2022-16457. Medicare Program; Prospective Payment System and Consolidated Billing for Skilled Nursing Facilities; Updates to the Quality Reporting Program and Value-Based Purchasing Program for Federal Fiscal Year 2023; Changes to the Requirements ...

AGENCY:

Centers for Medicare & Medicaid Services (CMS), Department of Health and Human Services (HHS).

ACTION:

Final rule.

SUMMARY:

This final rule updates payment rates; forecast error adjustments; diagnosis code mappings; the Patient Driven Payment Model (PDPM) parity adjustment; the SNF Quality Reporting Program (QRP); and the SNF Value-Based Purchasing (VBP) Program. It also establishes a permanent cap policy to smooth the impact of year-to-year changes in SNF payments related to changes in the SNF wage index. We also announce the application of a risk adjustment for the SNF Readmission Measure for COVID-19 beginning in FY 2023. We are finalizing changes to the long-term care facility fire safety provisions referencing the National Fire Protection Association (NFPA)® Life Safety Code, and Director of Food and Nutrition Services requirements.

DATES:

These regulations are effective on October 1, 2022.

FOR FURTHER INFORMATION CONTACT:

[e-mail] for issues related to the SNF PPS.

Heidi Magladry, [phone], for information related to the skilled nursing facility quality reporting program.

Alexandre Laberge, [phone], for information related to the skilled nursing facility value-based purchasing program.

Kristin Shifflett, [e-mail], and Cameron Ingram, [e-mail], for information related to the LTC requirements for participation.

SUPPLEMENTARY INFORMATION:

Availability of Certain Tables Exclusively Through the Internet on the CMS Website

As discussed in the FY 2014 SNF PPS final rule (78 FR 47936), tables setting forth the Wage Index for Urban Areas Based on CBSA Labor Market Areas and the Wage Index Based on CBSA Labor Market Areas for Rural Areas are no longer published in the Federal Register . Instead, these tables are available exclusively through the internet on the CMS website. The wage index tables for this final rule can be accessed on the SNF PPS Wage Index home page, at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​WageIndex.html.

Readers who experience any problems accessing any of these online SNF PPS wage index tables should contact Kia Burwell at [phone].

To assist readers in referencing sections contained in this document, we are providing the following Table of Contents.

Table of Contents

I. Executive Summary

A. Purpose

B. Summary of Major Provisions

C. Summary of Cost and Benefits

D. Advancing Health Information Exchange

II. Background on SNF PPS

A. Statutory Basis and Scope

B. Initial Transition for the SNF PPS

C. Required Annual Rate Updates

III. Analysis and Responses to Public Comments on the FY 2023 SNF PPS Proposed Rule

A. General Comments on the FY 2023 SNF PPS Proposed Rule

IV. SNF PPS Rate Setting Methodology and FY 2023 Update

A. Federal Base Rates

B. SNF Market Basket Update

C. Case-Mix Adjustment

D. Wage Index Adjustment

E. SNF Value-Based Purchasing Program

F. Adjusted Rate Computation Example

V. Additional Aspects of the SNF PPS

A. SNF Level of Care—Administrative Presumption

B. Consolidated Billing

C. Payment for SNF-Level Swing-Bed Services

D. Revisions to the Regulation Text

VI. Other SNF PPS Issues

A. Permanent Cap on Wage Index Decreases

B. Technical Updates to PDPM ICD-10 Mappings

C. Recalibrating the PDPM Parity Adjustment

D. Request for Information: Infection Isolation

VII. Skilled Nursing Facility Quality Reporting Program (SNF QRP)

A. Background and Statutory Authority

B. General Considerations Used for the Selection of Measures for the SNF QRP

C. SNF QRP Quality Measure Beginning With the FY 2025 SNF QRP

D. SNF QRP Quality Measures Under Consideration for Future Years: Request for Information (RFI)

E. Overarching Principles for Measuring Equity and Healthcare Quality Disparities across CMS Quality Programs—Request for Information (RFI)

F. Inclusion of the CoreQ: Short Stay Discharge Measure in a Future SNF QRP Program Year—Request for Information (RFI)

G. Form, Manner, and Timing of Data Submission Under the SNF QRP

H. Policies Regarding Public Display of Measure Data for the SNF QRP

VIII. Skilled Nursing Facility Value-Based Purchasing Program (SNF VBP)

A. Statutory Background

B. SNF VBP Program Measures

C. SNF VBP Performance Period and Baseline Period

D. Performance Standards

E. SNF VBP Performance Scoring

F. Adoption of a Validation Process for the SNF VBP Program Beginning With the FY 2023 Program Year

G. SNF Value-Based Incentive Payments for FY 2023

H. Public Reporting on the Provider Data Catalog website

I. Requests for Comment Related to Future SNF VBP Program Expansion Policies

IX. Changes to the Requirements for the Director of Food and Nutrition Services and Physical Environment Requirements in Long-Term (LTC) Facilities and Summary of Public Comments and Responses to the Request for Information on Revising the Requirements for Long-Term Care Facilities to Establish Mandatory Minimum Staffing Levels

X. Collection of Information Requirements

XI. Economic Analyses

A. Regulatory Impact Analysis

B. Regulatory Flexibility Act Analysis

C. Unfunded Mandates Reform Act Analysis

D. Federalism Analysis

E. Regulatory Review Costs

I. Executive Summary

A. Purpose

This final rule updates the SNF prospective payment rates for fiscal year (FY) 2023, as required under section 1888(e)(4)(E) of the Social Security Act (the Act). It also responds to section 1888(e)(4)(H) of the Act, which requires the Secretary to provide for publication of certain specified information relating to the payment update (see section II.C. of this final rule) in the Federal Register , before the August 1 that precedes the start of each FY. In addition, this final rule includes requirements for the Skilled Nursing Facility Quality Reporting Program (SNF QRP) and the Skilled Nursing Facility Value-Based Purchasing Program (SNF VBP), including adopting new quality measures for the SNF VBP Program and finalizing several updates to the Program's scoring methodology. The SNF QRP adopts one new measure to promote patient safety, begins collection of information which will improve the quality of care for all SNF patients, and revises associated regulation text. We are revising the qualification requirements for the Director of Food and Nutrition Services and revising requirements for life safety from fire for long-term care facilities that previously used the Fire Safety Evaluation System (FSES) to demonstrate compliance with provisions of the Life Safety Code (LSC).

B. Summary of Major Provisions

In accordance with sections 1888(e)(4)(E)(ii)(IV) and (e)(5) of the Act, the Federal rates in this final rule will reflect an update to the rates that we published in the SNF PPS final rule for FY 2022 (86 FR 42424, August 4, 2021). In addition, the final rule includes a forecast error adjustment for FY 2023, updates to the diagnosis code mappings used under the Patient Driven Payment Model (PDPM), and includes a recalibration of the PDPM parity adjustment. This final rule also establishes a permanent cap policy to smooth the impact of year-to-year changes in SNF payments related to changes in the SNF wage index.

This final rule finalizes requirements for the SNF QRP, including the adoption of one new measure beginning with the FY 2024 SNF QRP: the Influenza Vaccination Coverage among Healthcare Personnel (HCP) (NQF #0431) measure. We are also revising the compliance date for the Transfer of Health Information measures and certain standardized patient assessment data elements. In addition, we are revising regulation text that pertains to data submission requirements for the SNF QRP.

We are also finalizing several updates for the SNF VBP Program, including a policy to suppress the Skilled Nursing Facility 30-Day All-Cause Readmission Measure (SNFRM) for the FY 2023 SNF VBP Program Year for scoring and payment adjustment purposes. We are also adding two new measures to the SNF VBP Program beginning with the FY 2026 SNF VBP program year and one new measure beginning with the FY 2027 program year. We are also finalizing several updates to the scoring methodology beginning with the FY 2026 program year. We are also revising our regulation text in accordance with our proposals.

In addition, we are finalizing LTC facilities LSC changes in § 483.90(a) to allow older exiting facilities to continue to use the 2001 FSES mandatory values when determining compliance for containment, extinguishment, and people movement requirements as set out in the LSC. Older facilities who may not meet the FSES requirements previously used the 2000 LSC FSES will be allowed to remain in compliance with the older FSES without incurring substantial expenses to change their construction types, while maintaining resident and staff safety.

Additionally, we are finalizing changes to the requirements for the Director of Food and Nutrition Services in LTC facilities in § 483.60. We are revising the required qualifications for a director of food and nutrition services to provide that those with several years of experience performing as the director of food and nutrition services in a facility can continue to do so. Specifically, we have added to the current requirements that individuals with 2 or more years of experience in the position of a director of food and nutrition services and who have also completed a minimum course of study in food safety that includes topics integral to managing dietary operations (such as, but not limited to: foodborne illness, sanitation procedures, food purchasing/receiving, etc.) can continue to qualify as a director of food and nutrition services. This will help address concerns related to costs associated with training for existing staff and the potential need to hire new staff.

C. Summary of Cost and Benefits

D. Advancing Health Information Exchange

The Department of Health and Human Services (HHS) has a number of initiatives designed to encourage and support the adoption of interoperable health information technology and to promote nationwide health information exchange to improve health care and patient access to their digital health information.

To further interoperability in post-acute care settings, CMS and the Office of the National Coordinator for Health Information Technology (ONC) participate in the Post-Acute Care Interoperability Workgroup (PACIO) to facilitate collaboration with interested parties to develop Health Level Seven International® (HL7) Fast Healthcare Interoperability Resource® (FHIR) standards. These standards could support the exchange and reuse of patient assessment data derived from the post-acute care (PAC) setting assessment tools, such as the minimum data set (MDS), inpatient rehabilitation facility -patient assessment instrument (IRF-PAI), Long-Term Care Hospital (LTCH) continuity assessment record and evaluation (CARE) Data Set (LCDS), outcome and assessment information set (OASIS), and other sources.^[1 2] The PACIO Project has focused on HL7 FHIR implementation guides for: functional status, cognitive status and new use cases on advance directives, re-assessment timepoints, and Speech, language, swallowing, cognitive communication and hearing (SPLASCH) pathology.^[3] We encourage PAC provider and health IT vendor participation as the efforts advance.

The CMS Data Element Library (DEL) continues to be updated and serves as a resource for PAC assessment data elements and their associated mappings to health IT standards such as Logical Observation Identifiers Names and Codes (LOINC) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED).^[4] The DEL furthers CMS' goal of data standardization and interoperability. Standards in the DEL can be referenced on the CMS website and in the ONC Interoperability Standards Advisory (ISA). The 2022 ISA is available at https://www.healthit.gov/​isa/​sites/​isa/​files/​inline-files/​2022-ISA-Reference-Edition.pdf.

The 21st Century Cures Act (Cures Act) (Pub. L. 114-255, enacted December 13, 2016) required HHS and ONC to take steps to promote adoption and use of electronic health record (EHR) technology.^[5] Specifically, section 4003(b) of the Cures Act required ONC to take steps to advance interoperability through the development of a Trusted Exchange Framework and Common Agreement aimed at establishing full network-to network exchange of health information nationally. On January 18, 2022, ONC announced a significant milestone by releasing the Trusted Exchange Framework ^[6] and Common Agreement Version 1.^[7] The Trusted Exchange Framework is a set of non-binding principles for health information exchange, and the Common Agreement is a contract that advances those principles. The Common Agreement and the Qualified Health Information Network Technical Framework Version 1 (incorporated by reference into the Common Agreement) establish the technical infrastructure model and governing approach for different health information networks and their users to securely share clinical information with each other, all under commonly agreed to terms. The technical and policy architecture of how exchange occurs under the Common Agreement follows a network-of-networks structure, which allows for connections at different levels and is inclusive of many different types of entities at those different levels, such as health information networks, healthcare practices, hospitals, public health agencies, and Individual Access Services (IAS) Providers.^[8] For more information, we refer readers to https://www.healthit.gov/​topic/​interoperability/​trusted-exchange-framework-and-common-agreement.

We invited providers to learn more about these important developments and how they are likely to affect SNFs.

Comment: We received one comment on the information provided in this section. The commenter expressed support for efforts across HHS to advance health information technology exchange and encouraged use of a standard set of data by providers and health IT vendors, including efforts through the PACIO project. The commenter also noted a recent National Academies report describing technology barriers for PAC settings due to not being eligible for previous incentives to purchase technology certified under the ONC Health IT Certification Program. The commenter supported recommendations in the report for HHS to pursue financial incentives for post-acute care settings to adopt certified health information technology in order to enable health information exchange.

Response: We will take this comment into consideration as we coordinate with Federal partners, including ONC, on interoperability initiatives, and to inform future rulemaking.

II. Background on SNF PPS

A. Statutory Basis and Scope

As amended by section 4432 of the Balanced Budget Act of 1997 (BBA 1997) (Pub. L. 105-33, enacted August 5, 1997), section 1888(e) of the Act provides for the implementation of a PPS for SNFs. This methodology uses prospective, case-mix adjusted per diem payment rates applicable to all covered SNF services defined in section 1888(e)(2)(A) of the Act. The SNF PPS is effective for cost reporting periods beginning on or after July 1, 1998, and covers all costs of furnishing covered SNF services (routine, ancillary, and capital-related costs) other than costs associated with approved educational activities and bad debts. Under section 1888(e)(2)(A)(i) of the Act, covered SNF services include post-hospital extended care services for which benefits are provided under Part A, as well as those items and services (other than a small number of excluded services, such as physicians' services) for which payment may otherwise be made under Part B and which are furnished to Medicare beneficiaries who are residents in a SNF during a covered Part A stay. A comprehensive discussion of these provisions appears in the May 12, 1998 interim final rule (63 FR 26252). In addition, a detailed discussion of the legislative history of the SNF PPS is available online at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​Downloads/​Legislative_​History_​2018-10-01.pdf.

Section 215(a) of the Protecting Access to Medicare Act of 2014 (PAMA) (Pub. L. 113-93, enacted April 1, 2014) added section 1888(g) to the Act requiring the Secretary to specify an all-cause all-condition hospital readmission measure and an all-condition risk-adjusted potentially preventable hospital readmission measure for the SNF setting. Additionally, section 215(b) of PAMA added section 1888(h) to the Act requiring the Secretary to implement a VBP program for SNFs. Finally, section 2(c)(4) of the IMPACT Act amended section 1888(e)(6) of the Act, which requires the Secretary to implement a QRP for SNFs under which SNFs report data on measures and resident assessment data. Finally, section 111 of the Consolidated Appropriations Act, 2021 (CAA) updated section 1888(h) of the Act, authorizing the Secretary to apply up to nine additional measures to the VBP program for SNFs.

B. Initial Transition for the SNF PPS

Under sections 1888(e)(1)(A) and (e)(11) of the Act, the SNF PPS included an initial, three-phase transition that blended a facility-specific rate (reflecting the individual facility's historical cost experience) with the Federal case-mix adjusted rate. The transition extended through the facility's first 3 cost reporting periods under the PPS, up to and including the one that began in FY 2001. Thus, the SNF PPS is no longer operating under the transition, as all facilities have been paid at the full Federal rate effective with cost reporting periods beginning in FY 2002. As we now base payments for SNFs entirely on the adjusted Federal per diem rates, we no longer include adjustment factors under the transition related to facility-specific rates for the upcoming FY.

C. Required Annual Rate Updates

Section 1888(e)(4)(E) of the Act requires the SNF PPS payment rates to be updated annually. The most recent annual update occurred in a final rule that set forth updates to the SNF PPS payment rates for FY 2022 (86 FR 42424, August 4, 2021).

Section 1888(e)(4)(H) of the Act specifies that we provide for publication annually in the Federal Register the following:

• The unadjusted Federal per diem rates to be applied to days of covered SNF services furnished during the upcoming FY.
• The case-mix classification system to be applied for these services during the upcoming FY.
• The factors to be applied in making the area wage adjustment for these services.

Along with other revisions discussed later in this preamble, this final rule provides the required annual updates to the per diem payment rates for SNFs for FY 2023.

III. Analysis and Responses to Public Comments on the FY 2023 SNF PPS Proposed Rule

In response to the publication of the FY 2023 SNF PPS proposed rule, we received 6,970 public comments from individuals, providers, corporations, government agencies, private citizens, trade associations, and major organizations. The following are brief summaries of each proposed provision, a summary of the public comments that we received related to that proposal, and our responses to the comments.

A. General Comments on the FY 2023 SNF PPS Proposed Rule

In addition to the comments we received on specific proposals contained within the proposed rule (which we address later in this final rule), commenters also submitted the following, more general, observations on the SNF PPS and SNF care generally. A discussion of these comments, along with our responses, appears below.

Comment: Commenters submitted comments and recommendations that are outside the scope of the proposed rule addressing a number of different policies, including the Coronavirus disease 2019 (COVID-19) pandemic. This included comments on the flexibilities provided to SNFs during the PHE, specifically through the waivers issued under sections 1135 of the Act and coverage flexibility provided under section 1812(f) of the Act. Commenters also expressed concerns about the substantial additional costs due to the PHE that they were concerned would be permanent due to changes in patient care, infection control staff and equipment, personal protective equipment (PPE), reporting requirements, increased wages, increased food prices, and other necessary costs. Some commenters who received CARES Act Provider Relief funds indicated that those funds were not enough to cover these costs. Additionally, a few commenters from rural areas stated that their facilities were heavily impacted from the additional costs, particularly the need to raise wages, and that this could affect patients' access to care.

Response: Because these comments are outside the scope of the current rulemaking, we are not addressing them in this final rule. We may take them under consideration in future rulemaking.

Comment: We received a number of comments related to monitoring Medicare Advantage Organizations (MAOs). These commenters referred to a recent OIG report, which discussed how some MAOs have reportedly denied or delayed beneficiary access to SNF services. These commenters encouraged CMS to review the requirements and policies surrounding the payment and practices of MAOs.

Response: Because these comments are outside the scope of the current rulemaking, we are not addressing them in this final rule. We may take them under consideration in future rulemaking.

Comment: One commenter requested that we consider including recreational therapy time provided to SNF residents by recreational therapists as part of the calculation of the resident's RUG-IV therapy classification or as part of determining the number of restorative nursing services provided to the resident.

Response: We appreciate the commenter raising this issue, but we do not believe there is sufficient evidence at this time regarding the efficacy of recreational therapy interventions or, more notably, data which would substantiate a determination of the effect on payment of such interventions, as such services were not considered separately, as were physical, occupational and speech-language pathology services, when RUG-IV was being developed. That is, we note that Medicare Part A originally paid for institutional care in various provider settings, including SNF, on a reasonable cost basis, but now makes payment using PPS methodologies, such as the SNF PPS. To the extent that one of these SNFs furnished recreational therapy to its inpatients under the previous, reasonable cost methodology, the cost of the services would have been included in the base payments when SNF PPS payment rates were derived. Under the PPS methodology, Part A makes a comprehensive payment for the bundled package of items and services that the facility furnishes during the course of a Medicare-covered stay. This package encompasses nearly all services that the beneficiary receives during the course of the stay—including any medically necessary recreational therapy—and payment for such services is included within the facility's comprehensive SNF PPS payment for the covered Part A stay itself.

Comment: One commenter encouraged CMS to monitor the use of concurrent and group therapy under PDPM and identify any facilities that are consistently exceeding the established group and concurrent therapy limit. This commenter referred to reports by their members to disregard the established limit on these therapy modalities, as well as the impact of the PHE on the provision of group and concurrent therapy.

Response: We continue to monitor all aspects of payment and service provision under PDPM. Should we discover any outliers in the provision of group and concurrent therapy that consistently exceed the established limit on these therapy modalities, we will refer such outliers for administrative action.

IV. SNF PPS Rate Setting Methodology and FY 2023 Update

A. Federal Base Rates

Under section 1888(e)(4) of the Act, the SNF PPS uses per diem Federal payment rates based on mean SNF costs in a base year (FY 1995) updated for inflation to the first effective period of the PPS. We developed the Federal payment rates using allowable costs from hospital-based and freestanding SNF cost reports for reporting periods beginning in FY 1995. The data used in developing the Federal rates also incorporated a Part B add-on, which is an estimate of the amounts that, prior to the SNF PPS, would be payable under Part B for covered SNF services furnished to individuals during the course of a covered Part A stay in a SNF.

In developing the rates for the initial period, we updated costs to the first effective year of the PPS (the 15-month period beginning July 1, 1998) using a SNF market basket index, and then standardized for geographic variations in wages and for the costs of facility differences in case-mix. In compiling the database used to compute the Federal payment rates, we excluded those providers that received new provider exemptions from the routine cost limits, as well as costs related to payments for exceptions to the routine cost limits. Using the formula that the BBA 1997 prescribed, we set the Federal rates at a level equal to the weighted mean of freestanding costs plus 50 percent of the difference between the freestanding mean and weighted mean of all SNF costs (hospital-based and freestanding) combined. We computed and applied separately the payment rates for facilities located in urban and rural areas, and adjusted the portion of the Federal rate attributable to wage-related costs by a wage index to reflect geographic variations in wages.

B. SNF Market Basket Update

1. SNF Market Basket Index

Section 1888(e)(5)(A) of the Act requires us to establish a SNF market basket index that reflects changes over time in the prices of an appropriate mix of goods and services included in covered SNF services. Accordingly, we have developed a SNF market basket index that encompasses the most commonly used cost categories for SNF routine services, ancillary services, and capital-related expenses. In the SNF PPS final rule for FY 2018 (82 FR 36548 through 36566), we rebased and revised the market basket index, which included updating the base year from FY 2010 to 2014. In the SNF PPS final rule for FY 2022 (86 FR 42444 through 42463), we rebased and revised the market basket index, which included updating the base year from 2014 to 2018.

The SNF market basket index is used to compute the market basket percentage change that is used to update the SNF Federal rates on an annual basis, as required by section 1888(e)(4)(E)(ii)(IV) of the Act. This market basket percentage update is adjusted by a forecast error correction, if applicable, and then further adjusted by the application of a productivity adjustment as required by section 1888(e)(5)(B)(ii) of the Act and described in section IV.B.4. of this final rule.

As outlined in the proposed rule, we proposed a FY 2023 SNF market basket percentage of 2.8 percent based on IHS Global Inc.'s (IGI's) fourth quarter 2021 forecast of the 2018-based SNF market basket (before application of the forecast error adjustment and productivity adjustment). We also proposed that if more recent data subsequently became available (for example, a more recent estimate of the market basket and/or the productivity adjustment), we would use such data, if appropriate, to determine the FY 2023 SNF market basket percentage change, labor-related share relative importance, forecast error adjustment, or productivity adjustment in the SNF PPS final rule.

Since the proposed rule, we have updated the FY 2023 market basket percentage increase based on IGI's second quarter 2022 forecast with historical data through the first quarter of 2022. The FY 2023 growth rate of the 2018-based SNF market basket is estimated to be 3.9 percent.

In section IV.B.5. of this final rule, we discussed the 2 percent reduction applied to the market basket update for those SNFs that fail to submit measures data as required by section 1888(e)(6)(A) of the Act.

2. Use of the SNF Market Basket Percentage

Section 1888(e)(5)(B) of the Act defines the SNF market basket percentage as the percentage change in the SNF market basket index from the midpoint of the previous FY to the midpoint of the current FY. For the Federal rates outlined in this final rule, we use the percentage change in the SNF market basket index to compute the update factor for FY 2023. This factor is based on the FY 2023 percentage increase in the 2018-based SNF market basket index reflecting routine, ancillary, and capital-related expenses. As stated previously, in the proposed rule, the SNF market basket percentage update was estimated to be 2.8 percent for FY 2023 based on IGI's fourth quarter 2021 forecast. For this final rule, based on IGI's second quarter 2022 forecast with historical data through the first quarter of 2022, the FY 2023 growth rate of the 2018-based SNF market basket is estimated to be 3.9 percent.

3. Forecast Error Adjustment

As discussed in the June 10, 2003 supplemental proposed rule (68 FR 34768) and finalized in the August 4, 2003 final rule (68 FR 46057 through 46059), § 413.337(d)(2) provides for an adjustment to account for market basket forecast error. The initial adjustment for market basket forecast error applied to the update of the FY 2003 rate for FY 2004 and took into account the cumulative forecast error for the period from FY 2000 through FY 2002, resulting in an increase of 3.26 percent to the FY 2004 update. Subsequent adjustments in succeeding FYs take into account the forecast error from the most recently available FY for which there is final data, and apply the difference between the forecasted and actual change in the market basket when the difference exceeds a specified threshold. We originally used a 0.25 percentage point threshold for this purpose; however, for the reasons specified in the FY 2008 SNF PPS final rule (72 FR 43425), we adopted a 0.5 percentage point threshold effective for FY 2008 and subsequent FYs. As we stated in the final rule for FY 2004 that first issued the market basket forecast error adjustment (68 FR 46058), the adjustment will reflect both upward and downward adjustments, as appropriate.

For FY 2021 (the most recently available FY for which there is final data), the forecasted or estimated increase in the SNF market basket index was 2.2 percent, and the actual increase for FY 2021 is 3.7 percent, resulting in the actual increase being 1.5 percentage point higher than the estimated increase. Accordingly, as the difference between the estimated and actual amount of change in the market basket index exceeds the 0.5 percentage point threshold, under the policy previously described (comparing the forecasted and actual increase in the market basket), the FY 2023 market basket percentage change of 3.9 percent would be adjusted upward to account for the forecast error correction of 1.5 percentage point, resulting in a SNF market basket percentage change of 5.1 percent after reducing the market basket update by the productivity adjustment of 0.3 percentage point, discussed later in this section of the preamble.

Table 2 shows the forecasted and actual market basket increases for FY 2021.

4. Productivity Adjustment

Section 1888(e)(5)(B)(ii) of the Act, as added by section 3401(b) of the Patient Protection and Affordable Care Act (Affordable Care Act) (Pub. L. 111-148, enacted March 23, 2010) requires that, in FY 2012 and in subsequent FYs, the market basket percentage under the SNF payment system (as described in section 1888(e)(5)(B)(i) of the Act) is to be reduced annually by the productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of the Act. Section 1886(b)(3)(B)(xi)(II) of the Act, in turn, defines the productivity adjustment to be equal to the 10-year moving average of changes in annual economy-wide, private nonfarm business multifactor productivity (MFP) (as projected by the Secretary for the 10-year period ending with the applicable FY, year, cost-reporting period, or other annual period). The U.S. Department of Labor's Bureau of Labor Statistics (BLS) publishes the official measure of productivity for the U.S. We note that previously the productivity measure referenced in section 1886(b)(3)(B)(xi)(II) of the Act was published by BLS as private nonfarm business multifactor productivity. Beginning with the November 18, 2021 release of productivity data, BLS replaced the term multifactor productivity (MFP) with total factor productivity (TFP). BLS noted that this is a change in terminology only and will not affect the data or methodology. As a result of the BLS name change, the productivity measure referenced in section 1886(b)(3)(B)(xi)(II) of the Act is now published by BLS as private nonfarm business total factor productivity. However, as mentioned previously in this section, the data and methods are unchanged. We refer readers to the BLS website at www.bls.gov for the BLS historical published TFP data.

A complete description of the TFP projection methodology is available on our website at https://www.cms.gov/​Research-Statistics-Data-and-Systems/​Statistics-Trends-and-Reports/​MedicareProgramRatesStats/​MarketBasketResearch. In addition, in the FY 2022 SNF final rule (86 FR 42429) we noted that, effective with FY 2022 and forward, we are changing the name of this adjustment to refer to it as the “productivity adjustment,” rather than the “MFP adjustment.”

a. Incorporating the Productivity Adjustment Into the Market Basket Update

Per section 1888(e)(5)(A) of the Act, the Secretary shall establish a SNF market basket index that reflects changes over time in the prices of an appropriate mix of goods and services included in covered SNF services. Section 1888(e)(5)(B)(ii) of the Act, added by section 3401(b) of the Affordable Care Act, requires that for FY 2012 and each subsequent FY, after determining the market basket percentage described in section 1888(e)(5)(B)(i) of the Act, the Secretary shall reduce such percentage by the productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of the Act. Section 1888(e)(5)(B)(ii) of the Act further states that the reduction of the market basket percentage by the productivity adjustment may result in the market basket percentage being less than zero for a FY, and may result in payment rates under section 1888(e) of the Act being less than such payment rates for the preceding fiscal year. Thus, if the application of the productivity adjustment to the market basket percentage calculated under section 1888(e)(5)(B)(i) of the Act results in a productivity-adjusted market basket percentage that is less than zero, then the annual update to the unadjusted Federal per diem rates under section 1888(e)(4)(E)(ii) of the Act would be negative, and such rates would decrease relative to the prior FY.

Based on the data available for the FY 2023 SNF PPS proposed rule, the proposed productivity adjustment (the 10-year moving average of changes in annual economy-wide private nonfarm business TFP for the period ending September 30, 2023) was projected to be 0.4 percentage point. However, for this final rule, based on IGI's second quarter 2022 forecast, the estimated 10-year moving average of changes in annual economy-wide private nonfarm business TFP for the period ending September 30, 2023 is 0.3 percentage point.

Consistent with section 1888(e)(5)(B)(i) of the Act and § 413.337(d)(2), as discussed previously, the market basket percentage for FY 2023 for the SNF PPS is based on IGI's second quarter 2022 forecast of the SNF market basket percentage, which is estimated to be 3.9 percent. This market basket percentage is then increased by 1.5 percentage point, due to application of the forecast error adjustment discussed earlier in this section of the preamble. Finally, as discussed earlier in this section of the preamble, we are applying a 0.3 percentage point productivity adjustment to the FY 2023 SNF market basket percentage. The resulting productivity-adjusted FY 2023 SNF market basket update is, therefore, equal to 5.1 percent, or 3.9 percent plus 1.5 percentage point to account for forecast error and less 0.3 percentage point to account for the productivity adjustment.

5. Market Basket Update Factor for FY 2023

Sections 1888(e)(4)(E)(ii)(IV) and (e)(5)(i) of the Act require that the update factor used to establish the FY 2023 unadjusted Federal rates be at a level equal to the market basket index percentage change. Accordingly, we determined the total growth from the average market basket level for the period of October 1, 2021 through September 30, 2022 to the average market basket level for the period of October 1, 2022 through September 30, 2023. This process yields a percentage change in the 2018-based SNF market basket of 3.9 percent.

As further explained in section IV.B.3. of this final rule, as applicable, we adjust the market basket percentage change by the forecast error from the most recently available FY for which there is final data and apply this adjustment whenever the difference between the forecasted and actual percentage change in the market basket exceeds a 0.5 percentage point threshold in absolute terms. Since the actual FY 2021 SNF market basket percentage change exceeded the forecasted FY 2021 SNF market basket percentage change (FY 2021 is the most recently available FY for which there is historical data) by more than the 0.5 percentage point threshold, we are adjusting the FY 2023 market basket percentage change upward by the forecast error correction. Applying the 1.5 percentage point forecast error correction results in an adjusted FY 2023 SNF market basket percentage change of 5.4 percent (3.9 percent market basket update plus 1.5 percentage point forecast error adjustment).

Section 1888(e)(5)(B)(ii) of the Act requires us to reduce the market basket percentage change by the productivity adjustment (10-year moving average of changes in annual economy-wide private nonfarm business TFP for the period ending September 30, 2023) which is estimated to be 0.3 percentage point, as described in section IV.B.4. of this final rule. Thus, we apply a net SNF market basket update factor of 5.1 percent in our determination of the FY 2023 SNF PPS unadjusted Federal per diem rates, which reflects a market basket increase factor of 3.9 percent, plus the 1.5 percentage point forecast error correction and less the 0.3 percentage point productivity adjustment.

As outlined in the proposed rule, we noted that if more recent data became available (for example, a more recent estimate of the SNF market basket and/or productivity adjustment), we would use such data, if appropriate, to determine the FY 2023 SNF market basket percentage change, labor-related share relative importance, forecast error adjustment, or productivity adjustment in the FY 2023 SNF PPS final rule. Since more recent data did become available since the proposed rule, as outlined above, we have updated the various adjustment factors described through this section accordingly.

We also noted that section 1888(e)(6)(A)(i) of the Act provides that, beginning with FY 2018, SNFs that fail to submit data, as applicable, in accordance with sections 1888(e)(6)(B)(i)(II) and (III) of the Act for a fiscal year will receive a 2.0 percentage point reduction to their market basket update for the fiscal year involved, after application of section 1888(e)(5)(B)(ii) of the Act (the productivity adjustment) and section 1888(e)(5)(B)(iii) of the Act (the 1 percent market basket increase for FY 2018). In addition, section 1888(e)(6)(A)(ii) of the Act states that application of the 2.0 percentage point reduction (after application of section 1888(e)(5)(B)(ii) and (iii) of the Act) may result in the market basket index percentage change being less than zero for a fiscal year, and may result in payment rates for a fiscal year being less than such payment rates for the preceding fiscal year. Section 1888(e)(6)(A)(iii) of the Act further specifies that the 2.0 percentage point reduction is applied in a noncumulative manner, so that any reduction made under section 1888(e)(6)(A)(i) of the Act applies only to the fiscal year involved, and that the reduction cannot be taken into account in computing the payment amount for a subsequent fiscal year.

A discussion of the public comments received on the FY 2023 SNF market basket percentage increase to the SNF PPS rates, along with our responses, may be found below.

Comment: One commenter supported and appreciated the proposed increase in Medicare rates as a result of the market basket and forecast error adjustment. Several commenters supported the increase and urged CMS to use the most recent economic data as it becomes available in finalizing the payment update to capture the significant cost increases and inflation being felt by the long-term care sector and across the economy. However, multiple commenters raised concerns about whether rising costs, and costs of labor, in particular, are being sufficiently accounted for in the SNF market basket. One commenter urged CMS to discuss in the final rule how the agency will account for these increased costs. One commenter shared that their State wage survey of nursing facilities, which is used to inform their Medicaid inflation adjustment each year, indicates a 14.8 percent increase in nursing compensation (a composite of employee and agency staff) from 2022 to 2023, along with non-nursing compensation growth of 7.3 percent.

Commenters were concerned that CMS' use of the historical Employment Cost Index (ECI) for Wages and Salaries for Private Industry Workers in Nursing Care Facilities to measure the price growth of wages and salaries may not be accurately capturing employment costs in nursing homes, or otherwise not in a timely manner. They stated that the quarterly updates of the price proxies do not address changes in staffing levels, changes in the occupational mix, increases in the use of contract labor or travel nurses, or other drivers of wage rate growth such as labor market tightness and consumer inflation.

One commenter calculated notable differences in Medicare Cost Report Direct Care Wage Data and the labor component of market basket updates, which they estimated to be about 6 percent between 1998 and 2021. The commenter suggested spreading an adjustment for this difference into the update equally over a 2 to 3-year period. In addition, they requested that CMS develop a methodology to account for rapidly escalating labor costs in a more timely fashion than the current price proxy calculation method captures. The commenter also noted faster growth of the BLS Current Employment Statistics (CES) average hourly earnings (AHE) series for Production and Non-Supervisory Nursing care facility employees (without seasonality adjustment), compared to the ECI for Wages and Salaries for Private Industry Workers in Nursing Care Facilities.

One commenter requested that CMS provide a labor-related market basket price add-on due to workforce shortages and other challenges not addressed by the current market basket methodology.

Response: We recognize the challenges facing SNFs in operating during a high inflationary environment. Due to SNF payments under PPS being set prospectively, we rely on a projection of the SNF market basket that reflects both recent historical trends, as well as forecast expectations over the next roughly 18 months. The forecast error for a market basket update is calculated as the actual market basket increase for a given year, less the forecasted market basket increase. Due to the uncertainty regarding future price trends, forecast errors can be both positive and negative. We are confident that the forecast error adjustments built into the SNF market basket update factor will account for these discrepancies over time.

In the FY 2023 SNF PPS proposed rule, we proposed a 2018-based SNF market basket increase of 2.8 percent based on IGI's fourth quarter 2021 forecast with historical data through third quarter 2021. For this final rule, based on IGI's second quarter 2022 forecast with historical data through first quarter 2022 we are finalizing a 2018-based SNF market basket increase of 3.9 percent, which is the highest market basket update we have implemented in a final rule since the beginning of the SNF PPS. The 3.9-percent increase reflects forecasted compensation price growth of 4.2 percent (which is approximately 2 percentage points higher than the 10-year historical average price growth for compensation), reflecting increased wage pressures due to various economic and industry-specific factors. Additionally, the FY 2023 productivity-adjusted SNF market basket update of 3.6 percent (3.9 percent less 0.3 percentage point) will be increased by the FY 2021 forecast error adjustment of 1.5 percentage point for a total FY 2023 update of 5.1 percent (3.6 percent plus 1.5 percentage points). A forecast error for FY 2022 cannot be calculated until historical data through third quarter 2022 are available; if there is a FY 2022 forecast error and a similar update approach is used for FY 2024, then a forecast error adjustment would be applied to the FY 2024 SNF PPS payment update.

Section 1888(e)(5)(A) of the Act states the Secretary shall establish a skilled nursing facility market basket index that reflects changes over time in the prices of an appropriate mix of goods and services included in covered skilled nursing facility services. The 2018-based SNF market basket is a fixed-weight, Laspeyres-type price index that measures the change in price, over time, of the same mix of goods and services purchased in the base period. Any changes in the quantity or mix of goods and services (that is, intensity) purchased over time relative to a base period are not measured. For the compensation cost weight in the 2018-based SNF market basket (which includes salaried and contract labor employees), we use the ECI for wages and salaries and benefits for nursing care facilities to proxy the price increase of SNF labor. The ECI (published by the BLS) measures the change in the hourly labor cost to employers, independent of the influence of employment shifts among occupations and industry categories. Therefore, we believe the ECI for nursing care facilities, which only reflects the price change associated with the labor used to provide SNF care and appropriately does not reflect other factors that might affect labor costs, is an appropriate measure to use in the SNF market basket.

We acknowledge the commenters' concerns regarding the ECI being based on 2012 occupational distribution. Our analysis of the 2021 BLS Occupational Employment Statistics data, the most recent data available (published at https://www.bls.gov/​oes/​), shows that the salary (estimated as the product of employment and average annual salary) distribution by occupation for skilled nursing care facilities (NAICS 6231) is similar to the BLS OES data for 2012. Specifically, we found that the healthcare occupational distribution among the major occupations—registered nurses (16 percent in 2021), licensed practical and vocational nurses (16 percent), nursing assistants (25 percent), and therapists (4 percent)—were notably similar between 2012 and 2021. Additionally, we found the split between healthcare (70 percent in 2021) and nonhealthcare (30 percent) salaries by occupation to be virtually unchanged.

We also recognize the commenters' concerns regarding the need for increased reliance on the use of contract labor and travel nurses due to the overall tightness in the labor market and the more specific labor constraints of healthcare staff in particular. The compensation cost weight of the SNF market basket includes expenses for wages and salaries, employee benefits, and contract labor, with the contract labor expenses apportioned to the Wages and Salaries and Employee Benefits cost category weights. We analyzed the 2020 Medicare Cost Report (MCR) data and found the Compensation cost weight decreased slightly from 60.2 percent in 2018 to 59.8 percent in 2020. This was due to a decrease in the Contract Labor cost weight from 7.5 percent in 2018 to 6.8 percent in 2020 offset by a 0.3 percentage point increase in employed wages and salaries and benefits combined. Our analysis found that while there was an increase in the contract nursing staff hours, there was an offsetting decrease in the use of contract therapy staff hours. We will continue to analyze the MCR data, including the 2021 data when available, and assess the appropriateness of rebasing and revising the SNF market basket. Any rebasing or revising of the SNF market basket, if deemed necessary, would be proposed in future rulemaking and subject to public comments.

Regarding commenters' request that CMS consider other methods and data sources to calculate the final rule market basket update by exercising administrative authority, we note that we did not propose to use other methods or data sources to calculate the final market basket update for FY 2023, and therefore, we are not finalizing such an approach for this final rule. Further, while the Secretary has the discretion under the statute to establish the methodology for determining the appropriate mix of goods and services that comprise the SNF market basket, the statute requires the SNF PPS payment rates to be annually updated by the SNF market basket percentage change. As discussed in section IV.B.1. of this final rule, the market basket used to update SNF PPS payments has been rebased and revised over the history of the SNF PPS to reflect more recent data on SNF cost structures, and we believe it continues to appropriately reflect SNF cost structures. Consistent with our proposal, we have used more recent data to calculate a final SNF market basket update of 5.1 percent for FY 2023. Additionally, MedPAC did a full analysis of payment adequacy for SNF providers in its March 2022 Report to Congress ( https://www.medpac.gov/​wp-content/​uploads/​2022/​03/​Mar22_​MedPAC_​ReportToCongress_​Ch7_​SEC.pdf) and determined that, even considering the cost increases that have occurred as a result of the PHE associated with the COVID-19 pandemic, payments to SNFs continue to be adequate.

Comment: One commenter recommended that CMS convene a technical expert panel to discuss a more long-range approach to collecting and imputing appropriate and timely data for market basket labor update calculations, in an attempt to encompass factors not captured by currently available price proxies.

Response: We are open to hearing from interested parties about any data or analyses available to achieve the shared goal of ensuring that the SNF market basket price proxies are technically appropriate. As required by statute, any proposed changes to improve and/or update the SNF market basket occur through the rulemaking process and interested parties have an opportunity to publicly comment and make recommendations regarding the appropriateness of proposed changes.

Comment: One commenter stated that CMS should update the SNF market basket more frequently than every 4 to 5 years. The commenter noted that the SNF market basket uses a 2018 base year to measure the labor vs. non-labor cost inputs of 2018, which was prior to the pandemic and related significant labor cost increases.

Response: We note that while there is no official schedule for updating the market baskets, we typically attempt to rebase a market basket every 4 to 5 years since we have found that the cost weights are relatively stable over time. As the commenter acknowledged, the SNF market basket was last rebased in the FY 2022 SNF final rule using 2018 Medicare cost reports (86 FR 42444 through 42463), the most recent year of complete data available at the time of the rebasing. As described in that final rule, the primary data source for the major cost weights (Wages and Salaries, Employee Benefits, Contract Labor, Pharmaceutical, Malpractice, Capital-related, and Home Office) for the 2018-based SNF market basket are the MCRs for freestanding SNFs (CMS Form 2540-10, OMB NO. 0938-0463). We also indicated in the FY 2022 SNF final rule that we planned to review the 2020 MCR data as soon as complete information was available, to ensure the market basket relative cost shares are still appropriate.

Our analysis of the MCR data for 2019 and 2020 showed little change in the reported cost weights with the exception of the Pharmaceuticals cost weight in 2020. The Pharmaceuticals cost weight (including the adjustment for Medicaid dual-eligible drug costs) decreased approximately one percentage point from 7.5 percent in 2018 to 6.4 percent in 2020. The decrease in the Pharmaceuticals cost weight is stemming from the estimated Part D drug costs per day for dual-eligible Medicare beneficiaries, which decreased in 2020 as a result of an increase in the proportion of generic drugs. More detail regarding this adjustment is described in the FY 2022 SNF PPS rule (86 FR 42447). The 2020 Medicare cost report data also indicates that the Compensation cost weight is slightly lower at 59.8 percent, compared to the 2018-based SNF market basket with 60.2 percent. MCR data for 2021 are incomplete at this time. Given that the changes to the Compensation cost weight for 2020 are minimal and it is unclear whether changes in the cost weights are temporary as a result of the PHE, we continue to believe it is premature at this time to use more recent MCR data to derive a rebased and revised SNF market basket. We will continue to monitor these data, and any necessary changes to the SNF market basket will be proposed in future rulemaking.

Comment: One commenter expressed concern about the proposed 0.4 percent reduction for productivity and asked CMS in the final rule to further elaborate on the specific productivity gains that are the basis for this proposed market basket offset. The commenter stated that the productivity adjustment contradicts their members' PHE experiences of actual losses in productivity during the pandemic.

Response: Section 1888(e)(5)(B)(ii) of the Act requires the application of a productivity adjustment to the SNF market basket update. As required by statute, the FY 2023 productivity adjustment is derived based on the 10-year moving average of changes in annual economy-wide private nonfarm business TFP for the period ending FY 2023, which is currently projected to be 0.3 percent.

Comment: One commenter stated that they do not support the triggering of automatic forecast error adjustments. They expressed concern that automatic forecast corrections would, in some years, result in making payment increases on top of the statutory increases to the payment rates, despite the industry having sizeable average Medicare margins. The commenter also noted that eliminating the automatic adjustments would result in more stable updates and consistency across settings because CMS does not apply automatic forecast error adjustments to any other market baskets. They noted that although CMS is required by statute to update the payment rates each year by the estimated change in the market basket index, it is not required to make automatic forecast error corrections.

Response: When forecast error adjustments for the SNF market basket were introduced in the FY 2004 SNF PPS final rule (68 FR 46035), we indicated the goal was “to pay the appropriate amount, to the correct provider, for the proper service, at the right time”. We note that since implementation, forecast errors have generally been relatively small and clustered near zero and that for FY 2008 and subsequent years, we increased the threshold at which adjustments are triggered from 0.25 to 0.5 percentage point. Our intent in raising the threshold was to distinguish typical statistical variances from more major unanticipated impacts, such as unforeseen disruptions of the economy (such as occurred during the recent PHE) or unexpected inflationary patterns (either at lower or higher than anticipated rates).

Comment: One commenter stated that the market basket update reflects the actual cost of delivering services and it should not be used to justify the severity of the parity adjustment.

Response: We are required to update SNF PPS payments annually by the market basket update as required under section 1888(e)(4)(E)(ii)(IV) and (e)(5)(B) of the Act, as amended by section 53111 of the BBA 2018. We refer readers to section VI.C for a full discussion of the need for and the implementation of the parity adjustment.

6. Unadjusted Federal Per Diem Rates for FY 2023

As discussed in the FY 2019 SNF PPS final rule (83 FR 39162), in FY 2020 we implemented a new case-mix classification system to classify SNF patients under the SNF PPS, the PDPM. As discussed in section V.B.1. of that final rule (83 FR 39189), under PDPM, the unadjusted Federal per diem rates are divided into six components, five of which are case-mix adjusted components (Physical Therapy (PT), Occupational Therapy (OT), Speech-Language Pathology (SLP), Nursing, and Non-Therapy Ancillaries (NTA)), and one of which is a non-case-mix component, as existed under the previous RUG-IV model. We proposed to use the SNF market basket, adjusted as described previously, to adjust each per diem component of the Federal rates forward to reflect the change in the average prices for FY 2023 from the average prices for FY 2022. We proposed to further adjust the rates by a wage index budget neutrality factor, described later in this section. Further, in the past, we used the revised Office of Management and Budget (OMB) delineations adopted in the FY 2015 SNF PPS final rule (79 FR 45632, 45634), with updates as reflected in OMB Bulletin Nos. 15-01 and 17-01, to identify a facility's urban or rural status for the purpose of determining which set of rate tables would apply to the facility. As discussed in the FY 2021 SNF PPS proposed and final rules, we adopted the revised OMB delineations identified in OMB Bulletin No. 18-04 (available at https://www.whitehouse.gov/​wp-content/​uploads/​2018/​09/​Bulletin-18-04.pdf) to identify a facility's urban or rural status effective beginning with FY 2021.

Tables 3 and 4 reflect the updated unadjusted Federal rates for FY 2023, prior to adjustment for case-mix.

Commenters submitted the following comments related to the proposed unadjusted federal per diem rates for FY 2021. A discussion of these comments, along with our responses, appears below.

Comment: One commenter stated that the case mix adjusted rates shown in Tables 5 and 6 for PT, OT, SLP and nursing rates are higher in urban areas than rural areas and noted this may be driving inequities and labor shortages between rural and urban nursing homes.

Response: We disagree with the commenter's statement that the case-mix adjusted rates for the PT, OT and SLP components are higher in urban than rural areas as shown in Tables 5 and 6. Additionally, the Federal per diem rates were established separately for urban and rural areas using allowable costs from FY 1995 cost reports, and therefore, account for and reflect the relative costs differences between urban and rural facilities. We note that the SNF PPS payment rates are updated annually by an increase factor that reflects changes over time in the prices of an appropriate mix of goods and services included in the covered SNF services and a portion of these rates are further adjusted by a wage index to reflect geographic variations in wages. We will continue to monitor our SNF payment policies to ensure they reflect as accurately as possible the current costs of care in the SNF setting.

Accordingly, after considering the comments received, for the reasons specified in this final rule and in the FY 2023 SNF PPS proposed rule, we are finalizing the unadjusted federal per diem rates set forth in Tables 3 and 4.

C. Case-Mix Adjustment

Under section 1888(e)(4)(G)(i) of the Act, the Federal rate also incorporates an adjustment to account for facility case-mix, using a classification system that accounts for the relative resource utilization of different patient types. The statute specifies that the adjustment is to reflect both a resident classification system that the Secretary establishes to account for the relative resource use of different patient types, as well as resident assessment data and other data that the Secretary considers appropriate. In the FY 2019 final rule (83 FR 39162, August 8, 2018), we finalized a new case-mix classification model, the PDPM, which took effect beginning October 1, 2019. The previous RUG-IV model classified most patients into a therapy payment group and primarily used the volume of therapy services provided to the patient as the basis for payment classification, thus creating an incentive for SNFs to furnish therapy regardless of the individual patient's unique characteristics, goals, or needs. PDPM eliminates this incentive and improves the overall accuracy and appropriateness of SNF payments by classifying patients into payment groups based on specific, data-driven patient characteristics, while simultaneously reducing the administrative burden on SNFs.

The PDPM uses clinical data from the MDS to assign case-mix classifiers to each patient that are then used to calculate a per diem payment under the SNF PPS, consistent with the provisions of section 1888(e)(4)(G)(i) of the Act. As discussed in section IV.A. of this final rule, the clinical orientation of the case-mix classification system supports the SNF PPS's use of an administrative presumption that considers a beneficiary's initial case-mix classification to assist in making certain SNF level of care determinations. Further, because the MDS is used as a basis for payment, as well as a clinical assessment, we have provided extensive training on proper coding and the timeframes for MDS completion in our Resident Assessment Instrument (RAI) Manual. As we have stated in prior rules, for an MDS to be considered valid for use in determining payment, the MDS assessment should be completed in compliance with the instructions in the RAI Manual in effect at the time the assessment is completed. For payment and quality monitoring purposes, the RAI Manual consists of both the Manual instructions and the interpretive guidance and policy clarifications posted on the appropriate MDS website at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​MDS30RAIManual.html.

Under section 1888(e)(4)(H) of the Act, each update of the payment rates must include the case-mix classification methodology applicable for the upcoming FY. The FY 2023 payment rates set forth in this proposed rule reflect the use of the PDPM case-mix classification system from October 1, 2022, through September 30, 2023. The case-mix adjusted PDPM payment rates for FY 2023 are listed separately for urban and rural SNFs, in Tables 5 and 6 with corresponding case-mix values.

Given the differences between the previous RUG-IV model and PDPM in terms of patient classification and billing, it was important that the format of Tables 5 and 6 reflect these differences. More specifically, under both RUG-IV and PDPM, providers use a Health Insurance Prospective Payment System (HIPPS) code on a claim to bill for covered SNF services. Under RUG-IV, the HIPPS code included the three-character RUG-IV group into which the patient classified as well as a two-character assessment indicator code that represented the assessment used to generate this code. Under PDPM, while providers still use a HIPPS code, the characters in that code represent different things. For example, the first character represents the PT and OT group into which the patient classifies. If the patient is classified into the PT and OT group “TA”, then the first character in the patient's HIPPS code would be an A. Similarly, if the patient is classified into the SLP group “SB”, then the second character in the patient's HIPPS code would be a B. The third character represents the Nursing group into which the patient classifies. The fourth character represents the NTA group into which the patient classifies. Finally, the fifth character represents the assessment used to generate the HIPPS code.

Tables 5 and 6 reflect the PDPM's structure. Accordingly, Column 1 of Tables 5 and 6 represents the character in the HIPPS code associated with a given PDPM component. Columns 2 and 3 provide the case-mix index and associated case-mix adjusted component rate, respectively, for the relevant PT group. Columns 4 and 5 provide the case-mix index and associated case-mix adjusted component rate, respectively, for the relevant OT group. Columns 6 and 7 provide the case-mix index and associated case-mix adjusted component rate, respectively, for the relevant SLP group. Column 8 provides the nursing case-mix group (CMG) that is connected with a given PDPM HIPPS character. For example, if the patient qualified for the nursing group CBC1, then the third character in the patient's HIPPS code would be a “P.” Columns 9 and 10 provide the case-mix index and associated case-mix adjusted component rate, respectively, for the relevant nursing group. Finally, columns 11 and 12 provide the case-mix index and associated case-mix adjusted component rate, respectively, for the relevant NTA group.

Tables 5 and 6 do not reflect adjustments which may be made to the SNF PPS rates as a result of the SNF VBP Program, discussed in section VII. of this final rule, or other adjustments, such as the variable per diem adjustment. Further, in the past, we used the revised OMB delineations adopted in the FY 2015 SNF PPS final rule (79 FR 45632, 45634), with updates as reflected in OMB Bulletin Nos, 15-01 and 17-01, to identify a facility's urban or rural status for the purpose of determining which set of rate tables would apply to the facility. As discussed in the FY 2021 SNF PPS final rule (85 FR 47594), we adopted the revised OMB delineations identified in OMB Bulletin No. 18-04 (available at https://www.whitehouse.gov/​wp-content/​uploads/​2018/​09/​Bulletin-18-04.pdf) to identify a facility's urban or rural status effective beginning with FY 2021.

As we noted in the FY 2022 SNF PPS final rule (86 FR 42434), we continue to monitor the impact of PDPM implementation on patient outcomes and program outlays. Because of this analysis, in section V.C. of the proposed rule, we proposed to recalibrate the PDPM parity adjustment discussed in the FY 2020 SNF PPS final rule (84 FR 38734). Following the methodology of this proposed change, Tables 5 and 6 incorporate the recalibration of the PDPM parity adjustment.

D. Wage Index Adjustment

Section 1888(e)(4)(G)(ii) of the Act requires that we adjust the Federal rates to account for differences in area wage levels, using a wage index that the Secretary determines appropriate. Since the inception of the SNF PPS, we have used hospital inpatient wage data in developing a wage index to be applied to SNFs. We proposed to continue this practice for FY 2023, as we continue to believe that in the absence of SNF-specific wage data, using the hospital inpatient wage index data is appropriate and reasonable for the SNF PPS. As explained in the update notice for FY 2005 (69 FR 45786), the SNF PPS does not use the hospital area wage index's occupational mix adjustment, as this adjustment serves specifically to define the occupational categories more clearly in a hospital setting; moreover, the collection of the occupational wage data under the inpatient prospective payment system (IPPS) also excludes any wage data related to SNFs. Therefore, we believe that using the updated wage data exclusive of the occupational mix adjustment continues to be appropriate for SNF payments. As in previous years, we would continue to use the pre-reclassified IPPS hospital wage data, without applying the occupational mix, rural floor, or outmigration adjustment, as the basis for the SNF PPS wage index. For FY 2023, the updated wage data are for hospital cost reporting periods beginning on or after October 1, 2018 and before October 1, 2019 (FY 2019 cost report data).

We note that section 315 of the Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000 (BIPA) (Pub. L. 106-554, enacted December 21, 2000) authorized us to establish a geographic reclassification procedure that is specific to SNFs, but only after collecting the data necessary to establish a SNF PPS wage index that is based on wage data from nursing homes. However, to date, this has proven to be unfeasible due to the volatility of existing SNF wage data and the significant amount of resources that would be required to improve the quality of the data. More specifically, auditing all SNF cost reports, similar to the process used to audit inpatient hospital cost reports for purposes of the IPPS wage index, would place a burden on providers in terms of recordkeeping and completion of the cost report worksheet. In addition, adopting such an approach would require a significant commitment of resources by CMS and the Medicare Administrative Contractors, potentially far in excess of those required under the IPPS, given that there are nearly five times as many SNFs as there are inpatient hospitals. While we continue to believe that the development of such an audit process could improve SNF cost reports in such a manner as to permit us to establish a SNF-specific wage index, we do not believe this undertaking is feasible at this time. Therefore, as discussed in the proposed rule, in the absence of a SNF-specific wage index, we believe the use of the pre-reclassified and pre-floor hospital wage data (without the occupational mix adjustment) continue to be an appropriate and reasonable proxy for the SNF PPS.

In addition, we proposed to continue to use the same methodology discussed in the SNF PPS final rule for FY 2008 (72 FR 43423) to address those geographic areas in which there are no hospitals, and thus, no hospital wage index data on which to base the calculation of the FY 2022 SNF PPS wage index. For rural geographic areas that do not have hospitals and, therefore, lack hospital wage data on which to base an area wage adjustment, we proposed to continue using the average wage index from all contiguous Core-Based Statistical Areas (CBSAs) as a reasonable proxy. For FY 2023, there are no rural geographic areas that do not have hospitals, and thus, this methodology will not be applied. For rural Puerto Rico, we proposed not to apply this methodology due to the distinct economic circumstances there (for example, due to the close proximity of almost all of Puerto Rico's various urban and non-urban areas, this methodology would produce a wage index for rural Puerto Rico that is higher than that in half of its urban areas). Instead, we would continue using the most recent wage index previously available for that area. For urban areas without specific hospital wage index data, we proposed that we would use the average wage indexes of all urban areas within the State to serve as a reasonable proxy for the wage index of that urban CBSA. For FY 2023, the only urban area without wage index data available is CBSA 25980, Hinesville-Fort Stewart, GA.

The wage index applicable to FY 2023 is set forth in Tables A and B available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​WageIndex.html.

In the SNF PPS final rule for FY 2006 (70 FR 45026, August 4, 2005), we adopted the changes discussed in OMB Bulletin No. 03-04 (June 6, 2003), which announced revised definitions for MSAs and the creation of micropolitan statistical areas and combined statistical areas. In adopting the CBSA geographic designations, we provided for a 1-year transition in FY 2006 with a blended wage index for all providers. For FY 2006, the wage index for each provider consisted of a blend of 50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index (both using FY 2002 hospital data). We referred to the blended wage index as the FY 2006 SNF PPS transition wage index. As discussed in the SNF PPS final rule for FY 2006 (70 FR 45041), after the expiration of this 1-year transition on September 30, 2006, we used the full CBSA-based wage index values.

In the FY 2015 SNF PPS final rule (79 FR 45644 through 45646), we finalized changes to the SNF PPS wage index based on the newest OMB delineations, as described in OMB Bulletin No. 13-01, beginning in FY 2015, including a 1-year transition with a blended wage index for FY 2015. OMB Bulletin No. 13-01 established revised delineations for Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Combined Statistical Areas in the United States and Puerto Rico based on the 2010 Census, and provided guidance on the use of the delineations of these statistical areas using standards published in the June 28, 2010 Federal Register (75 FR 37246 through 37252). Subsequently, on July 15, 2015, OMB issued OMB Bulletin No. 15-01, which provided minor updates to and superseded OMB Bulletin No. 13-01 that was issued on February 28, 2013. The attachment to OMB Bulletin No. 15-01 provided detailed information on the update to statistical areas since February 28, 2013. The updates provided in OMB Bulletin No. 15-01 were based on the application of the 2010 Standards for Delineating Metropolitan and Micropolitan Statistical Areas to Census Bureau population estimates for July 1, 2012 and July 1, 2013 and were adopted under the SNF PPS in the FY 2017 SNF PPS final rule (81 FR 51983, August 5, 2016). In addition, on August 15, 2017, OMB issued Bulletin No. 17-01 which announced a new urban CBSA, Twin Falls, Idaho (CBSA 46300) which was adopted in the SNF PPS final rule for FY 2019 (83 FR 39173, August 8, 2018).

As discussed in the FY 2021 SNF PPS final rule (85 FR 47594), we adopted the revised OMB delineations identified in OMB Bulletin No. 18-04 (available at https://www.whitehouse.gov/​wp-content/​uploads/​2018/​09/​Bulletin-18-04.pdf) beginning October 1, 2020, including a 1-year transition for FY 2021 under which we applied a 5 percent cap on any decrease in a hospital's wage index compared to its wage index for the prior fiscal year (FY 2020). The updated OMB delineations more accurately reflect the contemporary urban and rural nature of areas across the country, and the use of such delineations allows us to determine more accurately the appropriate wage index and rate tables to apply under the SNF PPS. For FY 2023 and subsequent years, we proposed to apply a permanent 5 percent cap on any decreases to a provider's wage index from its wage index in the prior year, regardless of the circumstances causing the decline, which was further discussed in section V.A. of the proposed rule.

As we previously stated in the FY 2008 SNF PPS proposed and final rules (72 FR 25538 through 25539, and 72 FR 43423), this and all subsequent SNF PPS rules and notices are considered to incorporate any updates and revisions set forth in the most recent OMB bulletin that applies to the hospital wage data used to determine the current SNF PPS wage index. We note that on March 6, 2020, OMB issued Bulletin No. 20-01, which provided updates to and superseded OMB Bulletin No. 18-04 that was issued on September 14, 2018. The attachments to OMB Bulletin No. 20-01 provided detailed information on the updates (available on the web at https://www.whitehouse.gov/​wp-content/​uploads/​2020/​03/​Bulletin-20-01.pdf). In the FY 2021 SNF PPS final rule (85 FR 47611), we stated that we intended to propose any updates from OMB Bulletin No. 20-01 in the FY 2022 SNF PPS proposed rule. After reviewing OMB Bulletin No. 20-01, we have determined that the changes in OMB Bulletin 20-01 encompassed delineation changes that do not impact the CBSA-based labor market area delineations adopted in FY 2021. Therefore, while we proposed to adopt the updates set forth in OMB Bulletin No. 20-01 consistent with our longstanding policy of adopting OMB delineation updates, we noted that specific wage index updates would not be necessary for FY 2022 as a result of adopting these OMB updates and for these reasons we did not make such a proposal for FY 2023.

The wage index applicable to FY 2023 is set forth in Tables A and B available on the CMS website at http://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​WageIndex.html.

Once calculated, we would apply the wage index adjustment to the labor-related portion of the Federal rate. Each year, we calculate a revised labor-related share, based on the relative importance of labor-related cost categories (that is, those cost categories that are labor-intensive and vary with the local labor market) in the input price index. In the SNF PPS final rule for FY 2018 (82 FR 36548 through 36566), we finalized a proposal to revise the labor-related share to reflect the relative importance of the 2014-based SNF market basket cost weights for the following cost categories: Wages and Salaries; Employee Benefits; Professional Fees: Labor-Related; Administrative and Facilities Support Services; Installation, Maintenance, and Repair Services; All Other: Labor-Related Services; and a proportion of Capital-Related expenses. Effective beginning FY 2022 (86 FR 42437), we rebased and revised the labor-related share to reflect the relative importance of the 2018-based SNF market basket cost weights for the following cost categories: Wages and Salaries; Employee Benefits; Professional Fees: Labor-Related; Administrative and Facilities Support services; Installation, Maintenance, and Repair Services; All Other: Labor-Related Services; and a proportion of Capital-Related expenses. The methodology for calculating the labor-related portion beginning in FY 2022 is discussed in detail in the FY 2022 SNF PPS final rule (86 FR 42424).

We calculate the labor-related relative importance from the SNF market basket, and it approximates the labor-related portion of the total costs after taking into account historical and projected price changes between the base year and FY 2023. The price proxies that move the different cost categories in the market basket do not necessarily change at the same rate, and the relative importance captures these changes. Accordingly, the relative importance figure more closely reflects the cost share weights for FY 2023 than the base year weights from the SNF market basket. We calculate the labor-related relative importance for FY 2023 in four steps. First, we compute the FY 2023 price index level for the total market basket and each cost category of the market basket. Second, we calculate a ratio for each cost category by dividing the FY 2023 price index level for that cost category by the total market basket price index level. Third, we determine the FY 2023 relative importance for each cost category by multiplying this ratio by the base year (2018) weight. Finally, we add the FY 2023 relative importance for each of the labor-related cost categories (Wages and Salaries; Employee Benefits; Professional Fees: Labor-Related; Administrative and Facilities Support Services; Installation, Maintenance, and Repair Services; All Other: Labor-Related Services; and a portion of Capital-Related expenses) to produce the FY 2023 labor-related relative importance.

For the proposed rule, the labor-related share for FY 2023 was based on IGI's fourth quarter 2021 forecast of the 2018-based SNF market basket with historical data through third quarter 2021. As outlined in the proposed rule, we noted that if more recent data became available (for example, a more recent estimate of the labor-related share relative importance) we would use such data if appropriate for the SNF final rule. For this final rule, we base the labor-related share for FY 2023 on IGI's second quarter 2022 forecast, with historical data through the first quarter 2022. Table 7 summarizes the labor-related share for FY 2023, based on IGI's second quarter 2022 forecast of the 2018-based SNF market basket, compared to the labor-related share that was used for the FY 2022 SNF PPS final rule.

To calculate the labor portion of the case-mix adjusted per diem rate, we would multiply the total case-mix adjusted per diem rate, which is the sum of all five case-mix adjusted components into which a patient classifies, and the non-case-mix component rate, by the FY 2023 labor-related share percentage provided in Table 7. The remaining portion of the rate would be the non-labor portion. Under the previous RUG-IV model, we included tables which provided the case-mix adjusted RUG-IV rates, by RUG-IV group, broken out by total rate, labor portion and non-labor portion, such as Table 9 of the FY 2019 SNF PPS final rule (83 FR 39175). However, as we discussed in the FY 2020 final rule (84 FR 38738), under PDPM, as the total rate is calculated as a combination of six different component rates, five of which are case-mix adjusted, and given the sheer volume of possible combinations of these five case-mix adjusted components, it is not feasible to provide tables similar to those that existed in the prior rulemaking.

Therefore, to aid interested parties in understanding the effect of the wage index on the calculation of the SNF per diem rate, we have included a hypothetical rate calculation in Table 9.

Section 1888(e)(4)(G)(ii) of the Act also requires that we apply this wage index in a manner that does not result in aggregate payments under the SNF PPS that are greater or less than would otherwise be made if the wage adjustment had not been made. For FY 2023 (Federal rates effective October 1, 2022), we apply an adjustment to fulfill the budget neutrality requirement. We meet this requirement by multiplying each of the components of the unadjusted Federal rates by a budget neutrality factor, equal to the ratio of the weighted average wage adjustment factor for FY 2022 to the weighted average wage adjustment factor for FY 2023. For this calculation, we would use the same FY 2021 claims utilization data for both the numerator and denominator of this ratio. We define the wage adjustment factor used in this calculation as the labor portion of the rate component multiplied by the wage index plus the non-labor portion of the rate component. The proposed budget neutrality factor for FY 2023 set forth in the proposed rule was 1.0011.

We noted that if more recent data became available (for example, revised wage data), we would use such data, as appropriate, to determine the wage index budget neutrality factor in the SNF PPS final rule. Since the proposed rule, we have updated the wage adjustment factor for FY 2023. Based on this updated information, the budget neutrality factor for FY 2023 is 1.0005.

The following is a summary of the public comments we received on the proposed revisions to the Wage Index Adjustment and our responses.

Comment: Several commenters recommended that CMS develop a SNF-specific wage index utilizing SNF wage data rather than relying on hospital wage data. Most of these commenters recommended CMS utilize BLS data, while one commenter recommended CMS focus on Payroll-Based Journaling (PBJ) data.

Response: We appreciate the commenters' suggestion that we develop a SNF-specific wage index utilizing SNF wage data instead of hospital wage data while considering the use of BLS and PBJ data. We note that, consistent with the discussion published most recently in the FY 2021 SNF PPS final rule (86 FR 42436 through 42439), and in further detail in the FY 2019 SNF PPS final rule (83 FR 39172 through 39178) to these recurring comments, developing such a wage index would require a resource-intensive audit process similar to that used for IPPS hospital data, to improve the quality of the SNF cost report data in order for it to be used as part of this analysis. We also discussed in the FY 2019 SNF PPS why utilizing concepts such as BLS data and PBJ are unfeasible or not applicable to SNF policy.

We continue to believe that in the absence of the appropriate SNF-specific wage data, using the pre-reclassified, pre-rural floor hospital inpatient wage data (without the occupational mix adjustment) is appropriate and reasonable for the SNF PPS.

Comment: Several comments suggested that CMS revise the SNF wage index to adopt the same geographic reclassification and rural floor polices that are used to adjust the IPPS wage index.

Response: We note that until the development of a SNF-specific wage index, the SNF PPS does not account for geographic reclassification under section 315 of the Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000 (BIPA) (Pub. L. 106-554, enacted December 21, 2000).

With regard to implementing a rural floor under the SNF PPS, we do not believe it would be prudent at this time to adopt such a policy, particularly because MedPAC has repeatedly recommended eliminating the rural floor policy from the calculation of the IPPS wage index. For example, Chapter 3 of MedPAC's March 2013 Report to Congress on Medicare Payment Policy, available at http://www.medpac.gov/​docs/​default-source/​reports/​mar13_​ch03.pdf, notes on page 65 that, in 2007, MedPAC had recommended eliminating these special wage index adjustments and adopting a new wage index system to avoid geographic inequities that can occur due to current wage index policies (Medicare Payment Advisory Commission 2007b)). If we adopted the rural floor policy at this time, the SNF PPS wage index could become vulnerable to problems similar to those MedPAC identified in its March 2013 Report to Congress.

Furthermore, as we do not have an SNF-specific wage index, we are unable to determine the degree, if any, to which a geographic reclassification adjustment or a rural floor policy under the SNF PPS would be appropriate. The rationale for our current wage index policies was most recently published in the FY 2022 SNF PPS final rule (86 FR 42436) and previously described in the FY 2016 SNF PPS final rule (80 FR 45401 through 46402).

After consideration of public comments, we are finalizing our proposal to continue to use the updated pre-reclassification and pre-floor IPPS wage index data to develop the FY 2023 SNF PPS wage index.

E. SNF Value-Based Purchasing Program

Beginning with payment for services furnished on October 1, 2018, section 1888(h) of the Act requires the Secretary to reduce the adjusted Federal per diem rate determined under section 1888(e)(4)(G) of the Act otherwise applicable to a SNF for services furnished during a fiscal year by 2 percent, and to adjust the resulting rate for a SNF by the value-based incentive payment amount earned by the SNF based on the SNF's performance score for that fiscal year under the SNF VBP Program. To implement these requirements, we finalized in the FY 2019 SNF PPS final rule the addition of § 413.337(f) to our regulations (83 FR 39178).

Please see section VIII. of this final rule for further discussion of our policies for the SNF VBP Program.

F. Adjusted Rate Computation Example

Tables 8 through 10 provide examples generally illustrating payment calculations during FY 2023 under PDPM for a hypothetical 30-day SNF stay, involving the hypothetical SNF XYZ, located in Frederick, MD (Urban CBSA 23224), for a hypothetical patient who is classified into such groups that the patient's HIPPS code is NHNC1. Table 8 shows the adjustments made to the Federal per diem rates (prior to application of any adjustments under the SNF VBP Program as discussed previously and taking into account the proposed parity adjustment discussed in section VI.C. of this final rule) to compute the provider's case-mix adjusted per diem rate for FY 2023, based on the patient's PDPM classification, as well as how the variable per diem (VPD) adjustment factor affects calculation of the per diem rate for a given day of the stay. Table 9 shows the adjustments made to the case-mix adjusted per diem rate from Table 8 to account for the provider's wage index. The wage index used in this example is based on the FY 2023 SNF PPS wage index that appears in Table A available on the CMS website at http://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​WageIndex.html. Finally, Table 10 provides the case-mix and wage index adjusted per-diem rate for this patient for each day of the 30-day stay, as well as the total payment for this stay. Table 10 also includes the VPD adjustment factors for each day of the patient's stay, to clarify why the patient's per diem rate changes for certain days of the stay. As illustrated in Table 8, SNF XYZ's total PPS payment for this particular patient's stay would equal $20,821.69.

V. Additional Aspects of the SNF PPS

A. SNF Level of Care—Administrative Presumption

The establishment of the SNF PPS did not change Medicare's fundamental requirements for SNF coverage. However, because the case-mix classification is based, in part, on the beneficiary's need for skilled nursing care and therapy, we have attempted, where possible, to coordinate claims review procedures with the existing resident assessment process and case-mix classification system discussed in section IV.C. of this final rule. This approach includes an administrative presumption that utilizes a beneficiary's correct assignment, at the outset of the SNF stay, of one of the case-mix classifiers designated for this purpose to assist in making certain SNF level of care determinations.

In accordance with § 413.345, we include in each update of the Federal payment rates in the Federal Register a discussion of the resident classification system that provides the basis for case-mix adjustment. We also designate those specific classifiers under the case-mix classification system that represent the required SNF level of care, as provided in 42 CFR 409.30. This designation reflects an administrative presumption that those beneficiaries who are correctly assigned one of the designated case-mix classifiers on the initial Medicare assessment are automatically classified as meeting the SNF level of care definition up to and including the assessment reference date (ARD) for that assessment.

A beneficiary who does not qualify for the presumption is not automatically classified as either meeting or not meeting the level of care definition, but instead receives an individual determination on this point using the existing administrative criteria. This presumption recognizes the strong likelihood that those beneficiaries who are correctly assigned one of the designated case-mix classifiers during the immediate post-hospital period would require a covered level of care, which would be less likely for other beneficiaries.

In the July 30, 1999 final rule (64 FR 41670), we indicated that we would announce any changes to the guidelines for Medicare level of care determinations related to modifications in the case-mix classification structure. The FY 2018 final rule (82 FR 36544) further specified that we would henceforth disseminate the standard description of the administrative presumption's designated groups via the SNF PPS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​ index.html (where such designations appear in the paragraph entitled “Case Mix Adjustment”), and would publish such designations in rulemaking only to the extent that we actually intend to propose changes in them. Under that approach, the set of case-mix classifiers designated for this purpose under PDPM was finalized in the FY 2019 SNF PPS final rule (83 FR 39253) and is posted on the SNF PPS website ( https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​index.html), in the paragraph entitled “Case Mix Adjustment.”

However, we note that this administrative presumption policy does not supersede the SNF's responsibility to ensure that its decisions relating to level of care are appropriate and timely, including a review to confirm that any services prompting the assignment of one of the designated case-mix classifiers (which, in turn, serves to trigger the administrative presumption) are themselves medically necessary. As we explained in the FY 2000 SNF PPS final rule (64 FR 41667), the administrative presumption is itself rebuttable in those individual cases in which the services actually received by the resident do not meet the basic statutory criterion of being reasonable and necessary to diagnose or treat a beneficiary's condition (according to section 1862(a)(1) of the Act). Accordingly, the presumption would not apply, for example, in those situations where the sole classifier that triggers the presumption is itself assigned through the receipt of services that are subsequently determined to be not reasonable and necessary. Moreover, we want to stress the importance of careful monitoring for changes in each patient's condition to determine the continuing need for Part A SNF benefits after the ARD of the initial Medicare assessment.

B. Consolidated Billing

Sections 1842(b)(6)(E) and 1862(a)(18) of the Act (as added by section 4432(b) of the BBA 1997) require a SNF to submit consolidated Medicare bills to its Medicare Administrative Contractor (MAC) for almost all of the services that its residents receive during the course of a covered Part A stay. In addition, section 1862(a)(18) of the Act places the responsibility with the SNF for billing Medicare for physical therapy, occupational therapy, and speech-language pathology services that the resident receives during a noncovered stay. Section 1888(e)(2)(A) of the Act excludes a small list of services from the consolidated billing provision (primarily those services furnished by physicians and certain other types of practitioners), which remain separately billable under Part B when furnished to a SNF's Part A resident. These excluded service categories are discussed in greater detail in section V.B.2. of the May 12, 1998 interim final rule (63 FR 26295 through 26297).

A detailed discussion of the legislative history of the consolidated billing provision is available on the SNF PPS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​Downloads/​Legislative_​History_​2018-10-01.pdf. In particular, section 103 of the Medicare, Medicaid, and SCHIP Balanced Budget Refinement Act of 1999 (BBRA 1999) (Pub. L. 106-113, enacted November 29, 1999) amended section 1888(e)(2)(A)(iii) of the Act by further excluding a number of individual high-cost, low probability services, identified by HCPCS codes, within several broader categories (chemotherapy items, chemotherapy administration services, radioisotope services, and customized prosthetic devices) that otherwise remained subject to the provision. We discuss this BBRA 1999 amendment in greater detail in the SNF PPS proposed and final rules for FY 2001 (65 FR 19231 through 19232, April 10, 2000, and 65 FR 46790 through 46795, July 31, 2000), as well as in Program Memorandum AB-00-18 (Change Request #1070), issued March 2000, which is available online at www.cms.gov/​transmittals/​downloads/​ab001860.pdf.

As explained in the FY 2001 proposed rule (65 FR 19232), the amendments enacted in section 103 of the BBRA 1999 not only identified for exclusion from this provision a number of particular service codes within four specified categories (that is, chemotherapy items, chemotherapy administration services, radioisotope services, and customized prosthetic devices), but also gave the Secretary the authority to designate additional, individual services for exclusion within each of these four specified service categories. In the proposed rule for FY 2001, we also noted that the BBRA 1999 Conference report (H.R. Rep. No. 106-479 at 854 (1999) (Conf. Rep.)) characterizes the individual services that this legislation targets for exclusion as high-cost, low probability events that could have devastating financial impacts because their costs far exceed the payment SNFs receive under the PPS. According to the conferees, section 103(a) of the BBRA 1999 is an attempt to exclude from the PPS certain services and costly items that are provided infrequently in SNFs. By contrast, the amendments enacted in section 103 of the BBRA 1999 do not designate for exclusion any of the remaining services within those four categories (thus, leaving all of those services subject to SNF consolidated billing), because they are relatively inexpensive and are furnished routinely in SNFs.

As we further explained in the final rule for FY 2001 (65 FR 46790), and as is consistent with our longstanding policy, any additional service codes that we might designate for exclusion under our discretionary authority must meet the same statutory criteria used in identifying the original codes excluded from consolidated billing under section 103(a) of the BBRA 1999: they must fall within one of the four service categories specified in the BBRA 1999; and they also must meet the same standards of high cost and low probability in the SNF setting, as discussed in the BBRA 1999 Conference report. Accordingly, we characterized this statutory authority to identify additional service codes for exclusion as essentially affording the flexibility to revise the list of excluded codes in response to changes of major significance that may occur over time (for example, the development of new medical technologies or other advances in the state of medical practice) (65 FR 46791).

Effective with items and services furnished on or after October 1, 2021, section 134 in Division CC of the CAA established an additional category of excluded codes in section 1888(e)(2)(A)(iii)(VI) of the Act, for certain blood clotting factors for the treatment of patients with hemophilia and other bleeding disorders along with items and services related to the furnishing of such factors under section 1842(o)(5)(C) of the Act. Like the provisions enacted in the BBRA 1999, new section 1888(e)(2)(A)(iii)(VI) of the Act gives the Secretary the authority to designate additional items and services for exclusion within the category of items and services described in that section.

In the proposed rule, we specifically solicited public comments identifying HCPCS codes in any of these five service categories (chemotherapy items, chemotherapy administration services, radioisotope services, customized prosthetic devices, and blood clotting factors) representing recent medical advances that might meet our criteria for exclusion from SNF consolidated billing. In the proposed rule, we noted that we may consider excluding a particular service if it meets our criteria for exclusion as specified previously. We requested that commenters identify in their comments the specific HCPCS code that is associated with the service in question, as well as their rationale for requesting that the identified HCPCS code(s) be excluded.

In the proposed rule, we noted that the original BBRA amendment and the CAA identified a set of excluded items and services by means of specifying individual HCPCS codes within the designated categories that were in effect as of a particular date (in the case of the BBRA 1999, July 1, 1999, and in the case of the CAA, July 1, 2020), as subsequently modified by the Secretary. In addition, as noted in this section of the preamble, the statute (sections 1888(e)(2)(A)(iii)(II) through (VI) of the Act) gives the Secretary authority to identify additional items and services for exclusion within the categories of items and services described in the statute, which are also designated by HCPCS code. Designating the excluded services in this manner makes it possible for us to utilize program issuances as the vehicle for accomplishing routine updates to the excluded codes to reflect any minor revisions that might subsequently occur in the coding system itself, such as the assignment of a different code number to a service already designated as excluded, or the creation of a new code for a type of service that falls within one of the established exclusion categories and meets our criteria for exclusion.

Accordingly, in the event that we identify through the current rulemaking cycle any new services that would actually represent a substantive change in the scope of the exclusions from SNF consolidated billing, we would identify these additional excluded services by means of the HCPCS codes that are in effect as of a specific date (in this case, October 1, 2022). By making any new exclusions in this manner, we could similarly accomplish routine future updates of these additional codes through the issuance of program instructions. The latest list of excluded codes can be found on the SNF Consolidated Billing website at https://www.cms.gov/​Medicare/​Billing/​SNFConsolidatedBilling.

The following is a summary of the public comments we received on the proposed revisions to Consolidated Billing and our responses.

Comment: One commenter stated that consolidated billing exclusions remain inadequate and should be revised. The commenter stated that there continue to be outlier drug costs that need to be considered for exclusion from consolidated billing. The commenter stated that certain classes of drugs considered “Specialty” drugs are the largest exposure items for SNFs and need to be evaluated by CMS. The commenter further stated that many pharmaceutical therapies in use today were not in existence at the time that consolidated billing PPDs were created. Therefore, they cannot be considered “included” within the Medicare A FFS rate.

Response: As we noted in the proposed rule, sections 1888(e)(2)(A)(iii)(II) through (VI) of the Act give the Secretary authority to identify additional items and services for exclusion only within the categories of items and services described in the statute. Accordingly, it is beyond the statutory authority of CMS to exclude services that do not fit these categories, or to create additional categories of excluded services. Such changes would require Congressional action.

Comment: A commenter requested that CMS to consider agents that have evolving indications for use for different malignancies. In particular, the commenter requested consideration for both Leuprolide Acetate (HCPCS J9217) as well as Denosumab (HCPCS J0897) which previously was indicated as an osteoporosis medication but now has broader uses. The commenter also requested continued consideration of covering expensive antibiotics in Skilled Nursing Facilities as part of a Part A covered stay. The commenter stated that use of antibiotics such as ceftolozane 50 mg and tazobactam 25 mg (HCPCS J0695) are prohibitively expensive for facilities to cover outside of SNF consolidated billing and limit beneficiaries' abilities to access these skilled rehab services.

Response: For the reasons discussed previously in prior rulemaking, the particular drugs cited in these comments remain subject to consolidated billing. In the case of leuprolide acetate, we have addressed this when suggested in past rulemaking cycles, most recently in the SNF PPS final rules for FY 2019 (83 FR 39162, August 8, 2018) and FY 2015 (79 FR 45642, August 5, 2014). In those rules, we explained that this drug is unlikely to meet the criterion of “low probability” specified in the BBRA. With regard to denosumab, it would similarly be unlikely to meet the criterion of “low probability.” One of the indications for treatment is for bone metastases from solid tumors such as bone or prostate cancer. This can occur in up to 70 to 90 percent of patients with breast or prostate cancer.

With regard to the suggestion that CMS should exclude antibiotics, we note again that it is beyond the statutory authority of CMS to exclude services that do not fit the categories for exclusion defined by statute, or to create additional categories of excluded services. Such changes would require Congressional action.

C. Payment for SNF-Level Swing-Bed Services

Section 1883 of the Act permits certain small, rural hospitals to enter into a Medicare swing-bed agreement, under which the hospital can use its beds to provide either acute- or SNF-level care, as needed. For critical access hospitals (CAHs), Part A pays on a reasonable cost basis for SNF-level services furnished under a swing-bed agreement. However, in accordance with section 1888(e)(7) of the Act, SNF-level services furnished by non-CAH rural hospitals are paid under the SNF PPS, effective with cost reporting periods beginning on or after July 1, 2002. As explained in the FY 2002 final rule (66 FR 39562), this effective date is consistent with the statutory provision to integrate swing-bed rural hospitals into the SNF PPS by the end of the transition period, June 30, 2002.

Accordingly, all non-CAH swing-bed rural hospitals have now come under the SNF PPS. Therefore, all rates and wage indexes outlined in earlier sections of this final rule for the SNF PPS also apply to all non-CAH swing-bed rural hospitals. As finalized in the FY 2010 SNF PPS final rule (74 FR 40356 through 40357), effective October 1, 2010, non-CAH swing-bed rural hospitals are required to complete an MDS 3.0 swing-bed assessment which is limited to the required demographic, payment, and quality items. As discussed in the FY 2019 SNF PPS final rule (83 FR 39235), revisions were made to the swing bed assessment to support implementation of PDPM, effective October 1, 2019. A discussion of the assessment schedule and the MDS effective beginning FY 2020 appears in the FY 2019 SNF PPS final rule (83 FR 39229 through 39237). The latest changes in the MDS for swing-bed rural hospitals appear on the SNF PPS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​index.html.

D. Revisions to the Regulation Text

We proposed to make certain revisions in the regulation text itself. Specifically, we proposed to revise § 413.337(b)(4) and add new paragraphs (b)(4)(i) through (iii). These proposed revisions reflect that the application of the wage index would be made on the basis of the location of the facility in an urban or rural area as defined in § 413.333, and that starting on October 1, 2022, we would apply a cap on decreases to the wage index such that the wage index applied to a SNF is not less than 95 percent of the wage index applied to that SNF in the prior FY, as discussed in section VI.A. of this final rule.

We did not receive public comments specific to the proposed revisions to the regulation text, and therefore, we are finalizing as proposed. We discuss comments received on the wage index cap policy itself in section VI.A. of this final rule.

VI. Other SNF PPS Issues

A. Permanent Cap on Wage Index Decreases

As outlined in section III.D. of the proposed rule, we proposed and finalized temporary transition policies in the past to mitigate significant changes to payments due to changes to the SNF PPS wage index. Specifically, for FY 2015 (79 FR 45644 through 45646), we implemented a 50/50 blend for all geographic areas consisting of the wage index values computed using the then-current OMB area delineations and the wage index values computed using new area delineations based on OMB Bulletin No. 13-01. In FY 2021 (85 FR 47594, 47617), we implemented a 1-year transition to mitigate any negative effects of wage index changes by applying a 5 percent cap on any decrease in a SNF's wage index from the final wage index from FY 2020. We explained that we believed the 5-percent cap would provide greater transparency and would be administratively less complex than the prior methodology of applying a 50/50 blended wage index. We indicated that no cap would be applied to the reduction in the wage index for FY 2022, and we noted that this transition approach struck an appropriate balance by providing a transition period to mitigate the resulting short-term instability and negative impacts on providers and time for them to adjust to their new labor market area delineations and wage index values.

In the FY 2022 final rule (86 FR 42424, 42439), commenters recommended that CMS extend the transition period adopted in the FY 2021 SNF PPS final rule so that SNFs could offset the cuts scheduled for FY 2022. Although, we acknowledged that certain changes to wage index policy could affect Medicare payment. In addition, we reiterated that our policy principles with regard to the wage index include generally using the most current data and information available and providing that data and information, as well as any approaches to addressing any significant effects on Medicare payments resulting from these potential scenarios around SNF payment volatility, in notice and comment rulemaking. We did not propose to modify the transition policy that was finalized in the FY 2021 SNF PPS final rule, and therefore, did not extend the transition period for FY 2022. With these policy principles in mind for this FY 2023 proposed rule, we considered how best to address commenters' concerns discussed in the FY 2022 final rule around SNF payment volatility; that is, scenarios in which changes to wage index policy may significantly affect Medicare payments.

In the past, we have established transition policies of limited duration to phase in significant changes to labor market. In taking this approach in the past, we have sought to strike an appropriate balance between maintaining the accuracy of the overall labor market area wage index system and mitigating short-term instability and negative impacts on providers due to wage index changes. In accordance with the requirements of the SNF PPS wage index regulations at § 413.337(a)(1), we use an appropriate wage index based on the best available data, including the best available labor market area delineations, to adjust SNF PPS payments for wage differences. We have previously stated that, because the wage index is a relative measure of the value of labor in prescribed labor market areas, we believe it is important to implement new labor market area delineations with as minimal a transition as is reasonably possible. However, we recognize that changes to the wage index have the potential to create instability and significant negative impacts on certain providers even when labor market areas do not change. In addition, year-to-year fluctuations in an area's wage index can occur due to external factors beyond a provider's control, such as the COVID-19 public health emergency (PHE). For an individual provider, these fluctuations can be difficult to predict. So, we also recognize that predictability in Medicare payments is important to enable providers to budget and plan their operations.

In light of these considerations, we proposed a permanent approach to smooth year-to-year changes in providers' wage indexes. We proposed a policy that we believe increases the predictability of SNF PPS payments for providers, and mitigates instability and significant negative impacts to providers resulting from changes to the wage index.

As previously discussed, we believed applying a 5-percent cap on wage index decreases for FY 2021 provided greater transparency and was administratively less complex than prior transition methodologies. In addition, we believed this methodology mitigated short-term instability and fluctuations that can negatively impact providers due to wage index changes. Lastly, we have noted that we believed the 5-percent cap we applied to all wage index decreases for FY 2021 provided an adequate safeguard against significant payment reductions related to the adoption of the revised CBSAs. However, we recognize there are circumstances that a 1-year mitigation policy, like the one adopted for FY 2021, would not effectively address future years where providers continue to be negatively affected by significant wage index decreases.

Typical year-to-year variation in the SNF PPS wage index has historically been within 5 percent, and we expect this will continue to be the case in future years. For FY 2023, the provider level impact analysis indicates that approximately 97 percent of SNFs will experience a wage index change within 5 percent. Because providers are usually experienced with this level of wage index fluctuation, we believe applying a 5-percent cap on all wage index decreases each year, regardless of the reason for the decrease, would effectively mitigate instability in SNF PPS payments due to any significant wage index decreases that may affect providers in any year. We believe this approach would address concerns about instability that commenters raised in the FY 2022 SNF PPS rule. Additionally, as noted in the proposed rule, we believe that applying a 5-percent cap on all wage index decreases would support increased predictability about SNF PPS payments for providers, enabling them to more effectively budget and plan their operations. Lastly, because applying a 5-percent cap on all wage index decreases would represent a small overall impact on the labor market area wage index system we believe it would ensure the wage index is a relative measure of the value of labor in prescribed labor market wage areas. As outlined in detail in section XI.A.4. of the proposed rule, we estimated that applying a 5-percent cap on all wage index decreases will have a very small effect on the wage index budget neutrality factor for FY 2023. Because the wage index is a measure of the value of labor (wage and wage-related costs) in a prescribed labor market area relative to the national average, we anticipate that in the absence of proposed policy changes most providers will not experience year-to-year wage index declines greater than 5 percent in any given year. As noted in the proposed rule, we also believe that when the 5-percent cap would be applied under this proposal, it is likely that it would be applied similarly to all SNFs in the same labor market area, as the hospital average hourly wage data in the CBSA (and any relative decreases compared to the national average hourly wage) would be similar. While this policy may result in SNFs in a CBSA receiving a higher wage index than others in the same area (such as situations when delineations change), we believe the impact would be temporary. Therefore, we anticipate that the impact to the wage index budget neutrality factor in future years would continue to be minimal.

The Secretary has broad authority to establish appropriate payment adjustments under the SNF PPS, including the wage index adjustment. As discussed earlier in this section, the SNF PPS regulations require us to use an appropriate wage index based on the best available data. For the reasons discussed earlier in this section, we believe that a 5-percent cap on wage index decreases would be appropriate for the SNF PPS. Therefore, for FY 2023 and subsequent years, we proposed to apply a permanent 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, regardless of the circumstances causing the decline. That is, we proposed that SNF's wage index for FY 2023 would not be less than 95 percent of its final wage index for FY 2022, regardless of whether the SNF is part of an updated CBSA, and that for subsequent years, a provider's wage index would not be less than 95 percent of its wage index calculated in the prior FY. This means, if a SNF's prior FY wage index is calculated with the application of the 5-percent cap, then the following year's wage index would not be less than 95 percent of the SNF's capped wage index in the prior FY. For example, if a SNF's wage index for FY 2023 is calculated with the application of the 5-percent cap, then its wage index for FY 2024 would not be less than 95 percent of its capped wage index in FY 2023. Lastly, we proposed that a new SNF would be paid the wage index for the area in which it is geographically located for its first full or partial FY with no cap applied, because a new SNF would not have a wage index in the prior FY. As we outlined in the proposed rule, we believe this proposed methodology would maintain the SNF PPS wage index as a relative measure of the value of labor in prescribed labor market areas, increase the predictability of SNF PPS payments for providers, and mitigate instability and significant negative impacts to providers resulting from significant changes to the wage index. In section XI. of the proposed rule, we estimated the impact to payments for providers in FY 2023 based on this proposed policy. We also noted that we would examine the effects of this policy on an ongoing basis in the future in order to assess its continued appropriateness.

Subject to the aforementioned proposal becoming final, we also proposed to revise the regulation text at § 413.337(a)(1) to provide that starting October 1, 2022, we would apply a cap on decreases to the wage index such that the wage index applied is not less than 95 percent of the wage index applied to that SNF in the prior year.

We invited public comments on this proposal. The following is a summary of the comments we received on the proposed permanent cap on wage index decreases and our responses.

Comment: MedPAC expressed support for the 5-percent permanent cap on wage index decreases policy, but recommended that the 5-percent cap limit should apply to both increases and decreases in the wage index because they stated that no provider should have its wage index value increase or decrease by more than 5 percent.

Response: We appreciate MedPAC's suggestion that the cap on wage index changes of more than 5 percent should also be applied to increases in the wage index. However, as we discussed in the FY 2023 SNF PPS proposed rule (87 FR 22735), one purpose of the proposed policy is to help mitigate the significant negative impacts of certain wage index changes. Likewise, we explained that we believe that applying a 5-percent cap on all wage index decreases would support increased predictability about SNF PPS payments for providers, enabling them to more effectively budget and plan their operations. That is, we proposed to cap decreases because we believe that a provider would be able to more effectively budget and plan when there is predictability about its expected minimum level of SNF PPS payments in the upcoming fiscal year. We did not propose to limit wage index increases, because we do not believe such a policy would enable SNFs to more effectively budget and plan their operations. So, we believe it is appropriate for providers that experience an increase in their wage index value to receive the full benefit of their increased wage index value.

Comment: A few commenters requested that CMS retroactively apply the 5 percent cap policy to the FY 2022 wage index.

Response: In the FY 2021 SNF PPS rulemaking cycle, CMS proposed and finalized a one-time, 1-year transition policy to mitigate the effects of adopting OMB delineations updated in OMB Bulletin 18-04. In the FY 2023 SNF PPS proposed rule we did not propose to modify the one-time transition policy that was finalized in the FY 2021 SNF PPS final rule, nor did we propose to extend the transition period for FY 2022. We have historically implemented 1-year transitions, as discussed in the FY 2006 (70 FR 45026) and FY 2015 (79 FR 45644) final rules, to address CBSA changes due to substantial updates to OMB delineations. Our policy principles, as noted in the FY 2022 final rule (86 FR 42439), with regard to the wage index are to use the most updated data and information available. Therefore, the FY 2023 wage index policy proposal is prospective and is designed to mitigate any significant decreases beginning in FY 2023, not retroactively.

Comment: A number of commenters suggested the 5-percent cap be applied in a non-budget neutral manner.

Response: The statute at section 1888(e)(4)(G)(ii) of the Act requires that adjustments for geographic variations in labor costs for a FY are made in a budget-neutral. We are required to apply the permanent 5-percent cap policy in a budget-neutral manner.

Comment: A commenter recommended the percentage cap be lower than the proposed 5-percent stating they found that most wage indices do not swing by 5-percent.

Response: We appreciate the commenter's suggestion that the permanent cap percentage should be lower than 5-percent. However, as we discussed in the proposed rule, for FY 2023, the provider level impact analysis indicates that approximately 97 percent of SNFs will experience a wage index change within 5 percent. Because providers are usually experienced with this level of wage index fluctuation, we believe applying a 5-percent cap on all wage index decreases each year, regardless of the reason for the decrease, would effectively mitigate instability in SNF PPS payments due to any significant wage index decreases that may affect providers in any year.

Comment: One commenter was opposed to the implementation of the permanent 5-percent cap on wage index decreases at this time, stating that the industry struggled prior to the PHE.

Response: We appreciate the concern with implementing the permanent 5-percent cap on wage index decreases. However, as we discussed in the proposed rule, we believe moving forward with the permanent cap on wage index decreases would effectively mitigate instability in SNF PPS payments due to any significant wage index decreases that may affect providers in any year.

After consideration of the comments we received, we are finalizing the proposed permanent 5-percent cap on wage index decreases for the SNF PPS, beginning in FY 2023.

B. Technical Updates to PDPM ICD-10 Mappings

In the FY 2019 SNF PPS final rule (83 FR 39162), we finalized the implementation of the Patient Driven Payment Model (PDPM), effective October 1, 2019. The PDPM utilizes International Classification of Diseases, Version 10 (ICD-10) codes in several ways, including to assign patients to clinical categories under several PDPM components, specifically the PT, OT, SLP and NTA components. The ICD-10 code mappings and lists used under PDPM are available on the PDPM website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​PDPM.

Each year, the ICD-10 Coordination and Maintenance Committee, a Federal interdepartmental committee that is chaired by representatives from the National Center for Health Statistics (NCHS) and by representatives from CMS, meets biannually and publishes updates to the ICD-10 medical code data sets in June of each year. These changes become effective October 1 of the year in which these updates are issued by the committee. The ICD-10 Coordination and Maintenance Committee also can make changes to the ICD-10 medical code data sets effective on April 1 of each year.

In the FY 2020 SNF PPS final rule (84 FR 38750), we outlined the process by which we maintain and update the ICD-10 code mappings and lists associated with the PDPM, as well as the SNF Grouper software and other such products related to patient classification and billing, to ensure that they reflect the most up to date codes possible. Beginning with the updates for FY 2020, we apply nonsubstantive changes to the ICD-10 codes included on the PDPM code mappings and lists through a subregulatory process consisting of posting updated code mappings and lists on the PDPM website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​PDPM. Such nonsubstantive changes are limited to those specific changes that are necessary to maintain consistency with the most current ICD-10 medical code data set. On the other hand, substantive changes, or those that go beyond the intention of maintaining consistency with the most current ICD-10 medical code data set, will be proposed through notice and comment rulemaking. For instance, changes to the assignment of a code to a comorbidity list or other changes that amount to changes in policy are considered substantive changes for which we would undergo notice and comment rulemaking.

We proposed several changes to the PDPM ICD-10 code mappings and lists. We note that, in the case of any diagnoses that are either currently mapped to “Return to Provider” or that we proposed to classify into this category, this is not intended to reflect any judgment on the importance of recognizing and treating these conditions, but merely that there are more specific diagnoses than those mapped to “Return to Provider” or that we do not believe that the diagnosis should serve as the primary diagnosis for a Part-A covered SNF stay. Our proposed changes were as follows:

On October 1, 2021, D75.839 “ Thrombocytosis, unspecified, ” took effect and was mapped to the clinical category of “Cardiovascular and Coagulations.” However, there are more specific codes to indicate why a patient with thrombocytosis would require SNF care. If the cause is unknown, the SNF could use D47.3, “ Essential (hemorrhagic) thrombocythemia ” or D75.838, “ other thrombocytosis ” which is a new code that took effect on October 1, 2021. Further, elevated platelet count without other symptoms is not reason enough for SNF skilled care so this would not be used as a primary diagnosis. For this reason, we proposed to change the assignment of D75.839 to “ Return to Provider. ”

On October 1, 2021, D89.44, “ Hereditary alpha tryptasemia ” went into effect and was mapped to the clinical category, “Medical Management.” However, this is not a diagnosis that would be treated as a primary condition in the SNF, rather it would be treated in the outpatient setting. Therefore, we proposed to change the assignment of D89.44 to “ Return to Provider. ”

On October 1, 2021, F32.A, “ Depression, unspecified ” went into effect and was mapped to “Medical Management.” However, there are more specific codes that would more adequately capture the diagnosis of depression. Further, as we noted in the proposed rule, while we believe that SNFs serve an important role in providing services to those beneficiaries suffering from mental illness, the SNF setting is not the setting that would be most appropriate to treat a patient whose primary diagnosis is depression. For this reason, we proposed to change the assignment of F32.A to “Return to Provider.”

On October 1, 2021, G92.9, “ Unspecified toxic encephalopathy ” took effect and was mapped to the clinical category of “Acute Neurologic.” However, there are more specific codes that should be used to describe encephalopathy treated in a SNF. Therefore, we proposed to change the assignment of G92.9 to “ Return to Provider. ”

On October 1, 2021, M54.50, “ Low back pain, unspecified ” went into effect and was mapped to the clinical category of “Non-surgical Orthopedic/Musculoskeletal.” However, if low back pain were the primary diagnosis, the SNF should have a greater understanding of what is causing the pain. There are more specific codes to address this condition. Therefore, we proposed to change the assignment of M54.50 to “Return to Provider.”

In the FY 2022 proposed rule (86 FR 19984 through 19985), we proposed to reclassify K20.81, “ Other esophagitis with bleeding, ” K20.91, “ Esophagitis, unspecified with bleeding, ” and K21.01, “ Gastro-esophageal reflux disease with esophagitis, with bleeding ” from “Return to Provider” to “Medical Management.” Our rationale for the change was a recognition that these codes represent these esophageal conditions with more specificity than originally considered because of the bleeding that is part of the conditions and that they would more likely be found in SNF patients. We received one comment suggesting additional changes to similar ICD-10 code mappings and comorbidity lists that at the time were outside the scope of rulemaking. This commenter suggested that we consider remapping the following similar diagnosis codes that frequently require SNF skilled care, from “Return to Provider” to “Medical Management”: K22.11, “ Ulcer of esophagus with bleeding; ” K25.0, “ Acute gastric ulcer with hemorrhage; ” K25.1, “ Acute gastric ulcer with perforation; ” K25.2, “ Acute gastric ulcer with both hemorrhage and perforation; ” K26.0, “ Acute duodenal ulcer with hemorrhage; ” K26.1, “ Acute duodenal ulcer with perforation; ” K26.2, “ Acute duodenal ulcer with both hemorrhage and perforation; ” K27.0 “ Acute peptic ulcer, site unspecified with hemorrhage; ” K27.1, “ Acute peptic ulcer, site unspecified with perforation; ” K27.2, “ Acute peptic ulcer, site unspecified with both hemorrhage and perforation; ” K28.0, “ Acute gastrojejunal ulcer with hemorrhage; ” K28.1, “ Acute gastrojejunal ulcer with perforation; ” K28.2, “ Acute gastrojejunal ulcer with both hemorrhage and perforation; ” and K29.01, “ Acute gastritis with bleeding. ” Upon review of these codes, we recognize that they represent conditions with more specificity than originally considered because of the bleeding (or perforation) that is part of the conditions and that they would more likely be found in SNF patients.” Therefore, we proposed to remap these ICD-10 codes to “Medical Management.”

We also received a comment requesting we consider remapping M62.81, “ Muscle weakness (generalized) ” from “Return to Provider” to “Non-orthopedic Surgery” with the rationale that there is currently no sequela or late-effects ICD-10 code available when patients require skilled nursing and therapy due to late effects of resolved infections such as pneumonia or urinary tract infections. We considered the request and determined that muscle weakness (generalized) is nonspecific and if the original condition is resolved, but the resulting muscle weakness persists because of the known original diagnosis, there are more specific codes that exist that would account for why the muscle weakness is on-going, such as muscle wasting or atrophy. Therefore, we did not propose this specific remapping. This commenter also requested that that we consider remapping R62.7, “ Adult failure to thrive ” from “Return to Provider” to “Medical Management.” According to this commenter, physicians often diagnose adult failure to thrive when a resident has been unable to have oral intake sufficient for survival. Typically, this diagnosis is appended when the physician has determined that a feeding tube should be considered to provide sufficient intake for survival. According to the commenter, it would then appropriately become the primary diagnosis for a skilled stay. We considered this request and believe that R6.2 is a nonspecific code and SNF primary diagnoses should be coded to the highest level of specificity. If the patient has been unable to have oral intake, the primary diagnosis (for example, Ulcerative Colitis) for admission to a SNF should explain why the patient is unable to have oral intake sufficient for survival. Therefore, we did not propose this specific remapping.

We solicited comments on the proposed substantive changes to the ICD-10 code mappings discussed previously in this section, as well as comments on additional substantive and non-substantive changes that commenters believe are necessary. We received public comments on these proposals. The following is a summary of the comments we received and our responses.

Comment: Several commenters supported the proposed changes to the PDPM ICD-10 mappings. Some commenters expressed concerns with the proposed reclassification of certain conditions from a given clinical category to a Return to Provider status. For example, some commenters stated that, in the case of code F32.A ( Depression, unspecified), this may be the most appropriate diagnosis, based on the information provided in the medical record. These commenters also stated that while it may be appropriate to remap code D75.839 to Return to Provider, they do not believe the more specific codes discussed in the proposed rule for this condition would be appropriate. Similarly, some commenters opposed remapping code D89.44 to Return to Provider, as skilled care may be necessary to treat the symptoms associated with this condition.

Response: We appreciate the support for these proposed changes. Regarding the comments related to the potential lack of additional documentation to support more specific diagnoses, ICD 10 coding guidance indicates to code with the highest specificity. The suggestion of codes, D47.3 and D75.838, was given to provide examples of more specific coding that could potentially be used if appropriate. SNF primary diagnoses should be coded to the highest level of specificity. By the time a person is in the SNF, the reason for thrombocytosis, should be known and since ICD 10 guidelines state that coding should be to the highest specificity, the reason for thrombocytosis could be listed as the principal diagnosis. Additionally, our goal is to ensure that Medicare beneficiaries receive the best care in the appropriate place. If a patient requires treatment in a facility for the primary reason of depression, Not Otherwise Specified (NOS), then their Medicare benefits provide access to treatment in an inpatient psychiatric hospital so that the type of depression, as well as treatment can be determined by specialists in the field. We remind commenters that the ICD-10 mapping reflects diagnoses which may be used as the primary diagnosis for a Part-A covered stay, not merely for a comorbidity associated with the patient's care. For conditions like D89.44 ( Hereditary Alpha Tryptasemia), if there are symptoms or manifestations of this condition that require skilled care, then those symptoms should be provided as the primary diagnosis for the SNF stay, rather than the underlying condition which, often times, may be treated using oral medications.

Comment: Some commenters stated that CMS should reconsider mapping code M62.81 ( Muscle weakness, generalized) and R62.7 ( Adult failure to thrive) to a clinical category, as these conditions may serve as the source of treatment to maintain the patient's existing functional status before further decline.

Response: We considered this request and continue to believe that muscle weakness (generalized) is nonspecific and if the original condition is resolved, but the resulting muscle weakness persists because of the known original diagnosis, there are more specific codes that exist that would account for why the muscle weakness is on-going. This symptom, without any specification of the etiology or severity, is not a reason for daily skilled care in a SNF. Patients with generalized weakness should obtain a more specific diagnosis causing the generalized weakness. The specific diagnosis should be used to develop an appropriate care plan can for the patient. Similarly, in the case of a failure to thrive, this diagnosis is nonspecific and does not suggest the interventions needed to care for the patient, thus it should not be used as a reason for SNF admission. It may indicate that the patient's condition has not been thoroughly investigated which would be needed to develop an appropriate treatment plan.

Comment: Several commenters recommended that CMS consider revising the PDPM ICD-10 mapping to reclassify certain humeral fracture codes. These commenters highlighted that certain select encounter codes for humeral fracture are permitted to be coded under the current ICD-10 mapping, but not other encounter codes. The commenters suggested that all the encounter codes associated with these fracture codes be included in the appropriate clinical category.

Response: We appreciate the commenters' suggestion and agree that the various encounter codes should be treated in the same manner. We will examine the specific codes suggested to determine the most efficient manner for addressing this discrepancy.

Comment: Several commenters raised concerns with areas of discordance between the PDPM ICD-10 mapping and the Medicare Code Edits (MCE) listing used by Medicare Administrative Contractors (MACs) when evaluating the primary diagnosis codes listed on claims. These commenters referred to instances when claims were denied for including a primary diagnosis code that may be found in the PDPM ICD-10 mapping as a valid code but is not accepted by the MACs. These commenters recommended that CMS seek to align these two code lists.

Response: We appreciate commenters raising this concern. While outside the scope of this rule, we intend to consult with MACs on this issue to determine an appropriate path forward.

After consideration of public comments, we finalize the proposed changes to the PDPM ICD-10 mappings, as proposed.

C. Recalibrating the PDPM Parity Adjustment

1. Background

On October 1, 2019, we implemented the Patient Driven Payment Model (PDPM) under the SNF PPS, a new case-mix classification model that replaced the prior case-mix classification model, the Resource Utilization Groups, Version IV (RUG-IV). As discussed in the FY 2019 SNF PPS final rule (83 FR 39256), as with prior system transitions, we proposed and finalized implementing PDPM in a budget neutral manner. This means that the transition to PDPM, along with the related policies finalized in the FY 2019 SNF PPS final rule, were not intended to result in an increase or decrease in the aggregate amount of Medicare Part A payment to SNFs. We believe ensuring parity is integral to the process of providing “for an appropriate adjustment to account for case mix” that is based on appropriate data in accordance with section 1888(e)(4)(G)(i) of the Act. Section V.I. of the FY 2019 SNF PPS final rule (83 FR 39255 through 39256) discusses the methodology that we used to implement PDPM in a budget neutral manner. Specifically, we multiplied each of the PDPM case-mix indexes (CMIs) by an adjustment factor that was calculated by comparing total payments under RUG-IV using FY 2017 claims and assessment data (the most recent final claims data available at the time) to what we expected total payments would be under PDPM based on that same FY 2017 claims and assessment data. In the FY 2020 SNF PPS final rule (84 FR 38734 through 38735), we finalized an updated standardization multiplier and parity adjustment based on FY 2018 claims and assessment data. This analysis resulted in an adjustment factor of 1.46, by which all the PDPM CMIs were multiplied so that total estimated payments under PDPM would be equal to total actual payments under RUG-IV, assuming no changes in the population, provider behavior, and coding. By multiplying each CMI by 1.46, the CMIs were inflated by 46 percent to achieve budget neutrality.

We used a similar type of parity adjustment in FY 2011 when we transitioned from RUG-III to RUG-IV. As discussed in the FY 2012 SNF PPS final rule (76 FR 48492 through 48500), we observed that once actual RUG-IV utilization data became available, the actual RUG-IV utilization patterns differed significantly from those we had projected using the historical data that grounded the RUG-IV parity adjustment. We then used actual FY 2011 RUG-IV utilization data to recalibrate the RUG-IV parity adjustment and decreased the nursing CMIs for all RUG-IV therapy groups from an adjustment factor of 61 percent to an adjustment factor of 19.84 percent, while maintaining the original 61 percent total nursing CMI increase for all non-therapy RUG-IV groups. As a result of this recalibration, FY 2012 SNF PPS rates were reduced by 12.5 percent, or $4.47 billion, in order to achieve budget neutrality under RUG-IV prospectively.

Since PDPM implementation, we have closely monitored SNF utilization data to determine if the parity adjustment finalized in the FY 2020 SNF PPS final rule (84 FR 38734 through 38735) provided for a budget neutral transition between RUG-IV and PDPM as intended. Similar to what occurred in FY 2011 with RUG-IV implementation, we observed significant differences between the expected SNF PPS payments and case-mix utilization based on historical data, and the actual SNF PPS payments and case-mix utilization under PDPM, based on FY 2020 and FY 2021 utilization data. As discussed in the FY 2022 SNF PPS final rule (86 FR 42466 through 42469), we initially estimated that PDPM may have inadvertently triggered a significant increase in overall payment levels under the SNF PPS of approximately 5 percent and that recalibration of the parity adjustment may be warranted.

Following the methodology utilized in calculating the initial PDPM parity adjustment, we would typically use claims and assessment data for a given year to classify patients under both the current system and the prior system to compare aggregate payments and determine an appropriate adjustment factor to achieve parity. However, we acknowledged that the typical methodology for recalibrating the parity adjustment may not provide an accurate recalibration under PDPM for several reasons. First, the ongoing COVID-19 PHE has had impacts on nursing home care protocols and many other aspects of SNF operations that affected utilization data in FY 2020 and FY 2021. Second, given the significant differences in payment incentives and patient assessment requirements between RUG-IV and PDPM, using the same methodology that we have used in the past to calculate a recalibrated PDPM parity adjustment could lead to a potential overcorrection in the recalibration.

In the FY 2022 SNF PPS proposed rule (86 FR 19987 through 19989), we solicited comments from interested parties on a potential methodology for recalibrating the PDPM parity adjustment to account for these potential effects without compromising the accuracy of the adjustment. After considering the feedback and recommendations received, summarized in the FY 2022 SNF PPS final rule (86 FR 42469 through 42471), we proposed an updated recalibration methodology and presented results from our data monitoring efforts to provide transparency on our efforts to parse out the effects of PDPM implementation from the effects of the COVID-19 PHE in section V.C.2.d. of the proposed rule. We solicited comments on this proposal for recalibrating the PDPM parity adjustment to ensure that PDPM is implemented in a budget neutral manner, as originally intended. We received public comments on these proposals. The following is a summary of the comments we received and our responses.

Comment: Some commenters noted that they understood the need to implement PDPM in a budget neutral manner, but requested that CMS reconsider the necessity of the parity adjustment. These commenters stated that it was unreasonable to expect a budget-neutral transition given the “new normal” that includes the impacts of COVID-19 and questioned the appropriateness of comparing a pre-COVID-19 RUG-IV system to a COVID-19 era PDPM system. Other commenters stated that even if the COVID-19 PHE had not occurred, it was unreasonable to expect a budget-neutral transition given that PDPM encourages providers to put a greater emphasis on capturing all patient characteristics. That is, while providers have always treated and considered such highly individualized characteristics, commenters noted that these were not necessarily captured by the MDS under the previous RUG-IV payment system and were underrepresented in the data. Therefore, commenters disagreed with the notion that an overpayment is occurring between the PDPM model and RUG-IV model; rather, they stated the increased cost is an appropriate reflection of better capturing of patient complexities on the MDS.

Response: We believe there were significant changes in the coding of patient acuity directly following PDPM implementation and before the COVID-19 PHE that would have warranted a parity adjustment. In section V.C.2.d. of the proposed rule, we described numerous changes observed in the data that demonstrate the different impacts of PDPM implementation and the COVID-19 PHE on reported patient clinical acuity. For example, commenters stated that limitations regarding visitation and other infection control protocols due to the PHE led to higher levels of mood distress, cognitive decline, functional decline, compromised skin integrity, change in appetite, and weight loss requiring diet modifications among the non-COVID-19 population. However, our data show that many of these metrics had already exhibited clear changes concurrent with PDPM implementation and well before the start of the COVID-19 PHE. For example, the data showed an average of 4 percent of stays with depression and 5 percent of stays with a swallowing disorder in the fiscal year prior to PDPM implementation (FY 2019). In the 3 months directly following PDPM implementation and before the start of the COVID-19 PHE (October 2019 through December 2019), these averages increased to 11 percent of stays with depression and 17 percent of stays with a swallowing disorder.

The parity adjustment is meant to correct for the very changes in coding intensity of patient characteristics that these commenters describe, and similar changes in provider behavior and coding in response to payment incentives have occurred in past transitions from one payment system to another. As discussed in the FY 2012 SNF PPS final rule (76 FR 48492 through 48500), we implemented a similar type of parity adjustment in 2011 after observing a large difference between expected and actual utilization patterns in the transition from the RUG-III to RUG-IV payment system. As with prior system transitions, we proposed and finalized implementing PDPM in a budget neutral manner in the FY 2019 SNF PPS final rule (83 FR 39256). This meant that the transition to PDPM was not intended to result in an increase or decrease in the aggregate amount of Medicare Part A payment to SNFs.

Comment: Some commenters pointed to unintended consequences of implementing the parity adjustment on Medicare beneficiaries and other residents. Medicare's reimbursement rates for SNF care are higher than those of other payers such as Medicaid, and therefore, are a crucial support for an otherwise financially challenged SNF industry, particularly given the ongoing COVID-19 PHE. Any decrease to those rates would be acutely detrimental, especially to smaller, independent providers serving low-income populations, possibly resulting in facility closures and decreased access to care for beneficiaries.

Response: We remind commenters that Medicare Part A payments under the SNF PPS are solely intended to reflect the costs of providing care to beneficiaries covered under Medicare Part A and are not intended to augment payments from other payers that may be lower than Medicare Part A payment rates.

After consideration of public comments, we are finalizing our proposal to recalibrate the PDPM parity adjustment to ensure that PDPM is implemented in a budget neutral manner, as originally intended.

2. Methodology for Recalibrating the PDPM Parity Adjustment

a. Effect of COVID-19 Public Health Emergency

FY 2020 was a year of significant change under the SNF PPS. In addition to implementing PDPM on October 1, 2019, a national COVID-19 PHE was declared beginning January 27, 2020. With the announcement of the COVID-19 PHE, and under authority granted us by section 1812(f) of the Act, we issued two temporary modifications to the limitations of section 1861(i) of the Act beginning March 1, 2020, that affected SNF coverage. The 3-day prior hospitalization modification allows a SNF to furnish Medicare Part A services without requiring a 3-day qualifying hospital stay, and the benefit period exhaustion modification allows a one-time renewal of benefits for an additional 100 days of Part A SNF coverage without a 60-day break in a spell of illness. These COVID-19 PHE-related modifications allow coverage for beneficiaries who would not typically be able to access the Part A SNF benefit, such as community and long-term care nursing home patients without a prior qualifying hospitalization.

We acknowledged that the COVID-19 PHE had significant impacts on nursing home care protocols and many other aspects of SNF operations. For months, infection and mortality rates were high among nursing home residents. Additionally, facilities were often unable to access testing and affordable personal protective equipment (PPE) and were effectively closed to visitors and barred from conducting communal events to help control infections (March 2021 MedPAC Report to Congress, 204, available at https://www.medpac.gov/​wp-content/​uploads/​2021/​10/​mar21_​medpac_​report_​ch7_​sec.pdf). As described in the FY 2022 SNF PPS final rule (86 FR 42427), many commenters voiced concerns about additional costs due to the COVID-19 PHE that could be permanent due to changes in patient care, infection control staff and equipment, personal protective equipment, reporting requirements, increased wages, increased food prices, and other necessary costs. Some commenters who received CARES Act Provider Relief funds indicated that those funds were not enough to cover these additional costs. Additionally, a few commenters from rural areas stated that their facilities were heavily impacted from the additional costs, particularly the need to raise wages, and that this could affect patients' access to care.

However, we noted that the relevant issue for a recalibration of the PDPM parity adjustment is whether or not the COVID-19 PHE caused changes in the SNF case-mix distribution. In other words, the issue is whether patient classification, or the relative percentages of beneficiaries in each PDPM group, was different than what it would have been if not for the COVID-19 PHE. The parity adjustment addresses only to the transition between case-mix classification models (in this case, from RUG-IV to PDPM) and is not intended to include other unrelated SNF policies such as the market basket increase, which is intended to capture the change over time in the input prices for skilled nursing facility services described previously. A key aspect of our recalibration methodology, described in further detail later in this section, involved parsing out the impacts of the COVID-19 PHE and the PHE-related modifications from those that occurred solely, or at least principally, due to the implementation of PDPM.

b. Effect of PDPM Implementation

As discussed in the FY 2022 SNF PPS final rule (86 FR 42467), we presented evidence that the transition to PDPM impacted certain aspects of SNF patient classification and care provision prior to the beginning of the COVID-19 PHE. For example, our data showed that SNF patients received an average of approximately 93 therapy minutes per utilization day in FY 2019. Between October 2019 and December 2019, the 3 months after PDPM implementation and before the onset of the COVID-19 PHE, the average number of therapy minutes SNF patients received per day dropped to approximately 68 minutes per utilization day, a decrease of approximately 27 percent. Given this reduction in therapy provision since PDPM implementation, we found that using patient assessment data collected under PDPM would lead to a significant underestimation of what RUG-IV case-mix and payments would have been (for example, the Ultra-High and Very-High Rehabilitation assignments are not nearly as prevalent using PDPM-reported data), which would in turn lead to an overcorrection in the parity adjustment. Additionally, there were significant changes in the patient assessment schedule such as the removal of the Change of Therapy Other Medicare Required Assessment (COT-OMRA). Without having an interim assessment between the 5-day assessment and the patient's discharge from the facility, we were unable to determine if the RUG-IV group into which the patient classified on the 5-day assessment changed during the stay, or if the patient continued to receive an amount of therapy services consistent with the initial RUG-IV classification.

Therefore, given the significant differences in payment incentives and patient assessment requirements between RUG-IV and PDPM, using the same methodology that we have used in the past to calculate a recalibrated PDPM parity adjustment could lead to a potential overcorrection in the recalibration. In the FY 2022 SNF PPS proposed rule (86 FR 19988), we described an alternative recalibration methodology that used FY 2019 RUG-IV case-mix distribution as a proxy for what total RUG-IV payments would have been absent PDPM implementation. We believed that this methodology provided a more accurate representation of what RUG-IV payments would have been, were it not for the changes precipitated by PDPM implementation, than using data reported under PDPM to reclassify these patients under RUG-IV. We solicited comments from interested parties on this aspect of our potential methodology for recalibrating the PDPM parity adjustment and they were generally receptive to this approach, as described in the FY 2022 SNF PPS final rule (86 FR 42468 through 42470).

c. FY 2022 SNF PPS Proposed Rule Potential Parity Adjustment Methodology and Comments

In the FY 2022 SNF PPS proposed rule (86 FR 19986 through 19987), we presented a potential methodology that attempted to account for the effects of the COVID-19 PHE by removing those stays with a COVID-19 diagnosis and those stays using a PHE-related modification from our data set, and we solicited comment on how interested parties believed the COVID-19 PHE affected the distribution of patient case-mix in ways that were not sufficiently captured by our subset population methodology. According to our data analysis, 10 percent of SNF stays in FY 2020 and 17 percent of SNF stays in FY 2021 included a COVID-19 ICD-10 diagnosis code either as a primary or secondary diagnosis, while 17 percent of SNF stays in FY 2020 and 27 percent of SNF stays in FY 2021 utilized a PHE-related modification (with the majority of these cases using the prior hospitalization modification), as identified by the presence of a “Disaster Relief (DR)” condition code on the SNF claim. As compared to prior years, when approximately 98 percent of SNF beneficiaries had a qualifying prior hospital stay, approximately 86 percent and 81 percent of SNF beneficiaries had a qualifying prior hospitalization in FY 2020 and FY 2021, respectively. These general statistics are important, as they highlight that while the PHE for COVID-19 certainly impacted many aspects of nursing home operations, the large majority of SNF beneficiaries entered into Part A SNF stays in FY 2020 and FY 2021 as they would have in any other year; that is, without using a PHE-related modification, with a prior hospitalization, and without a COVID-19 diagnosis.

Moreover, as discussed FY 2022 SNF PPS proposed rule (86 FR 19988), we found that even after removing those using a PHE-related modification and those with a COVID-19 diagnosis from our data set, the observed inadvertent increase in SNF payments since PDPM was implemented was approximately the same. To calculate expected total payments under RUG-IV, we used the percentage of stays in each RUG-IV group in FY 2019 and multiplied these percentages by the total number of FY 2020 days of service. We then multiplied the number of days for each RUG-IV group by the RUG-IV per diem rate, which we obtained by inflating the FY 2019 SNF PPS RUG-IV rates by the FY 2020 market basket update factor. The total payments under RUG-IV also accounted for the human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) add-on of a 128 percent increase in the PPS per diem payment under RUG-IV, and a provider's FY 2020 urban or rural status. To calculate the actual total payments under PDPM, we used data reported on FY 2020 claims. Specifically, we used the Health Insurance Prospective Payment System (HIPPS) code on the SNF claim to identify the patient's case-mix assignment and associated CMIs, utilization days on the claim to calculate stay payments and the variable per diem adjustment, the presence of an HIV diagnosis on the claim to account for the PDPM AIDS add-on of 18 percent to the nursing component and the highest point value (8 points) to the NTA component, and a provider's urban or rural status. Using this approach, and as described in the FY 2022 SNF PPS final rule (86 FR 42468 through 42469), we initially estimated a 5.3 percent increase in aggregate spending under PDPM as compared to expected total payments under RUG-IV for FY 2020 when considering the full SNF population, and a 5 percent increase in aggregate spending under PDPM for FY 2020 when considering the subset population. This finding suggested that a large portion of the changes observed in SNF utilization are due to PDPM and not the PHE for COVID-19, as the “new” population of SNF beneficiaries (that is, COVID-19 patients and those using a PHE-related modification) did not appear to be the main cause of the increase in SNF payments after implementation of PDPM. Although these results are similar, we believed it would be more appropriate to pursue a potential recalibration using the subset population.

As described in the FY 2022 SNF PPS final rule (86 FR 42469 through 42471), some commenters agreed with our approach, stating that our subset population was a reasonable method to account for the effect of the COVID-19 PHE, and made a few suggestions for improvements. They stated that our analysis may have undercounted COVID-19 patients because there was no COVID-19 specific diagnosis code available before April 2020 and a shortage of tests at the beginning of the PHE led to SNFs being unable to report COVID-19 cases. To address these issues, commenters suggested that CMS consider using non-specific respiratory diagnoses or depression as proxies for COVID-19 cases. While we considered this option, we believed that such a change would overestimate the population to be excluded due to the non-specific nature of those diagnoses. Additionally, because we did not provide our COVID-19 population definition in the FY 2022 SNF PPS proposed or final rules, commenters were concerned that our methodology did not include COVID-19 diagnoses from the Minimum Data Set (MDS) patient assessments in addition to SNF claims. Commenters were also concerned that we did not exclude transitional stays resulting from CMS' instruction to assess all patients anew in October 2019 using the PDPM MDS assessment, even though some patients were in the middle or end of their Medicare Part A coverage. We addressed these concerns by sharing a revised COVID-19 population definition in section V.C.2.d. of the proposed rule.

However, many commenters expressed concern that our subset population methodology would not accurately represent what the SNF patient case-mix would look like outside of the COVID-19 PHE environment, stating that data collected during the PHE was entirely too laden with COVID-19 related effects on the entire SNF population to be utilized and pointing to multiple reasons for greater clinical acuity even among our subset population. For example, because elective surgeries were halted, those admitted were the most compromised who could not be cared for at home. Additionally, limitations regarding visitation and other infection control protocols led to higher levels of mood distress, cognitive decline, functional decline, compromised skin integrity, change in appetite, and weight loss requiring diet modifications. In response to these comments, we conducted comprehensive data analysis and monitoring to identify changes in provider behavior and payments since implementing PDPM and presented a revised parity adjustment methodology in section V.C.2.d. of the proposed rule that we believed more accurately accounted for these changes while excluding the effect of the COVID-19 PHE on the SNF population.

d. FY 2023 SNF PPS Proposed Parity Adjustment Methodology

As outlined in section V.C.2.d. of the proposed rule, we proposed a revised methodology for the calculating the parity adjustment that considers the comments received in response to the potential methodology described in the FY 2022 SNF PPS proposed rule (86 FR 19986 through 19987). In response to the comments received about the subset population methodology, we modified our definition of COVID-19, which we derived from the Centers for Disease Control and Prevention (CDC) coding guidelines, to align with the definition used by publicly available datasets from CMS's Office of Enterprise Data and Analytics (OEDA) and found no significant impact on our calculations. For the FY 2022 SNF proposed rule, we defined the COVID-19 population to include stays that have either the interim COVID-19 code B97.29 recorded as a primary or secondary diagnosis in addition to one of the symptom codes J12.89, J20.8, J22, or J80, or the new COVID-19 code U07.1 recorded as a primary or secondary diagnosis on their SNF claims or MDS 5-day admission assessments. For the FY 2023 SNF proposed rule, we defined the COVID-19 population to include stays that have the interim COVID-19 code B97.29 from January 1, 2020 to March 31, 2020 or the new COVID-19 code U07.1 from April 1, 2020 onward recorded as a primary or secondary diagnosis on their SNF claims, MDS 5-day admission assessments, or MDS interim payment assessments. Both FY 2022 and FY 2023 definitions of the COVID-19 population excluded transitional stays. We noted that we found no significant impact on our calculations, as the COVID-19 population definition change only increased the stay count of our subset population by less than 1 percent.

In response to the comments described previously and based on additional data collection through FY 2021, we identified a recalibration methodology that we believed better accounted for COVID-19 related effects. We proposed to use the same type of subset population discussed in the FY 2022 SNF PPS proposed rule (86 FR 19960), which excluded stays that either used a section 1812(f) of the Act modification or that included a COVID-19 diagnosis, with a 1-year “control period” derived from both FY 2020 and FY 2021 data. Specifically, we used 6 months of FY 2020 data from October 2019 through March 2020 and 6 months of FY 2021 data from April 2021 through September 2021 (which our data suggests were periods with relatively low COVID-19 prevalence) to create a full 1-year period with no repeated months to account for seasonality effects. As shown in Table 11, we believed this combined approach provided the most accurate representation of what the SNF case-mix distribution would look like under PDPM outside of a COVID-19 PHE environment. While using the subset population method alone for FY 2020 and FY 2021 data results in differences of 0.31 percent and 0.40 percent between the full and subset populations, respectively, introducing the control period closed the gap between the full and subset population adjustment factors to 0.02 percent, suggesting that the control period captures additional COVID-19 related effects on patient acuity that the subset population method alone does not. Accordingly, the combined methodology of using the subset population with data from the control period resulted in the lowest parity adjustment factor. Table 12 shows that while using the subset population method would lead to a 4.9 percent adjustment factor ($1.6 billion) using FY 2020 data and a 5.3 percent adjustment factor ($1.8 billion) using FY 2021 data, introducing the control period reduced the adjustment factor to 4.6 percent ($1.5 billion). We note that these estimates are revised from those provided in the FY 2023 SNF PPS proposed rule, based on a more recent SNF baseline budget estimate provided by the CMS Office of the Actuary. The robustness of the control period approach was further demonstrated by the fact that using data from the control period, with either the full or subset population, would lead to approximately the same parity adjustment factor of 4.58 percent as compared to 4.6 percent.

Our data analysis and monitoring efforts provided further support for the accuracy and appropriateness of a 4.6 percent parity adjustment factor, as we have identified numerous changes that demonstrate the different impacts of PDPM implementation and the COVID-19 PHE on reported patient clinical acuity. As described earlier, commenters stated that limitations regarding visitation and other infection control protocols due to the PHE led to higher levels of mood distress, cognitive decline, functional decline, compromised skin integrity, change in appetite, and weight loss requiring diet modifications among the non-COVID-19 population. However, our data showed that most of these metrics, with the exception of functional decline and compromised skin integrity, had already exhibited clear changes concurrent with PDPM implementation and well before the start of the COVID-19 PHE. For example, in regard to higher levels of mood distress and cognitive decline, we observed an average of 4 percent of stays with depression and 40 percent of stays with cognitive impairment, with an average mood score of 1.9, in the fiscal year prior to PDPM implementation (FY 2019). In the 3 months directly following PDPM implementation and before the start of the COVID-19 PHE (October 2019 to December 2019), these averages increased to 11 percent of stays with depression and 44 percent of stays with cognitive impairment, with an average mood scale of 2.9. As for change in appetite and weight loss requiring diet modifications, we observed an average of 15 percent of stays with any SLP comorbidity, 5 percent of stays with a swallowing disorder, and 22 percent of stays with a mechanically altered diet in FY 2019. In the 3 months directly following PDPM implementation, these averages increased to 19 percent of stays with any SLP comorbidity, 17 percent of stays with a swallowing disorder, and 25 percent of stays with a mechanically altered diet. Notably, we also observed that the percentage of stays with a swallowing disorder that did not also receive a mechanically altered diet increased from 1 percent in FY 2019 to 5 percent in the 3 months directly following PDPM implementation. While many of these metrics increased further after the start of the COVID-19 PHE, they remained elevated at around their post-PDPM implementation levels even during periods of low COVID-19 prevalence. As a result, our parity adjustment calculations remained much the same even during months when rates of COVID-19 cases were quite low, suggesting that patient case mix classification has stabilized independent of the ongoing COVID-19 PHE.

Another reason that commenters cited to explain the greater clinical acuity among the subset population is that, because elective surgeries were halted, patients who were admitted were more severely ill and could not be treated at home. We acknowledged that the subset population methodology, or any method predicated on data from the COVID-19 PHE period, may not accurately represent what SNF patient case-mix would look like outside of the COVID-19 PHE environment because while we could remove data that we believed were due to COVID-19 impacts, it was more difficult to add data back in that was missing due to the COVID-19 PHE.

However, we believed that the addition of the control period to the subset population methodology helped to resolve this issue. For example, there likely would have been more joint replacements were it not for the COVID-19 PHE. Our data showed that the rate of major joint replacement or spinal surgery decreased from 7.6 percent of stays in FY 2019, to 5.5 percent of stays in FY 2021, to 5.2 percent of stays in FY 2022. Similarly, rates of orthopedic surgery decreased from 9.1 percent of stays in FY 2019, to 9.0 percent of stays in FY 2021, to 8.8 percent of stays in FY 2022. Using the control period, which excluded the periods of highest COVID-19 prevalence and lowest rates of elective surgeries, we arrived at rates of 6.4 percent of stays with major joint replacement or spinal surgery, and 9.5 percent of stays with orthopedic surgery. Therefore, as we noted in section V.C.2.d. the proposed rule, we believed that using the control period would be a closer representation of SNF patient case-mix outside of a COVID-19 PHE environment than using either FY 2021 or FY 2022 data alone.

Given the results of our data analyses, we proposed adopting the methodology based upon the subset population during the control period and lowering the PDPM parity adjustment factor from 46 percent to 38 percent for each of the PDPM case-mix adjusted components if we were to implement the 4.6 percent parity adjustment factor in FY 2023. We noted that the parity adjustment would be calculated and applied at a systemic level to all facilities paid under the SNF PPS, and there may be variation between facilities based on their unique patient population, share of non-case- mix component payment, and urban or rural status. We invited comments on the methodology outlined in section V.C.2.d. of the proposed rule for recalibrating the PDPM parity adjustment, as well as the findings of our analysis described throughout section V.C.2. of the proposed rule.

To assist commenters in providing comments on this issue, we also posted a file on the CMS website at https://www.cms.gov/​medicare/​medicare-fee-for-service-payment/​snfpps, which provided the FY 2019 RUG IV case-mix distribution and calculation of total payments under RUG-IV, as well as PDPM case-mix utilization data at the case mix group and component level to demonstrate the calculation of total payments under PDPM.

We invited comments on our proposed combined methodology of using the subset population and data from the control period for the purposes of calculating the recalibrated parity adjustment factor. The following is a summary of the comments we received and our responses.

Comment: A few commenters provided comments in relation to the proposed methodology for calculating the parity adjustment. Some commenters noted our proposed methodology to be a reasonable and much improved approach compared to the approach proposed in FY 2022 SNF PPS proposed rule, as our revised methodology addresses many of the key issues raised by interested parties (86 FR 42469 through 42471).

However, one commenter suggested removing August and September 2021 due to the Delta variant. Another commenter suggested a modified control period to eliminate April and May 2021 as patients and healthcare personnel were still in the process of receiving the initial dose of the COVID-19 vaccine, and August and September 2021 due to early phase of the Delta variant surge. The commenter also provided analysis regarding COVID-19 spillover effects, which they defined as effects that occur in non-COVID-19 patient CMIs when MDS patient assessment patterns change from what would have occurred if not for the pandemic, using the percentage change over time in various patient clinical and zip-code level demographic characteristics, the latter used as proxies for the demographics of the SNF population in a particular zip code. The commenter stated that some metrics, such as HCC risk scores, English proficiency, educational level, and poverty level returned to or dropped below pre-COVID-19 PHE baseline levels, suggesting that the revised parity adjustment factor is adequate to account for COVID-19 spillover effects. However, the commenter also stated that other metrics, such as PDPM component CMI trends; MDS items for respiratory failure, pressure ulcers, and depression; and claim items for age, race, dual, and disability status did not return to pre-COVID-19 PHE baseline levels, suggesting that the revised parity adjustment factor may not be adequate to account for COVID-19 spillover effects. Based on these findings, the commenters stated that they believed that there are COVID-19 spillover effects that remain despite CMS's improved parity adjustment approach, and they recommended that CMS further evaluate the data to exclude the months of April, May, August, and September 2021 from the parity adjustment calculations, as discussed above. The commenter also stated that modifying the control period in this way would mitigate most of the remaining spillover effects and would result in an additional 0.1 to 0.2 percent reduction below the proposed 4.6 percent parity adjustment amount.

Response: We note that many of the differences shown in the data the commenter provided are quite small (some less than a small fraction of 1 percent) and could be attributed to the continuation of the impact of PDPM implementation or regular year-to-year variations in the composition of the SNF population (or zip-code level population more generally), rather than true COVID-19 spillover effects. We also note that the commenter did not consider data from before PDPM implementation to support what they believe should be a more appropriate parity adjustment factor, as they used data from October 2019 to February 2020 to define their “pre-pandemic” study population.

In contrast, the data analyses we presented earlier in the preamble show significant changes in the coding of patient case mix concurrent with PDPM implementation. For example, in the year prior to PDPM implementation (FY 2019), we observed an average of 4 percent of stays coded with depression and 5 percent of stays coded with a swallowing disorder. In the 3 months directly following PDPM implementation and before the start of the COVID-19 PHE (October 2019 to December 2019), these averages increased to 11 percent of stays coded with depression and 17 percent of stays coded with a swallowing disorder. While these and other clinical metrics increased in acuity after the start of the COVID 19 PHE in January 2020, they remained elevated at around their immediate post-PDPM implementation levels even during periods of low COVID-19 prevalence. As a result, our parity adjustment calculations remained much the same even during months when rates of COVID-19 cases were quite low, suggesting that the 4.6 percent parity adjustment factor captures the effect of PDPM implementation and excludes the effects of the COVID-19 PHE.

Moreover, we believe that it is important to have an adequate and representative amount of time in both 2020 and 2021 upon which to calculate a parity adjustment factor, rather than choosing specific months that would result in the lowest possible parity adjustment factor. Our analysis of Medicare Part A data from SNFs in April, May, August, and September 2021 show that these were months of low COVID-19 prevalence in SNFs compared to other months in FY 2020 and FY 2021. We intentionally chose 6 months of FY 2020 data from October 2019 through March 2020 and 6 months of FY 2021 data from April 2021 through September 2021, which our Medicare Part A monitoring data showed were periods with the lowest COVID-19 prevalence in SNFs, to create a full 1-year period with no repeated months to account for seasonality effects. While we used less than a year of data in calculating the recalibration of the RUG-IV parity adjustment when transitioning between RUG-III and RUG-IV in FY 2012 (76 FR 48493), that change was between two payment models that were, in several ways, very similar (for example, the relationship between therapy intensity and payment classification). This time, in light of the significant differences between the PDPM and the RUG-IV payment models, in addition to the impact of the COVID-19 PHE, we believe it is necessary to use a full year of data.

After consideration of these public comments, we are finalizing a parity adjustment factor of 4.6 percent using the combined subset population and control period methodology, as proposed. As discussed later in section VI.C.4. of this final rule, we are finalizing the implementation of the parity adjustment with a 2-year phase-in period, which means that, for each of the PDPM case-mix adjusted components, we would lower the PDPM parity adjustment factor from 46 percent to 42 percent in FY 2023 and we would further lower the PDPM parity adjustment factor from 42 percent to 38 percent in FY 2024.

3. Methodology for Applying the Recalibrated PDPM Parity Adjustment

As discussed in the FY 2022 SNF PPS proposed rule (86 FR 19988), we believed it would be appropriate to apply the recalibrated parity adjustment across all PDPM CMIs in equal measure, as the initial increase to the PDPM CMIs to achieve budget neutrality was applied equally, and therefore, this method would properly implement and maintain the integrity of the PDPM classification methodology as it was originally designed. Tables 5 and 6 in section III.C. of the proposed rule set forth what the PDPM CMIs and case-mix adjusted rates would be if we apply the recalibration methodology in equal measure in FY 2023.

We acknowledged that we received several comments in response to last year's rule objecting to this approach given that our data analysis, presented in Table 23 of the FY 2022 SNF PPS proposed rule (86 FR 19987), showed significant increases in the average CMI for the SLP, Nursing, and NTA components for both the full and subset FY 2020 populations as compared to what was expected, with increases of 22.6 percent, 16.8 percent, and 5.6 percent, respectively, for the full FY 2020 SNF population. As described in the FY 2022 SNF PPS final rule (86 FR 42471), some commenters disagreed with adjusting the CMIs across all case-mix adjusted components in equal measure, suggesting that this approach would harm patient care by further reducing PT and OT therapy minutes. Instead, the commenters recommended a targeted approach that focuses the parity adjustment on the SLP, Nursing, and NTA components in proportion to how they are driving the unintended increase observed under PDPM.

We considered these comments, but believe that it would be most appropriate to propose applying the parity adjustment across all components equally. First, as described earlier, the initial increase to the PDPM CMIs to achieve budget neutrality was applied across all components, and therefore, it would be appropriate to implement a revision to the CMIs in the same way. Second, the reason we did not observe the same magnitude of change in the PT and OT components is that, in designing the PDPM payment system, the data used to help determine what payment groups SNF patients would classify into under PDPM was collected under the prior payment model (RUG-IV), which included incentives that encouraged significant amounts of PT and OT. Given that PT and OT were furnished in such high amounts under RUG-IV, we had already assumed that a significant portion of patients would be classified into the higher paying PT and OT groups corresponding to having a Section GG function score of 10 to 23. Therefore, this left little room for additional increases in PT and OT classification after PDPM implementation. In other words, the PT and OT components results were as expected according to the original design of PDPM, while the SLP, Nursing, and NTA results were not.

However, to fully explore the alternative targeted approach that commenters suggested, we updated our analysis of the average CMI by PDPM component from Table 23 of the FY 2022 SNF PPS proposed rule (86 FR 19987) and found that a similar pattern still holds when comparing the expected average CMIs for FY 2019 and the expected actual CMIs for the subset population during the control period. Table 13 shows significant increases in average case-mix of 18.6 percent for the SLP component and the 10.8 percent for the Nursing component, a moderate increase of 3.0 percent for the NTA component, and a slight increase of 0.4 percent for the PT and OT components, respectively. We also provided Table 14 to show the potential impact of applying the 4.6 percent PDPM parity adjustment factor to the PDPM CMIs in a targeted manner in FY 2023, instead of an equal approach as presented in Tables 5 and 6 in section III.C. of the proposed rule. We invited comments on whether interested parties believe a targeted approach is preferable to our proposed equal approach.

We received public comments on these proposals. The following is a summary of the comments we received and our responses.

Comment: A few commenters supported our proposal to apply the parity adjustment evenly over all CMIs for all case-mix groups, the same approach that was taken when the original adjustment was implemented. One commenter stated that the targeted approach, which results in a larger reduction for some CMIs than others, may have unintended adverse effects on some facilities and that an equally distributed percentage reduction would have a more equitable impact on all facilities. Another commenter believed an equal approach would be the least disruptive policy implementation, rather than set a precedent for potential future changes to the individual CMI components. The commenter also added that regardless of which CMIs are reduced, facilities are still receiving a single per-diem payment. A third commenter agreed that, in the absence of re-designing the PDPM payment model from the ground-up based on observed PDPM CMIs, the adoption of an even distribution for the parity adjustment would best maintain the stability of the PDPM payment model. A fourth commenter strongly opposed a targeted approach to all categories, believing that SLP services were undervalued in the RUG-IV system and utilization of SLP services appropriately meets beneficiary needs under PDPM, but were not previously reported since there were no financial incentives for SNFs to report SLP services under RUG-IV.

Two commenters supported a targeted approach and expressed concern about a reduction in payment for the PT and OT components, given that the majority of increased spending is not attributed to these components, leading to a reduction in PT and OT services. The commenters urged CMS to use the data to adjust PDPM in an accurate and precise manner, rather than simply reducing every CMI.

Response: We agree that applying the parity adjustment equally across all PDPM CMIs would be the most equitable and least disruptive policy implementation, rather than set a precedent for potential future changes to the individual CMI components. We also agree that regardless of which CMIs are reduced, facilities are still receiving a single per-diem payment and a reduction in the PT and OT CMIs should not impact the provision of these services, as the main driver for determining the appropriate provision of these services should the unique characteristics, goals, or needs, of each SNF patient. As we stated in the FY 2020 SNF PPS final rule (84 FR 38748), financial motives should not override the clinical judgment of a therapist or therapy assistant or pressure a therapist or therapy assistant to provide less than appropriate therapy.

After consideration of public comments, we are finalizing the application of the parity adjustment equally across all components, as proposed.

4. Delayed and Phased Implementation

As we noted in the FY 2012 SNF PPS final rule (76 FR 48493), we believe it is imperative that we act in a well-considered but expedient manner once excess payments are identified, as we did in FY 2012. However, we acknowledged that applying a reduction in payments without time to prepare could create a financial burden for providers, particularly considering the ongoing COVID-19 PHE. Therefore, in the FY 2022 SNF PPS proposed rule (86 FR 19988 through 19990), we solicited comments on two potential mitigation strategies to ease the transition to prospective budget neutrality: delayed implementation and phased implementation. We noted that for either of these options, the adjustment would be applied prospectively, and the CMIs would not be adjusted to account for deviations from budget neutrality in years before the payment adjustments are implemented.

A delayed implementation strategy would mean that we would implement the reduction in payment in a later year than the year the reduction is finalized. For example, considering the 4.6 percent reduction discussed previously in this preamble, if this reduction is finalized in FY 2023 with a 1-year delayed implementation, this would mean that the full 4.6 percent reduction will be applied prospectively to the PDPM CMIs in FY 2024. By comparison, a phased implementation strategy would mean that the amount of the reduction would be spread out over some number of years. For example, if we were to implement a 2-year phase-in period to the 4.6 percent reduction discussed previously in the proposed rule with no delayed implementation, this would mean that the PDPM CMIs would be reduced by 2.3 percent in the first year of implementation in FY 2023 and then reduced by the remaining 2.3 percent in the second and final year of implementation in FY 2024. We could also use a combination of both mitigation strategies, such as a 1-year delayed implementation with a 2-year phase-in period, would mean that the PDPM CMIs would be reduced by 2.3 percent in the first year of implementation in FY 2024 and then reduced by the remaining 2.3 percent in the second and final year of implementation in FY 2025.

In the FY 2022 SNF PPS proposed rule (86 FR 19988 through 19990), we solicited comments on the possibility of combining the delayed and phased implementation approaches and what interested parties believed would be appropriate to appropriately mitigate the impact of the reduction in SNF PPS payments. As described in the FY 2022 SNF PPS final rule (86 FR 42470 through 42471), most commenters supported combining both mitigation strategies of delayed implementation of 2 years and a gradual phase-in of no more than 1 percent per year. MedPAC supported delayed implementation, but did not believe a phased-in approach was warranted given the high level of aggregate payment to SNFs. Further, MedPAC's March 2022 Report to Congress (available at https://www.medpac.gov/​wp-content/​uploads/​2022/​03/​Mar22_​MedPAC_​ReportToCongress_​Ch7_​SEC.pdf) has found that since 2000, the aggregate Medicare margin for freestanding SNFs has consistently been above 10 percent each year. In 2020, the aggregate Medicare margin was 16.5 percent, a sizable increase from 11.9 percent in 2019. Additionally, the aggregate Medicare margin in 2020 increased to an estimated 19.2 percent when including Federal relief funds for the COVID-19 PHE (March 2022 MedPAC Report to Congress, 251-252). Given these high Medicare margins, we did not believe that a delayed implementation or a phase-in approach was needed. Rather, these mitigation strategies would continue to pay facilities at levels that exceed intended SNF payments, had PDPM been implemented in a budget neutral manner as finalized by CMS in the FY 2019 SNF PPS final rule (83 FR 39256), which we cannot recoup.

It is also important to note that the parity adjustment recalibration would serve to remove an unintended increase in payments from moving to a new case mix classification system, rather than decreasing an otherwise appropriate payment amount. Thus, as we noted in section V.C.4. of the proposed rule, we did not believe that the recalibration should negatively affect facilities, beneficiaries, and quality of care, or create an undue hardship on providers.

Therefore, we proposed to recalibrate the parity adjustment in FY 2023 with no delayed implementation or phase-in period in order to allow for the most rapid establishment of payments at the appropriate level, ensuring that PDPM will be budget-neutral as intended and preventing the continued accumulation of excess SNF payments. We noted that while this proposal would lead to a prospective reduction in Medicare Part A SNF payments of approximately 4.6 percent in FY 2023, the reduction would be substantially mitigated by the proposed FY 2023 net SNF market basket update factor of 3.9 percent discussed in section III.B of the proposed rule. Taken together, we had stated that the preliminary net budget impact in FY 2023 would be an estimated decrease of $320 million in aggregate payment to SNFs if the parity adjustment is implemented in 1 year.

However, we continue to believe that in implementing PDPM, it is essential that we stabilize the baseline as quickly as possible without creating a significant adverse effect on the industry or to beneficiaries. Therefore, we solicited comments on our proposal to recalibrate the parity adjustment by 4.6 percent in FY 2023, and whether interested parties believe delayed implementation or a phase-in period are warranted, in light of the data analysis and policy considerations presented previously. We received public comments on these proposals. The following is a summary of the comments we received and our responses.

Comment: We received a few comments in support of the proposed parity adjustment with no phase-in period. The commenters indicated that the SNF industry has been on notice for a year that an additional reduction to the payment rates would be necessary to maintain budget neutrality and noted that the parity adjustment of 4.6 percent proposed for FY 2023 was smaller than the SNF industry might have expected, given CMS's initial estimate of 5 percent in the FY 2022 SNF PPS proposed rule (86 FR 19988). The commenters also stated that no phase-in period is warranted in FY 2023 as, based on CMS' final calculations, it has overpaid the industry about 4.6 percent per year since the PDPM was implemented in FY 2020, or approximately $5 billion over FY 2020, FY 2021, and FY 2022.

Response: We appreciate these comments and agree that the SNF industry was made aware of the potential for CMS to implement parity adjustment in prior rulemaking.

Comment: The majority of commenters strongly objected to implementing the 4.6 percent adjustment all in 1 year, instead requesting that CMS implement a mitigation strategy of phasing the parity adjustment in over a number of years, with the majority requesting a 3-year phase-in period and a significant number requesting a 2- to 3-year phase-in period. Some commenters requested a 1-year delay combined with a 4- to 5-year phase-in period of no more than 1 percent of the parity adjustment implemented per year.

The commenters stated that a phased-in approach would assure some predictability and stability to the SNF industry by making a negative net annual update less likely to occur each year of the phase-in. The commenters pointed to several reasons why the SNF industry could not withstand a negative payment adjustment at this time. Many commenters stated that their facilities are still facing financial difficulties due to the ongoing COVID-19 PHE, with decreased census numbers, the continued need to purchase PPE, and the discontinuation of CARES Act Provider Relief funds. Many commenters also pointed to the unfavorable current economic climate with inflation at above 8 percent and historically high fuel prices, which they did not believe were adequately accounted for in the market basket. Finally, the majority of commenters pointed to the high cost of labor, resulting in staffing shortages as healthcare workers opt for other healthcare or non-healthcare settings offering higher pay.

Response: We appreciate the comments raised on the potential impact on providers of finalizing this adjustment with no delay or phase-in period. We acknowledge the concerns raised about financial difficulties due to the ongoing COVID-19 PHE and due to the current economic climate. The parity adjustment addresses the transition between case-mix classification models (in this case, from RUG-IV to PDPM) and is not intended to include other unrelated SNF policies such as the market basket increase, which is intended to capture the change over time in the prices of skilled nursing facility services.

As stated in section V.C.4. of the proposed rule, we believe that it is essential to stabilize the baseline budget without creating a significant adverse effect on SNFs. While we understand the comments raised on the potential financial impact on providers of finalizing this adjustment with less than a 3-year phase-in period, we believe that it would be best to implement this adjustment as soon as possible in order to maintain budget neutrality in the SNF payment system. We remind commenters that, in the FY 2022 SNF PPS final rule, we stated it would be imperative to act in a well-considered but expedient manner once excess payments are identified (86 FR 42471).

However, we also recognize that the ongoing COVID-19 PHE provides a basis for taking a more cautious approach in order to mitigate the potential negative impacts on providers, such as the potential for facility closures or disproportionate impacts on rural and small facilities. Given this, we believe that it would be appropriate to implement a phased-in approach to recalibrating the PDPM parity adjustment. Therefore, after considering these comments, and in order to balance mitigating the financial impact on providers of recalibrating the PDPM parity adjustment with ensuring accurate Medicare Part A SNF payments, we are finalizing the proposed recalibration of the PDPM parity adjustment with a 2-year phase-in period, resulting in a 2.3 percent reduction in FY 2023 ($780 million) and a 2.3 percent reduction in FY 2024.

D. Request for Information: Infection Isolation

Under the SNF PPS, various patient characteristics are used to classify patients in Medicare-covered SNF stays into payment groups. One of these characteristics is isolation due to an active infection. In order for a patient to qualify to be coded as being isolated for an active infectious disease, the patient must meet all of the following criteria:

1. The patient has active infection with highly transmissible or epidemiologically significant pathogens that have been acquired by physical contact or airborne or droplet transmission.

2. Precautions are over and above standard precautions. That is, transmission-based precautions (contact, droplet, and/or airborne) must be in effect.

3. The patient is in a room alone because of active infection and cannot have a roommate. This means that the resident must be in the room alone and not cohorted with a roommate regardless of whether the roommate has a similar active infection that requires isolation.

4. The patient must remain in his or her room. This requires that all services be brought to the resident (for example, rehabilitation, activities, dining, etc.).

Being coded for infection isolation can have a significant impact on the Medicare payment rate for a patient's SNF stay. The increase in a SNF patient's payment rate as a result of being coded under infection isolation is driven by the increase in the relative costliness of treating a patient who must be isolated due to an infection. More specifically, in 2005, we initiated a national nursing home staff time measurement (STM) study, the Staff Time and Resource Intensity Verification (STRIVE) Project. The STRIVE project was the first nationwide time study for nursing homes in the United States to be conducted since 1997, and the data collected were used to establish payment systems for Medicare skilled nursing facilities (SNFs) as well as Medicaid nursing facilities (NFs).

In the STRIVE project final report, titled “Staff Time and Resource Intensity Verification Project Phase II” section 4.8 (available at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​TimeStudy), we discussed how infection isolation was categorized into the Extensive Services RUG-III category based on the high resource intensity that was required for treating patients for whom facilities would code this category on the MDS. The significant increase in payment associated with this item is intended to account for the increase in relative resource utilization and costs associated with treating a patient isolated due to an active infection, as well as the PPE and additional protocols which must be followed treating such a patient, which are significantly greater than treating patients outside of such an environment.

During the COVID-19 PHE, a number of interested parties raised concerns with the definition of “infection isolation”, as it relates to the treatment of SNF patients being cohorted due to either the diagnosis or suspected diagnosis of COVID-19. Specifically, interested parties took issue with criterion 1, which requires that the patient have an active infection, rather than suspicion of an active infection, and criterion 3, which requires that the patient be in the room alone, rather than being cohorted with other patients. To this point, we have maintained that the definition of “infection isolation” is appropriate and should not be changed in response to the circumstances of the COVID-19 PHE. Due to the ubiquitous nature of the PHE and precautions that are being taken throughout SNFs with regard to PPE and other COVID-19 related needs, we understand that the general costs for treating all SNF patients may have increased. However, as the case-mix classification model is intended to adjust payments based on relative differences in the cost of treating different SNF patients, we are unclear on if the relative increase in resource intensity for each patient being treated within a cohorted environment is the same relative increase as it would be for treating a single patient isolated due to an active infection.

We invited the public to submit their comments about isolation due to active infection and how the PHE has affected the relative staff time resources necessary for treating these patients. Specifically, we invited comments on whether or not the relative increase in resource utilization for each of the patients within a cohorted room, all with an active infection, is the same or comparable to that of the relative increase in resource utilization associated with a patient that is isolated due to an active infection. We received public comments on this request for information. The following is a summary of the comments we received and our responses.

Comment: We received several comments on this request for information. Commenters suggested that criterion 1 and criterion 3 above should be revised. More specifically, commenters recommended that criterion 1 be revised to allow for “suspected,” rather than only active, cases of infection. Additionally, commenters recommended that criterion 3 be revised to allow providers to code infection isolation in cases where patients are cohorted due to an active infection. These commenters provided evidence to suggest that the costs of caring for cohorted patients are similar to those of a patient that is isolated due to active infection. Some commenters further suggested that CMS consider adding items to the MDS that would allow coding for cohorted patients, with the possibility of a lower CMI adjustment for such patients, as compared to those in full isolation. Some commenters also recommended revisions to the MDS manual and coding guidance to ensure that coding for infection isolation is consistent with CDC guidance. Finally, some commenters suggested that CMS consider a new time study to evaluate the cost of treating cohorted patients isolated with an active infection.

Response: We appreciate the comments that we received on this request for information and will consider these comments as we plan for future rulemaking on this issue.

VII. Skilled Nursing Facility Quality Reporting Program (SNF QRP)

A. Background and Statutory Authority

The Skilled Nursing Facility Quality Reporting Program (SNF QRP) is authorized by section 1888(e)(6) of the Act, and it applies to freestanding SNFs, SNFs affiliated with acute care facilities, and all non-critical access hospital (CAH) swing-bed rural hospitals. Section 1888(e)(6)(A)(i) of the Act requires the Secretary to reduce by 2 percentage points the annual market basket percentage update described in section 1888(e)(5)(B)(i) of the Act applicable to a SNF for a fiscal year, after application of section 1888(e)(5)(B)(ii) of the Act (the productivity adjustment) and section 1888(e)(5)(B)(iii) of the Act, in the case of a SNF that does not submit data in accordance with sections 1888(e)(6)(B)(i)(II) and (III) of the Act for that fiscal year. For more information on the requirements we have adopted for the SNF QRP, we refer readers to the FY 2016 SNF PPS final rule (80 FR 46427 through 46429), FY 2017 SNF PPS final rule (81 FR 52009 through 52010), FY 2018 SNF PPS final rule (82 FR 36566 through 36605), FY 2019 SNF PPS final rule (83 FR 39162 through 39272), and FY 2020 SNF PPS final rule (84 FR 38728 through 38820).

B. General Considerations Used for the Selection of Measures for the SNF QRP

For a detailed discussion of the considerations we use for the selection of SNF QRP quality, resource use, or other measures, we refer readers to the FY 2016 SNF PPS final rule (80 FR 46429 through 46431).

1. Quality Measures Currently Adopted for the FY 2023 SNF QRP

The SNF QRP currently has 15 measures for the FY 2023 SNF QRP, which are outlined in Table 15. For a discussion of the factors used to evaluate whether a measure should be removed from the SNF QRP, we refer readers to § 413.360(b)(3).

C. SNF QRP Quality Measures Beginning With the FY 2025 SNF QRP

Section 1899B(h)(1) of the Act permits the Secretary to remove, suspend, or add quality measures or resource use or other measures described in sections 1899B(c)(1) and (d)(1) of the Act, respectively, so long as the Secretary publishes in the Federal Register (with a notice and comment period) a justification for such removal, suspension, or addition. Section 1899B(a)(1)(B) of the Act requires that all of the data that must be reported in accordance with section 1899B(a)(1)(A) of the Act (including resource use or other measure data under section 1899B(d)(1) of the Act) be standardized and interoperable to allow for the exchange of the information among post-acute care (PAC) providers and other providers and the use by such providers of such data to enable access to longitudinal information and to facilitate coordinated care.

We proposed to adopt one new measure for the SNF QRP beginning with the FY 2025 SNF QRP: the Influenza Vaccination Coverage among Healthcare Personnel (HCP) (NQF #0431) measure as an “other measure” under section 1899B(d)(1) of the Act. In accordance with section 1899B(a)(1)(B) of the Act, the data used to calculate this measure are standardized and interoperable. As proposed, the measure supports the “Preventive Care” Meaningful Measure area and the “Promote Effective Prevention and Treatment of Chronic Disease” healthcare priority.^[9] The Influenza Vaccination Coverage among HCP measure (the HCP Influenza Vaccine measure) is a process measure, developed by the Centers for Disease Control and Prevention (CDC), and reports on the percentage of HCP who receive the influenza vaccination. This measure is currently used in other post-acute care (PAC) Quality Reporting Programs (QRPs), including the Inpatient Rehabilitation Facility (IRF) QRP and the Long-Term Care Hospital (LTCH) QRP. The measure is described in more detail in section VII.C.1. of this final rule.

In addition, we proposed to revise the compliance date for the collection of the Transfer of Health (TOH) Information to the Provider-PAC measure, the TOH Information to the Patient-PAC measure, and certain standardized patient assessment data elements from October 1st of the year that is at least 2 full fiscal years after the end of the COVID-19 PHE to October 1, 2023. We believe the COVID-19 PHE revealed why the TOH Information measures and standardized patient assessment data elements are important to the SNF QRP. The new data elements will facilitate communication and coordination across care settings as well as provide information to support our mission of analyzing the impact of the COVID-19 PHE on patients to improve the quality of care in SNFs. We described the proposal in more detail in section VI.C.2. of the proposed rule.

We also proposed to make certain revisions to regulation text at § 413.360 to include a new paragraph to reflect all the data completion thresholds required for SNFs to meet the compliance threshold for the annual payment update (APU), as well as certain conforming revisions. We described the proposal in more detail in section VI.C.3. of the proposed rule.

1. Influenza Vaccination Coverage Among Healthcare Personnel (NQF #0431) Measure Beginning With the FY 2025 SNF QRP

a. Background

The CDC Advisory Committee on Immunization Practices (ACIP) recommends that all persons 6 months of age and older, including HCP and persons training for professions in healthcare, should be vaccinated annually against influenza.^[10] The basis of this recommendation stems from the spells of illness, hospitalizations, and mortality associated with the influenza virus. Between 2010 and 2020, the influenza virus resulted in 12,000 to 52,000 deaths in the United States each year, depending on the severity of the strain.^[11 12] Preliminary estimates from the CDC revealed 35 million cases, 380,000 hospitalizations, and 20,000 deaths linked to influenza in the United States during the 2019 to 2020 influenza season.^[13] Persons aged 65 years and older are at higher risk for experiencing burdens related to severe influenza due to the changes in immune defenses that come with increasing age.^[14 15] The CDC estimates that 70 to 85 percent of seasonal influenza-related deaths occur among people aged 65 years and older, and 50 to 70 percent of influenza-related hospitalizations occur among this age group.^[16] Residents of long-term care facilities, who are often of older age, have greater susceptibility for acquiring influenza due to general frailty and comorbidities, close contact with other residents, interactions with visitors, and exposure to staff who rotate between multiple facilities.^{[17 18 19]} Therefore, monitoring and reporting influenza vaccination rates among HCP is important as HCP are at risk for acquiring influenza from residents and exposing influenza to residents.^[20] For example, one early report of HCP influenza infections during the 2009 H1N1 influenza pandemic estimated 50 percent of HCP had contracted the influenza virus from patients or coworkers within the healthcare setting.^[21]

Despite the fact that influenza commonly spreads between HCP and SNF residents, vaccine hesitancy and organizational barriers often prevent influenza vaccination. For example, although the CDC emphasizes the importance for HCP to receive the influenza vaccine, the 2017 to 2018 influenza season shows higher influenza vaccination coverage among HCP working in hospitals (approximately 92 percent) and lower coverage among those working in long-term care facilities (approximately 68 percent).^[22 23] HCP working in long-term care facilities, including SNFs, have expressed concerns about the influenza vaccine's effectiveness and safety, fearing potential side effects and adverse reactions.^[24] Other HCP believe healthy individuals are not susceptible to infection and therefore find vaccination unnecessary.^[25] In addition, many HCP do not prioritize influenza vaccination, expressing a lack of time to get vaccinated.^[26] Lower HCP influenza vaccination in long-term care facilities also stems from organizational barriers, such as inadequate vaccine recordkeeping, frequent staff turnover, an absence of influenza vaccine mandates, a lack of communication about vaccination rates, and a lack of incentives encouraging HCP flu vaccination.^[27] Given the fact that influenza vaccination coverage among HCP is typically lower in long-term care settings, such as SNFs, when compared to other care settings, we noted in the proposed rule that we believe the measure as proposed has the potential to increase influenza vaccination coverage in SNFs, promote patient safety, and increase the transparency of quality of care in the SNF setting.

Although concerns about vaccine effectiveness often prevent some HCP from getting the influenza vaccine, the CDC notes that higher influenza vaccination rates reduce the risk of influenza-related illness between 40 to 60 percent among the overall population during seasons when the circulating influenza virus is well-matched to viruses used to make influenza vaccines.^[28] During the 2019 to 2020 influenza season, vaccinations prevented 7.5 million influenza-related illnesses, 105,000 influenza-related hospitalizations, and 6,300 deaths.^[29] Additionally, among adults with influenza-associated hospitalization, influenza vaccination is also associated with a 26 percent lower risk of intensive care unit admission, and a 31 percent lower risk of influenza-related deaths compared to individuals who were unvaccinated against influenza.^[30] Several cluster-randomized trials comparing HCP influenza vaccination groups to control groups demonstrate reductions in long-term care resident mortality rates as related to HCP influenza vaccination.^{[31 32 33 34]} To reduce vaccine hesitancy and organizational barriers to influenza vaccination, several strategies can be used to increase influenza vaccination among HCP. These include availability of on-site influenza vaccinations and educational campaigns about influenza risks and vaccination benefits.^{[35 36 37]}

Addressing HCP influenza vaccination in SNFs is particularly important as vulnerable populations often reside in SNFs. Vulnerable populations are less likely to receive the influenza vaccine, and thus, are susceptible to contracting the virus. For example, not only are Black residents more likely to receive care from facilities with lower overall influenza vaccination rates, but Black residents are also less likely to be offered and receive influenza vaccinations in comparison to White residents.^{[38 39 40 41]} Racial and ethnic disparities in influenza vaccination, specifically among Black and Hispanic populations, are also higher among short-stay residents receiving care for less than 100 days in the nursing home.^[42] Additionally, Medicare fee-for-service beneficiaries of Black, Hispanic, rural, and lower-income populations are less likely to receive inactivated influenza vaccines, and non-White beneficiaries are generally less likely to receive high-dose influenza vaccines in comparison to White beneficiaries.^{[43 44 45]} Therefore, the measure as proposed has the potential to increase influenza vaccination coverage of HCP in SNFs, as well as prevent the spread of the influenza virus to vulnerable populations who are less likely to receive influenza vaccinations.

The COVID-19 pandemic has exposed the importance of implementing infection prevention strategies, including the promotion of HCP influenza vaccination. Activity of the influenza virus has been lower during the COVID-19 pandemic as several strategies to reduce the spread of COVID-19 have also reduced the spread of influenza, including mask mandates, social distancing, and increased hand hygiene.^[46] However, even though more people are receiving COVID-19 vaccines, it is still important to encourage annual HCP influenza vaccination to prevent healthcare systems from getting overwhelmed by the co-circulation of COVID-19 and influenza viruses. A 2020 literature search revealed several studies in which those with severe cases of COVID-19, requiring hospitalization, were less likely to be vaccinated against influenza.^[47] HCP vaccinations against influenza may prevent the spread of illness between HCP and residents, thus reducing resident morbidities associated with influenza and pressure on already stressed healthcare systems. In fact, several thousand nursing homes voluntarily reported weekly influenza vaccination coverage through a National Healthcare Safety Network (NHSN) module based on the NQF #0431 measure during the overlapping 2020 to 2021 influenza season and COVID-19 pandemic. Even after the COVID-19 pandemic ends, promoting HCP influenza vaccination is important in preventing morbidity and mortality associated with influenza.

As discussed in the proposed rule, variation in influenza vaccination coverage rates indicate the proposed measure's usability and use. A CDC analysis during the 2020 to 2021 influenza season revealed that among 16,535 active, CMS-certified nursing homes, 17.3 percent voluntarily submitted data for the proposed measure through the NHSN. Average staff influenza vaccination coverage was approximately 64 percent, ranging from 0.3 percent to 100 percent with an interquartile range of 40 to 93.9 percent. Variation in influenza vaccination coverage rates by facility demonstrates the utility of the measure for resident choice of facility. Variation in influenza vaccination rates by type of HCP demonstrates the utility of the proposed measure for targeted quality improvement efforts.

For these reasons, we proposed to adopt the CDC-developed Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431) measure for the SNF QRP, as collected through the CDC's NHSN, to report the percentage of HCP who receive the influenza vaccine. We explained in the proposed rule that we believe this measure will encourage HCP to receive the influenza vaccine, resulting in fewer cases, less hospitalizations, and lower mortality associated with the virus.

b. Stakeholder Input and Pilot Testing

In the development and specification of this measure, a transparent process was employed to seek input from stakeholders and national experts and engage in a process that allows for pre-rulemaking input in accordance with section 1890A of the Act. To meet this requirement, opportunities were provided for stakeholder input by a Delphi panel and Steering Committee through the measure's pilot testing. The measure's pilot testing assessed reliability and validity among 234 facilities and five facility types (that is, long-term care facilities, acute care hospitals, ambulatory surgery centers, physician practices, and dialysis centers) across four jurisdictions (that is, California, New Mexico, New York City, and western Pennsylvania) between 2010 and 2011.^[48 49]

Two methods were used to conduct reliability testing, including interrater reliability testing and the use of case studies. Interrater reliability was assessed among 96 facilities, including 19 long-term care facilities, by comparing agreement between two raters: facility staff and project staff. Project staff reviewed individual-level records from randomly selected facilities to assess agreement with how facility staff classified HCP into numerator and denominator categories. For more information regarding numerator and denominator definitions, refer to section VI.C.1.e. of the proposed rule. Interrater reliability results demonstrated high adjusted agreement between facility and project staff for numerator data (91 percent) and denominator data (96 percent). Most numerator disagreements resulted from healthcare facilities reporting verbal declinations in the “declined vaccination” numerator rather than categorizing verbal declinations as “missing/unknown” as there was no written documentation of the declination. There was also numerator disagreement related to contraindications as HCP did not properly cite true medical contraindications. Adhering to true medical contraindications and tracking declinations of the influenza vaccine among HCP should additionally improve reliability.

Case studies were also used to assess reliability. Facilities received a series of 23 vignettes, in which they were instructed to select appropriate numerator and denominator categories for the hypothetical cases described in each vignette. Most numerator and denominator elements were categorized correctly. For example, 95.6 percent of facility staff correctly categorized employees that were vaccinated at the facility, 88.6 percent correctly categorized employees vaccinated elsewhere, etc.^[50] However, problematic denominator elements included poor facility understanding of how to classify physician-owners of healthcare facilities who work part-time and physicians who were credentialed by a facility but had not admitted patients in the past 12 months. Problematic numerator elements were related to confusion about reporting persistent deferrals of vaccination and verbal vaccine declinations for non-medical reasons.

Two methods were also used for validity testing: convergent validity assessments and face validity assessment. Convergent validity examined the association between the number of evidence-based strategies used by a healthcare facility to promote influenza vaccination and the facility's reported vaccination rate among each HCP denominator group. The association between employee vaccination rates and the number of strategies used was borderline significant. The association between credentialed non-employee vaccination rates and the number of strategies used was significant, and the association between other non-employee vaccination rates and the number of strategies used was also significant, demonstrating convergent validity.

Face validity was assessed through a Delphi panel, which convened in June 2011 and provided stakeholder input on the proposed measure. The Delphi panel, comprised of nine experts in influenza vaccination measurement and quality improvement from several public and private organizations, rated elements of the proposed measure using a Likert scale. The Delphi panel discussed pilot testing results from the first round of ratings during a one-hour moderated telephone conference. After the conference concluded, panelists individually rated a revised set of elements. Ultimately, the Delphi panel reached a consensus that the majority of the proposed measure's numerator definitions had strong face validity. However, the panel raised concerns regarding the accuracy of self-reported data and deemed validity lowest for denominator categories of credentialed and other nonemployees of the facility.

After the conclusion of measure testing, the proposed measure's specifications were revised in alignment with the Delphi panel's ratings and with guidance from a Steering Committee. The CDC-convened Steering Committee was comprised of representatives from several institutions, including CMS, the Joint Commission, the Federation of American Hospitals, the American Osteopathic Association, the American Medical Association, and others. To address concerns raised through pilot testing and to reduce institutional barriers to reporting, denominator specifications were revised to include a more limited number of HCP among whom vaccination could be measured with greater reliability and accuracy: employees; licensed independent practitioners; and adult students/trainees and volunteers. The measure was also revised to require vaccinations received outside of the facility to be documented, but allow for self-report of declinations and medical contraindications. Verbal declinations were assigned to the “declined” numerator category, and an “unknown” category was added to give facilities actionable data on unvaccinated HCP who may not have purposefully declined. For more information regarding pilot testing results and measure input from the Delphi panel and Steering Committee, refer to the article published in the Infection Control & Hospital Epidemiology journal by the measure developer.^[51]

c. Measure Applications Partnership (MAP) Review

Our pre-rulemaking process includes making publicly available a list of quality and efficiency measures, called the Measures under Consideration (MUC) List that the Secretary is considering adopting through the Federal rulemaking process for use in Medicare programs. This allows multi-stakeholder groups to provide recommendations to the Secretary on the measures included in the list.

We included the Influenza Vaccination Coverage among HCP measure under the SNF QRP Program in the publicly available “List of Measures Under Consideration for December 1, 2021” (MUC List).^[52] Shortly after, several National Quality Forum (NQF)-convened Measure Applications Partnership (MAP) workgroups met virtually to provide input on the proposed measure. The MAP Rural Health workgroup convened on December 8, 2021. Members generally agreed that the proposed measure would be suitable for use by rural providers within the SNF QRP program, noting the measure's rural relevance. Likewise, the MAP Health Equity workgroup met on December 9, 2021, in which the majority of voting members agreed that the proposed measure has potential for decreasing health disparities. The MAP Post-Acute Care/Long-Term Care (PAC/LTC) workgroup met on December 16, 2021, in which the majority of voting workgroup members supported rulemaking of the proposed measure. Finally, the MAP Coordinating Committee convened on January 19, 2022, in which the committee agreed with the MAP's preliminary measure recommendation of support for rulemaking.

In addition to receiving feedback from MAP workgroup and committee members, NQF received four comments by industry stakeholders during the proposed measure's MAP pre-rulemaking process. Commenters were generally supportive of the measure as SNF QRP adoption would promote measure interoperability, encourage vaccination, and likely decrease the spread of infection. One commenter was not supportive of the measure due to burdens of NHSN data submission.

Overall, the MAP offered support for rulemaking, noting that the measure aligns with the IRF and LTCH PAC QRPs and adds value to the current SNF QRP measure set since influenza vaccination among HCP is not currently addressed within the SNF QRP program. The MAP noted the importance of vaccination coverage among HCP as an actionable strategy that can decrease viral transmission, morbidity, and mortality within SNFs. The final MAP report is available at https://www.qualityforum.org/​Publications/​2022/​03/​MAP_​2021-2022_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx.

d. Competing and Related Measures

Section 1899B(e)(2)(A) of the Act requires that, absent an exception under section 1899B(e)(2)(B) of the Act, each measure specified under section 1899B of the Act be endorsed by the entity with a contract under section 1890(a) of the Act, currently the NQF. In the case of a specified area or medical topic determined appropriate by the Secretary for which a feasible and practical measure has not been endorsed, section 1899B(e)(2)(B) of the Act permits the Secretary to specify a measure that is not so endorsed, as long as due consideration is given to the measures that have been endorsed or adopted by a consensus organization identified by the Secretary.

The proposed Influenza Vaccination Coverage among HCP measure initially received NQF endorsement in 2008 as NQF #0431. Measure endorsement was renewed in 2017, and the measure is due for maintenance in the spring 2022 cycle. The measure was originally tested in nursing homes and has been endorsed by NQF for use in nursing home settings since the measure was first endorsed. No additional modifications were made to the proposed measure for the spring 2022 measure maintenance cycle, but as noted in section VI.C.1.a. of the proposed rule, several thousand nursing homes voluntarily reported weekly influenza vaccination coverage through an NHSN module based on the NQF #0431 measure during the overlapping 2020 to 2021 influenza season and COVID-19 pandemic. The measure is currently used in several of our programs, including the Hospital Inpatient and Prospective Payment System (PPS)-Exempt Cancer Hospital QRPs. Among PAC programs, the proposed measure is also reported in the IRF and LTCH QRPs as adopted in the FY 2014 IRF PPS final rule (78 FR 47905 through 47906) and the FY 2013 Inpatient Prospective Payment System (IPPS)/LTCH PPS final rule (77 FR 53630 through 53631), respectively.

After review of the NQF's consensus-endorsed measures, we were unable to identify any NQF-endorsed measures for SNFs focused on capturing influenza vaccinations among HCP. For example, although the Percent of Residents or Patients Who Were Assessed and Appropriately Given the Seasonal Influenza Vaccine (Short Stay) (NQF #0680) and the Percent of Residents Assessed and Appropriately Given the Seasonal Influenza Vaccine (Long Stay) (NQF #0681) measures are both NQF-endorsed and assess rates of influenza vaccination, they assess vaccination rates among residents in the nursing home rather than HCP in the SNF. Additionally, the Percent of Programs of All-Inclusive Care for the Elderly (PACE) Healthcare Personnel with Influenza Immunization measure resembles the proposed measure since it assesses influenza vaccination among HCP; however, it is not NQF-endorsed and is not specific to the SNF setting.

Therefore, after consideration of other available measures, we found the NQF-endorsed Influenza Vaccination Coverage among HCP measure appropriate for the SNF QRP, and we proposed the measure beginning with the FY 2025 SNF QRP. Application of the Influenza Vaccination Coverage among HCP measure within the SNF QRP promotes measure harmonization across quality reporting programs that also report this measure. This proposed measure has the potential to generate actionable data on vaccination rates that can be used to target quality improvement among SNF providers.

e. Quality Measure Calculation

The Influenza Vaccination Coverage among HCP measure is a process measure developed by the CDC to track influenza vaccination coverage among HCP in facilities such as SNFs. The measure reports on the percentage of HCP who receive influenza vaccination. The term “healthcare personnel” refers to all paid and unpaid persons working in a healthcare setting, contractual staff not employed by the healthcare facility, and persons not directly involved in patient care but potentially exposed to infectious agents that can be transmitted to and from HCP. As explained in the proposed rule, since the proposed measure is a process measure, rather than an outcome measure, it does not require risk-adjustment.

The proposed measure's denominator is the number of HCP who are physically present in the healthcare facility for at least 1 working day between October 1st and March 31st of the following year, regardless of clinical responsibility or patient contact. The proposed measure's reporting period is October 1st through March 31st; this reporting period refers to the proposed measure's denominator only. The denominator would be calculated separately for three required categories: Employees, meaning all persons who receive a direct paycheck from the reporting facility (that is, on the SNF's payroll); Licensed independent practitioners,^[53] such as physicians, advanced practice nurses, and physician assistants who are affiliated with the reporting facility, who do not receive a direct paycheck from the reporting facility; and Adult students/trainees and volunteers who do not receive a direct paycheck from the reporting facility. A denominator can be calculated for an optional category as well: Other contract personnel are defined as persons providing care, treatment, or services at the facility through a contract who do not fall into any of the three required denominator categories.

The proposed measure's numerator consists of all HCP included in the denominator population who received an influenza vaccine any time from when it first became available (such as August or September) through March 31st of the following year and who fall into one of the following categories: (a) received an influenza vaccination administered at the healthcare facility; (b) reported in writing (paper or electronic) or provided documentation that an influenza vaccination was received elsewhere; (c) were determined to have a medical contraindication/condition of severe allergic reaction to eggs or other component(s) of the vaccine, or a history of Guillain-Barré syndrome (GBS) within 6 weeks after a previous influenza vaccination; (d) were offered but declined the influenza vaccination; or (e) had an unknown vaccination status or did not meet any of the definitions of the other numerator categories (a through d). As described in the FY 2014 IRF PPS final rule, measure numerator data are required based on data collected from October 1st or whenever the vaccine becomes available.^[54] Therefore, if the vaccine is available prior to October 1st, any vaccine given before October 1st is credited toward vaccination coverage. Likewise, if the vaccine becomes available after October 1st, the vaccination counts are to begin as soon as possible after October 1st.

We proposed that SNFs submit data for the measure through the CDC/NHSN data collection and submission framework.^[55] In alignment with the data submission frameworks utilized for this measure in the IRF and LTCH QRPs, SNFs would use the HCP influenza data reporting module in the NHSN Healthcare Personnel Safety (HPS) Component and complete two forms. SNFs would complete the first form (CDC 57.203) to indicate the type of data they plan on reporting to the NHSN by selecting the “Influenza Vaccination Summary” option under “Healthcare Personnel Vaccination Module” to create a reporting plan. SNFs would then complete a second form (CDC 57.214) to report the number of HCP who have worked at the healthcare facility for at least 1 day between October 1st and March 31st (denominator) and the number of HCP who fall into each numerator category. To meet the minimum data submission requirements, SNFs would enter a single influenza vaccination summary report at the conclusion of the measure reporting period. If SNFs submit data more frequently, such as on a monthly basis, the information would be used to calculate one summary score for the proposed measure which would be publicly reported on Care Compare. See sections VI.G.2. and VI.H.2. of the proposed rule for more information regarding data submission requirements for this measure and its public reporting plan. Details related to the use of NHSN for data submission can be found at the CDC's NHSN HPS Component web page at https://www.cdc.gov/​nhsn/​hps/​vaccination/​index.html?​CDC_​AA_​refVal=​https%3A%2F%2Fwww.cdc.gov%2Fnhsn%2Finpatient-rehab%2Fvaccination%2Findex.html.

We solicited public comment on our proposal to add a new measure, Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431), to the SNF QRP beginning with the FY 2025 SNF QRP. The following is a summary of the comments we received and our responses.

Comment: We received several supportive comments for our proposal to adopt the Influenza Vaccination Coverage among Healthcare Personnel (HCP) (NQF #0431) measure for the SNF QRP. Several commenters agreed that regular reporting of influenza vaccination rates among SNF HCP would reduce the risk of infection transmission from HCP to SNF patients. Another commenter supported the measure, noting that (1) influenza causes significant healthcare costs and mortality of elderly patients and (2) the measure provides an opportunity for nursing leaders to educate their staff and use evidence-based strategies, such as motivational interviewing, to encourage staff to adopt a behavior change that is beneficial for public health. Two facilities supported the proposal, noting that they already require employees to receive annual influenza vaccinations unless there is an appropriate medical or religious exemption. Multiple commenters supported the reporting of HCP influenza vaccination rates as it may encourage SNFs to take responsibility for supporting HCP access to recommended immunizations, incentivize facilities to adopt programs encouraging workers to receive influenza vaccines, provide additional information about a SNF's infection response and readiness efforts, and increase the transparency of quality of care among SNFs. Other commenters supported the measure for other reasons, such as the fact that it is consistent with CDC guidelines for long-term care workers, promotes alignment and consistency across PAC QRPs, and is NQF-endorsed.

Response: We believe the proposed measure will promote the health and well-being of SNF patients and HCP, and that reporting this measure will contribute to overall infection control within SNFs.

Comment: One commenter supported the measure, but expressed concern that it could create an administrative burden for community and long-term care pharmacies or consultant pharmacists within long-term care settings. The commenter pointed out staffing issues experienced by long-term care pharmacies when pharmacists leave the pharmacy to perform on-site vaccinations at the SNF.

Response: We note that the measure neither requires the influenza vaccine to be administered to HCP at SNFs, nor does it require the vaccine to be administered by a pharmacist or a long-term care pharmacy in order for HCP to be captured in the measure's numerator.^[56] The influenza vaccination may either be received at the SNF or an HCP may provide written or electronic documentation that the vaccine was received elsewhere. We provide a full description of the measure numerator earlier in this section (VII.C.1.e.) of this final rule.

Comment: One commenter noted concern over payment reductions if a specified percentage of HCP are not vaccinated against influenza, and noted that SNFs are already struggling financially to overcome pandemic costs.

Response: The SNF QRP is a pay-for-reporting program, which means that SNFs are only financially penalized if they fail to comply with the QRP's data submission standards. For the HCP Influenza Vaccine measure, the data submission standard consists of one data submission per year at the conclusion of the measure reporting period. SNFs would not have to reach a particular threshold of HCP influenza vaccination among HCP to comply with measure data submission standards. Additionally, the HCP Influenza Vaccine measure would be submitted through the CDC's NHSN collection and submission framework, which is free to SNF providers. While we acknowledge the challenges the PHE has presented, we refer SNFs to section XI.A.5. of this final rule, where we estimate the measure will only require an annual cost of $9.38 per SNF for annual data submission. Because of the minimal cost associated with annual data submission and the fact that data submission requirements are not associated with vaccination thresholds, we believe that SNFs will be able to successfully meet the data submission requirements for the HCP Influenza Vaccine measure at a minimal cost.

Comment: One commenter supported CMS's increased focus on infection control but is concerned about whether the measure aligns with the Improving Medicare Post-Acute Care Transformation (IMPACT) Act. The commenter noted that the IMPACT Act requires the reporting of standardized patient assessment data, while the Influenza Vaccination Coverage among HCP measure collects HCP data rather than patient data, and therefore may not be useful to consumers.

Response: The IMPACT Act added section 1899B to the Act and requires the reporting of standardized patient assessment data with regard to quality measures and standardized patient assessment data elements.^[57] The IMPACT Act does not state that quality reporting programs can only report patient-level data. The Act also requires the submission of data pertaining to quality measures, resource use, and other domains. The Influenza Vaccination Coverage among HCP measure is proposed for adoption as an “other” measure under section 1899B(d)(1) of the Act. In accordance with section 1899B(a)(1)(B) of the Act, the data used to calculate this measure are standardized and interoperable. A similar NHSN-based measure, COVID-19 Vaccination Coverage among HCP, was added to the SNF QRP under the same statutory authority in the FY 2022 SNF PPS final rule.^[58] The statute intends for standardized PAC data to improve Medicare beneficiary outcomes through shared-decision making, care coordination, and enhanced discharge planning. As the Influenza Vaccination Coverage among HCP measure's purpose is to report HCP vaccination rates and encourage infection prevention and control within a facility, we disagree with the commenter and find the measure useful to consumers' shared decision-making processes.

Comment: Several commenters did not support the proposal to adopt the Influenza Vaccination Coverage among HCP (NQF #0431) measure due to staffing concerns. Some of these commenters noted that mandated HCP vaccination may hamper efforts to increase facility staffing levels, and one commenter questioned whether CMS intends to mandate influenza vaccination as a condition of employment at a later time. One commenter expressed concern that collecting vaccination information would invade staff's personal lives and intensify staff shortages.

Response: We disagree with the commenter that the HCP Influenza Vaccine measure may hamper efforts to increase facility staffing levels because CMS is not mandating SNF employees receive an influenza vaccine as a condition of employment. The SNF QRP is a pay-for-reporting program and the actual number of SNF HCP who have been vaccinated does not impact SNFs' ability to successfully report the measure. Additionally, hospitals, IRFs, and LTCHs have been collecting HCP influenza vaccination data for almost 10 years and have not reported to CMS that it hampers their hiring ability. In regards to privacy concerns, the NHSN HPS Component used to report HCP influenza data collects summary information and does not require SNFs to enter staff personal identifiable information.

Comment: Some commenters stated that the proposal to add the HCP Influenza Vaccine measure to the SNF QRP is an unfunded mandate. A few commenters were concerned about the amount of unfunded mandated reporting that has occurred over the course of the COVID-19 PHE, and another commenter urged CMS not to finalize new data reporting requirements during the COVID-19 PHE, because SNFs do not have the resources to manage another unfunded mandate.

Response: We acknowledge the commenters' concerns. However, we have examined the impacts of this proposed measure as required by Executive Order 12866 on Regulatory Planning and Review (September 30, 1993), Executive Order 13563 on Improving Regulation and Regulatory Review (January 18, 2011), and section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA, March 22, 1995; Pub. L. 104-4). Executive Orders 12866 and 13563 direct agencies to assess all costs and benefits of available regulatory alternatives and, if regulation is necessary, to select regulatory approaches that maximize net benefits.

As required, we have considered the benefits and costs of the proposed measure. This measure would facilitate patient care and care coordination during the discharge planning process. A discharging hospital or facility, in collaboration with the patient and family, could use this measure to coordinate care and ensure patient preferences are considered in the discharge plan. Patients at high risk for negative outcomes due to influenza (perhaps due to underlying conditions) can use healthcare provider vaccination rates when they are selecting a SNF for next-level care. Additionally, the data submission method is free to SNFs, and we estimate the annual data submission will require a cost $9.38 per SNF annually. We believe we have selected an approach that maximizes net benefits.

Comment: One commenter requested that CMS consider hybrid care delivery models where staff, including, but not limited to, respiratory therapists, physical therapists, or dieticians/dietary aides, may cross between different quality reporting programs on the same campus. The commenters requested that inclusion and exclusion criteria must be clearly stated for valid comparisons.

Response: We thank the commenter for their suggestion, and will take it under consideration. Further we note that the criteria for HCP included and excluded from the HCP Influenza Vaccine measure can be found in the NHSN Healthcare Personnel Safety Component Protocol at https://www.cdc.gov/​nhsn/​pdfs/​hps-manual/​vaccination/​hps-flu-vaccine-protocol.pdf.

Comment: Some commenters noted the importance of how the measure's denominator is defined. Specifically, two commenters suggested the measure's denominator should be modified to exclude non-employed staff, such as agency and contracted staff, and/or be limited to direct care staff in the SNF. One of these commenters noted that such modifications to the measure's denominator will better assess a SNF's ability to engage with and vaccinate its staff while not necessarily rewarding or penalizing SNFs based on vaccination coverage that may occur outside of the facility's control. Other commenters stated how CMS will define “employee” in reference to the measure's denominator will be significant.

Response: As described in section VII.G.2. of this final rule, the proposed measure does not require SNFs to report all facility contract personnel. The proposed measure requires vaccination information to be reported for three required categories of HCP who are physically present in the healthcare facility for at least 1 working day within the measure's data collection period. Healthcare personnel captured in the measure's denominator include: (1) employees of the SNF (or those who receive a direct paycheck from the reporting facility), (2) licensed independent practitioners (including MD, DO, advanced practice nurses, physician assistants, and post-residency fellows affiliated with the reporting facility, but who are not directly employed by the facility), and (3) adult students/trainees and volunteers regardless of clinical responsibility or patient contact. SNFs are not required (but have the option) to report influenza vaccination status on other contract personnel. Since the SNF QRP is a pay-for-reporting program, SNFs are not rewarded or penalized based on the rate of HCP vaccination. While CMS acknowledges that SNFs do not have direct control over an HCP's choice to receive a vaccine, the SNF does have direct control over reporting the data required for the HCP Influenza Vaccine measure, which is the only requirement to comply with the SNF QRP.

SNFs should use the specifications and data collection tools for the HCP Influenza Vaccine measure as required by CDC as of the time that the data are submitted. For more information about HCP included in the measure's denominator, please refer to the NHSN Manual Healthcare Personnel Safety Component Protocol Healthcare Personnel Vaccination Module: Influenza Vaccination Summary web page at https://www.cdc.gov/​nhsn/​pdfs/​hps-manual/​vaccination/​hps-flu-vaccine-protocol.pdf.

Comment: One commenter expressed concern about adopting infection-specific regulations for particular viruses as these actions could set a precedent for future regulations that potentially burden both CMS as well as SNFs.

Response: We strive to promote high quality and efficiency in the delivery of healthcare to the beneficiaries we serve. Valid, reliable, and relevant quality measures are fundamental to the effectiveness of our QRPs. We are aware of potential provider burdens and only implement quality initiatives that have the potential to assure quality healthcare for Medicare beneficiaries through accountability and public disclosure. The Influenza Vaccination Coverage among HCP measure is consistent with CMS's Meaningful Measures 2.0, which includes safety as a key component of achieving value-based care and promoting health equity. The COVID-19 PHE has exposed the threat that emerging infectious diseases pose, and the importance of implementing infection prevention strategies, including the promotion of HCP influenza vaccination. We believe the proposed measure has the potential to generate actionable data on vaccination rates that can be used to target quality improvement among SNF providers.

Comment: One commenter expressed concerns about the HCP Influenza Vaccine measure due to the commenter's belief that SNFs are already required to report vaccine status to CMS on a weekly basis and are financially penalized for a failure to report. The commenter was also concerned that SNFs would receive a double penalty if the proposal were finalized.

Response: It is unclear what the commenter means by the term “double penalty,” but we interpret the commenter to be concerned about being penalized twice: once for a failure to report COVID-19 vaccine data to CMS on a weekly basis and a second time for failure to report HCP influenza vaccine data. The LTC facility requirements of participation (requirements) at § 483.80(g) and the SNF QRP are two separate requirements. The LTC facility requirements require nursing homes to report weekly on the COVID-19 vaccination status of all residents and staff as well as COVID-19 therapeutic treatment administered to residents. As discussed in section VII.C.1.e. of this final rule, we proposed that SNFs would report the number of HCP who receive influenza vaccination. The reporting requirement for the HCP Influenza Vaccine measure is different from the COVID-19 vaccination information reporting requirement in the May 2021 IFC.^[59] Each system has its own methods of validation and carries separate penalties.

Comment: One commenter stated that evidence continues to support that the best measures to prevent transmission from person to person are consistent infection control measures by the healthcare providers and encouraged CMS to review literature evidence more critically, and be able to discern between conflicting evidence in a more effective manner. Another commenter noted that although vaccines are beneficial, other infection control practices, such as mask wearing, can prevent influenza outbreaks within the SNF.

Response: We appreciate the comment and agree with the commenter that evidence continues to support the use of consistent infection control measures. Evidence also points to the importance of vaccination as a part of a multi-pronged approach within SNF infection prevention and control programs, especially to prevent the transmission of highly contagious conditions, such as influenza. We will continue to critically review evidence in our measure development processes.

Comment: Commenters suggested CMS delay implementation of the measure due to the PHE and staffing crisis. One commenter stated the data may be misleading to consumers due to changes in staffing from one influenza season to the next, the effectiveness of the vaccine, and the fact that the measure includes all HCP regardless of possible contact with the Medicare beneficiary.

Response: The PHE further emphasizes the need for CMS to prioritize infection prevention and control initiatives, such as HCP influenza vaccination. HCP vaccinations against influenza may prevent the spread of illness between HCP and residents, thus reducing resident morbidities associated with influenza and pressure on already stressed healthcare systems. The HCP Influenza Vaccine measure has been successfully reported in the IRF QRP since 2014 and the LTCH QRP since 2013, and CMS has had no questions or complaints from consumers about the value of the information when selecting a PAC provider. We disagree with the commenter that including all HCP in the measure, regardless of possible contact with the Medicare beneficiary, could result in misleading measure data because it is possible for any and all HCP to come into contact with Medicare beneficiaries. We do not require SNFs to differentiate between HCP who come into contact with Medicare beneficiaries versus those who do not as this would place additional reporting burdens on SNFs. Therefore, as described in section VII.G.2. of this final rule, we proposed the Influenza Vaccination Coverage among HCP measure to include HCP (as defined by the measure's denominator) who are physically present in the healthcare facility for at least 1 working day within the measure's data collection period since all types of HCP may come into contact with SNF residents.

Comment: One commenter urged CMS to add the HCP Influenza Vaccine measure to the SNF QRP as soon as possible because influenza season is anticipated as an annual occurrence nationally. In addition, the commenter stated that because the data used to calculate the measure are standardized and interoperable, CMS should be able to support an earlier implementation than the FY 2025 QRP.

Response: We agree with the commenter that we should adopt the measure sooner than the FY 2025 SNF QRP because it has the potential to increase influenza vaccination coverage in SNFs, promote patient safety, and increase the transparency of quality of care in the SNF setting as described in section VII.C.1.a. of this final rule. Therefore, we are finalizing this measure beginning with the FY 2024 SNF QRP. We are also finalizing our proposal to require SNFs to begin reporting data on this measure for the period October 1, 2022 through March 31, 2023, with a reporting deadline of May 15, 2023. This initial data reporting deadline gives us sufficient time to calculate the first year of measure results for the FY 2024 SNF QRP. Accordingly, we are finalizing our adoption of the measure beginning with the FY 2024 SNF QRP rather than the FY 2025 SNF QRP as proposed.

Comment: We received several comments that were not related to our SNF QRP proposals. One commenter responded to several proposals from the FY 2022 SNF PPS proposed rule,^[60] while another commenter encouraged CMS to ensure immunizations are affordable and accessible. One commenter noted the number of measures currently reported on Care Compare and emphasized the importance of risk-adjusting measures due to COVID-19. Another commenter stated it is critical that changes to the QRP are accompanied with appropriate financial incentives so SNFs may invest in technologies that improve patient safety and compliance with data submission thresholds. Another commenter recommended the COVID-19 Vaccination Coverage among HCP numerator be aligned with the Influenza Vaccination Coverage among HCP measure. Finally, two commenters suggested CMS explore inclusion of Medicare Advantage patients in quality measure calculations.

Response: These comments fall outside the scope of the FY 2023 SNF PPS proposed rule.

After consideration of public comments, we are finalizing our proposal to adopt the Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431) measure beginning with the FY 2024 SNF QRP, since this measure influences patient safety and should be implemented within the SNF QRP as soon as possible.

2. Revised Compliance Date for Certain Skilled Nursing Facility Quality Reporting Program Requirements Beginning With the FY 2024 SNF QRP

a. Background

Section 1888(d)(6)(B)(i)(III) of the Act requires that, for FY 2019 and each subsequent year, SNFs must report standardized patient assessment data required under section 1899B(b)(1) of the Act. Section 1899B(a)(1)(C) of the Act requires, in part, the Secretary to modify the PAC assessment instruments in order for PAC providers, including SNFs, to submit standardized patient assessment data under the Medicare program. In the FY 2020 SNF PPS final rule (84 FR 38755 through 38817), we adopted two TOH Information quality measures as well as standardized patient assessment data that would satisfy five categories defined by section 1899B(c)(1). The TOH Information to the Provider—Post-Acute Care (PAC) measure and the TOH Information to the Patient—PAC measure are process-based measures that assess whether or not a current reconciled medication list is given to the subsequent provider when a patient is discharged or transferred from his or her current PAC setting or is given to the patient, family, or caregiver when the patient is discharged from a PAC setting to a private home/apartment, a board and care home, assisted living, a group home, or transitional living. Section 1899B(b)(1)(B) of the Act defines standardized patient assessment data as data required for at least the quality measures described in section 1899B(c)(1) of the Act and that is with respect to the following categories: (1) functional status; (2) cognitive function; (3) special services, treatments, and interventions; (4) medical conditions and comorbidities; (5) impairments; and (6) other categories deemed necessary and appropriate by the Secretary.

The interim final rule with comment period that appeared in the May 8, 2020 Federal Register (85 FR 27550) (hereafter referred to as the “May 8th COVID-19 IFC”), delayed the compliance date for certain reporting requirements under the SNF QRP (85 FR 27596 through 27597). Specifically, we delayed the requirement for SNFs to begin reporting the TOH Information to the Provider-PAC and the TOH Information to the Patient-PAC measures and the requirement for SNFs to begin reporting certain standardized patient assessment data elements from October 1, 2020, to October 1st of the year that is at least 2 full fiscal years after the end of the COVID-19 PHE. We also delayed the adoption of the updated version of the Minimum Data Set (MDS) 3.0 v1.18.1 ^[61] which SNFs would have used to report the TOH Information measures and certain standardized patient assessment data elements.

Currently, SNFs must use the MDS 3.0 v1.18.11 to begin collecting data on the two TOH Information measures beginning with discharges on October 1st of the year that is at least 2 full fiscal years after the end of the COVID-19 PHE. SNFs must also begin collecting data on certain standardized patient assessment data elements on the MDS 3.0 v1.18.11, beginning with admissions and discharges (except for the preferred language, need for interpreter services, hearing, vision, race, and ethnicity standardized patient assessment data elements, which would be collected at admission only) on October 1st of the year that is at least 2 full fiscal years after the end of the COVID-19 PHE. The delay to begin collecting data for these measures was intended to provide relief to SNFs from the added burden of implementing an updated instrument during the COVID-19 PHE. As discussed in the proposed rule, we wanted to provide maximum flexibilities for SNFs to respond to the public health threats posed by the COVID-19 PHE, and to reduce the burden in administrative efforts associated with attending trainings, training their staff, and working with their vendors to incorporate the updated assessment instruments into their operations.

At the time the May 8th COVID-19 IFC was published, we believed this delay would not have a significant impact on the SNF QRP. However, we were in the initial months of the COVID-19 PHE, and very little was known about the SARS-CoV-2 virus. Additionally, we believed the delay in the collection of the TOH Information measures and standardized patient assessment data elements were necessary to allow SNFs to focus on patient care and staff safety. However, the COVID-19 PHE has illustrated the important need for these TOH Information measures and standardized patient assessment data elements under the SNF QRP. The PHE's disproportionate impact among non-Hispanic Black, and Hispanic and Latino persons ^{[62 63 64 65 66 67 68]} demonstrates the importance of analyzing this impact in order to improve quality of care within SNFs especially during a crisis. One important strategy for addressing these important inequities is by improving data collection to allow for better measurement and reporting on equity across post-acute care programs and policies. The information will inform our Meaningful Measures framework.

b. Current Assessment of SNFs' Capabilities

To accommodate the COVID-19 PHE, we provided additional guidance and flexibilities, and as a result SNFs have had the opportunity to adopt new processes and modify existing processes to accommodate the significant health crisis presented by the COVID-19 PHE. For example, we held regular “Office Hours” conference calls to provide SNFs regular updates on the availability of supplies, as well as answer questions about delivery of care, reporting, and billing. We also supported PAC providers, including SNFs, by providing flexibilities in the delivery of care in response to the PHE,^[69] such as waiving the requirements at § 483.30 for physician and non-physician practitioners to perform in-person visits, allowing them to use telehealth methods where deemed appropriate. We also waived the nurse aide training and certification requirements § 483.35(d) (with the exception of § 483.35(d)(1)(i)), allowing SNFs to employ nurse aides for longer than 4 months even when they have yet not met the standard training and certification requirements, and we waived the requirement at § 483.95(g)(1) for nursing aides to receive at least 12 hours of in-service training annually. To reduce provider burden, we waived the Pre-Admission Screening and Annual Resident Review (PASARR) at § 483.20(k), allowing SNFs more flexibility in scheduling Level 1 assessments. We narrowed the scope of requirements for a SNF's Quality Assurance and Performance Improvement (QAPI) program to the aspects of care most associated with COVID-19 (§ 483.75), that is infection control and adverse events. Additionally, we waived timeframe requirements on MDS assessments and transmission at § 483.20, along with waiving requirements for submitting staffing data through the Payroll-Based Journal (PBJ) system at § 483.70(q), to grant SNFs the greater flexibility needed to adapt to the rapidly evolving burdens of the PHE. While the MDS and PBJ requirements have since been terminated, many of these waivers for SNFs are still in effect today.

In addition, as of March 1, 2022, 86.2 percent of the population aged 12 and older (81.3 percent of those 5 and older) had received at least one COVID-19 vaccination.^[70] Further, although there was a recent increase in COVID-19 cases, vaccinated individuals aged 18 years and older through March 4, 2022 were 3.2 times less likely to test positive, over 9 times less likely to be hospitalized, and experienced 41 times lower risk of death, compared to unvaccinated individuals.^[71] We also believe that SNFs have more information and interventions to deploy to effectively prevent and treat COVID-19 than they had at the time the May 8th COVID-19 IFC was finalized,^{[72 73 74 75]} including three vaccines that are either approved or authorized in the United States to prevent COVID-19, and antiviral drugs that are approved or authorized to treat COVID-19.^{[76 77 78 79 80]} Also, recent reports suggest that the rollout of COVID-19 vaccines has alleviated some of the burden on SNFs imposed by the PHE.^[81 82]

Despite the COVID-19 PHE, we must maintain our commitment to the quality of care for all patients, and we continue to believe that the collection of the standardized patient assessment data elements and TOH Information measures will contribute to this effort. That includes an ongoing commitment to achieving health equity by improving data collection to better measure and analyze disparities across programs and policies.^{[83 84 85 86 87 88 89 90]} We also note that in response to the “Request for Information to Close the Health Equity Gap” in the FY 2022 SNF PPS proposed rule (86 FR 20000), we heard from stakeholders that it is important to gather additional information about race, ethnicity, gender, language, and other social determinants of health (SDOH). Some SNFs noted they had already begun to collect some of this information for use in their operations. Our commitment to the quality of care for all patients also includes improving the quality of care in SNFs through a reduction in preventable adverse events. Health information, such as medication information, that is incomplete or missing increases the likelihood of a patient or resident safety risk, and is often life-threatening.^{[91 92 93 94 95 96]} Poor communication and coordination across healthcare settings contributes to patient complications, hospital readmissions, emergency department visits, and medication errors.^{97 98 99 100 101 102 103 104 105 106} Further delaying the data collection has the potential to further exacerbate these issues. We believe the benefit of having this information available in a standardized format outweighs the potential burden of collecting these data, as data availability is a necessary step in addressing health disparities in SNFs.

Given the flexibilities described earlier in this section, SNFs' increased knowledge and interventions to deploy to effectively prevent and treat COVID-19, and the trending data on COVID-19, we believe that SNFs are in a better position to accommodate the reporting of the TOH Information measures and certain standardized patient assessment data elements. Specifically, we believe SNFs have learned how to adapt and now have the administrative capacity to attend training, train their staff, and work with their vendors to incorporate the updated assessment instruments into their operations. Moreover, these standardized patient assessment data elements are reflective of patient characteristics that providers may already be recording for their own purposes, such as preferred language, race, ethnicity, hearing, vision, health literacy, and cognitive function. It is also important to align the collection of these data with the IRFs and LTCHs that will begin collecting this information on October 1, 2022, and home health agencies (HHAs) that will begin collecting this information on January 1, 2023.^[107]

c. Collection of the Transfer of Health (TOH) Information to the Provider-PAC Measure, the Transfer of Health (TOH) Information to the Patient-PAC Measure and Certain Standardized Patient Assessment Data Elements Beginning October 1, 2023

We proposed to revise the compliance date specified in the May 8th COVID-19 IFC from October 1st of the year that is at least 2 full FYs after the end of the COVID-19 PHE to October 1, 2023. This revised date would begin the collection of data on the TOH Information to the Provider-PAC measure and TOH Information to the Patient-PAC measure, and certain standardized patient assessment data elements on the updated version of the MDS assessment instrument referred to as MDS 3.0 v1.18.11. We believe this revised date of October 1, 2023, which is a 3-year delay from the original compliance date finalized in the FY 2020 SNF PPS final rule (84 FR 38755 through 38764), balances the support that SNFs have needed during much of the COVID-19 PHE, the flexibilities we provided to support SNFs, and the time necessary to develop preventive and treatment options along with the need to collect these important data. We believe this date is sufficiently far in advance for SNFs to make the necessary preparations to begin reporting these data elements and the TOH Information measures. As described in section VI.C.2 of the proposed rule, the need for the standardized patient assessment data elements and TOH Information measures has been shown to be even more pressing with issues of health inequities, exacerbated by the COVID-19 PHE. These data, which include information on SDOH, provides information that is expected to improve quality of care for all, and is not already found in assessment or claims data currently available. Consequently, we proposed to revise the compliance date to reflect this balance and assure that data collection begins on October 1, 2023.

As stated in the FY 2020 SNF PPS final rule (84 FR 38774), we will provide the training and education for SNFs to be prepared for this implementation date. In addition, if we adopt an October 1, 2023 compliance date, we would release a draft of the updated version of the MDS 3.0 v1.18.11 in early 2023 with sufficient lead time to prepare for the October 1, 2023 start date.

Based upon our evaluation, we proposed that SNFs collect the TOH Information to the Provider-PAC measure, the TOH Information to the Patient-PAC measure, and certain standardized patient assessment data elements beginning October 1, 2023. We also proposed that SNFs begin collecting data on the two TOH Information measures beginning with discharges on October 1, 2023. We proposed that SNFs begin collecting data on the six categories of standardized patient assessment data elements on the MDS 3.0 v1.18.11, beginning with admissions and discharges (except for the preferred language, need for interpreter services, hearing, vision, race, and ethnicity standardized patient assessment data elements, which would be collected at admission only) on October 1, 2023. We solicited public comment on this proposal. The following is a summary of the comments we received and our responses.

Comment: Several commenters supported our proposal to revise the compliance date for the TOH Information measures and certain standardized patient assessment data elements beginning with the FY 2024 QRP. One commenter acknowledged that CMS must maintain its commitment to quality of care for all patients and they support the collection of certain standardized patient assessment data as an important part of improving patient care. Two commenters stated that they recognize the importance of collecting these data to advance health equity and improve quality of care for all beneficiaries. These commenters also noted that the date was further into the future than the IRF and LTCH QRPs, and therefore they appreciated CMS's acknowledgement of the unique support needs of SNFs during the COVID-19 public health emergency. Other commenters noted that despite the ongoing challenges of the pandemic, they believe SNFs will be able to report this information. Another commenter supported the prompt initiation of the data collection to enhance holistic care, call attention to impairments to be mitigated or resolved, and to facilitate clear communication between residents and providers. Further, the commenters noted that such data collection could allow for examination of SNF performance stratified for factors associated with healthcare disparities, such as race and ethnicity.

Response: We agree that the data will advance quality of care for all patients. We believe that as the healthcare community continues to learn about the enormous impact that social determinants of health (SDOH) and social risk factors (SRFs) have on patient health and health outcomes,^[108] it becomes more critical to collect this information to better understand the impact of the PHE on our healthcare system, as well as how to address the inequities that the PHE has made so visible. We believe it will help SNFs, physicians, and other practitioners caring for patients in SNFs better prepare for the complex and resource-intensive care needs of patients with new and emerging viruses.

We also agree with the commenter that despite the COVID-19 PHE, SNFs will be able to successfully report the standardized patient assessment data and TOH Information measures. As of July 6, 2022, 89.86 percent of the population aged 12 and older (83.3 percent of those 5 and older) had received at least one COVID-19 vaccination, indicating an increase of 3.5 percent and 2 percent, respectively in the last 4 months.^[109] Further strengthening our conclusion that SNFs are able to meet the revised compliance date is that there are even more treatments available to treat COVID-19.^[110] As of May 31, 2022, there are two treatments currently approved by the FDA for use in COVID-19 and 13 COVID-19 treatments authorized for Emergency Use.^[111]

Comment: Several commenters supported the proposal to revise the compliance date for the TOH Information measures and certain standardized patient assessment data elements beginning with the FY 2024 QRP, but at the same time reminding CMS that concerns exist around the timing for the release of the newer version of the MDS 3.0, which contains new data elements. The commenters specifically raised questions about the ability of providers and health IT developers to develop, test, and implement software for the new MDS and its associated reporting requirements. One commenter requested adequate time to develop, test, and deploy new software, noting that in the past, health IT developers have indicated they need 18 months for this process. Two commenters also urged CMS to provide adequate lead time for training staff on the changes required by the new assessment items.

Response: We understand providers' concerns with developing software for the new MDS and the need to train staff. However, SNFs have known since July 30, 2019 ^[112] that CMS would be implementing an updated version of the MDS to collect the TOH Information measures and certain standardized patient assessment data elements. As described in section VII.C.2.a., the May 8th COVID-19 IFC only delayed the compliance date for these reporting requirements.

On July 31, 2019, we posted the specifications for the TOH Information measures and standardized patient assessment data elements on the IMPACT Act Downloads and Videos web page which SNFs could use to begin developing their software and train their staff. Specifically, the Final Specifications for SNF QRP Quality Measures and SPADEs document,^[113] provides information on each of the TOH Information measures, including the items' description, measure numerator and denominator, as well as the assessment items and responses. Additionally, each of the new standardized patient assessment data elements is described and accompanied by the assessment item and response(s). We also suggest SNF information technology (IT) vendors look at the Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF-PAI) Version 4.0 and the Long-Term Care Hospital (LTCH) Continuity Assessment Record and Evaluation (CARE) Data Set (LCDS) Version 5.0 to see how these assessment items are embedded into those assessment tools. As we discussed in section VI.2.b. of the SNF PPS proposed rule, the new items that will be collected are standardized and interoperable data elements. As such, the items that would be collected by the MDS are the same items that will be collected by IRFs and LTCHs on October 1, 2022, and home health agencies (HHAs) on January 1, 2023.^[114] Since the Final Specifications for SNF QRP Quality Measures and SPADEs document has been available to SNFs since July 31, 2019, we believe IT vendors will have enough time to update their software prior to October 1, 2023. We also note that since IT vendors for IRFs, LTCHs and HH agencies will have already updated their systems, IT vendors in SNFs may benefit from their experience.

In response to the comment that health IT vendors need 18 months to develop, test, and deploy new software, we note that historically we have tried to provide vendors with the information they need to make adjustments to their software well ahead of the implementation date. This was especially important in the early years of assessment data submission to CMS, but we have found in recent years, vendors are very mature in the software development process for MDS and do not require such extensive lead times. The time, form, and manner in which the MDS will be submitted is not changing; rather it is a variation in the data elements being collected. Therefore, the implementation of this proposal should not require health IT vendors to completely rewrite their software.

In response to the commenters' concerns for training staff, we plan to provide multiple training resources and opportunities for SNFs to take advantage of, reducing the burden to SNFs in creating their own training resources. These training resources may include online learning modules, tip sheets, questions and answers documents, and/or recorded webinars and videos, and would be available to providers in early 2023, allowing SNFs several months to ensure their staff take advantage of the learning opportunities. Having the materials online and on-demand would give staff the flexibility of learning about the new items at times that minimize disruption to patient care schedules. The SNF QRP Helpdesk would also be available for providers to submit their follow-up questions by email, further enhancing the educational resources.

We received several comments urging us not to revise the compliance date for the TOH Information measures and certain standardized patient assessment data elements beginning with the FY 2024 QRP. We will address each of these comments here.

Comment: Many commenters raised concerns with revising the compliance date from October 1st of the year that is at least 2 full fiscal years after the end of the PHE to October 1, 2023 given the fact that the PHE is still in effect as of the date of our proposal, while another suggested no new quality metrics should be implemented within 1 calendar year from the date the COVID-19 PHE officially ends. One commenter stated that the delay was intended to provide relief to SNFs, and it would be inappropriate to move up the date while the PHE is still in effect. Another commenter supported the implementation of the TOH Information measures since it reflects a process already being completed in SNFs, but stated the proposed implementation of the MDS 3.0 with the new standardized patient assessment data elements would be overwhelming to facilities at this time given the impact on quality measures, care area triggers, and care plans. One commenter disagreed with CMS's assertion that the flexibilities and assistance granted by the agency during the PHE, as well as the promising trends in COVID-19 vaccination and death rates, have left providers in a better position to collect the standardized patient assessment data. Another commenter pointed to the uncertainty around current therapeutics' and vaccines' effectiveness against new variants, which they believe leave the SNF population potentially susceptible to an ever-changing COVID-19 ecosystem, and stated that further stressing SNFs with additional reporting at a time when the COVID-19 PHE may still be burdening SNFs and their residents may lead to unforeseen consequences like inaccurate and inconsistent data lessening the value of this reporting. Other commenters acknowledged that the acute impacts of COVID-19 have lessened but are concerned that COVID-19's rippling effects continue to impact SNF operations.

Response: As stated in section VI.C.2 of the FY 2023 SNF PPS proposed rule (87 FR 22750 through 22754), we have provided SNFs a number of flexibilities to accommodate the COVID-19 PHE, including delaying the adoption of the updated version of the MDS 3.0 v1.18.0 with which SNFs would have used to report the TOH Information measures and standardized patient assessment data elements (85 FR 27595 through 27596). Despite the COVID-19 PHE, we must maintain our commitment to quality of care for all patients, and we continue to believe that the collection of the standardized patient assessment data elements and TOH Information measures will contribute to this effort. That includes staying committed to achieving health equity by improving data collection to better measure and analyze disparities across programs and policies ^{[115 116 117 118 119 120]} and improving the quality of care in SNFs through a reduction in preventable adverse events. Health information, such as medication information, that is incomplete or missing increases the likelihood of a patient or resident safety risk, and is often life-threatening.^{[121 122 123 124 125 126]} Poor communication and coordination across healthcare settings contribute to patient complications, hospital readmissions, emergency department visits, and medication errors.^{[127 128 129 130 131 132 133 134 135 136]} While we understand that there are concerns related to the timeline proposed, we believe specifying an earlier date for the data collection is necessary to maintain our commitment to quality of care for all patients. Furthermore, it is important to align the collection of these data with the IRFs and LTCHs that will begin collecting this information on October 1, 2022, and HHAs that will begin collecting this information on January 1, 2023.^[137] We have strived to balance the scope and level of detail of the data elements against the potential burden placed on SNFs.

Comment: Several commenters stated that implementing the MDS 3.0 v1.18.11 would require additional staffing, specifically nursing staff, at a time when there is a national staffing crisis. Two commenters noted that the workforce shortages have been compounded by burnout among SNF workers resulting in experienced professionals leaving the workforce earlier than expected, with one stating it would take years to replace them. Another commenter cited a Kaiser Family Foundation study reporting more than a quarter of nursing homes have reported staffing shortages as recently as March of this year.

Response: The impacts of the COVID-19 PHE on the healthcare system, including staffing shortages, make it especially important now to monitor quality of care.^[138] Still, we are mindful of burden that may occur from the collection and reporting of our measures. We emphasize, however, that the TOH Information Provider-PAC and TOH Information Patient-PAC measures consist of one item each, and further, the activities associated with the measures align with the existing Requirements of Participation for SNFs related to transferring information at the time of discharge to safeguard patients.^[139] As a result, the information gathered will reflect a process that SNFs should already be conducting, and will demonstrate the quality of care provided by SNFs.

We do not believe that shortages in staffing will affect implementation of the new MDS because many of the data elements adopted as standardized patient assessment data elements in the FY 2020 SNF PPS final rule are already collected on the MDS 1.17.2 using current SNF staffing levels. For example, the hearing, vision, preferred language, Brief Interview for Mental Status (BIMS), Confusion Assessment Method (CAM©), and the Patient Health Questionnaire (PHQ) are items that were finalized as standardized patient assessment data elements in the FY 2020 SNF PPS final rule and are already being collected by SNFs on the MDS 1.17.2. However, those items have not historically been collected in the IRF and LTCH settings, and therefore will be “new” items to collect beginning October 1, 2022. Therefore, MDS 1.18.11 results in fewer “new” standardized patient assessment data elements for SNFs, as compared to other PAC settings.

Examples of the “new” standardized patient assessment data elements that will be collected on the MDS 1.18.11 include ethnicity, access to transportation, health literacy, social isolation, and pain interference.^[140] We note that in response to the “Request for Information to Close the Health Equity Gap” in the FY 2022 SNF PPS proposed rule (86 FR 20000), we heard from SNFs that they had already started collecting additional information about race, ethnicity, gender, language, and other SDOH. Given the fact that some SNFs are able to collect this information at current staffing levels and many of the items categorized as standardized patient assessment data elements will not be new items for SNFs, we do not believe that staff shortages will interfere with implementing the MDS 3.0 v1.18.11.

Comment: Two commenters noted that the length of the revised MDS assessment instrument is expected to increase from 51 pages to approximately 61 pages, a change they believe will require significant investments in staff education and training, which would divert these resources from direct patient care.

Response: As stated earlier in this final rule, many of the data elements that would be adopted as standardized patient assessment data elements are already collected by SNFs. The increase in the number of pages is the result of providing additional response options for several of the existing data elements and does not necessarily translate to additional time and burden. Additionally, the new version of the MDS 3.0 is expected to be 58 pages, rather than 61 pages.

We plan to provide multiple training resources and opportunities for SNFs on the revised MDS assessment tool, which may include online learning modules, tip sheets, questions and answers documents, and/or recorded webinars and videos. We plan to make these training resources available to SNFs in early 2023, allowing SNFs several months to ensure their staff take advantage of the learning opportunities, and to allow SNFs to spread the cost of training out over several quarters.

Comment: One commenter supported collecting, analyzing, and using data on social risk factors. This commenter noted, however, that it would create confusion and unnecessary administrative burden for CMS to quickly add data elements to the MDS because they happen to be available now, only to replace them with other data elements developed with the feedback from CMS's Requests for Information (RFIs) and its ongoing work with its Disparity Methods.^[141]

Response: To clarify, the standardized patient assessment data elements that would be collected in the MDS 3.0 v1.18.11 were finalized in the FY 2020 SNF PPS final rule (84 FR 38755 through 38817). The RFI published in section VI.E. of the FY 2023 SNF PPS proposed rule (87 FR 22754 through 22761) requested public comment on Overarching Principles for Measuring Equity and Healthcare Quality Disparities across CMS Quality Programs and on Approaches to Assessing Drivers of Healthcare Quality Disparities and Developing Measures of Healthcare Equity in the SNF QRP, which may or may not include using standardized patient assessment data elements. Any new data elements that may come out of the RFI would have to go through the public notice and comment period before being implemented. Therefore, we do not anticipate confusion or unnecessary administrative burden as a result of the feedback received to the FY 2023 SNF RFI.

Comment: Two commenters urged CMS to delay the implementation of the MDS 3.0 v1.18.11 until it has received the first full year of data collection on the TOH Information measures and standardized patient assessment data elements in the IRF and LTCH settings in order to better inform provider education and technical assistance for SNF providers.

Response: The revised date of October 1, 2023, is a 3-year delay from the original compliance date finalized in the FY 2020 SNF PPS final rule (84 FR 38755 through 38764), and balances the support that SNFs have needed during the COVID-19 PHE with the need to collect this important data. We believe the revised date is sufficiently far in advance for SNFs to make the necessary preparations to begin reporting these data elements and the TOH Information measures. As stated earlier, the IRF and LTCH will begin collecting the TOH Information measures and the standardized patient assessment data elements on October 1, 2022. CMS began answering questions from providers in November 2021, after the proposal was finalized.^[142] CMS released virtual trainings programs for IRF and LTCH providers in April 2022 that reviewed the updated guidance for their respective updated assessment tools, and hosted two live Question and Answer sessions on June 15 and June 16, 2022. A major focus of the trainings was on the cross-setting implementation of the standardized patient assessment data elements they begin collecting October 1, 2022. Therefore, CMS would have over a year to inform provider education and technical assistance for SNF providers prior to implementation.

We also note that in response to the “Request for Information to Close the Health Equity Gap” in the FY 2022 SNF PPS proposed rule (86 FR 20000), interested parties stressed the importance of gathering additional information about race, ethnicity, gender, language, and other SDOH. Some SNFs noted they had already begun to collect some of this information for use in their operations. We do not believe further delaying the data collection would provide any additional information to better inform provider education and technical assistance for SNF providers.

Comment: We received comments regarding states' and other payer programs use of section G data elements, the impact of changes to SNF regulations and requirements on the demands of these other payment systems, and the need for CMS to provide more infrastructure support to adopt certified electronic technology to facilitate meaningful data exchange.

Response: These comments fall outside the scope of the FY 2023 SNF PPS proposed rule.

Comment: One commenter stated their support for CMS' proposed update to the denominator of the TOH Information to the Patient-PAC measure.

Response: We believe this comment was directed at the proposals in the FY 2022 SNF proposed rule (86 FR 19998), and we thank the commenter for their support. In the FY 2022 SNF PPS Final Rule (86 FR 42490), we finalized the proposal to remove the location of home under the care of an organized home health service organization or hospice from the denominator of the TOH Information to the Patient-PAC measure.

After consideration of the comments received, we are finalizing our proposal that SNFs begin collecting the TOH Information to the Provider-PAC measure, the TOH Information to the Patient-PAC measure, and the six categories of standardized patient assessment data elements on the MDS v1.18.11 for admissions and discharges (except for the hearing, vision, race, and ethnicity standardized patient assessment data elements, which would be collected at admission only) on or after October 1, 2023.

3. Revisions to the Regulation Text (§ 413.360)

The FY 2022 SNF PPS final rule (86 FR 42480 through 42489) added the COVID-19 Vaccination Coverage among Healthcare Personnel (HCP COVID-19 Vaccine) measure to the SNF QRP beginning with the FY 2024 QRP. The data submission method for the HCP COVID-19 Vaccine measure is the NHSN. The NHSN is a system maintained by the CDC, whose mission it is to protect the health security of the nation. The NHSN is used to collect and report on healthcare-acquired infections, such as catheter-associated urinary tract infections and central-line-associated bloodstream infections. The NHSN also collects vaccination information since vaccines play a major role in preventing the spread of harmful infections. Healthcare-acquired infections are a threat to beneficiaries, SNFs, and the public. Given the significance of the information collected through the NHSN, and the fact that infection prevention affects all beneficiaries, 100 percent of the information required to calculate the HCP COVID-19 Vaccine measure must be submitted to the NHSN. The HCP COVID-19 Vaccine measure is an important part of the nation's response to the COVID-19 PHE, and therefore 100 percent of the information is necessary to monitor the health and safety of beneficiaries.

For consistency in our regulations, we proposed conforming revisions to the Requirements under the SNF QRP at § 413.360. Specifically, we proposed to redesignate § 413.360(b)(2) to § 413.360(f)(2) and add a new paragraph (f) for the SNF QRP data completeness thresholds. The new paragraph would reflect all data completion thresholds required for SNFs to meet or exceed in order to avoid receiving a 2-percentage-point reduction to their APU for a given fiscal year.

At § 413.360(b), Data submission requirement, we proposed to remove paragraph (b)(2) and redesignate paragraph (b)(3) as paragraph (b)(2). At § 413.360, we proposed to add a new paragraph (f), Data completion thresholds.

At § 413.360(f)(1), we proposed to add new language to state that SNFs must meet or exceed two separate data completeness thresholds: One threshold set at 80 percent for completion of required quality measures data and standardized patient assessment data collected using the MDS submitted through the CMS-designated data submission system, beginning with FY 2018 and for all subsequent payment updates; and a second threshold set at 100 percent for measures data collected and submitted using the CDC NHSN, beginning with FY 2023 and for all subsequent payment updates.

At § 413.360(f)(2), we proposed to add new language to state that these thresholds (80 percent for completion of required quality measures data and standardized patient assessment data on the MDS; 100 percent for CDC NHSN data) will apply to all measures and standardized patient assessment data requirements adopted into the SNF QRP.

At § 413.360(f)(3), we proposed to add new language to state that a SNF must meet or exceed both thresholds to avoid receiving a 2-percentage-point reduction to their APU for a given fiscal year.

We solicited public comment on this proposal. The following is a summary of the comments we received and our responses.

Comment: One commenter urged CMS not to establish a 100 percent compliance threshold for measures submitted to the QRP using the NHSN. The commenter stated that SNFs need more experience with submitting data through the NHSN and that NHSN reporting requirements should be simplified in order to make a 100 percent compliance threshold more reasonable.

Response: We disagree that SNFs need more experience with submitting data through the NHSN before we finalize the proposal. Since May 21, 2021, SNFs have been submitting the COVID-19 vaccination status of all residents and staff through the NHSN on a weekly basis.^[143] Similarly, SNFs would submit the HCP Influenza Vaccine measure through the NHSN at the conclusion of the measure reporting period.

If SNFs experience data submission issues, the NHSN has a Helpdesk to which providers can submit questions about data submission. If a facility continues to have questions or experience additional issues after a ticket has closed, the CDC encourages providers to submit a new email with a detailed subject line to ensure an expeditious Helpdesk reply with input from a subject matter expert team.

Comment: Several commenters requested that CMS clarify what 100 percent reporting means for purposes of meeting the compliance threshold.

Response: To meet the minimum data submission requirements for measure data collected and submitted using the CDC NHSN, SNFs must submit 100 percent of the data to the NHSN in order to calculate the measure. For example, NHSN is the data submission method for the HCP COVID-19 Vaccine measure for the SNF QRP. Therefore, SNFs must submit to the NHSN 100 percent of the information required to calculate the HCP COVID-19 Vaccine measure in order to meet the compliance threshold.

Similarly, for the HCP Influenza Vaccine measure, SNFs must submit to the NHSN 100 percent of the information required to calculate the measure. To meet the minimum data submission requirements for the HCP Influenza Vaccine measure, SNFs must enter a single influenza vaccination summary report at the conclusion of the measure reporting period. If SNFs submit data more frequently, such as on a monthly basis, the information would be used to calculate one summary score for the proposed measure which would be publicly reported on Care Compare and used to determine compliance with the SNF QRP.

Comment: One commenter requested clarification on the proposed conforming language to the regulatory text at § 413.360. Specifically, the commenter requested that CMS clarify the procedural steps SNFs must take to meet or exceed the two separate data completeness thresholds.^[144] The commenter inquired how many files a SNF must submit and how often in order to meet the 100 percent completion threshold.

Response: The proposed revisions to the regulatory text at § 413.360 would add language to state that SNFs must meet or exceed two separate data completeness thresholds depending on the data submission method: (1) an 80 percent threshold for completion of required data elements collected using the MDS submitted through the CMS designated data submission system; and (2) a 100 percent threshold for measures collected and submitted using the NHSN.

With the addition of the HCP Influenza Vaccine measure adopted in this final rule, the SNF QRP would have two measures submitted via the NHSN: (1) the HCP COVID-19 Vaccine measure and (2) the HCP Influenza Vaccine measure. SNFs must follow separate data submission guidelines for each measure to meet the 100 percent completion threshold. For the HCP COVID-19 Vaccine measure, SNFs use the COVID-19 vaccination data collection module in the NHSN Long-term Care Component to report the number of HCP eligible to work at the facility for at least 1 day during the reporting period excluding persons with contraindications to COVID-19 vaccination that are described by the CDC ^[145] (denominator) and the number of those people who have received a completed COVID-19 vaccination course (numerator). To meet the minimum data submission requirements for the HCP COVID-19 Vaccine measure, SNFs submit COVID-19 vaccination data through the NHSN for at least 1 week each month. For example, if a SNF only submitted COVID-19 vaccination data for 1 week each month from January through September of a given calendar year, but failed to submit information for October, November, and December of that same calendar year, that SNF would not meet the 100 percent completion threshold for this measure and would face a 2-percentage-point reduction to its APU.

Similarly, for the HCP Influenza Vaccine measure, SNFs would use the HCP influenza data reporting module in the NHSN HPS Component and complete two forms. The first form (CDC 57.203) would indicate the type of data SNFs plan on reporting to the NHSN by selecting the “Influenza Vaccination Summary” option under “Healthcare Personnel Vaccination Module” to create a reporting plan. The second form (CDC 57.214) would report the number of HCP who have worked at the healthcare facility for at least 1 day between October 1st and March 31st (denominator) and the number of HCP who fall into each numerator category. To meet the minimum data submission requirements for the HCP Influenza Vaccine measure, SNFs would enter a single influenza vaccination summary report at the conclusion of the measure reporting period. If SNFs submit data more frequently, such as on a monthly basis, the information would be used to calculate one summary score for the proposed measure which would be publicly reported on Care Compare and used to determine compliance with the SNF QRP.

To meet the 100 percent compliance threshold for the HCP Influenza Vaccine measure, a SNF must submit a single influenza vaccination summary report at the conclusion of the reporting period. A SNF that submits an influenza vaccination summary report for October through December of an influenza season, but not for the remainder of the influenza season, would not meet the 100 percent completion threshold for this measure.

To meet the 80 percent compliance threshold for purposes of calculating the SNF's APU, a SNF would need to submit a minimum of 80 percent of its MDS with 100 percent of the required data elements collected during the reporting period to the CMS Quality Improvement and Evaluation System (QIES) Assessment Submission and Processing (ASAP) system or a successor system. The SNF QRP Table for Reporting Assessment-Based Measures for each FY SNF QRP APU is available for download on the SNF Quality Reporting Measures and Technical Information web page in the Downloads section.^[146]

Comment: One commenter questioned whether a SNF would be compliant if it meets the 80 percent requirements but fails to meet the 100 percent requirements.

Response: We interpret the comment to be referring to the 80 percent compliance threshold for the required data elements submitted using the MDS 3.0 and the 100 percent compliance threshold proposed for measures submitted using the NHSN data submission framework. In accordance with section 1888(e)(6)(A)(i) of the Act, the Secretary must reduce by 2 percentage points the APU applicable to a SNF for a fiscal year if the SNF does not comply with the requirements of the SNF QRP for that fiscal year. Consistent with the measures we are finalizing, SNF providers must meet both the 80 percent and 100 percent compliance thresholds for that applicable fiscal year to comply with the requirements of the SNF QRP beginning with FY 2023 QRP and for all subsequent payment updates.

After consideration of the comments received, we are finalizing our proposal to make conforming revisions to the requirements under the SNF QRP at § 413.360. Specifically, we are redesignating § 413.360(b)(2) to § 413.360(f)(2) and adding a new paragraph (f) for the SNF QRP data completeness thresholds.

D. SNF QRP Quality Measures Under Consideration for Future Years: Request for Information (RFI)

We solicited input on the importance, relevance, and applicability of the concepts under consideration listed in Table 16 in the SNF QRP. More specifically, we solicited input on a cross-setting functional measure that would incorporate the domains of self-care and mobility. Our measure development contractor for the cross-setting functional outcome measure convened a Technical Expert Panel (TEP) on June 15 and June 16, 2021 to obtain expert input on the development of a functional outcome measure for PAC. During this meeting, the possibility of creating one measure to capture both self-care and mobility was discussed. We also solicited input on measures of health equity, such as structural measures that assess an organization's leadership in advancing equity goals or assess progress toward achieving equity priorities. Finally, we solicited input on the value of a COVID-19 Vaccination Coverage measure that would assess whether SNF patients were up to date on their COVID-19 vaccine.

Comment: Most commenters supported the concept of a cross-setting functional outcome measure that is inclusive of both self-care and mobility items. Commenters provided information relative to potential risk adjustment methodologies as well as other tests and measures that could be used to capture functional outcomes. Commenters were mixed on whether they supported the measure concept of a PAC-COVID-19 vaccination coverage among patients. Two commenters noted the measure should account for other variables, such as whether the vaccine was offered, as well as patients with medical contraindications to the vaccine. Comments were generally supportive of the concept of measuring health equity in the SNF QRP. In addition, several commenters suggested other measures and measure concepts CMS should consider.

Response: As discussed in the proposed rule, we are not responding to specific comments submitted in response to this RFI in this final rule, but we intend to use this input to inform our future measure development efforts.

E. Overarching Principles for Measuring Equity and Healthcare Quality Disparities Across CMS Quality Programs—Request for Information (RFI)

1. Solicitation of Public Comments

The goal of the request for information was to describe some key principles and approaches that we would consider when advancing the use of quality measure development and stratification to address healthcare disparities and advance health equity across our programs.

We invited general comments on the principles and approaches described previously in this section of the rule, as well as additional thoughts about disparity measurement guidelines suitable for overarching consideration across CMS's QRP programs. Specifically, we invited comments on:

• Identification of Goals and Approaches for Measuring Healthcare Disparities and Using Measure Stratification Across CMS Quality Reporting Programs:

++ The use of the within- and between-provider disparity methods in SNFs to present stratified measure results.

++ The use of decomposition approaches to explain possible causes of measure performance disparities.

++ Alternative methods to identify disparities and the drivers of disparities.

• Guiding Principles for Selecting and Prioritizing Measures for Disparity Reporting:

++ Principles to consider for prioritization of health equity measures and measures for disparity reporting, including prioritizing stratification for validated clinical quality measures, those measures with established disparities in care, measures that have adequate sample size and representation among healthcare providers and outcomes, and measures of appropriate access and care.

• Principles for SRF and Demographic Data Selection and Use:

++ Principles to be considered for the selection of SRFs and demographic data for use in collecting disparity data including the importance of expanding variables used in measure stratification to consider a wide range of SRFs, demographic variables, and other markers of historic disadvantage. In the absence of patient-reported data we will consider use of administrative data, area-based indicators, and imputed variables as appropriate.

• Identification of Meaningful Performance Differences:

++ Ways that meaningful difference in disparity results should be considered.

• Guiding Principles for Reporting Disparity Measures:

++ Guiding principles for the use and application of the results of disparity measurement.

• Measures Related to Health Equity:

++ The usefulness of a Health Equity Summary Score (HESS) for SNFs, both in terms of provider actionability to improve health equity, and in terms of whether this information would support Care Compare website users in making informed healthcare decisions.

++ The potential for a structural measure assessing a SNF's commitment to health equity, the specific domains that should be captured, and options for reporting these data in a manner that would minimize burden.

++ Options to collect facility-level information that could be used to support the calculation of a structural measure of health equity.

++ Other options for measures that address health equity.

We received several comments on the RFI for Overarching Principles for Measuring Equity and Healthcare Quality Disparities Across CMS Quality Programs. While we will not be responding to specific comments submitted in response to this RFI, the following is a summary of some comments received:

Comment: Several commenters provided feedback on the use of the within-provider and between-provider disparity methods to present stratified measure results. Overall, comments were generally supportive of implementing both methods in order to provide a more complete picture of the quality of care provided to beneficiaries with SRFs. In terms of specific feedback related to the implementation of these stratification approaches, one commenter noted that when making between-facility comparisons, CMS should appropriately account for the share of patients within a facility with various risk factors. Another commenter noted that in the hospital setting, some stratification metrics moved widely across deciles when only a few patients improved performance, suggesting the importance of evaluating the statistical reliability of stratification methodologies implemented in the SNF setting.

One commenter expressed support for the measure performance disparity decomposition approach because it will likely provide valuable data while placing minimal burden on SNFs. Several commenters emphasized that providing stratified results alone to providers does not provide sufficient information to identify underlying factors that contribute to health inequities. While these commenters did not explicitly point to the disparity decomposition approach as a solution, the decomposition approach described could be a promising method to identify specific drivers of performance disparities, which would increase the actionability of stratified measure information while providing no additional burden to providers.

A handful of commenters responded to CMS's request for information about measures CMS could develop to assess and encourage health equity, including comments regarding the usefulness and actionability of a HESS and the potential for a structural measure to assess SNFs' commitment to health equity. We first summarize the comments regarding the HESS, then summarize comments related to a structural measure to assess commitment to equity.

Three commenters specifically addressed the HESS. One commenter encouraged CMS to clarify that the HESS would assess individual SNFs as opposed to the individual clinicians within each SNF. The two remaining commenters either supported or appreciated the HESS in concept, but raised several concerns pertaining to technical barriers, ambiguity in the methodology, and usability of the measure. In terms of technical concerns, one commenter noted that a standardized set of demographic data elements must be available for each patient, and stated that demographic data elements are not yet standardized across healthcare settings and organizations. Regarding methodological concerns, one commenter questioned how one could combine within-facility disparities and disparities across facilities into a single summary score in a manner that would accurately reflect the individual factors that may lead to these different types of disparities, without masking other factors. Other commenters raised similar concerns about the usability of the HESS, primarily stemming from the extent to which disparities across multiple measures and SRFs are aggregated into a single score. Specifically, one commenter noted that one SRF included in the HESS could mask the effects of other SRFs, which could potentially lead to misinterpretation of the overall score. Similarly, another commenter noted that performance on the composite HESS might obscure measure-level and SRF-specific disparities.

Two commenters addressed the potential for a structural measure to assess health equity. One commenter noted that the development of a structural measure to assess engagement and commitment of leadership toward advancing health equity should be included as one of several guiding principles to address health disparities and achieve health equity. Another commenter cautioned against the development of structural measures, suggesting that such measures would only demonstrate whether an organization is “good at checking the box” for the purpose of meeting the requirements of a measure.

Several commenters addressed the selection of SRFs and demographic data in collecting disparity data. One commenter supported the Center for Outcomes Research and Evaluation's (CORE's) efforts to categorize SDOH. Several commenters supported collecting data through current standardized resident assessment processes using variables with robust, established data sources. They believe revisions to an item already used across settings would capitalize on existing workflows and be easily updated within electronic health record (EHR) systems, resulting in minimal staff burden. One commenter recommended using existing items such as A1000 in Section A of the MDS assessment that addresses Race and Ethnicity, and revising gender identification options in MDS item A0800—Gender, which currently only includes binary Male/Female options. Another commenter recommended CMS consider how to best capture sexual orientation and gender identity among Medicare and Medicaid beneficiaries.

Several commenters preferred using self-reported social, economic, and demographic tools over imputed data sources, but also recognized the challenges with collecting self-reported data, and so they stated that in the absence of self-reported data, they would support the use of certain proxies, such as the Area Deprivation Index (ADI) or other area-based indicators of social risk. One commenter also suggested utilizing indexes from the Agency for Healthcare Research and Quality, CDC, and the Health Resources and Services Administration to incorporate data about area-based indicators of social risk would reduce burden on organizations or clinicians.

One commenter noted that using both methods of capturing data might be the best option: (1) a self-report demographic like the social determinants of health reported through the standardized patient assessment data elements that gives a picture of the unique resident's perspective, while (2) the area-based indices provide objective data on the risk factors present in the resident's usual environment.

Two commenters did not support selecting race and ethnicity for collecting disparity data. One commenter stated that “race” and “ethnicity” are social constructs that have no reliable biological basis in the clinical context, and are so overly broad, vague, and ill-defined that, even in combination with other indicators, they are unlikely to provide useful information and may even obscure individual experience to the detriment of individualized patient care. Another commenter also had significant reservations about using race and ethnicity data as the basis for stratifying measures and explaining differences in health and outcomes due to concerns about the variation in the manner in which race and ethnicity are defined and the categories collected by institutions.

Commenters suggested collecting other SRFs, including dual eligibility for Medicare and Medicaid, and detailed standardized demographic and language data. The Medicare Payment Advisory Commission (MedPAC) commented on its recent work to expand its definition of “low-income” as a proxy for beneficiary social risk. It defined “low-income” beneficiaries as those who are eligible for full or partial Medicaid benefits or receive the Part D low-income subsidy (LIS). This expanded definition includes beneficiaries who do not qualify for Medicaid benefits in their states but who do qualify for the LIS based on having limited assets and an income below 150 percent of the federal poverty level. MedPAC found that compared to the non-LIS Medicare population, LIS beneficiaries have relatively low incomes and differ in other regards, including being twice as likely to be Black or Hispanic and three times as likely to be disabled.

Commenters spoke to the importance of considering how SRF data could be interoperable and constructed in a way to facilitate exchange. One commenter suggested that CMS consider recommendations from The Gravity Project. Another requested that CMS make a concerted effort to advance standards for the collection of socio-demographic information, using existing tools such as the United States Core Data for Interoperability (USCDI), Z-codes, HL7, and Fast Healthcare Interoperability Resources (FHIR) standards.

We received several comments on the topic of confidential reporting of stratified and unstratified measure results. Most commenters supported the concept of selecting and prioritizing measures for disparity reporting. One commenter stated they want meaningful, actionable data, while another commenter recommended that, in addition to providing confidential feedback to nursing homes on stratified measure results, CMS should also provide information to make this feedback meaningful to nursing homes, such as how to interpret the information and what can be done to address identified disparities. This commenter suggested using the cumulative data to identify disparities at a regional or national level on which targeted training and resources could be provided, either by CMS or by the Quality Improvement Organizations (QIOs). Another commenter urged CMS to use ease of data access as an additional guiding principle when making disparity reporting decisions.

As for public reporting of stratified and unstratified results, many commenters urged CMS to carefully evaluate performance using the confidential reporting of data prior to applying the same methodologies to public reporting of stratified measure results. Another commenter recommended CMS outline a clear plan for transitioning to public reporting as it plans for the initial private reporting. MedPAC, however, supported it because MedPAC believes it should enable comparisons of individual providers with State and national averages to give consumers meaningful reference points.

Response: We appreciate all of the comments and interest in this important topic. Public input is very valuable in the continuing development of our health equity quality measurement efforts and broader commitment to health equity, a key pillar of our strategic vision as well as a core agency function. Thus, we will continue to take all concerns, comments, and suggestions into account for future development and expansion of policies to advance health equity across the SNF QRP, including by supporting SNFs in their efforts to ensure equity for all of their patients, and to identify opportunities for improvements in health outcomes. Any updates to specific program requirements related to quality measurement and reporting provisions would be addressed through separate and future notice-and-comment rulemaking, as necessary.

F. Inclusion of the CoreQ: Short Stay Discharge Measure in a Future SNF QRP Program Year-Request for Information (RFI)

1. Solicitation of Public Comment

In the proposed rule, we requested stakeholder feedback on future adoption and implementation of the CoreQ: Short Stay Discharge Measure (CoreQ) into the SNF QRP.

Specifically, we sought comment on the following:

• Would you support utilizing the CoreQ to collect patient-reported outcomes (PROs)?
• Do SNFs believe the questions asked in the CoreQ would add value to their patient engagement and quality-of-care goals?
• Should CMS establish a minimum number of surveys to be collected per reporting period or a waiver for small providers?
• How long would facilities and customer satisfaction vendors need to accommodate data collection and reporting for all participating SNFs?
• What specific challenges do SNFs anticipate for collecting the CoreQ measure? What are potential solutions for those challenges?

Comment: We received a few comments on this RFI that were generally supportive of the addition of a PRO measure or patient experience measure to the SNF QRP. However, support for the CoreQ measure specifically was mixed among commenters. One commenter stated that since the CoreQ has a limited number of questions, it may not fully reflect patient experience at a given facility. Another commenter would not support the CoreQ since it excludes residents who leave a facility against medical advice and residents with guardians, and this commenter stated it would be important to hear from both of these resident populations. Two commenters cautioned CMS to consider the burden associated with contracting with vendors to administer such a measure.

Response: We are not responding to specific comments submitted in response to this RFI in this final rule, but we intend to use this input to inform our future measure development efforts.

G. Form, Manner, and Timing of Data Submission Under the SNF QRP

1. Background

We refer readers to the current regulatory text at § 413.360(b) for information regarding the policies for reporting SNF QRP data.

2. Proposed Schedule for Data Submission of the Influenza Vaccination Coverage Among Healthcare Personnel (NQF #0431) Measure Beginning With the FY 2024 SNF QRP

As discussed in section VI.C.1. of the proposed rule, we proposed to adopt the Influenza Vaccination Coverage among HCP quality measure beginning with the FY 2025 SNF QRP. However, after consideration of public comments, we are finalizing our proposal to adopt the Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431) measure beginning with the FY 2024 SNF QRP. The CDC has determined that the influenza vaccination season begins on October 1st (or when the vaccine becomes available) and ends on March 31st of the following year. Therefore, we proposed an initial data submission period from October 1, 2022 through March 31, 2023. We also noted that in subsequent years, data collection for this measure will be from October 1st through March 31st of the following year.

This measure requires that the provider submit a minimum of one report to the NHSN by the data submission deadline of May 15th for each influenza season following the close of the data collection period each year to meet our requirements. Although facilities may edit their data after May 15th, the revised data will not be shared with us.^[147] SNFs would submit data for the measure through the CDC/NHSN web-based surveillance system. SNFs would use the Influenza Vaccination Summary option under the NHSN HPS Component to report the number of HCP who receive the influenza vaccination (numerator) among the total number of HCP in the facility for at least 1 working day between October 1st and March 31st of the following year, regardless of clinical responsibility or patient contact (denominator).

We sought public comment on this proposal. The following is a summary of the comments we received and our responses.

Comment: Several commenters urged CMS to be cautious in executing reporting for this measure since HCP influenza vaccination data are not currently reported by nursing homes and new processes will need to be implemented for measure data collection. Commenters recommended that (1) CMS provide ample notification to providers to ensure timely reporting of the measure, (2) reporting requirements of the measure should align with what is outlined in the proposed rule, and (3) CMS should only require reporting of the measure once per influenza season. Commenters also cautioned CMS that enforcement of any requirement must follow a traditional citation route without automatic financial penalties, given that SNFs that fail to report measure data will be penalized through the QRP framework itself.

One commenter expressed concerns that SNFs would be required to verify the influenza vaccination status of every employee, especially those who are immunized by an outside provider, and that the increase in administrative burden may take away resources to care for residents. Another commenter sought clarification about the measure's data collection process, noting that CMS must be clear and allow for ongoing flexibility in data collection and potential dispute.

Response: The HCP Influenza Vaccine measure reporting requirements would align with those outlined in the proposed rule. Specifically, the data collection period is October 1st to March 31st of the following year, with an annual data submission deadline due no later than May 15th. Additionally, we provide an updated SNF QRP Deadlines for Data Collection and Final Submission document on an annual basis. These deadlines provide sufficient notification to providers to ensure timely reporting of the measure. Providers may refer to this document on the SNF QRP Data Submission Deadlines web page at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Skilled-Nursing-Facility-Quality-Reporting-Program/​SNF-Quality-Reporting-Program-Data-Submission-Deadlines#:~:text=​When%20does%20SNF%20quality%20data,day%20of%20the%20submission%20deadline. We also send out reminders of the data submission deadlines via CMS listserv announcements. Providers can subscribe to the listserv to receive these email updates and for the latest SNF quality reporting program information on the CMS Email Updates web page at https://public.govdelivery.com/​accounts/​USCMS/​subscriber/​new?​pop=​t&​topic_​id=​USCMS_​7819.

To report HCP influenza vaccination summary data to the NHSN, all facilities must complete two required forms: (1) HCP Safety Monthly Reporting Plan Form (57.203), and (2) HCP Influenza Vaccination Summary Form (57.214). Facilities reporting annual HCP influenza vaccination data would report through the NHSN's Healthcare Personnel Safety (HPS) Component; therefore, providers should use form 57.203 and select the “Influenza Vaccination Summary” option under the “Healthcare Personnel Vaccination Module” to create a reporting plan. For more data collection and submission details, we refer providers to the HCP Influenza Vaccination Summary Reporting FAQs on the CDC NHSN web page at https://www.cdc.gov/​nhsn/​faqs/​vaccination/​faq-influenza-vaccination-summary-reporting.html. We also provide additional information regarding provider trainings later in this section.

Although the measure may require that SNFs spend additional time obtaining verification of HCP influenza vaccination, the importance of preventing infection among susceptible residents warrants collection of HCP influenza vaccination rates. We note that SNFs already have a process in place for tracking employee vaccinations, since they have been reporting HCP COVID-19 vaccination since October 1, 2021. We emphasize that tracking influenza vaccination rates among HCP is less burdensome than tracking COVID-19 vaccination rates, since SNFs are only required to track and submit data for one annual vaccination per HCP instead of potentially multiple vaccinations and boosters for the COVID-19 vaccination.

Comment: Several commenters requested CMS not to finalize the Influenza Vaccination Coverage among HCP measure due to the burden associated with reporting it. Commenters expressed concern that additional NHSN reporting will place burden on facilities on top of the existing NHSN reporting requirement of COVID-19 data. One commenter noted provider confusion with NHSN data submission requirements as some have unintentionally submitted data for certain modules that were not required. This commenter also highlighted the burdens associated with obtaining Secure Access Management Services (SAMS) Level 3 access in accordance with the CDC's reporting requirements for SNFs. A final commenter recommended using National Immunization Records as a data source for the measure, rather than spending additional time to report HCP vaccination status to the NHSN.

Response: We emphasize that the Influenza Vaccination Coverage among HCP measure only requires providers to submit a minimum of one report to the NHSN for each influenza season. We also clarify a statement in section VI.C.1.a. of the FY 2023 SNF PPS proposed rule that a CDC analysis of the 2020 through 2021 influenza season revealed that among 16,535 active, CMS-certified nursing homes, 17.3 percent voluntarily submitted at least 1 weekly influenza vaccination measurement through the NHSN. We believe such voluntary reporting supports the feasibility of annual measure data collection and reporting by nursing homes. We also believe that the burden of submitting data should be reduced since providers will have some familiarity with the NHSN through their experience of reporting of the COVID-19 Vaccination Coverage among HCP measure.^[148]

In response to provider confusion with NHSN data submission requirements, facilities may refer to the Healthcare Personnel Safety Component—Healthcare Personnel Vaccination Module Influenza Vaccination Summary Comprehensive Training Slides at https://www.cdc.gov/​nhsn/​pdfs/​training/​hcp/​hcp-flu-vaccination-summary-reporting-general-training.pdf, to learn how to report required data. To view provider trainings that are specific to long-term care facilities, providers may refer to the Healthcare Personnel Safety Component—Healthcare Personnel Vaccination Module Influenza Vaccination Summary Long-Term Care Facilities training slides at the following CDC web page at https://www.cdc.gov/​nhsn/​pdfs/​training/​vaccination/​hcp-flu-vax-summary-reporting-ltc.pdf. The CDC also plans to offer additional training in the fall of 2022 to review annual influenza vaccination reporting and answer provider questions in real time via a webinar chat feature.

In regard to concerns about provider requirements to obtain SAMS Level 3 access, we would like to highlight that 14,849 long-term care facilities (98 percent) with a CMS Certification Number (CCN) already have at least one SAMS Level 3 user. We additionally note that 12,133 long-term care facilities (80 percent) have two or more SAMS level 3 users. Therefore, many facilities will not need to spend additional time requesting SAMS Level 3 access to meet the data submission requirements of the Influenza Vaccination Coverage among HCP measure. Additionally, SAMS has expedited the timeline for gaining Level 3 access by allowing users to submit identity verification documents to the CDC using Experian. More information for gaining SAMS Level 3 access can be retrieved at the About SAMS CDC web page at https://www.cdc.gov/​nhsn/​sams/​about-sams.html.

Lastly, regarding commenter suggestions to retrieve HCP influenza vaccination from national immunization records, there is no such national organization.^[149] While some vaccine providers participate in immunization registries such as the Immunization Information Systems (IIS), the HCP Influenza Vaccine measure would not require SNFs to participate in such registries,^[150] making the NHSN the comprehensive method for tracking HCP influenza vaccination rates for purposes of the SNF QRP.

Comment: One commenter noted technical issues encountered with the NHSN reporting system since SNFs began using it in May 2021, suggesting that CMS should implement provider protections to mitigate NHSN data submission issues that may be beyond providers' control. Another commenter opposed the measure proposal due to technical issues with the NHSN reporting system that are beyond providers' control. One commenter outlined several NHSN technical issues experienced by providers, such as (1) frequent changing of NHSN module tables and required content, (2) NHSN acceptance of incomplete data resulting in SNF non-compliance, (3) mislabeling SNF CMS Certification Numbers (CCNs) by the NSHN, (4) errors with comma-separated items on group NHSN uploads, (5) auto-populated NHSN error messages that do not identify which portion of the submission may have an error, (6) delays in NHSN Helpdesk response and/or closing a ticket without ensuring the issue has been resolved, (7) provider software incompatibility and ransomware attacks which have prevented transmission of files, and (8) unavailability of telecommunication due to weather-related interruptions.

Response: We discussed providers' concerns regarding technical difficulties that may arise in submitting data to the NHSN. The CDC has provided responses to each concern as outlined throughout the remainder of this response. First, the CDC highlights that the NHSN conducted surveillance of annual influenza vaccination beginning with the 2012 through 2013 influenza season. Results of the surveillance reveal that multiple facility types (for example, acute care facilities, inpatient rehabilitation facilities, long-term acute care facilities, etc.) have successfully reported these data over several years. Surveillance to track influenza vaccination has not required frequent changes to NHSN module tables and required content because annual influenza vaccination recommendations for healthcare workers have not changed for several years, unlike COVID-19 vaccination data reporting where guidance is still evolving and changing.

Regarding concerns about NHSN acceptance of incomplete data submission leading to provider non-compliance, the CDC notes that fields are set as required in the current NHSN annual influenza module, which prevents incomplete data submission for this reporting metric. Resources and training materials for annual influenza surveillance are available on the NHSN Healthcare Personnel (HCP) Flu Vaccination CDC web page at https://www.cdc.gov/​nhsn/​hps/​vaccination/​index.html.

In response to concerns about mislabeled CMS CCNs, the CDC emphasizes that providers are responsible for correctly entering their CCNs into the NHSN application. If a SNF has correctly entered its CCN and influenza surveillance data appropriately, data will automatically be sent to CMS to meet SNF QRP data submission requirements. The NHSN continues to provide support and education to SNFs when they reach out about correcting their CCN in the NHSN application. SNFs may view checklists to ensure their annual influenza vaccination data are reported accurately on the NHSN Healthcare Personnel (HCP) Flu Vaccination CDC web page at https://www.cdc.gov/​nhsn/​hps/​vaccination/​index.html, under the “Annual Flu Summary” heading. In addition, providers can view information regarding data verification on the following CDC web page: Submission of Healthcare Personnel (HCP) Influenza Vaccination Summary Data in NHSN at https://www.cdc.gov/​nhsn/​pdfs/​hps-manual/​vaccination/​verification-hcp-flu-data.pdf. If a provider seeks to change the CCN listed for a SNF in the NHSN, the provider may refer to the following CDC NHSN guidance document: Long-Term Care Facility (LTCF) How to Add and Edit Facility CMS Certification Number (CCN) within NHSN at the following web page at https://www.cdc.gov/​nhsn/​pdfs/​ltc/​ccn-guidance-508.pdf. Lastly, providers may view additional NHSN resources at the CDC NHSN CMS Quality Reporting Program Frequently Asked Questions web page at https://www.cdc.gov/​nhsn/​faqs/​cms/​faq_​cms_​hai.html.

Regarding concerns with comma-separated items on group uploads, the CDC notes that uploading data via a comma-separated values (CSV) file is not an option for annual influenza vaccination data reporting. However, the CDC anticipates having this option available in the upcoming 2022 through 2023 influenza season. The CDC acknowledged that as COVID-19 surveillance needs evolved, data fields changed accordingly, and at times it led to unexpected issues with CSV upload and short delays in reporting. The CDC prioritizes resolving such issues quickly and communicating with users and partners. The NHSN continues to offer support to facilitate data uploading.

Moreover, in response to concerns about auto-populated error messages, the NHSN continues to work to make error messages detailed and clear for users. For example, common errors are covered during user trainings ( i.e., webinars, email blasts, etc.). The CDC continues to revise error messages based on user feedback, encouraging plain language detailed messages. If there are specific alerts causing confusion for annual influenza vaccination data, providers are encouraged to contact [e-mail].

Regarding NHSN Helpdesk concerns, if a SNF continues to have questions or experience additional issues after a ticket has closed, the CDC encourages providers to submit a new email with a detailed subject line to ensure an expeditious Helpdesk reply with input from a subject matter expert team. When submitting annual influenza vaccination data, SNFs have been instructed to include “HPS Flu Summary” along with their facility type in the subject line of the email for a more immediate response.

In regard to general submission concerns such as software incompatibility and ransomware attacks that have prevented the transmission of data files, the NHSN provides CSV templates and CSV template example files if SNFs prefer to upload data directly to the platform. CSV templates will be made available to SNFs reporting annual influenza vaccination data for the 2022 through 2023 influenza season. Once available, CSV templates will appear similarly to how the COVID-19 Vaccination Coverage among HCP resources appear on the Weekly HCP & Resident COVID-19 Vaccination CDC NHSN web page https://www.cdc.gov/​nhsn/​ltc/​weekly-covid-vac/​index.html, under a CSV Data Import header.

Lastly, in response to concerns about technical data submission issues that may arise beyond providers' control, such as telecommunication issues resulting from weather-related interruptions, the CMS reconsideration and exception and extension process is available to SNFs if they are found to be non-compliant with the SNF QRP data submission requirements and believe they have a valid reason for an exception. For information about the reconsideration and exception and extension request process, please visit the SNF QRP Reconsideration and Exception & Extension CMS web page at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Skilled-Nursing-Facility-Quality-Reporting-Program/​SNF-QR-Reconsideration-and-Exception-and-Extension.

Comment: Two commenters expressed concern over the quality of provider-submitted data to the NHSN, noting the importance of data validation efforts, and oppose the adoption of the measure until there are data validation and provider Review and Correct Reports comparable to other provider-submitted SNF QRP data. The commenters noted that since SNFs receive their provider preview reports in July, SNFs do not have an opportunity to correct any discrepancies that could be found if given more time to review their data. Another commenter supported the measure concept but would like clarification regarding Review and Correct Reports.

Response: The Influenza Vaccination Coverage among HCP measure is stewarded by the CDC NHSN. To date, we have never added any of the CDC NHSN measures to the Review and Correct Report, as the data for these measures are at the CDC. In lieu of this, the CDC makes accessible to PAC providers, including SNFs, reports that are similar to the Review and Correct Reports that allow for real-time review of data submissions for all CDC NHSN measures adopted for use in the CMS PAC QRPs, including the SNF QRP. These reports are referred to as “CMS Reports” within the “Analysis Reports” page in the NHSN Application. Such a report exists for each CDC NHSN measure within each of the PAC programs, and each report is intended to mimic the data that will be sent to CMS on their behalf. This report will exist to serve the same “review and correct” purposes for the Influenza Vaccination Coverage among HCP measure. The CDC publishes reference guides for each facility type (including SNFs) and each NHSN measure, which explain how to run and interpret the reports.

Additionally, we will make available to SNFs a preview of SNF performance on the Influenza Vaccination Coverage among HCP measure on the SNF Provider Preview Report, which will be issued approximately 3 months prior to displaying the measure on Care Compare. As always, SNFs will have a full 30 days to preview their data. Should SNFs disagree with their measure results, they can request a formal review of their data by us. Instructions for submitting such a request are available on the CMS SNF Quality Reporting Program Public Reporting web page at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Skilled-Nursing-Facility-Quality-Reporting-Program/​SNF-Quality-Reporting-Program-Public-Reporting.

After consideration of public comments, we are finalizing the schedule of data submission for the Influenza Vaccination Coverage among HCP Measure (NQF #0431) as proposed.

H. Policies Regarding Public Display of Measure Data for the SNF QRP

1. Background

Section 1899B(g) of the Act requires the Secretary to establish procedures for making the SNF QRP data available to the public, including the performance of individual SNFs, after ensuring that SNFs have the opportunity to review their data prior to public display. SNF QRP measure data are currently displayed on the Nursing homes including rehab services website within Care Compare and the Provider Data Catalog. Both Care Compare and the Provider Data Catalog replaced Nursing Home Compare and Data.Medicare.gov, which were retired in December 2020. For a more detailed discussion about our policies regarding public display of SNF QRP measure data and procedures for the opportunity to review and correct data and information, we refer readers to the FY 2017 SNF PPS final rule (81 FR 52045 through 52048).

2. Public Reporting of the Influenza Vaccination Coverage Among Healthcare Personnel (NQF #0431) Measure Beginning With the FY 2024 SNF QRP

We proposed to publicly report the Influenza Vaccination Coverage among HCP (NQF #0431) measure beginning with the October 2023 Care Compare refresh or as soon as technically feasible using data collected from October 1, 2022 through March 31, 2023. If finalized as proposed, a SNF's Influenza Vaccination Coverage among HCP rate would be displayed based on 6 months of data. Provider preview reports would be distributed in July 2023. Thereafter, Influenza Vaccination Coverage among HCP rates would be displayed based on 6 months of data, reflecting the reporting period of October 1st through March 31st, updated annually. We invited public comment on this proposal for the public display of the Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431) measure on Care Compare.

The following is a summary of the comments we received and our responses.

Comment: One commenter noted that public reporting of this measure would provide the previous influenza season's data to consumers and would not reflect the vaccination rates of the current influenza year.

Response: The measure's public reporting schedule is in alignment with those of the IRF and LTCH QRPs, supporting the standardized and interoperable requirement of the IMPACT Act, and the ability to compare data for the same time period across PAC providers when using Care Compare. Additionally, the public display of HCP influenza vaccine data in October 2023 allows for a 6-month data collection period (October 1, 2022 through March 31, 2023), a period of 6 weeks for providers to submit their data to the NHSN, our analysis of the data, and a period of time for SNFs to review their Provider Preview Report and alert us if they believe there are errors in the data. We believe this reporting schedule, outlined in section VI.G.2. of the proposed rule, is reasonable, and expediting this schedule may establish undue burden on providers and jeopardize the integrity of the data.

After consideration of public comments, we are finalizing the proposal to publicly report the Influenza Vaccination Coverage among Healthcare Personnel (NQF #0413) measure beginning with the October 2023 refresh or as soon as technically feasible, as proposed.

VIII. Skilled Nursing Facility Value-Based Purchasing (SNF VBP) Program

A. Statutory Background

Section 215(b) of the Protecting Access to Medicare Act of 2014 (Pub. L. 113-93) authorized the SNF VBP Program (the “Program”) by adding section 1888(h) to the Act. Additionally, section 111 of the Consolidated Appropriations Act, 2021 authorized the Secretary to apply additional measures to the SNF VBP Program for payments for services furnished on or after October 1, 2023. The SNF VBP Program applies to freestanding SNFs, SNFs affiliated with acute care facilities, and all non-CAH swing bed rural hospitals. We believe the SNF VBP Program has helped to transform how payment is made for care, moving increasingly towards rewarding better value, outcomes, and innovations instead of merely rewarding volume.

As a prerequisite to implementing the SNF VBP Program, in the FY 2016 SNF PPS final rule (80 FR 46409 through 46426), we adopted an all-cause, all-condition hospital readmission measure, as required by section 1888(g)(1) of the Act and discussed other policies to implement the Program such as performance standards, the performance period and baseline period, and scoring. SNF VBP Program policies have been codified in our regulations at 42 CFR 413.338. For additional background information on the SNF VBP Program, including an overview of the SNF VBP Report to Congress and a summary of the Program's statutory requirements, we refer readers to the following prior final rules:

• In the FY 2017 SNF PPS final rule (81 FR 51986 through 52009), we adopted an all-condition, risk-adjusted potentially preventable hospital readmission measure for SNFs, as required by section 1888(g)(2) of the Act, adopted policies on performance standards, performance scoring, and sought comment on an exchange function methodology to translate SNF performance scores into value-based incentive payments, among other topics.
• In the FY 2018 SNF PPS final rule (82 FR 36608 through 36623), we adopted additional policies for the Program, including an exchange function methodology for disbursing value-based incentive payments.
• In the FY 2019 SNF PPS final rule (83 FR 39272 through 39282), we adopted more policies for the Program, including a scoring adjustment for low-volume facilities.
• In the FY 2020 SNF PPS final rule (84 FR 38820 through 38825), we adopted additional policies for the Program, including a change to our public reporting policy and an update to the deadline for the Phase One Review and Correction process. We also adopted a data suppression policy for low-volume SNFs.

• In the FY 2021 SNF PPS final rule (85 FR 47624 through 47627), we amended regulatory text definitions at § 413.338(a)(9) and (11) to reflect the definition of Performance Standards and the updated Skilled Nursing Facility Potentially Preventable Readmissions after Hospital Discharge measure name, respectively. We also updated the Phase One Review and Correction deadline and codified that update at § 413.338(e)(1). Additionally, we codified the data suppression policy for low-volume SNFs at § 413.338(e)(3)(i) through (iii) and amended § 413.338(e)(3) to reflect that SNF performance information will be publicly reported on the Nursing Home Compare website and/or successor website (84 FR 38823 through 38824), which since December 2020 is the Provider Data Catalog website ( https://data.cms.gov/​provider-data/​).

• In the September 2nd interim final rule with comment (IFC) (85 FR 54837), we revised the performance period for the FY 2022 SNF VBP Program to be April 1, 2019 through December 31, 2019 and July 1, 2020 through September 30, 2020, in response to the COVID-19 Public Health Emergency (PHE).
• In the FY 2022 SNF PPS final rule (86 FR 42502 through 42517), we adopted additional policies for the Program, including a measure suppression policy to offer flexibility in response to the COVID-19 PHE. We adopted policies to suppress the SNFRM for scoring and payment purposes for the FY 2022 SNF VBP program year, to revise the SNFRM risk-adjustment lookback period for the FY 2023 SNF VBP program year, and to use FY 2019 data for the baseline period for the FY 2024 SNF VBP program year. We also updated the Phase One Review and Correction process and updated the instructions for requesting an Extraordinary Circumstances Exception (ECE). Finally, we finalized a special scoring policy assigning all SNFs a performance score of zero, effectively ranking all SNFs equally in the FY 2022 SNF VBP program year. This policy was codified at § 413.338(g) of our regulations.

To improve the clarity of our regulations, we proposed to update and renumber the “Definitions” used in § 413.338 by revising paragraphs (a)(1) and (4) through (17). We invited public comment on these proposed updates.

We did not receive any public comments on our proposal to update and renumber the “Definitions” used in § 413.338 by revising paragraphs (a)(1) and (4) through (17) and therefore, we are finalizing the updates as proposed.

B. SNF VBP Program Measures

For background on the measures we have adopted for the SNF VBP Program, we refer readers to the FY 2016 SNF PPS final rule (80 FR 46419), where we finalized the Skilled Nursing Facility 30-Day All-Cause Readmission Measure (SNFRM) (NQF #2510) that we are currently using for the SNF VBP Program. We also refer readers to the FY 2017 SNF PPS final rule (81 FR 51987 through 51995), where we finalized the Skilled Nursing Facility 30-Day Potentially Preventable Readmission Measure (SNFPPR) that we will use for the SNF VBP Program instead of the SNFRM as soon as practicable, as required by statute. The SNFPPR measure's name is now “Skilled Nursing Facility Potentially Preventable Readmissions after Hospital Discharge measure” (§ 413.338(a)(11)). We intend to submit the SNFPPR measure for NQF endorsement review as soon as practicable, and to assess transition timing of the SNFPPR measure to the SNF VBP Program after NQF endorsement review is complete.

1. Suppression of the SNFRM for the FY 2023 Program Year

a. Background

As discussed in the FY 2023 SNF proposed rule, we remain concerned about the effects of the PHE for COVID-19 on our ability to assess performance on the SNFRM in the SNF VBP Program. As of mid-December 2021, more than 50 million COVID-19 cases and 800,000 COVID-19 deaths have been reported in the United States (U.S.).^[151] COVID-19 has overtaken the 1918 influenza pandemic as the deadliest disease in American history.^[152] Moreover, the individual and public health ramifications of COVID-19 extend beyond the direct effects of COVID-19 infections. Several studies have demonstrated significant mortality increases in 2020, beyond those attributable to COVID-19 deaths. One paper quantifies the net impact (direct and indirect effects) of the pandemic on the U.S. population during 2020 using three metrics: excess deaths, life expectancy, and total years of life lost. The findings indicate there were 375,235 excess deaths, with 83 percent attributable to direct effects, and 17 percent attributable to indirect effects, of COVID-19. The decrease in life expectancy was 1.67 years, translating to a reversion of 14 years in historical life expectancy gains. Total years of life lost in 2020 was 7,362,555 across the U.S. (73 percent directly attributable, 27 percent indirectly attributable to COVID-19), with considerable heterogeneity at the individual State level.^[153]

b. Suppression of the SNFRM for the FY 2023 SNF VBP Program Year

In the FY 2022 SNF PPS final rule (86 FR 42503 through 42505), we adopted a quality measure suppression policy for the duration of the PHE for COVID-19 that enables us to suppress the use of the SNFRM for purposes of scoring and payment adjustments in the SNF VBP Program if we determine that circumstances caused by the PHE for COVID-19 have affected the measure and the resulting performance scores significantly.

We also adopted a series of Measure Suppression Factors to guide our determination of whether to propose to suppress the SNF readmission measure for one or more program years that overlap with the PHE for COVID-19. The Measure Suppression Factors that we adopted are:

• Measure Suppression Factor 1: Significant deviation in national performance on the measure during the PHE for COVID-19, which could be significantly better or significantly worse compared to historical performance during the immediately preceding program years.
• Measure Suppression Factor 2: Clinical proximity of the measure's focus to the relevant disease, pathogen, or health impacts of the PHE for COVID-19.
• Measure Suppression Factor 3: Rapid or unprecedented changes in:

++ Clinical guidelines, care delivery or practice, treatments, drugs, or related protocols, or equipment or diagnostic tools or materials; or

++ The generally accepted scientific understanding of the nature or biological pathway of the disease or pathogen, particularly for a novel disease or pathogen of unknown origin.

• Measure Suppression Factor 4: Significant national shortages or rapid or unprecedented changes in:

++ Healthcare personnel.

++ Medical supplies, equipment, or diagnostic tools or materials.

++ Patient case volumes or facility-level case-mix.

We refer readers to the FY 2022 SNF PPS final rule (86 FR 42503 through 42505) for additional details on this policy, including summaries of the public comments that we received and our responses.

Additionally, in the FY 2022 SNF PPS final rule (86 FR 42505 through 42507), we suppressed the SNFRM for the FY 2022 SNF VBP program year under Measure Suppression Factor (4): Significant national shortages or rapid or unprecedented changes in: (iii) Patient case volumes or facility-level case mix. We refer readers to that final rule for additional discussion of the analyses we conducted of SNFRM performance during the PHE for COVID-19, how the measure's reliability changed, how its current risk-adjustment model does not factor in COVID-19, and how the PHE affected different regions of the country at different times, as well as summaries of the public comments that we received on that proposal and our responses.

The PHE for COVID-19 has had direct, significant, and continuing effects on our ability to measure SNFs' performance on the SNFRM. SNFs are experiencing a significant downward trend in admissions compared with their pre-COVID-19 admission rates. For the FY 2021 program year, a total of 1,566,540 SNF admissions were eligible for inclusion in the SNFRM (based on FY 2019 data). We have estimated that approximately 1,069,789 admissions would be eligible for inclusion for the FY 2023 program year (based on currently available data, which ranged from July 1, 2020 through June 30, 2021), representing a volume decrease of approximately 32 percent. Based on this lower number of eligible SNF admissions, we have estimated that only 75.2 percent of SNFs would be eligible to be scored on the SNFRM for FY 2021, compared with 82.4 percent that were eligible to be scored for FY 2019. As discussed in the FY 2023 SNF PPS proposed rule, given the significant decrease in SNF admissions during FY 2021, we remain concerned that using FY 2021 data to calculate SNFRM rates for the FY 2023 program year will have significant negative impacts on the measure's reliability. Our contractor's analysis using FY 2019 data showed that such changes may lead to a 15 percent decrease in the measure reliability, assessed by the intra-class correlation coefficient (ICC).

As discussed in the FY 2023 SNF PPS proposed rule, we also remain concerned that the pandemic's disparate effects on different regions of the country throughout the PHE have presented challenges to our assessments of performance on the SNFRM. According to CDC data,^[154] for example, new COVID-19 cases at the beginning of FY 2021 (October 1, 2020) were highest in Texas (3,534 cases), California (3,062 cases), and Wisconsin (3,000 cases). By April 1, 2021, however, new cases were highest in Michigan (6,669 cases), Florida (6,377 cases), and New Jersey (5,606 cases). This variation in COVID-19 case rates throughout the PHE has also been demonstrated in several studies. For example, studies have found widespread geographic variation in county-level COVID-19 cases across the U.S.^{[155 156 157]} Specifically, one study found that, across U.S. census regions, counties in the Midwest had the greatest cumulative rate of COVID-19 cases.^[158] Another study found that U.S. counties with more immigrant residents, as well as more Central American or Black residents, have more COVID-19 cases.^[159] These geographic variations in COVID-19 case rates are often linked to a wide range of county-level characteristics, including sociodemographic and health-related factors.^[160] In addition, these studies have found evidence of temporal variation in county-level COVID-19 cases. For example, one study found that while many county-level factors show persistent effects on COVID-19 severity over time, some factors have varying effects on COVID-19 severity over time.^[161] The significant variation in COVID-19 case rates across the U.S. can affect the validity of performance data. Therefore, we do not believe it would be fair or equitable to assess SNFs' performance on the measure using FY 2021 data, which has been affected by these variations in COVID-19 case rates.

Increases in the number of COVID-19 cases are typically followed by an increase in the number of COVID-19 related hospitalizations, especially among the unvaccinated. Although COVID-19 vaccines began to come available in December of 2020, it was only readily available in early summer 2021 resulting in less than half of eligible Americans being fully vaccinated by the beginning of the fourth quarter of FY 2021. In addition, the vaccination rates were not evenly distributed across the country. Regions with significantly lower vaccination rates experienced higher hospitalization and ICU rates making them more prone to capacity challenges. Hospital capacity challenges have the potential to influence decisions that impact their downstream post-acute partners. As a result, for the first 3 quarters of FY 2021 performance year, low vaccinated regions' SNFs could have faced care coordination challenges with their partnering hospitals that regions with high vaccination rates did not experience. The continuation of the pandemic into 2021 did not necessarily impact all measures in the post-acute space, but measures related to hospital care may be impacted because of how closely the surge in COVID-19 cases was related to the surge in COVID-19 related hospital cases. Unlike other value-based purchasing programs that have multiple measures, the SNF VBP Program's single-measure requirement, currently the SNFRM, means that suppression of the measure will directly impact the payment adjustment.

The combination of fewer admissions to SNFs, regional differences in the prevalence of COVID-19 throughout the PHE and changes in hospitalization patterns in FY 2021 has impacted our ability to use the SNFRM to calculate payments for the FY 2023 program year.

Based on the significant and continued decrease in the number of patients admitted to SNFs, which likely reflects shifts in utilization patterns due to the risk of spreading COVID-19 in SNFs, we proposed to suppress the SNFRM for the FY 2023 SNF VBP program year under Measure Suppression Factor (4): Significant national shortages or rapid or unprecedented changes in: (iii) Patient case volumes or facility-level case-mix.

As with the suppression policy that we adopted for the FY 2022 SNF VBP Program, we proposed for the FY 2023 SNF VBP Program that we will use the previously finalized performance period (FY 2021) and baseline period (FY 2019) to calculate each SNF's RSRR for the SNFRM. We also proposed to suppress the use of SNF readmission measure data for purposes of scoring and payment adjustments. We further proposed to assign all participating SNFs a performance score of zero in the FY 2023 SNF VBP Program Year. We stated that this assignment would result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier.

We proposed to reduce each participating SNF's adjusted Federal per diem rate for FY 2023 by 2 percentage points and award each participating SNF 60 percent of that 2 percent withhold, resulting in a 1.2 percent payback for the FY 2023 SNF VBP Program Year. We continue to believe that this continued application of the 2 percent withhold is required under section 1888(h)(5)(C)(ii)(III) of the Act and that a payback percentage that is spread evenly across all participating SNFs is the most equitable way to reduce the impact of the withhold in light of our proposal to award a performance score of zero to all SNFs.

However, as discussed in the proposed rule, we further proposed to remove the low-volume adjustment policy from the SNF VBP Program beginning with the FY 2023 program year, and instead, implement case and measure minimums that SNFs must meet in order to be eligible to participate in the SNF VBP Program for a program year.

We proposed that SNFs that do not report a minimum of 25 eligible stays for the SNFRM for the FY 2023 program year will not be included in the SNF VBP Program for that program year. As a result, the payback percentage for FY 2023 will remain at 60.00 percent.

For the FY 2023 program year, we also proposed to provide quarterly confidential feedback reports to SNFs and to publicly report the SNFRM rates for the FY 2023 SNF VBP Program Year. However, in the proposed rule, we stated that we will make clear in the public presentation of those data that the measure has been suppressed for purposes of scoring and payment adjustments because of the effects of the PHE for COVID-19 on the data used to calculate the measure (87 FR 22765). We stated in the proposed rule that the public presentation will be limited to SNFs that reported the minimum number of eligible stays. Finally, we proposed to codify these policies for the FY 2023 SNF VBP in our regulation text at § 413.338(i).

As stated in the proposed rule, we continue to be concerned about effects of the COVID-19 PHE but are encouraged by the rollout of COVID-19 vaccinations and treatment for those diagnosed with COVID-19 and believe that SNFs are better prepared to adapt to this virus. Our measure suppression policy focuses on a short-term, equitable approach during this unprecedented PHE, and it was not intended for indefinite application. Additionally, we emphasized the importance of value-based care and incentivizing quality care tied to payment. The SNF VBP Program is an example of our effort to link payments to healthcare quality in the SNF setting. We stated our understanding that the COVID-19 PHE is ongoing and unpredictable in nature; however, we also stated our belief that 2022 presents a more promising outlook in the fight against COVID-19. Over the course of the pandemic, providers have gained experience managing the disease, surges of COVID-19 infection, and supply chain fluctuations.^[162] While COVID-19 cases among nursing home staff reached a recent peak in January of 2022, those case counts dropped significantly by the week ending February 6, 2022, to 22,206.^[163] COVID-19 vaccinations and boosters have also been taken up by a significant majority of nursing home residents, and according to CDC, by February 6, 2022, more than 68 percent of completely vaccinated nursing home residents had received boosters.^[164] Finally, the Biden-Harris Administration has mobilized efforts to distribute home test kits,^[165] N-95 masks,^[166] and increase COVID-19 testing in schools.^[167] In light of this more promising outlook, we stated in the proposed rule that we intend to resume the use of the SNFRM for scoring and payment adjustment purposes beginning with the FY 2024 program year. That is, for FY 2024, for each SNF, we will calculate measure scores in the SNF VBP Program. We will then calculate a SNF performance score for each SNF and convert the SNF performance scores to value-based incentive payments.

We invited public comment on our proposal to suppress the SNFRM for the FY 2023 program year and to codify our scoring and payment proposals for FY 2023 in our regulation text. We received the following comments and provide our responses:

Comment: Many commenters supported our proposal to suppress the SNFRM for FY 2023 and our plans to resume use of the SNFRM beginning with FY 2024 noting the impacts of COVID-19 on readmission rates. One commenter suggested that we consider alternative quality measures in the long term that would encourage providers to use SNFs as a short-term care venue for patients likely to be readmitted. Another commenter recommended that we provide confidential feedback reports to providers rather than publicly reporting SNFRM rates until we end our measure suppression policy and that we delay calculating SNF performance scores in FY 2024 until the end of the PHE.

Response: We appreciate the support for our proposal to suppress the SNFRM for FY 2023 and our plans to resume use of the SNFRM beginning with FY 2024 noting the impacts of COVID-19 on readmission rates. We disagree with the commenter's suggestion to provide only confidential feedback reports to SNFs until we end the suppression policy. We continue to believe that stakeholders benefit immensely from access to quality data, and as we stated in the proposed rule, we will include appropriate caveats on the suppressed measure data when published. We will consider additional quality measurement topics for the Program in future rulemaking.

Comment: Many commenters recommended that we increase the Program's payback percentage to 70 percent while we suppress the SNFRM for FY 2023. One commenter suggested that we return the full 2 percent withheld from SNFs' Medicare payments, while another suggested that we extend suppression through the end of any future PHE.

Response: We did not propose to change the previously finalized payback percentage for the SNF VBP Program in the proposed rule, and we view comments requesting that we change that policy to be beyond the scope of the proposed rule. We believe this continued application of the 2 percent withhold is required under section 1888(h)(5)(C)(ii)(III) of the Act and that a payback percentage that is spread evenly across all qualifying SNFs is the most equitable way to reduce the impact of the withhold in light of our proposal, which we are finalizing in this final rule, to award a performance score of zero to all SNFs. We also do not believe it would be appropriate to preemptively extend the quality measure suppression policy through the end of any future PHE, as the suppression policy focuses on identifying how quality measurement has been affected by a specific PHE.

After considering the public comments, we are finalizing our proposal to suppress the SNFRM for the FY 2023 SNF VBP Program as proposed and codifying it, as well as finalizing the special scoring and payment policies for FY 2023, at § 413.338(i) of our regulations.

2. Technical Updates to the SNFRM To Risk-Adjust for COVID-19 Patients Beginning With the FY 2023 Program Year

The emergence of the COVID-19 PHE, along with the high prevalence of COVID-19 in patients admitted to SNFs, has prompted us to examine whether we should develop an adjustment to the SNFRM that would properly account for COVID-19 patients. As detailed in the proposed rule, we considered four options that such an adjustment could take. After careful examination of each of the four options, we are updating the technical specifications of the SNFRM such that COVID-19 patients (diagnosed at any time within 12 months prior to or during the prior proximal hospitalization [PPH]) will remain in the measure's cohort, but we will add a variable to the risk-adjustment model that accounts for the clinical differences in outcomes for these patients. We stated that we believe this change is technical in nature and does not substantively change the SNFRM.

In order to determine whether and how to update the SNFRM, we first sought to understand the frequency of COVID-19 diagnoses in patients admitted to a SNF between July 1, 2020 and June 30, 2021. Of the 1,069,789 SNF stays included in the year of data, 134,674 (13 percent) had a primary or secondary diagnosis of COVID-19. Of those patients with COVID-19, 108,859 (81 percent) had a primary or secondary COVID-19 diagnosis during the PPH and 25,815 (19 percent) had a COVID-19 diagnosis in their history only (within 12 months of the SNF admission).

We then compared clinical and demographic characteristics between patients with and without COVID-19 between July 1, 2020, and June 30, 2021. When compared to the 30-day readmission rate for patients without COVID-19 (20.2 percent), the observed 30-day readmission rate was noticeably higher for patients with COVID-19 during the PPH (23.4 percent) and patients with a history of COVID-19 (26.9 percent). Both groups also experienced higher 30-day mortality rates compared to patients without COVID-19 (14.9 percent versus 8.8 percent and 10.7 percent versus 8.8 percent, respectively). Admissions for patients with COVID-19 during the PPH or a history of COVID-19 were also much more likely to be for patients who were dual-eligible (40.3 percent versus 28.9 percent and 45.2 percent versus 28.9 percent, respectively) and for patients who were non-white (21.1 percent versus 15.2 percent and 24.4 percent versus 15.2 percent, respectively).

Next, we compared readmission odds ratios for patients with COVID-19 during the PPH and for patients with a history of COVID-19. Patients with COVID-19 during the PPH had significantly higher odds of readmission (1.18), while patients with a history of COVID-19 but no COVID-19 during the PPH had significantly lower odds of readmission (0.84), after adjusting for all other variables in the SNFRM risk-adjustment model.

Although patients with only a history of COVID-19 had higher observed readmission rates than patients with COVID-19 during the PPH (26.9 percent versus 23.4 percent), they experienced lower readmission odds ratios (0.84 versus 1.18). This is because patients with a history of COVID-19 during the 12 months prior to the SNF admission are generally much sicker and have a substantially higher number of average comorbidities (15) compared to patients with COVID-19 during the PPH (10). We expect unadjusted readmission rates for patients with a history of COVID-19 to be higher because they are suffering from many more comorbidities, making it more likely they will be readmitted to the hospital. After adjusting for all their other comorbidities, we concluded that COVID-19 is not a significant reason for why they return to the hospital. Instead, their other comorbidities are a more significant cause of their readmission; that is, patients with a history of COVID-19 but no COVID-19 during the PPH have lower odds of being readmitted to a hospital once they've been admitted to the SNF. However, we stated in the proposed rule that we believed it was important to keep the history of COVID-19 variable in the model for two reasons: (1) to address any potential concerns with the face validity of the measure if it did not adjust for history of COVID-19; and (2) to account for long COVID-19 and other possible long-term effects of the virus. On the other hand, patients with a COVID-19 diagnosis during the PPH remain at higher odds of readmission even after accounting for their other comorbidities. Even when all other comorbidities are taken into account in the current risk-adjustment model, a COVID-19 diagnosis during the PPH still raises a patient's odds of being readmitted compared to patients who did not have any COVID-19 diagnosis during the PPH.

After having examined the prevalence of COVID-19 in SNF patients and the differences between patients with and without COVID-19, we then evaluated several options for how to account for COVID-19 in the measure. We evaluated four options.

• Under Option 1, we considered and tested whether to add a binary risk-adjustment variable for patients who had a primary or secondary diagnosis of COVID-19 during the PPH.
• Under Option 2, we considered and tested whether to add a binary risk-adjustment variable for patients who had a history of COVID-19 in the 12 months prior to the PPH.
• Under Option 3, we combined the first 2 options into a categorical risk-adjustment variable. The reference category is patients without a history of COVID-19 and no COVID-19 diagnosis during the PPH. The first comparison category is patients who had a history of COVID-19 in the 12 months prior to the PPH and no COVID-19 diagnosis during the PPH. The second comparison category is patients who had a primary or secondary diagnosis of COVID-19 during the PPH. If a patient had both a history of COVID-19 and a COVID-19 diagnosis during the PPH, they would be included in the second comparison category.
• Under Option 4, we considered and tested removing patients with a COVID-19 diagnosis during the PPH from the measure cohort.

We compared how well the model predicted whether patients were readmitted or not (model fit and performance) for these four options to a reference period (FY 2019) that predated COVID-19. Ideally, whichever option we chose would perform as similarly as possible to the reference period, providing us with confidence that the emergence of COVID-19 has not caused the model to perform worse.

The percentage of SNFs that would receive a measure score (75 percent), measure reliability (0.45), and C-statistic (0.66) was identical for the first 3 risk-adjustment options. The percentage of SNFs with a measure score, measure reliability score, and C-statistic values was 71 percent, 0.41, and 0.67 for Option 4 (excluding COVID-19 patients), respectively. The percentage of SNFs with a measure score was lower for the first 3 options than the baseline period (75 percent versus 82 percent), but the measure reliability was nearly identical (0.45 versus 0.46), as was the C-statistic (0.66 versus 0.68).

We also considered removing readmissions from the outcome for patients with a primary or secondary diagnosis of COVID-19 during the readmission hospital stay but decided it would not be appropriate for this measure. Community spread of COVID-19 in SNFs is a possible marker of poor infection control and patients who are admitted to a SNF without any COVID-19 diagnoses but then potentially acquire COVID-19 in a SNF should not be excluded from the readmission outcome.

After careful examination, we selected Option 3 and are modifying the SNFRM beginning with the FY 2023 SNF VBP program year by adding a risk-adjustment variable for both COVID-19 during the PPH and patients with a history of COVID-19. As we stated, this option both maintains the integrity of the model (as demonstrated by nearly identical measure reliability and C-statistic values) and allows the measure to appropriately adjust for SNF patients with COVID-19. In the proposed rule, we stated our belief that this approach will continue to maintain the validity and reliability of the SNFRM. This approach will retain COVID-19 patients in the measure cohort and prevent a further decrease in the sample size, which would harm the measure's reliability.

As discussed in the proposed rule and in section VIII.B.2.c. of this final rule, though we believe risk-adjusting the SNFRM for COVID-19 is an important step in maintaining the validity and reliability of the SNFRM, this risk-adjustment alone is not sufficient for ensuring a reliable SNF performance score in light of the overall decrease in SNF admissions in FY 2021. That is, the risk-adjustment is designed to maintain the scientific reliability of the measure, but it does not mitigate the effects of the PHE on patient case volumes and the resulting impact on the validity of the SNFRM.

We received several public comments on our technical update to the SNFRM to risk-adjust for COVID-19 patients beginning with the FY 2023 program year.

Comment: Some commenters supported our proposal to update the SNFRM to risk-adjust for COVID-19 patients. One commenter agreed with our approach but noted that removing COVID-19 patients from the measure may reduce the sample sizes and result in excluding more facilities from the Program, which may mean missing important indicators of quality performance. Another commenter stated that our proposed risk-adjustment best allows the measure's calculation by removing beneficiaries that were affected directly by a COVID-19 infection. One commenter also recommended that we continue to review COVID-19 data and refine our risk-adjustment policies as we learn more about the impacts and prevalence of “long” COVID-19.

Response: We clarify that we selected Option 3, which retains COVID-19 patients in the measure cohort and prevents a decrease in the sample size, while also adjusting for patients with a COVID-19 diagnosis. Furthermore, we decided to risk-adjust for patients with a history of COVID-19 because of the evolving evidence on the impact of “long” COVID-19 and the recognition that we still have much to learn about the long-term effects of COVID-19. We will continue to review the impacts of COVID-19 on the measure's data and will make technical updates to the risk-adjustment methodology for the SNFRM as appropriate.

3. Adoption of Quality Measures for the SNF VBP Expansion Beginning With the FY 2026 Program Year

a. Background

Section 1888(h)(2)(A)(ii) of the Act (as amended by section 111(a)(2)(C) of the Consolidated Appropriations Act, 2021 (Pub. L. 116-120)) allows the Secretary to add up to nine new measures to the SNF VBP Program with respect to payments for services furnished on or after October 1, 2023. These measures may include measures of functional status, patient safety, care coordination, or patient experience. Section 1888(h)(2)(A)(ii) of the Act also requires that the Secretary consider and apply, as appropriate, quality measures specified under section 1899B(c)(1) of the Act.

Currently, the SNF VBP Program includes only a single quality measure, the SNFRM, which we intend to transition to the SNFPPR as soon as practicable. Both the SNFRM and the SNFPPR assess the rate of hospital readmissions. In considering which measures might be appropriate to add to the SNF VBP Program, we requested public comment on potential future measures to include in the expanded SNF VBP Program in the FY 2022 SNF PPS proposed rule (86 FR 20009 through 20011). We refer readers to summaries of input from interested parties in the FY 2022 SNF PPS final rule (86 FR 42507 through 42511). As stated in the proposed rule, we considered this input as we developed our quality measure proposals for this year's proposed rule.

In the FY 2023 SNF PPS proposed rule (87 FR 22767 through 22777), we proposed to adopt three new quality measures for the SNF VBP Program. Specifically, we proposed to adopt two new quality measures for the SNF VBP Program beginning with the FY 2026 program year: (1) Skilled Nursing Facility (SNF) Healthcare Associated Infections (HAI) Requiring Hospitalization (SNF HAI) measure; and (2) Total Nursing Hours per Resident Day Staffing (Total Nurse Staffing) measure. We also proposed to adopt an additional quality measure for the SNF VBP Program beginning with the FY 2027 program year: Discharge to Community (DTC)—Post-Acute Care (PAC) Measure for Skilled Nursing Facilities (NQF #3481). We are finalizing the adoption of these measures, and we discuss each in more detail in the following sections.

We stated in the proposed rule that although none of these quality measures have been specified under section 1899B(c)(1) of the Act, we determined after consideration of those measures that none are appropriate for adoption into the SNF VBP Program until, at a minimum, we have had sufficient time to review their specifications and conduct further analyses to ensure that they are suited for meeting the objectives of the SNF VBP Program. We stated that we are currently reviewing measures of patient falls and functional status, which are both specified under section 1899B(c)(1) of the Act, to determine whether any of them would be appropriate for the SNF VBP Program. We also stated our belief that it is important to cover the full range of SNF services in the SNF VBP Program, which includes measure topics beyond those specified under section 1899B(c)(1) of the Act. Since we have determined that the measures specified under section 1899B(c)(1) of the Act are not yet appropriate for the SNF VBP Program, we proposed to begin the Program expansion with measures that address other important indicators of SNF care quality, including measures that align with the topics listed under section 1888(h)(2)(A)(ii) of the Act and align with HHS priorities.

As proposed, the SNF HAI measure is a patient safety measure, and the DTC PAC SNF measure is a care coordination measure. Regarding the proposed Total Nurse Staffing measure, we stated in the proposed rule that many studies have found that the level of nurse staffing is associated with patient safety,^[168] patient functional status,^[169 170] and patient experience.^[171 172] Nursing home staffing, including SNF staffing, is also a high priority for the Department of Health and Human Services (HHS) and the Biden-Harris Administration because of its central role in the quality of care for Medicare beneficiaries.^[173]

We stated in the proposed rule that we believe adopting these measures to begin affecting SNF payments in the FY 2026 program year would provide SNFs with sufficient time to prepare and become familiar with the quality measures, as well as with the numerous other programmatic changes that we proposed would take effect in the FY 2023 program year.

As we discussed in the FY 2023 SNF PPS proposed rule (87 FR 22786 through 22787), we also considered and requested public comment on additional quality measures for potential adoption in the SNF VBP Program through future rulemaking.

We received a general comment on the SNF VBP Program's measures.

Comment: One commenter supported the concept of adding new measures to the Program but expressed concern about the increase in estimated savings to Medicare via reduced payments to SNFs. The commenter stated that adding new measures effectively reduces provider reimbursement rates because they must absorb the burden and costs of reporting new measures.

Response: We carefully consider the reporting burden for all quality measures that we propose to adopt in the SNF VBP Program. Specifically, we weigh a measure's reporting burden against the benefits of adopting that measure in the Program. Our goal is to minimize the reporting burdens that we impose on SNFs under the SNF VBP Program and we will continue considering this topic as we explore proposing additional measures for the Program. We also note that the SNF HAI and DTC PAC SNF measures that we are finalizing in this final rule are calculated using Medicare claims data and do not impose any new reporting burdens on SNFs. In addition, the Total Nurse Staffing measure that we are finalizing in this final rule is calculated using information that SNFs already submit to us for the Nursing Home Five-Star Quality Rating System, and therefore, this measure will not impose any new reporting burdens on SNFs.

We proposed to update our regulations at § 413.338(d)(5) to note that, for a given fiscal year, we will specify the measures for the SNF VBP Program. We did not receive any public comments on our proposal to update § 413.338(d)(5) of our regulations, and therefore, we are finalizing our proposal as proposed.

b. Adoption of the Skilled Nursing Facility Healthcare-Associated Infections (HAI) Requiring Hospitalization Measure Beginning With the FY 2026 SNF VBP Program Year

As part of the SNF VBP Program expansion authorized under the CAA, we proposed to adopt the SNF HAI measure for the FY 2026 SNF VBP Program and subsequent years. The SNF HAI measure is an outcome measure that estimates the risk-standardized rate of HAIs that are acquired during SNF care and result in hospitalization using 1 year of Medicare fee-for-service (FFS) claims data. As proposed, the SNF HAI measure assesses SNF performance on infection prevention and management, which will align the Program with the Patient Safety domain of CMS's Meaningful Measures 2.0 Framework. In addition, the SNF HAI measure is currently part of the SNF QRP measure set. For more information on this measure in the SNF QRP, please visit https://www.cms.gov/​medicare/​quality-initiatives-patient-assessment-instruments/​nursinghomequalityinits/​skilled-nursing-facility-quality-reporting-program/​snf-quality-reporting-program-measures-and-technical-information. We also refer readers to the SNF HAI Measure Technical Report, available at https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf, for the measure specifications, which we proposed to adopt as the SNF HAI measure specifications for the SNF VBP Program.

(1) Background

Healthcare-associated infections (HAIs) are defined as infections acquired while receiving care at a health care facility that were not present or incubating at the time of admission.^[174] As stated in the proposed rule, HAIs are a particular concern in the SNF setting, and thus, monitoring the occurrence of HAIs among SNF residents can provide valuable information about a SNF's quality of care. A 2014 report from the Office of the Inspector General (OIG) estimated that one in four adverse events among SNF residents is due to HAIs, and approximately half of all HAIs are potentially preventable.^[175] In addition, analyses from FY 2019 found a wide variation in facility-level HAI rates among SNF providers with 25 or more stays, which indicates a performance gap. Specifically, among the 14,102 SNFs included in the sample, the FY 2019 facility-level, risk-adjusted rate of SNF HAIs requiring hospitalization ranged from 2.36 percent to 17.62 percent.^[176]

While HAIs are not considered “never events,” or serious adverse errors in the provision of health care services that should never occur, most are preventable.^[177] HAIs are most often the result of poor processes and structures of care. Specifically, evidence suggests that inadequate patient management following a medical intervention, such as surgery or device implantation, and poor adherence to infection control protocols and antibiotic stewardship guidelines contribute to the occurrence of HAIs.^{[178 179 180]} In addition, several provider characteristics relate to the occurrence of HAIs, including staffing levels (for example, low staff-to-resident ratios), facility structure characteristics (for example, high occupancy rates), and adoption, or lack thereof, of infection surveillance and prevention policies.^{[181 182 183 184 185 186]}

Inadequate prevention and treatment of HAIs is likely to result in poor health care outcomes for SNF residents, as well as wasteful resource use. Specifically, studies find that HAIs are associated with longer lengths of stay, use of higher-intensity care (for example, critical care services and hospital readmissions), increased mortality, and higher health care costs.^{[187 188 189 190]} Addressing HAIs in SNFs is particularly important as several factors place SNF residents at increased risk for infections, including increased age, cognitive and functional decline, use of indwelling devices, frequent care transitions, and close contact with other residents and healthcare workers.^[191 192] Further, infection prevention and control deficiencies are consistently among the most frequently cited deficiencies in surveys conducted to assess SNF compliance with Federal quality standards.^[193] Infection prevention and control deficiencies can include practices directly related to the occurrence and risks of HAIs, such as inconsistent use of hand hygiene practices or improper use of protective equipment or procedures during an infectious disease outbreak, which further underscores the importance of efforts to improve practices to reduce the prevalence of HAIs.

Given the effects of HAIs, preventing and reducing their occurrence in SNFs is critical to delivering safe and high-quality care. As discussed in the proposed rule, we continue to believe the SNF HAI measure, as proposed, aligns with this goal by monitoring the occurrence of HAIs and assessing SNFs on their performance on infection prevention and control efforts. In doing so, we continue to believe the measure may promote patient safety and increase the transparency of care quality in the SNF setting, which aligns the SNF VBP Program with the Patient Safety domain of CMS's Meaningful Measures 2.0 Framework. Prevention and reduction of HAIs has also been a priority at Federal, State, and local levels. For example, the HHS Office of Disease Prevention and Health Promotion has created a National Action Plan to Prevent HAIs, with specific attention to HAIs in LTC facilities. We refer readers to additional information on the National Action Plan available at https://www.hhs.gov/​oidp/​topics/​health-care-associated-infections/​hai-action-plan/​index.html.

Evidence suggests there are several interventions that SNFs may utilize to effectively reduce HAI rates among their residents and thus, improve quality of care. These interventions include adoption of infection surveillance and prevention policies, safety procedures, antibiotic stewardship, and staff education and training programs.^{[194 195 196 197 198 199 200]} In addition, infection prevention and control programs with core components in education, monitoring, and feedback have been found to be successful in reducing HAI rates.^[201] The effectiveness of these interventions suggest improvement of HAI rates among SNF residents is possible through modification of provider-led processes and interventions, which supports the overall goal of the SNF VBP Program.

(2) Overview of Measure

The SNF HAI measure, which was finalized for adoption in the SNF QRP in the FY 2022 SNF PPS final rule (86 FR 42473 through 42480), is an outcome measure that estimates the risk-standardized rate of HAIs that are acquired during SNF care and result in hospitalization using 1 year of Medicare FFS claims data. A HAI is defined, for the purposes of this measure, as an infection that is likely to be acquired during SNF care and severe enough to require hospitalization, or an infection related to invasive (not implanted) medical devices (for example, catheters, insulin pumps, and central lines). Several types of infections are excluded from the measure, which we discuss in section VIII.B.2.b.(4). of this final rule. In addition, all SNF stays with an admission date during the 1-year period are included in the measure cohort, except those meeting the exclusion criteria, which we also discuss in section VIII.B.2.b.(4). of this final rule.

Unlike other HAI measures that target specific infections, this measure targets all HAIs serious enough to require admission to an acute care hospital.

The goal of this measure is to identify SNFs that have notably higher rates of HAIs acquired during SNF care, when compared to their peers and to the national average HAI rate.

Validity and reliability testing has been conducted for this measure. For example, split-half testing on the SNF HAI measure indicated moderate reliability. In addition, validity testing showed good model discrimination as the HAI model can accurately predict HAI cases while controlling for differences in resident case-mix. We refer readers to the SNF HAI Measure Technical Report for further details on the measure testing results available at https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf.

(a) Measure Applications Partnership (MAP) Review

The SNF HAI measure was included as a SNF VBP measure under consideration in the publicly available “List of Measures Under Consideration for December 1, 2021.” ^[202]

The MAP offered conditional support of the SNF HAI measure for rulemaking, contingent upon NQF endorsement, noting that the measure would add value to the Program due to the addition of an overall measurement of all HAIs acquired within SNFs requiring hospitalization. We refer readers to the final 2021-2022 MAP report available at https://www.qualityforum.org/​Publications/​2022/​03/​MAP_​2021-2022_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx. We are preparing to submit the SNF HAI measure for NQF endorsement, consistent with the MAP recommendation.

(3) Data Sources

As proposed, the SNF HAI measure uses Medicare FFS claims data to estimate the risk-adjusted rate of HAIs that are acquired during SNF care and result in hospitalization. Specifically, this measure uses data from the Medicare Enrollment Database (EDB), as well as Medicare SNF and inpatient hospital claims from the CMS Common Working File (CWF). HAIs are identified using the principal diagnosis code and the Present on Admission (POA) indicators on the Medicare inpatient rehospitalization claim within a specified incubation window. We refer readers to the SNF HAI Measure Technical Report for further details on how these data components are utilized in calculating the SNF HAI measure available at https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf. We note that the proposed SNF HAI measure is calculated entirely using administrative data and therefore, it will not impose any additional data collection or submission burden for SNFs.

(4) Inclusion and Exclusion Criteria

The measure's cohort includes all Part A FFS Medicare SNF residents 18 years and older who have a SNF admission date during the 1-year measure period and who do not meet any of the exclusion criteria, which we describe next. Additionally, the hospital admission must occur during the time period which begins on day 4 after SNF admission and ends 3 days after SNF discharge. We note that residents who died during the SNF stay or during the post-discharge window (3 days after SNF discharge), and residents with a missing discharge date (or have “active” SNF stays) are included in the measure's cohort.

There are several scenarios in which a SNF stay is excluded from the measure cohort and thus, excluded from the measure denominator. Specifically, any SNF stay that meets one or more of the following criteria is excluded from the cohort and measure denominator:

• Resident is less than 18 years old at SNF admission.
• The SNF length of stay was shorter than 4 days.
• Residents who were not continuously enrolled in Part A FFS Medicare during the SNF stay, 12 months prior to the measure period, and 3 days after the end of the SNF stay.
• Residents who did not have a Part A short-term acute care hospital stay within 30 days prior to the SNF admission date. The short-term stay must have positive payment and positive length of stay.
• Residents who were transferred to a Federal hospital from a SNF as determined by the discharge status code on the SNF claim.
• Residents who received care from a provider located outside the U.S., Puerto Rico, or another U.S. territory as determined from the first 2 characters of the SNF CMS Certification Number.
• SNF stays in which data were missing on any variable used in the measure calculation or risk-adjustment. This also included stays where Medicare did not pay for the stay, which is identified by non-positive payment on the SNF claim.

The measure numerator includes several HAI conditions. We refer readers to Appendix A of the SNF HAI Measure Technical Report, available at https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf, for a complete list of the ICD-10 codes that correspond to the HAI conditions included in the measure numerator. There are also several types of HAIs that are excluded from the proposed measure numerator. For example, HAIs reported during emergency department visits and observations stays are excluded from the numerator. In addition, the HAI definition excludes infections that meet any of the following criteria:

• Chronic infections (for example, chronic viral hepatitis B).
• Infections that typically require a long period of time to present (for example, typhoid arthritis).
• Infections that are likely related to the prior hospital stay (for example, postprocedural retroperitoneal abscess).
• Sequela (a condition which is the consequence of a previous disease or injury) and subsequent encounter codes.
• Codes that include “cause disease classified elsewhere.”
• Codes likely to represent secondary infection, where the primary infection would likely already be coded (for example, pericarditis, myocarditis, or cardiomyopathy).
• Infections likely to be community acquired.
• Infections common in other countries and/or acquired through animal contact.

• Preexisting infections that fall within the CDC's National Healthcare Safety Network (NHSN) Repeat Infection Timeframe (RIT) of 14 days. We refer readers to the SNF HAI Measure Technical Report for additional information on the repeat infection timeframe (RIT) and conditions that are considered preexisting ( https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf).

(5) Risk-Adjustment

Risk-adjustment is a statistical process used to account for risk factor differences across SNF residents. By controlling for these differences in resident case-mix, we can better isolate the proposed measure's outcome and its relationship to the quality of care delivered by SNFs. As proposed, the SNF HAI measure's numerator and denominator are both risk-adjusted. Specifically, the denominator is risk-adjusted for resident characteristics excluding the SNF effect. The numerator is risk-adjusted for resident characteristics, as well as a statistical estimate of the SNF effect beyond resident case-mix. The SNF effect, or the provider-specific behaviors that influence a SNF's HAI rates, accounts for clustering of patients within the same SNF and captures variation in the measure outcome across SNFs, which helps isolate differences in measure performance. The risk-adjustment model for this measure includes the following resident characteristic variables:

• Age and sex category.
• Original reason for Medicare entitlement.
• Surgery or procedure category from the prior proximal inpatient (IP) stay.
• Dialysis treatment, but not end-stage renal disease (ESRD) on the prior proximal IP claim.
• Principal diagnosis on the prior proximal IP hospital claim.
• Hierarchical Condition Categories (HCC) comorbidities.
• Length of stay of the prior proximal IP stay.
• Prior intensive care or coronary care utilization during the prior proximal IP stay.
• The number of prior IP stays within a 1-year lookback period from SNF admission.

(6) Measure Calculation

(a) Numerator

The risk-adjusted numerator is the estimated number of SNF stays predicted to have a HAI that is acquired during SNF care and results in hospitalization. This estimate begins with the unadjusted, observed count of the measure outcome, or the raw number of stays with a HAI acquired during SNF care and resulting in hospitalization. The unadjusted, observed count of the measure outcome is then risk-adjusted for resident characteristics and a statistical estimate of the SNF effect beyond resident case-mix, which we discussed in section VIII.B.3.b.(5). of this final rule.

(b) Denominator

The risk-adjusted denominator is the expected number of SNF stays with the measure outcome, which represents the predicted number of SNF stays with the measure outcome if the same SNF residents were treated at an “average” SNF. The calculation of the risk-adjusted denominator begins with the total eligible Medicare Part A FFS SNF stays during the measurement period and then applying risk-adjustment for resident characteristics, excluding the SNF effect, as we discussed in section VIII.B.3.b.(5). of this final rule.

The SNF HAI measure rate, which is reported at the facility-level, is the risk-standardized rate of HAIs that are acquired during SNF care and result in hospitalization. This risk-adjusted HAI rate is calculated by multiplying the standardized risk ratio (SRR) for a given SNF by the national average observed rate of HAIs for all SNFs. The SRR is a ratio that measures excess HAIs and is the predicted number of HAIs (adjusted numerator) divided by the expected number of HAIs (adjusted denominator). A lower measure score for the SNF HAI measure indicates better performance in prevention and management of HAIs. For technical information on the proposed measure's calculation, we refer readers to the SNF HAI Measure Technical Report available at https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf.

Because a “lower is better” rate could cause confusion among SNFs and the public, we proposed to invert SNF HAI measure rates, similar to the approach used for the SNFRM, for scoring. Specifically, we proposed to invert SNF HAI measure rates using the following calculation:

SNF HAI Inverted Rate = 1 - Facility's SNF HAI rate

This calculation will invert SNFs' HAI measure rates such that higher SNF HAI measure rates will reflect better performance. In the proposed rule, we stated our belief that this inversion is important to incentivize improvement in a clear and understandable manner, so that “higher is better” for all measure rates included in the Program.

(7) Confidential Feedback Reports and Public Reporting

We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 through 52007) for discussion of our policy to provide quarterly confidential feedback reports to SNFs on their measure performance. We also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 through 42517) for a summary of our two-phase review and corrections policy for SNFs' quality measure data. Furthermore, we refer readers to the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to publicly report SNF measure performance information under the SNF VBP Program on the Provider Data Catalog website currently hosted by HHS and available at https://data.cms.gov/​provider-data/​. We proposed to update and redesignate the confidential feedback report and public reporting policies, which are currently codified at § 413.338(e)(1) through (3), to § 413.338(f), to include the SNF HAI measure.

We invited public comment on our proposal to adopt the SNF HAI measure beginning with the FY 2026 SNF VBP program year. We received the following comments and provide our responses:

Comment: Many commenters supported our proposal to adopt the SNF HAI measure beginning with the FY 2026 SNF VBP program year. Commenters noted that the SNF HAI measure is an important quality indicator, that the measure imposes a low reporting burden on SNFs, and that SNFs are already familiar with the measure because it is currently adopted in the SNF QRP.

Response: We agree that the SNF HAI measure is an important quality indicator. Monitoring SNF HAI rates provides valuable information on a SNF's infection prevention and management practices, and the overall quality of care. We also agree that SNFs are already familiar with the SNF HAI measure and that because the measure is calculated using Medicare FFS claims data, the adoption of the measure for the SNF VBP Program would impose no new reporting burden on SNFs.

Comment: Several commenters offered qualified support for our proposal to adopt the SNF HAI measure and offered recommendations for improving the measure. Several commenters noted that the SNF HAI measure has not been endorsed by NQF and a few commenters suggested that we delay finalizing the measure until it has received NQF endorsement. A few commenters also recommended that we update the measure's specifications to exclude hospital- and community-acquired infections, as well as to exclude or risk-adjust for hospitalizations due to COVID-19 infection. One commenter recommended that we collect SNF HAI measure data but not publicly report those data until the PHE for COVID-19 has expired. Another commenter suggested that we develop a better reporting system in CASPER for the measure. Lastly, one commenter recommended that we link SNF HAI measure data to race and ethnicity information to assess care disparities.

Response: We thank the commenters for their recommendations. As part of our routine measure monitoring work, we intend to consider whether any of these recommendations would warrant further analysis or potential updates to the measure's specifications.

We intend to submit the SNF HAI measure to the NQF for consideration of endorsement. However, we also believe that the SNF HAI measure provides valuable quality of care information. For example, the HHS Office of Inspector General estimated that one in four adverse events among SNF residents is due to HAIs with approximately half of all HAIs being potentially preventable.^[203] The identification of HAIs by SNFs provides actionable information that SNFs can use to improve their quality of care and prevent their residents from having to be hospitalized. For these reasons, we continue to believe that it is important to include this measure in the SNF VBP Program.

Comment: Several commenters opposed the use of Medicare FFS claims data for calculating the SNF HAI measure and expressed concerns about the validity and accuracy of those claims data. Some commenters recommended that we adopt NHSN-based measures instead of claims-based measures. Another commenter recommended that the measure undergo additional testing before its inclusion in the Program.

Response: As we discussed in the proposed rule (87 FR 22769), validity and reliability testing results showed that the SNF HAI measure has acceptable reliability and validity when calculated from Medicare FFS claims data. In addition, during development of this measure, the TEP considered the appropriateness of using alternative data sources, including NHSN data. The TEP ultimately recommended against using those sources because they would increase the reporting burden on SNFs. We refer commenters to the SNF HAI Final TEP Summary Report, available at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Downloads/​SNF-HAI-Final-TEP-Report-7-15-19_​508C.pdf for more information.

Comment: One commenter expressed concern that SNFs must rely on hospitals accurately capturing HAIs because the measure is calculated using hospital claims data. Another commenter noted that performance scores may be inaccurate because there is variation in hospital documentation of HAIs.

Response: We use inpatient hospital claims to calculate the SNF HAI measure because the measure's main outcome is HAIs that require hospitalization. In addition, we commissioned a medical record review for the purpose of analyzing the accuracy of hospital coding of Hospital Acquired Conditions (HACs), which include HAIs, and Present on Admission (POA) conditions. This study did not find patterns of widespread underreporting of HACs or overreporting of POA status.^[204] The study found that only 3 percent of HAC cases were underreported and 91 percent of all cases coded POA were accurate. Another medical record review we conducted assessed the accuracy of the principal diagnosis coded on a Medicare claim to identify whether a patient was admitted for a diagnosis included in our list of potentially preventable readmission (PPR) diagnoses.^[205] The study analyzed inpatient discharges from October 2015 through September 2017 and found high agreement between principal diagnoses in Medicare claims and corresponding medical records. Specifically, the agreement rate between principal diagnoses in Medicare claims and information in the corresponding medical records ranged from 83 percent to 94 percent by study hospital. Additionally, 91 percent to 97 percent of principal diagnoses from the corresponding medical records were included in our list of PPR diagnoses. Therefore, we disagree with commenters' concerns about the accuracy of hospital inpatient claims data.

Comment: Several commenters opposed our proposal to adopt the SNF HAI measure, stating that SNFs will experience a significant time lag between claims submission and when data derived from those claims are used to measure quality performance. One commenter stated that while measuring HAIs in the SNF setting is “vital,” the topic is so important and complex that CMS should develop a measure that delivers more timely, accurate and actionable information. Another commenter was concerned that SNFs have not had time to review their performance data on this measure, thus making improvement plans difficult to implement. One commenter questioned whether providers would be able to use data from this measure to improve the quality of their care.

Response: We understand commenters' concerns regarding the time gap. As we discuss in section VIII.C.3. of this final rule, we are finalizing our proposal to adopt FY 2022 as the baseline period and FY 2024 as the performance period for the SNF HAI measure for the FY 2026 SNF VBP Program. Under section 1888(h)(3)(C) of the Act, we are required to calculate and announce performance standards no later than 60 days prior to the start of the performance period. To meet this statutory requirement, we need sufficient time between the end of the baseline period and the start of the performance period to calculate and announce performance standards, which are derived from baseline period data. Therefore, we continue to believe that a baseline period that occurs 2 fiscal years prior to the start of the performance period is most appropriate for this measure. In addition, under section 1888(h)(7) of the Act, we are required to announce the net results of the Program's adjustments to a SNF's Medicare payment no later than 60 days prior to the fiscal year involved. To meet this statutory requirement, we need sufficient time between the end of the performance period and the applicable fiscal program year to calculate and announce the net results of the Program's adjustments to a SNF's Medicare payment. Therefore, we continue to believe that a performance period that occurs two fiscal years prior to the applicable fiscal program year is most appropriate for this measure We refer readers to section VIII.C.3. of this final rule for further details on the baseline and performance periods for the SNF HAI measure. Given these statutory requirements, and the time needed to calculate valid and reliable measure rates, we have narrowed the time gap to the extent feasible at this time.

We continue to believe that the data provided by the SNF HAI measure will be valuable to SNFs and their efforts to improve care quality. Specifically, a SNF's HAI rate provides information on the effectiveness of its current infection prevention and management practices, as well as provides information regarding opportunities for improvement. As we discussed in the FY 2023 SNF PPS proposed rule (87 FR 22769), evidence suggests that there are several interventions that SNFs may utilize to effectively reduce HAI rates among their residents to improve quality of care, including infection surveillance and prevention policies, safety procedures, antibiotic stewardship, and staff education and training programs. The effectiveness of these interventions suggest that improvement of HAI rates among SNF residents is possible through modification of provider-led processes, which further demonstrates the value in measuring HAI rates among SNF residents.

Comment: One commenter opposed our proposal to adopt the SNF HAI measure because of their belief that the SNF HAI measure only captures HAIs that result in hospitalization and does not prioritize other HAIs and their underlying causes.

Response: We agree with the commenter that detecting all HAIs in the measure definition would provide additional data to SNFs and empower additional quality improvement. However, we decided to include only those HAIs requiring hospitalization in the SNF HAI measure to avoid the risk of overloading SNFs with information on every possible HAI in their SNF HAI measure rate.^[206] This decision was consistent with the recommendation of our TEP, which concluded that a concentrated list of severe infections would be more valuable to SNFs and would make the measure more actionable.

Comment: A few commenters expressed concern that the SNF HAI measure does not account for other resident characteristics, including social risk factors, or provider characteristics, such as facility size, location, and teaching status, that influence HAI rates.

Response: We understand commenters' concerns regarding the risk-adjustment model for the SNF HAI measure. As part of our routine measure monitoring work, we intend to continue assessing the appropriateness of the risk-adjustment model. In addition, as described in our RFI in the proposed rule (87 FR 22789), we are considering whether it would be appropriate to incorporate adjustments in the SNF VBP Program, beyond an individual measure's risk-adjustment model, to account for social risk factors as part of our efforts to measure and improve health equity. Further, we note that the risk-adjustment model for the SNF HAI accounts for the following resident characteristic variables: age and sex category; original reason for Medicare entitlement; surgery or procedure category from the prior proximal inpatient (IP) stay; dialysis treatment, but not end-stage renal disease (ESRD) on the prior proximal IP claim; principal diagnosis on the prior proximal IP hospital claim; hierarchical condition categories (HCC) comorbidities; length of stay of the prior proximal IP stay; prior intensive care or coronary care utilization during the prior proximal IP stay; and the number of prior IP stays within a 1-year lookback period from SNF admission. We refer the commenters to section VIII.B.3.b.(5). of this final rule for further discussion of the risk-adjustment model.

Comment: Some commenters opposed our proposal to adopt the SNF HAI measure due to various concerns with the measure specifications. Some commenters expressed validity concerns, stating that the measure's list of exclusion criteria is incomplete. One commenter stated that the inability to define the magnitude of the clinical problem addressed by the SNF HAI measure makes it difficult for SNFs to identify benchmarks and goals. Another commenter suggested that the proposed time window for excluding infections prior to SNF admission is not long enough.

Response: We disagree with commenters' concerns regarding the validity of the measure. As we discussed in the FY 2023 SNF PPS proposed rule (87 FR 22769), the validity testing for this measure showed that the HAI model can accurately predict HAI cases while controlling for differences in resident case-mix.

Our measure contractor developed the exclusion criteria with input from subject matter experts with clinical expertise specific to infectious diseases and the SNF population. We continue to believe the set of exclusion criteria helps ensure that we only capture HAIs requiring hospitalization that can be directly attributed to care during a SNF stay. We also agree with the members of the SNF HAI measure TEP, which found that the exclusion criteria were realistic and comprehensive.^[207] With regard to identifying benchmarks and goals for the SNF HAI measure, we note that our analysis of FY 2019 data demonstrated that there is a performance gap in HAI rates across SNFs. Specifically, among the 14,102 SNFs included in the sample, risk-adjusted SNF HAI measure rates ranged from a minimum of 2.36 percent to a maximum of 17.62 percent.^[208] In addition, we calculate specific performance standards, based on data gathered from all participating SNFs, that we use as benchmarks and achievement thresholds. We continue to believe each SNF can use this information to set goals for quality improvement that meet the needs of their facility. As we discuss in detail in the next comment response, we have made several resources available to assist SNFs with reducing HAIs and improving their quality of care.

Comment: A few commenters expressed concerns about a lack of resources in SNFs currently. One commenter noted that no new measures should be adopted because of current staffing burdens. Another commenter stated that SNFs may not have the resources for quality improvement efforts and recommended that CMS offer quality improvement support to reduce HAIs.

Response: We note that the SNF HAI measure, as well as the DTC PAC SNF and Total Nurse Staffing measures, will not impose any new reporting burdens on SNFs. In addition, as finalized, the SNF HAI and Total Nurse Staffing measures will not begin affecting SNF payments until the FY 2026 program year, and the DTC PAC SNF measure will not begin affecting SNF payments until the FY 2027 program year. We continue to believe that this provides SNFs with sufficient time to prepare for implementation of these measures.

We also note that we have made several resources available to assist SNFs with reducing HAIs and improving quality of care. These include training in partnership with the CDC and Quality Improvement Organizations (QIOs), many of which are available at https://www.cdc.gov/​longtermcare/​prevention/​index.html and https://www.cdc.gov/​longtermcare/​prevention/​index.html. Additionally, the CMS Office of Minority Health (OMH) offers a Disparity Impact Statement, which is a tool that all health care stakeholders can use to identify and address health disparities: https://www.cms.gov/​About-CMS/​Agency-Information/​OMH/​Downloads/​Disparities-Impact-Statement-508-rev102018.pdf.

After considering the public comments, we are finalizing our proposal to adopt the SNF HAI Requiring Hospitalization Measure beginning with the FY 2026 SNF VBP program year as proposed.

c. Adoption of the Total Nursing Hours per Resident Day Staffing Measure Beginning With the FY 2026 SNF VBP Program Year

We proposed to adopt the Total Nursing Hours per Resident Day Staffing (Total Nurse Staffing) measure for the FY 2026 program year and subsequent years. The Total Nurse Staffing measure is a structural measure that uses auditable electronic data reported to CMS's Payroll Based Journal (PBJ) system to calculate total nursing hours per resident day. Given the well-documented impact of nurse staffing on patient outcomes and quality of care, this measure, as proposed, will align the Program with the Person-Centered Care domain of CMS's Meaningful Measures 2.0 Framework. In addition, the Total Nurse Staffing measure is currently included in the Five-Star Quality Rating System. For more information on the Five-Star Quality Rating System, see https://www.cms.gov/​Medicare/​Provider-Enrollment-and-Certification/​CertificationandComplianc/​FSQRS.

(1) Background

Staffing is a crucial component of quality care for nursing home residents. Numerous studies have explored the relationship between nursing home staffing levels and quality of care. The findings and methods of these studies have varied, but most have found a strong, positive relationship between staffing and quality outcomes.^{[209 210 211 212 213]} Specifically, studies have shown an association between nurse staffing levels and hospitalizations,^[214 215] pressure ulcers,^{[216 217 218]} weight loss,^[219 220] functional status,^[221 222] and survey deficiencies,^[223 224] among other quality and clinical outcomes. The strongest relationships have been identified for registered nurse (RN) staffing; several studies have found that higher RN staffing is associated with better care quality.^[225 226] We recognize that the relationship between nurse staffing and quality of care is multi-faceted, with elements such as staff turnover playing a critical role.^[227] We refer readers to additional discussion of staffing turnover in section VIII.I.1.a. of this final rule.

The PHE due to COVID-19 has further underscored the critical importance of sufficient staffing to quality and clinical outcomes. Several recent studies have found that higher staffing is associated with lower COVID-19 incidence and fewer deaths.^{[228 229 230]}

Multiple Institute of Medicine (IOM) reports have examined the complex array of factors that influence care quality in nursing homes, including staffing variables such as staffing levels and turnover.^[231 232] In the 2004 report, “Keeping Patients Safe: Transforming the Work Environment of Nurses,” the IOM's Committee on the Work Environment for Nurses and Patient Safety highlighted the positive relationships between higher nursing staffing levels, particularly RN levels, and better patient outcomes, and recognized the need for minimum staffing standards to support appropriate levels of nursing staff in nursing homes.^[233]

Previously published Phase I and Phase II “Reports to Congress on the Appropriateness of Minimum Staffing Ratios in Nursing Homes” further studied the relationship between quality and nurse staffing levels and provided compelling evidence of the relationship between staffing ratios and quality of care.^[234 235] The Phase II report, completed in 2001, identified staffing thresholds that maximized quality outcomes, demonstrating a pattern of incremental benefits of increased nurse staffing until a threshold was reached. Specifically, the Phase II study used Medicaid Cost Report data from a representative sample of 10 states, including over 5,000 facilities, to identify staffing thresholds below which quality of care was compromised and above which there was no further benefit of additional staffing with respect to quality. The study found evidence of a relationship between higher staffing and better outcomes for total nurse staffing levels up to 4.08 hours per resident day and RN staffing levels up to 0.75 RN hours per resident day. In the 2001 study, minimum staffing levels at any level up to these thresholds were associated with incremental quality improvements, and no significant quality improvements were observed for staffing levels above these thresholds. The findings were also supported by case studies of individual facilities, units, and residents.

We have long identified staffing as one of the vital components of a nursing home's ability to provide quality care and used staffing data to gauge its impact on quality of care in nursing homes more accurately and effectively. In 2003, the National Quality Forum Nursing Home Steering Committee recommended that a nurse staffing quality measure be included in the set of nursing home quality measures that are publicly reported by us. The Total Nurse Staffing measure is currently used in the Nursing Home Five-Star Quality Rating System, as one of two measures that comprise the staffing domain. For more information on the Five-Star Quality Rating System, we refer readers to https://www.cms.gov/​Medicare/​Provider-Enrollment-and-Certification/​CertificationandComplianc/​FSQRS.

Current Federal requirements for nurse staffing are outlined in the LTC facility requirements for participation (requirements).^[236] The regulations at 42 CFR 483.35 specify, in part, that every facility must have sufficient nursing staff with the appropriate competencies and skill sets to provide nursing and related services to assure resident safety and attain or maintain the highest practicable physical, mental, and psychosocial well-being of each resident, as determined by resident assessments and individual plans of care and considering the number, acuity and diagnoses of the facility's resident population in accordance with the facility assessment required at § 483.70(e). We adopted this competency-based approach to sufficient staffing to ensure every nursing home provides the staffing levels needed to meet the specific needs of their resident population, including their person-centered care goals. We also note that current regulations require (unless these requirements are waived) facilities to have an RN onsite at least 8 consecutive hours a day, 7 days a week and around-the-clock services from licensed nursing staff under sections 1819(b)(4)(C) and 1919(b)(4)(C) of the Act, and § 483.35(a) and (b).

Section 1128I(g) of the Act requires facilities to electronically submit direct care staffing information (including agency and contract staff) based on payroll and other auditable data. In August 2015, we amended the requirements for LTC facilities at § 483.70(q) to require the electronic submission of payroll-based staffing data, which includes RNs, licensed practical nurses (LPNs) or vocational nurses, certified nursing assistants, and other types of medical personnel as specified by us, along with census data, data on agency and contract staff, and information on turnover, tenure and hours of care provided by each category of staff per resident day.^[237] We developed the PBJ system to enable facilities to submit the required staffing information in a format that is auditable to ensure accuracy. Development of the PBJ system built on several earlier studies that included extensive testing of payroll-based staffing measures. The first mandatory PBJ reporting period began July 1, 2016.

We post staffing information publicly to help consumers understand staffing levels and how they differ across nursing homes. See sections 1819(i)(1)(A)(i) and 1919(i)(1)(A)(i) of the Act. However, there are currently no staffing measures in the SNF VBP Program.

Given the strong evidence regarding the relationship between sufficient staffing levels and improved care for residents, inclusion of this measure in the SNF VBP Program adds an important new dimension to provide a more comprehensive assessment of and accountability for the quality of care provided to residents and serves to drive improvements in staffing that are likely to translate into better resident care. PBJ data show that there is variability across SNFs in performance on this measure, and that there is an opportunity and potential for many SNFs to improve their staffing levels. For Q4 CY 2020, average total nurse staffing was 4.09 hours per resident day for the case-mix adjusted Total Nurse Staffing measure, with considerable variability across facilities ranging from 2.81 hours per resident day to 5.93 hours per resident day. Staffing levels increased after April 2018, when we first reported PBJ-based staffing measures on Nursing Home Compare and using them in the Five-Star Quality Rating System. Average nursing staffing hours per resident day increased from 3.85 in Q4 CY 2017 (publicly reported in April 2018) to 4.08 for Q4 CY 2020 (publicly reported in April 2021).

Inclusion of this measure in the SNF VBP Program also aligns with our current priorities and focus areas for the Program and optimizing the use of measures that SNFs are already reporting to us. Because the measure is currently used in the Nursing Home Five-Star Quality Rating System, inclusion of this measure in the Program does not add reporting or administrative burden to SNFs. Recognizing the importance of staffing to supporting and advancing person-centered care needs, this measure will align the Program with the Person-Centered Care domain of CMS's Meaningful Measures 2.0 Framework.

(2) Overview of Measure

The Total Nurse Staffing measure is a structural measure that uses auditable electronic data reported to CMS's PBJ system to calculate total nursing hours, which includes RNs, LPNs, and certified nurse aides (CNA), per resident day. The measure uses a count of daily resident census derived from Minimum Data Set (MDS) resident assessments and is case-mix adjusted based on the distribution of MDS resident assessments by Resource Utilization Groups, version IV (RUG-IV groups). The measure was specified and originally tested at the facility level with SNFs as the care setting. The measure is not currently NQF endorsed; however, we plan to submit it for endorsement in the next 1 to 2 years.

Data on the measure have been publicly reported on the Provider Data Catalog website currently hosted by HHS, available at https://data.cms.gov/​provider-data/​, for many years and have been used in the Nursing Home Five-Star Quality Rating System since its inception in 2008. The data source for the measure changed in 2018, when we started collecting payroll-based staffing data through the PBJ system. Since April 2018, we have been using PBJ and the MDS as the data sources for this measure for public reporting and for use in the Five-Star Quality Rating System. For more information, see the Final Specifications for the SNF VBP Program Total Nursing Hours per Resident Day Measure, at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​Measure.

The CMS report “Appropriateness of Minimum Nurse Staffing Ratios in Nursing Homes, Phase II,” described earlier in this section, showed the relationship between quality and nurse staffing levels using several methods, establishing the face validity of the Total Nurse Staffing measure. The study included an analysis of data from 10 states including over 5,000 facilities and found evidence of a relationship between staffing ratios and the quality of nursing home care.

We note that payroll data are considered the gold standard for nurse staffing measures and a significant improvement over the manual data previously used, wherein staffing information was calculated based on a form (CMS-671) filled out manually by the facility.^[238] In contrast, PBJ staffing data are electronically submitted and are auditable back to payroll and other verifiable sources. Analyses of PBJ-based staffing measures show a relationship between higher nurse staffing levels and higher ratings for other dimensions of quality such as health inspection survey results and quality measures.^[239]

(a) Interested Parties and TEP Input

In considering whether the total nurse staffing measure would be appropriate for the SNF VBP Program, we looked at the developmental history of the measure in which we employed a transparent process that provided interested parties and national experts the opportunity to provide pre-rulemaking input. We convened meetings with interested parties and offered engagement opportunities at all phases of measure development, from 2004 through 2019. Calls and meetings with interested parties have included patient/consumer advocates and a wide range of facilities throughout the country including large and small, rural and urban, independently owned facilities and national chains. In addition to input obtained through meetings with interested parties, we solicited input through a dedicated email address ( [e-mail]).

(b) MAP Review

The Total Nurse Staffing measure was included in the publicly available “List of Measures Under Consideration for December 1, 2021.” ^[240] The MAP conditionally supported the Total Nurse Staffing measure for rulemaking, pending NQF endorsement. We refer readers to the final 2021-2022 MAP report available at https://www.qualityforum.org/​Publications/​2022/​03/​MAP_​2021-2022_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx.

(3) Data Sources

As proposed, the Total Nurse Staffing measure is calculated using auditable, electronic staffing data submitted by each SNF for each quarter through the PBJ system, along with daily resident census information derived from Minimum Data Set, Version 3.0 (MDS 3.0) standardized patient assessments. We refer readers to the Final Specifications for the SNF VBP Program Total Nursing Hours per Resident Day Measure, at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​Measure. We noted that the Total Nurse Staffing measure is already reported on the Provider Data Catalog website and used as part of the Five-Star Quality Rating System and thus, there will be no additional data collection or submission burdens for SNFs.

(4) Inclusion and Exclusion Criteria

The target population for the measure is all SNFs to whom the SNF VBP applies and that are not excluded for the reasons listed below. A set of exclusion criteria are used to identify facilities with highly improbable staffing data and these facilities are excluded. The exclusion criteria are as follows:

• Total nurse staffing, aggregated over all days in the quarter that the facility reported both residents and staff is excessively low (<1.5 hours per resident day).
• Total nurse staffing, aggregated over all days in the quarter that the facility reported both residents and staff is excessively high (>12 hours per resident day).
• Nurse aide staffing, aggregated over all days in the quarter that the facility reported both residents and staff is excessively high (>5.25 hours per resident day).

(5) Measure Calculation and Case-Mix Adjustment

We proposed to calculate case-mix adjusted hours per resident day for each facility for each staff type using this formula:

Hours _Adjusted = (Hours _Reported /Hours_Case-Mix) * Hours _{National Average}

The reported hours are those reported by the facility through PBJ. National average hours for a given staff type represent the national mean of case-mix hours across all facilities active on the last day of the quarter that submitted valid nurse staffing data for the quarter.

The measure is case-mix adjusted based on the distribution of MDS assessments by RUG-IV groups. The CMS Staff Time Resource Intensity Verification (STRIVE) Study measured the average number of RN, LPN, and NA minutes associated with each RUG-IV group (using the 66-group version of RUG-IV).^[241] We refer to these as “case-mix hours.” The case-mix values for each facility are based on the daily distribution of residents by RUG-IV group in the quarter covered by the PBJ reported staffing and estimates of daily RN, LPN, and NA hours from the CMS STRIVE Study. This adjustment is based on the distribution of MDS assessments by RUG-IV groups to account for differences in acuity, functional status, and care needs of residents, and therefore is appropriate for the SNF VBP Program. For more information, see the Final Specifications for the SNF VBP Program Total Nursing Hours per Resident Day Measure, at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​Measure.

(a) Numerator

The numerator for the measure is total nursing hours (RN + LPN + NA hours). RN hours include the RN director of nursing, RNs with administrative duties, and RNs. LPN hours include licensed practical and licensed vocational nurses with administrative duties and licensed practical and licensed vocational nurses. NA hours include certified nurse aides (CNAs), aides in training, and medication aides/technicians. We noted that the proposed PBJ staffing data include both facility employees (full-time and part-time) and individuals under an organization (agency) contract or an individual contract. The proposed PBJ staffing data do not include “private duty” nursing staff reimbursed by a resident or his/her family. Also, hospice staff and feeding assistants are not included.

(b) Denominator

The denominator for the measure is a count of daily resident census derived from MDS resident assessments. It is calculated by: (1) identifying the reporting period (quarter) for which the census will be calculated; (2) extracting MDS assessment data for all residents of a facility beginning 1 year prior to the reporting period to identify all residents that may reside in the facility (that is, any resident with an MDS assessment); and (3) identifying discharged or deceased residents using specified criteria. For any date, residents whose assessments do not meet the criteria for being identified as discharged or deceased prior to that date are assumed to reside in the facility. The count of these residents is the census for that particular day. We refer readers to the Final Specifications for the SNF VBP Program Total Nursing Hours per Resident Day Measure for more information on the calculation of daily resident census used in the denominator of the reported nurse staffing ratios, at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​Measure.

The currently publicly reported Total Nurse Staffing measure is reported on a quarterly basis. To align with other quality measures for the expanded SNF VBP Program, we proposed to report the measure rate for the SNF VBP Program for each SNF as a simple average rate of total nurse staffing per resident day across available quarters in the 1-year performance period.

(6) Confidential Feedback Reports and Public Reporting

We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 through 52007) for discussion of our policy to provide quarterly confidential feedback reports to SNFs on their measure performance. We also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 through 42517) for a summary of our two-phase review and corrections policy for SNFs' quality measure data. Furthermore, we refer readers to the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to publicly report SNF measure performance information under the SNF VBP Program on the Provider Data Catalog website currently hosted by HHS and available at https://data.cms.gov/​provider-data/​. We proposed to update and redesignate the confidential feedback report and public reporting policies, which are currently codified at § 413.338(e)(1) through (3) as § 413.338(f), to include the Total Nurse Staffing measure.

We invited public comment on our proposal to adopt the Total Nurse Staffing measure beginning with the FY 2026 SNF VBP program year. We received the following comments and provide our responses:

Comment: Many commenters supported our proposal to adopt a measure of Total Nurse Staffing, citing the strong relationship between higher nurse staffing levels and improved quality of care. Some commenters noted that they supported inclusion of the measure because, although it a structural measure, not an outcome measure, staffing levels are tied to multiple outcomes such as hospitalizations, pressure ulcers, emergency department use, functional improvement, weight loss and dehydration, and COVID-19 infection rates and deaths. Another commenter noted that adding the measure allows for more accountability for SNFs without adding data collection burden.

Response: We agree that there is a strong, positive relationship between nurse staffing levels, quality of care, and patient outcomes and that the adoption of this measure adds an important dimension of quality to the Program. We refer readers to the evidence discussed in our proposed rule (87 FR 22771 through 22772) which demonstrates that nurse staffing levels are associated with various patient outcomes, such as hospitalizations and functional status. We also note that analyses of PBJ-based staffing data show a relationship between higher nurse staffing levels and higher ratings on other dimensions of quality such as health inspection survey results and various quality measures.^[242] We agree that the measure allows for more accountability for quality outcomes without adding data reporting or administrative burden, as SNFs already report nurse staffing data on which the measure is based through the PBJ system, and the Total Nurse Staffing measure is currently used in the Nursing Home Five-Star Quality Rating System.

Comment: Many commenters opposed our proposal to adopt a measure of Total Nurse Staffing. Several commenters stated that staff shortages have made it difficult for facilities to operate, potentially impacting SNFs for years to come, and suggested that we delay the measure's implementation in the Program.

Response: We recognize that the COVID-19 PHE has had significant impacts on SNF operations and staffing. We also note that facilities with data indicating excessively low staffing levels are excluded from the measure, and based on the proposed exclusion criteria, facilities with <1.5 nursing hours per resident day will be excluded from the measure on the basis that those data are at high risk for inaccuracy.^[243] We refer readers to our proposed rule for further information on the inclusion and exclusion criteria for this measure (87 FR 22773). We also remain committed to the importance of value-based care and incentivizing quality care tied to payment. SNF staffing is a high priority because of its central role in the quality of care for Medicare beneficiaries, and therefore, we continue to believe that this measure will provide a more comprehensive assessment of, and accountability for, the quality of care provided to residents.

Comment: One commenter stated that an operational measure is not appropriate for the SNF VBP Program, while another stated that the Program's purpose to link payments to outcomes is not served by a structural measure.

Response: We recognize that the Total Nurse Staffing measure is a structural measure, not a patient outcome measure. However, numerous studies have shown that higher staffing levels are associated with better patient outcomes, such as fewer hospitalizations ^[244 245] , fewer pressure ulcers ^{[246 247 248]} , more weight loss ^[249 250] , and better functional status ^[251 252] . As a result, we believe that this measure is a strong indicator of quality of care and is an appropriate and important addition to the Program.

Comment: One commenter noted that the measure is unlikely to provide an accurate assessment of care quality because it simplifies the relationship between staffing levels and improved care. Another commenter stated that we should adopt measures of the clinical outcomes that are associated with nurse staffing and not reward facilities for simply increasing staffing rather than achieving better clinical outcomes. Another commenter stated that there is less evidence of the relationship between patient outcomes and certain types of facility staff, such as LPNs and nurse aides, than there is of the relationship between patient outcomes and RNs.

Response: We recognize the relationship between nurse staffing and quality of care is multi-faceted. We refer commenters to our proposed rule (87 FR 22771 through 22772) where we discussed several studies that emphasize the evidence of a relationship between staffing levels, quality of care, and patient outcomes. We have selected this measure as a first step towards addressing this complex relationship between nurse staffing and quality of care. Furthermore, we are examining additional staffing measures to include in a future Program year to further account for the multi-faceted nature of the relationship between staffing and care quality and outcomes. We refer readers to our RFI on the potential inclusion of a staff turnover measure in section VII.I.1.a. of the proposed rule (87 FR 22786 through 22787). In addition, as we discussed in the proposed rule (87 FR 22771 through 22772), several studies have identified a strong relationship between higher RN staffing and better quality of care. Also, studies support that other nursing staff, including certified nursing assistants and LPNs, play a critical role in providing care to Medicare beneficiaries in SNFs and, therefore, certified nursing assistants and LPNs, in addition to RNs, are also included in our proposed Total Nurse Staffing measure.^[253]

Comment: A few commenters recommended that the measure should be endorsed by NQF as soon as possible or prior to its adoption.

Response: We intend to submit the measure for NQF endorsement in the next 1 to 2 years, which we believe is the most feasible timeline. We continue to believe the Total Nurse Staffing measure provides vital quality of care information; as mentioned in the proposed rule (87 FR 22771 through 22772), studies demonstrate a strong relationship between nurse staffing levels, quality of care, and patient outcomes. Given its relationship to quality of care, we believe it is important to include this measure in the Program despite the lack of current NQF endorsement.

Comment: One commenter expressed concern that a staffing measure may exacerbate care disparities because SNFs with larger minority patient populations tend to have lower staffing levels. Another commenter was concerned that the measure could cause SNFs to close, especially if they serve underserved populations and rural communities. The commenter suggested that we reexamine staffing and wage reimbursement levels and economic conditions before implementing the measure.

Response: We recognize the commenters' concerns that this measure could impact disparities in care provided to SNF residents, especially with respect to SNFs that serve large proportions of minority patient populations and other underserved communities. We will monitor and evaluate the measure's impact on health disparities as it is implemented in the SNF VBP Program. Addressing and improving health equity is an important priority for us, and as discussed in our RFI on the Program's approach to measuring and improving health equity (87 FR 22789), we remain committed to examining ways to incorporate health equity measurement and adjustments in our quality reporting and value-based purchasing programs. Further, we share the commenter's concerns about rural health disparities and note that we remain committed to providing support to rural communities in an effort to improve quality of care. We also note that in November 2021, the US Department of Health and Human Services began distributing $7.5 billion in American Rescue Plan (ARP) Rural payments to providers and suppliers who serve rural Medicaid, Children's Health Insurance Program (CHIP), and Medicare beneficiaries.^[254] In addition, we will continue to examine staffing and wage reimbursement levels and economic conditions as part of our ongoing evaluation of the Program.

Comment: One commenter recommended that we should only reward facilities with the highest staffing levels. Another commenter noted that literature on the effects of nursing facility staffing incentives is mixed and suggested that incentives may be too small or too complex to administer to motivate behavioral changes. Other commenters suggested that staffing requirements be set based on residents' acuity, stating that facilities that successfully provide quality services without increasing staffing should not be penalized.

Response: We agree that it is important to incentivize staffing levels that foster the highest quality outcomes for SNF residents. As a reminder, the proposed Total Nurse Staffing measure calculates total nursing hours per resident day, and we refer readers to our proposed rule (87 FR 22774) to review the specific measure calculations. We continue to believe that scoring facilities based on their achievement on the Program's quality measures provides strong incentives in this program for those facilities already providing higher quality of care without prescribing specific staffing levels or practices. We believe this type of clinical quality assessment, which allows participating facilities to decide how best to achieve better care outcomes, is an important feature in our quality programs. However, we also believe that it is important to offer SNFs that provide lower levels of care quality in the baseline period with incentives for their successes in substantially improving the quality of care they provide based on their investments in quality improvement. Providing incentives for both achievement and improvement in staffing levels and other quality metrics provides the opportunity for the program to increase the quality of care for all SNF residents, and not only those residents who receive care from higher performing SNFs. We will continue to evaluate the impact on SNFs' behaviors, staffing levels, and quality outcomes as the measure is implemented in the Program. Regarding the commenter's concern that SNFs could be penalized for failing to increase staffing while still providing quality services, we do not believe this measure would penalize those SNFs as long as staffing levels are not low enough to imperil services provided to SNF residents. Finally, we note that the Total Nurse Staffing measure is case-mix adjusted based on resident assessments to account for differences in acuity, functional status, and care needs of residents.

Comment: One commenter suggested that we use targeted surveillance of PBJ staffing data to monitor SNFs' staffing rather than using a broad count of general staff hours, noting that CMS currently monitors PBJ staffing data for trends such as differences in weekend and weekday staffing. Another commenter recommended that we align the Program's staffing requirements with the Five-Star Quality Rating System.

Response: We agree that it is important to align the Program's measures with other quality and public reporting programs and note that the proposed Total Nurse Staffing measure is currently used in the Nursing Home Five-Star Quality Rating System. We agree that targeted oversight and auditing of PBJ staffing data, such as weekend staffing levels and staff turnover, is an important element of our efforts to assure sufficient staffing, and we refer readers to this memorandum for more information on these efforts: https://www.cms.gov/​files/​document/​qso-22-08-nh.pdf.

Comment: Several commenters offered technical views on the measure, particularly around the type of staff that are included and excluded. One commenter suggested that nursing hours should exclude RNs with administrative duties, medication aides, technicians, aides in training, or private duty nurses. One commenter recommended that the measure should include only Medicare Part A beneficiaries because the commenter believes that is the scope of the SNF VBP Program. Some commenters recommended that we exclude Temporary Nurse Aides (TNAs) from the measure's calculation, or otherwise measure CNA, LPN, and RN time separately. Some commenters recommended that we weight agency staff lower in the measure.

Response: We refer readers to the proposed rule where we more thoroughly discuss inclusion and exclusion criteria for SNFs under this measure (87 FR 22773). All SNFs to whom the SNF VBP Program applies are included in the measure, except for facilities where total nurse staffing or nurse aide staffing is excessively low or excessively high. As mentioned in our proposed rule (87 FR 22773), facilities where total nurse staffing is <1.5 hours per resident day or >12 hours per resident day are excluded. Also, facilities where nurse aide staffing is >5.25 hours per resident day are excluded. Furthermore, staff included in the measure are RNs, LPNs, and nurse aides, such as certified nurse aides (CNAs), aides in training, and medication aides/technicians. We included a variety of SNF staff in the proposed measure, because as discussed in our proposed rule (87 FR 22771-22772), several studies demonstrate the strong relationship between these types of staff and patient outcomes. Private duty nurses are not included in the measure calculation at this time, because they are not included in PBJ staffing data. We will also take commenters' suggestions around excluding certain types of nurse staffing or calculating CNA, LPN, and RN time separately into account as we monitor implementation of the measure. In response to the commenter suggesting that we limit the measure to Medicare Part A beneficiaries only, we note our continued belief that our quality programs drive quality improvement for all patients, meaning that we do not believe any such limitation is appropriate at this time.

Comment: A few commenters expressed concerns about the measure's case-mix adjustment. One commenter suggested CMS should report both actual staffing levels and case-mix adjusted staffing levels. Another commenter noted that the measure's case-mix adjustment information is outdated and has not been reviewed by a TEP or by NQF.

Response: We note that the proposed case-mix adjustment is consistent with that currently used for the measure in the Nursing Home Five-Star Quality Rating System and was originally reviewed by a TEP (see https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​TimeStudy). The case-mix values for each facility are based on the daily distribution of residents by RUG-IV group in the quarter covered by the PBJ reported staffing and estimates of daily RN, LPN and NA hours from the CMS STRIVE Study. We also believe it is important to include the case-mix adjustment to account for differences in acuity, functional status, and care needs of residents. For more information, we refer commenters to our proposed rule (87 FR 22774). We will consider whether any changes or updates are needed to the case-mix adjustment.

Comment: One commenter expressed concern that PBJ data may not capture salaried individuals who work more than 40 hours per work week and variations in how lunch breaks are captured in the PBJ system. Another commenter recommended that we allow the PBJ system to capture patient care hours provided by other types of professionals such as mental health support service workers, music therapists, or respiratory therapists. One commenter noted that the proposed exclusion criteria are not appropriate for the VBP Program and should be accompanied by an appeals process.

Response: We recognize the importance of various types of professionals in providing care and services to Medicare beneficiaries in SNFs, but we emphasize the strong relationship identified in the literature between nursing professionals and quality of care. For this reason, we proposed to adopt the Total Nurse Staffing measure, which includes the time worked by RNs, LPNs, and nurse aides, in the FY 2026 Program. We intend to assess the impact of other types of professionals on quality of care. We also note that we will continue to assess the measure and if needed, propose measure updates in future rulemaking.

After considering the public comments, we are finalizing our proposal to adopt the Total Nursing Hours per Resident Day Staffing (Total Nurse Staffing) measure beginning with the FY 2026 SNF VBP program year as proposed.

d. Adoption of the DTC—PAC Measure for SNFs (NQF #3481) Beginning With the FY 2027 SNF VBP Program Year

As part of the SNF VBP Program expansion authorized under the CAA, we proposed to adopt the DTC PAC SNF measure for the FY 2027 SNF VBP Program and subsequent years. The DTC PAC SNF measure (NQF #3481) is an outcome measure that assesses the rate of successful discharges to community from a SNF setting, using 2 years of Medicare FFS claims data. As proposed, the measure addresses an important health care outcome for many SNF residents (returning to a previous living situation and avoiding further institutionalization) and will align the Program with the Seamless Care Coordination domain of CMS's Meaningful Measures 2.0 Framework. In addition, the DTC PAC SNF measure is currently part of the SNF QRP measure set.^[255] For more information on this measure in the SNF QRP, see https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Skilled-Nursing-Facility-Quality-Reporting-Program/​SNF-Quality-Reporting-Program-Measures-and-Technical-Information.

(1) Background

As we stated in the proposed rule, we believe it is an important goal in post-acute care settings to return patients to their previous levels of independence and functioning with discharge to community being one of the primary goals for post-acute patients. We also stated our belief that it is important to improve access to community discharge options for SNF residents. Discharge to community is considered a valuable outcome to measure because it provides important information about patient outcomes after being discharged from a SNF and is a multifaceted measure that captures the patient's functional status, cognitive capacity, physical ability, and availability of social support at home.

In 2019, 1.5 million of Medicare's FFS beneficiaries (4 percent of all Medicare FFS beneficiaries) utilized Medicare coverage for a SNF stay.^[256] However, almost half of the older adults that are admitted to SNFs are not discharged to the community, and for a significant proportion of those that are discharged back to the community, it may take up to 365 days.^[257 258] In 2017, the SNF QRP and other PAC QRP programs adopted this measure; however, there remains considerable variation in performance on this measure. In 2019, the lowest performing SNFs had risk-adjusted rates of successful discharge to the community at or below 39.5 percent, while the best performing SNFs had rates of 53.5 percent or higher, indicating considerable room for improvement.^[259]

In addition to being an important outcome from a resident and family perspective, residents discharged to community settings, on average, incur lower costs over the recovery episode, compared with those discharged to institutional settings.^[260 261] As stated in the proposed rule, we believe including this measure in the SNF VBP Program will further encourage SNFs to prepare residents for discharge to community, when clinically appropriate, which may have significant cost-saving implications for the Medicare program given the high costs of care in institutional settings. Also, providers have discovered that successful discharge to community is a key factor in their ability to achieve savings, where capitated payments for post-acute care were in place.^[262] For residents who require LTC due to persistent disability, discharge to community could result in lower LTC costs for Medicaid and for residents' out-of-pocket expenditures.^[263]

Discharge to community is also an actionable health care outcome, as targeted interventions have been shown to successfully increase discharge to community rates in a variety of post-acute settings. Many of these interventions involve discharge planning or specific rehabilitation strategies, such as addressing discharge barriers and improving medical and functional status.^{[264 265 266 267]} Other factors that have shown positive associations with successful discharge to community include patient safety culture within the SNF and availability of home and community-based services.^[268 269] The effectiveness of these interventions suggests that improvement in discharge to community rates among post-acute care residents is possible through modifying provider-led processes and interventions. Therefore, including the DTC PAC SNF measure in the SNF VBP Program may provide further incentive for providers to continue improving on current interventions or implement new interventions.

(2) Overview of Measure

This measure, which was finalized for adoption under the SNF QRP (81 FR 52021 through 52029), reports a SNF's risk-standardized rate of Medicare FFS residents who are discharged to the community following a SNF stay, do not have an unplanned readmission to an acute care hospital or LTCH in the 31 days following discharge to community, and remain alive during the 31 days following discharge to community. Community, for this measure, is defined as home or selfcare, with or without home health services. We proposed to adopt this measure beginning with the FY 2027 program year. We note that including this measure in the FY 2027 program year provides advanced notice for facilities to prepare for the inclusion of this measure in the SNF VBP Program. This also provides the necessary time to incorporate the operational processes associated with including this two-year measure in the SNF VBP Program.

(a) Interested Parties and TEP Input

In considering the selection of this measure for the SNF VBP Program, we reviewed the developmental history of the measure, which employed a transparent process that provided interested parties and national experts the opportunity to provide pre-rulemaking input. Our measure development contractor convened a TEP, which was strongly supportive of the importance of measuring discharge to community outcomes and implementing the measure, Discharge to Community PAC SNF QRP in the SNF QRP. The panel provided input on the technical specifications of this measure, including the feasibility of implementing the measure, as well as the overall measure reliability and validity. We refer readers to the FY 2017 SNF PPS final rule (81 FR 52023), as well as a summary of the TEP proceedings available on the PAC Quality Initiatives Downloads and Videos website available at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Post-Acute-Care-Quality-Initiatives/​IMPACT-Act-of-2014/​IMPACT-Act-Downloads-and-Videos for additional information.

(b) MAP Review

The DTC PAC SNF measure was included in the publicly available “List of Measures Under Consideration for December 1, 2021,” ^[270] and the MAP supported the DTC PAC SNF measure for rulemaking for the SNF VBP Program. We refer readers to the final MAP report available at https://www.qualityforum.org/​Publications/​2022/​03/​MAP_​2021-2022_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx.

(3) Data Sources

We proposed to use data from the Medicare FFS claims and Medicare eligibility files to calculate this measure. We will use data from the “Patient Discharge Status Code” on Medicare FFS claims to determine whether a resident was discharged to a community setting for calculation of this measure. The eligibility files provide information such as date of birth, date of death, sex, reasons for Medicare eligibility, periods of Part A coverage, and periods in the Medicare FFS program. The data elements from the Medicare FFS claims are those basic to the operation of the Medicare payment systems and include data such as date of admission, date of discharge, diagnoses, procedures, indicators for use of dialysis services, and indicators of whether the Part A benefit was exhausted. The inpatient claims data files contain patient-level PAC and other hospital records. SNFs will not need to report additional data for us to calculate this measure.^[271]

We refer readers to the FY 2017 SNF PPS final rule where we adopted the DTC measure for use in the SNF QRP (81 FR 52021 through 52029). In that rule, we provided an analysis related to the accuracy of using the “Patient Discharge Status Code” in determining discharge to a community setting. Specifically, in all PAC settings, we tested the accuracy of determining discharge to a community setting using the “Patient Discharge Status Code” on the PAC claim by examining whether discharge to community coding based on PAC claim data agreed with discharge to community coding based on PAC assessment data. We found agreement between the two data sources in all PAC settings, ranging from 94.6 percent to 98.8 percent. Specifically, in the SNF setting, using 2013 data, we found 94.6 percent agreement in discharge to community codes when comparing discharge status codes on claims and the Discharge Status (A2100) on the Minimum Data Set (MDS) 3.0 discharge assessment, when the claims and MDS assessment had the same discharge date. We further examined the accuracy of the “Patient Discharge Status Code” on the PAC claim by assessing how frequently discharges to an acute care hospital were confirmed by follow-up acute care claims. We discovered that 88 percent to 91 percent of IRF, LTCH, and SNF claims with acute care discharge status codes were followed by an acute care claim on the day of, or day after, PAC discharge. We believe these data support the use of the claims “Patient Discharge Status Code” for determining discharge to a community setting for this measure. In addition, this measure can feasibly be implemented in the SNF VBP Program because all data used for measure calculation are derived from Medicare FFS claims and eligibility files, which are already available to us.

(4) Inclusion and Exclusion Criteria

We proposed that the DTC PAC SNF measure will use the same specifications under the SNF VBP Program as the Discharge to Community—PAC SNF QRP measure used in the SNF QRP, which are available at https://www.cms.gov/​files/​zip/​snf-qrp-measure-calculations-and-reporting-users-manual-v301-addendum-effective-10-01-2020.zip. The target population for the measure is the group of Medicare FFS residents who are admitted to a SNF and are not excluded for the reasons listed in this paragraph. The measure exclusion criteria are determined by processing Medicare claims and eligibility data to determine whether the individual exclusion criteria are met. All measure exclusion criteria are based on administrative data. Only SNF stays that are preceded by a short-term acute care stay in the 30 days prior to the SNF admission date are included in the measure. Stays ending in transfers to the same level of care are excluded. The measure excludes residents for which the following conditions are true:

• Age under 18 years;
• No short-term acute care stay within the 30 days preceding SNF admission;
• Discharges to a psychiatric hospital;
• Discharges against medical advice;
• Discharges to disaster alternative care sites or Federal hospitals;
• Discharges to court/law enforcement;
• Residents discharged to hospice and those with a hospice benefit in the post-discharge observation window;
• Residents not continuously enrolled in Part A FFS Medicare for the 12 months prior to the post-acute admission date, and at least 31 days after post-acute discharge date;
• Residents whose prior short-term acute care stay was for non-surgical treatment of cancer;
• Post-acute stays that end in transfer to the same level of care;
• Post-acute stays with claims data that are problematic (for example, anomalous records for stays that overlap wholly or in part, or are otherwise erroneous or contradictory);
• Planned discharges to an acute or LTCH setting;
• Medicare Part A benefits exhausted;
• Residents who received care from a facility located outside of the U.S., Puerto Rico or a U.S. territory; and
• Swing Bed Stays in Critical Access Hospitals.

This measure also excludes residents who had a long-term nursing facility stay in the 180 days preceding their hospitalization and SNF stay, with no intervening community discharge between the long-term nursing facility stay and qualifying hospitalization.

(5) Risk-Adjustment

The measure is risk-adjusted for variables including demographic and eligibility characteristics, such as age and sex, principal diagnosis, types of surgery or procedures from the prior short-term acute care stay, comorbidities, length of stay and intensive care utilization from the prior short-term acute care stay, ventilator status, ESRD status, and dialysis, among other variables. For additional technical information about the measure, including information about the measure calculation, risk-adjustment, and denominator exclusions, we refer readers to the document titled, Final Specifications for SNF QRP Quality Measures and Standardized Patient Assessment Data Elements, available at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Downloads/​Final-Specifications-for-SNF-QRP-Quality-Measures-and-SPADEs.pdf. We note that we proposed to use the technical information and specifications found in this document for purposes of calculating this measure in the SNF VBP Program.

(6) Measure Calculation

We proposed to adopt the DTC PAC SNF measure for the SNF VBP Program for FY 2027 and subsequent years. This measure is calculated using 2 years of data. Since Medicare FFS claims data are already reported to the Medicare program for payment purposes, and Medicare eligibility files are also available, SNFs will not be required to report any additional data to us for calculation of this measure.

(a) Numerator

The measure numerator is the risk-adjusted estimate of the number of residents who are discharged to the community, do not have an unplanned readmission to an acute care hospital or LTCH in the 31-day post-discharge observation window, and who remain alive during the post-discharge observation window. This estimate starts with the observed discharges to community and is risk-adjusted for patient/resident characteristics and a statistical estimate of the facility effect beyond case-mix. A patient/resident who is discharged to the community is considered to have an unfavorable outcome if they have a subsequent unplanned readmission to an acute care hospital or LTCH in the post-discharge observation window, which includes the day of discharge and the 31 days following day of discharge. Discharge to community is determined based on the “Patient Discharge Status Code” from the PAC claim. Discharge to community is defined as discharge to home or self-care with or without home health services, which includes the following Patient Discharge Status Codes: 01 Discharged to home or self-care (routine discharge); 06 Discharged/transferred to home under care of organized home health service organization; 81 Discharged to home or self-care with a planned acute care hospital readmission; and 86 Discharged/transferred to home under care of organized home health service organization with a planned acute care hospital inpatient readmission. Residents who are discharged to the community are also considered to have an unfavorable outcome if they die in the post-discharge window, which includes the day of discharge and the 31 days following day of discharge. Death in the post-discharge window is identified based on date of death from Medicare eligibility files.

(b) Denominator

The denominator for the DTC PAC SNF measure is the risk-adjusted expected number of discharges to community. This estimate includes risk-adjustment for patient/resident characteristics with the facility effect removed. The “expected” number of discharges to community is the predicted number of risk-adjusted discharges to community if the same residents were treated at the average facility appropriate to the measure.

(7) Confidential Feedback Reports and Public Reporting

We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 through 52007) for discussion of our policy to provide quarterly confidential feedback reports to SNFs on their measure performance. We also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 through 42517) for a summary of our two-phase review and corrections policy for SNFs' quality measure data. Furthermore, we refer readers to the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to publicly report SNF measure performance information under the SNF VBP Program on the Provider Data Catalog website currently hosted by HHS and available at https://data.cms.gov/​provider-data/​. We proposed to update and redesignate the confidential feedback report and public reporting policies, which are currently codified at § 413.338(e)(1) through (3) to § 413.338(f), to include the DTC PAC SNF measure.

We invited public comment on this proposal to adopt the DTC PAC SNF measure beginning with the FY 2027 SNF VBP program year. We received the following comments and provide our responses:

Comment: Many commenters supported our proposal to adopt the DTC PAC SNF measure, noting its endorsement by NQF, its use in other quality programs, and its usefulness as an indicator of health outcomes. A few commenters recommended that we modify the measure to include post-discharge ER and observation visits within 31 days because they could be indicators of premature discharge from the SNF. One commenter suggested that we include assisted living and personal care homes as community settings for the measure. One commenter expressed concern about the length of time between baseline, performance, and payment periods and suggested that facilities would benefit from real-time, actionable quality data. Another commenter suggested that we include those nursing home residents discharged back to the same nursing home in the measure's calculation. One commenter also suggested that we monitor how the measure will affect SNFs that care for patients experiencing homelessness.

Response: We agree the measure is an important indicator of quality. We appreciate commenters' recommendations regarding adjustments to the measure specifications and we will take this into consideration in future rulemaking.

Comment: Some commenters opposed our proposal to adopt the DTC PAC SNF measure. One commenter noted that not all Medicare beneficiaries are able to return home, that the measure may disadvantage those residents that continue to need SNF care to maintain functions or slow declines or deterioration in function, and that the measure only captures fee-for-service Medicare beneficiaries. Another commenter recommended that we consider a measure that assesses care coordination between SNFs and post-SNF care, while another commenter worried that the DTC PAC SNF measure may penalize SNFs based on whether a patient complied with discharge instructions and services.

Response: As discussed in the proposed rule (87 FR 22774 through 22776), returning patients to their previous levels of independence and functioning is a key goal of post-acute care and an important indicator for patients and families. When we convened a TEP for this measure's inclusion in the SNF QRP, experts agreed with this assessment. Additionally, as discussed in the proposed rule (87 FR 22775), this measure addresses multiple components including cognitive capacity, physical ability, social support as home, and other actionable elements, incentivizing providers to continue improving care in these various domains. Although we agree that not all residents will be able to return home or will follow all discharge instructions, the variability in current rates of the measure among different SNFs indicate that there is room for improvement. This measure is risk adjusted for several variables, including principal diagnosis. This measure should not disadvantage patients that continue to need SNF care to maintain functioning as it includes readmissions within 30 days of discharge. Thus, providers will not be incentivized to discharge patients inappropriately. Lastly, this measure is calculated using Medicare FFS claims data, which does not require SNFs to report any additional data. Including residents for which claims data is not currently available would add considerable data burden to SNFs. We will consider whether to address care coordination among SNFs for the SNF VBP Program in future rulemaking.

Comment: Some commenters offered technical comments on the measure. One commenter stated that an unplanned readmission post-SNF discharge may not be the best measure of whether a discharge was successful. A few commenters suggested that we consider using the discharge planning process or discharge to a lower level of care instead of discharge to communities, noting that not all admissions are appropriate for community discharge. One commenter also requested clarification on whether we plan to adjust the measure for COVID-19.

Response: As noted above, we recognize that not all admissions are appropriate for community discharge, but discharge to the community is an important goal for residents and families, as well as a key indicator of care. The measure is risk adjusted and has several exclusions to ensure that the appropriate population is being measured. Additionally, this is an NQF endorsed measure and varying performance rates observed among SNFs for this measure suggest that it is actionable. This measure also adjusts for principal diagnosis.

After considering the public comments, we are finalizing our proposal to adopt the DTC PAC SNF measure (NQF #3481) beginning with the FY 2027 SNF VBP program year as proposed.

C. SNF VBP Performance Periods and Baseline Periods

1. Background

We refer readers to the FY 2016 SNF PPS final rule (80 FR 46422) for a discussion of our considerations for determining performance periods under the SNF VBP Program. In the FY 2019 SNF PPS final rule (83 FR 39277 through 39278), we adopted a policy whereby we will automatically adopt the performance period and baseline period for a SNF VBP Program Year by advancing the performance period and baseline period by 1 year from the previous program year. We also refer readers to the FY 2022 SNF PPS final rule, where we finalized our proposal to use FY 2019 data for the FY 2024 baseline period (86 FR 42512 through 42513).

2. Revised Baseline Period for the FY 2025 SNF VBP Program

Under the policy finalized in the FY 2019 SNF PPS final rule (83 FR 39277 through 39278), the baseline period for the SNFRM for the FY 2025 program year will be FY 2021. However, as more fully described in the proposed rule (87 FR 22764 through 22765), we have determined that the significant decrease in SNF admissions, regional variability in COVID-19 case rates, and changes in hospitalization patterns associated with the PHE for COVID-19 in FY 2021 has impacted SNFRM validity and reliability. Because the baseline period for this measure is used to calculate the performance standards under the SNF VBP Program, we stated that we were concerned about using COVID-19 impacted data for the FY 2025 baseline period for scoring and payment purposes.

Therefore, we proposed to use a baseline period of FY 2019 for the FY 2025 program year. We stated that we believe using data from this period will provide sufficiently valid and reliable data for evaluating SNF performance that can be used for FY 2025 scoring. We also proposed to select this revised data period because it captures a full year of data, including any seasonal effects.

As stated in the proposed rule, we considered using FY 2020 as the baseline period for the FY 2025 program. However, under the ECE, SNF qualifying claims for a 6-month period in FY 2020 (January 1, 2020 through June 30, 2020) are excepted from the calculation of the SNFRM, which means that we will not have a full year of data to calculate the SNFRM for a FY 2020 baseline period.

We also considered using FY 2022 as the baseline period for the FY 2025 program year, which will be the baseline period for the FY 2026 program year for the SNFRM under the previously established policy for adopting baseline periods for future years (83 FR 39277). However, it is operationally infeasible for us to calculate performance standards using a FY 2022 baseline period for the FY 2025 program year because performance standards must be published at least 60 days prior to the start of the performance period, currently planned as FY 2023, as required under section 1888(h)(3)(C) of the Act. We invited public comment on this proposal to update the baseline period for the FY 2025 SNF VBP Program. We received the following comments and provide our responses:

Comment: Some commenters supported the proposal to revise the baseline period for the FY 2025 program year. One commenter recommended that we consider the accuracy of pre- and post-pandemic quality comparisons to ensure that SNFs are not penalized based on factors out of their control, such as lower occupancy levels, patient case-mix, and staffing concerns.

Response: We appreciate the support. We will continue to consider for future rulemaking whether and how to take the lasting impacts of the COVID-19 pandemic into consideration.

After considering the public comments, we are finalizing our proposal to update the baseline period to FY 2019 for the FY 2025 SNF VBP Program.

3. Performance Periods and Baseline Periods for the SNF HAI Measure Beginning With the FY 2026 SNF VBP Program

a. Performance Period for the SNF HAI Measure for the FY 2026 SNF VBP Program and Subsequent Years

As stated in the proposed rule, in considering the appropriate performance period for the SNF HAI measure for the FY 2026 SNF VBP Program, we recognized that we must balance the length of the performance period with our need to calculate valid and reliable performance scores and announce the resulting payment adjustments no later than 60 days prior to the program year involved, in accordance with section 1888(h)(7) of the Act. In our testing of the measure, we found that a 1-year performance period produced moderately reliable performance scores. We refer readers to the SNF HAI Measure Technical Report for further information on measure testing results, available at https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf. In addition, we refer readers to the FY 2017 SNF PPS final rule (81 FR 51998 through 51999) for a discussion of the factors we should consider when specifying performance periods for the SNF VBP Program, as well as our stated preference for 1-year performance periods. Based on these considerations, we believed that a 1-year performance period for the SNF HAI measure is operationally feasible for the SNF VBP Program and provides sufficiently accurate and reliable SNF HAI measure rates and resulting performance scores.

We also recognized that we must balance our desire to specify a performance period for a fiscal year as close to the fiscal year's start date as possible to ensure clear connections between quality measurement and value-based payment with our need to announce the net results of the Program's adjustments to Medicare payments not later than 60 days prior to the fiscal year involved, in accordance with section 1888(h)(7) of the Act. In considering these constraints, and in alignment with the SNFRM, we believed that a performance period that occurs 2 fiscal years prior to the applicable fiscal program year is most appropriate for the SNF HAI measure.

For these reasons, we proposed to adopt a 1-year performance period for the SNF HAI measure. In addition, we proposed to adopt FY 2024 (October 1, 2023 through September 30, 2024) as the performance period for the SNF HAI measure for the FY 2026 SNF VBP Program.

In alignment with the current Program measure, we also proposed that, for the SNF HAI measure, we would automatically adopt the performance period for a SNF VBP program year by advancing the beginning of the performance period by 1 year from the previous program year's performance period.

We invited public comment on these proposals related to the performance period for the SNF HAI measure for the FY 2026 program year and subsequent years. We received one public comment related to the performance periods for the SNF HAI measure. We summarized that comment and provide our response below in section VIII.C.3.b. of this final rule. As stated in that section, we are finalizing our proposal to adopt FY 2024 (October 1, 2023 through September 30, 2024) as the performance period for the SNF HAI measure for the FY 2026 program year and finalizing our proposal to adopt performance periods for the SNF HAI measure for subsequent program years by advancing the beginning of the performance period by 1 year from the previous program year's performance period.

b. Baseline Period for the SNF HAI Measure for the FY 2026 SNF VBP Program and Subsequent Years

We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, as with other Medicare quality programs, we generally adopt a baseline period for a fiscal year that occurs prior to the performance period for that fiscal year to establish measure performance standards. In the FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent to adopt baseline periods that are as close as possible in duration as the performance period for a fiscal year as well as our intent to seasonally align baseline periods with the performance period to avoid any effects on quality measurement that may result from tracking SNF performance during different times in a year. Therefore, to align with the proposed performance period length for the SNF HAI measure, we believed a 1-year baseline period is most appropriate for the SNF HAI measure.

We also recognized that we are required to calculate and announce performance standards no later than 60 days prior to the start of the performance period, as required by section 1888(h)(3)(C) of the Act. Therefore, in alignment with the SNFRM baseline period, we believed that a baseline period that occurs 4 fiscal years prior to the applicable fiscal program year, and 2 fiscal years prior to the performance period, is most appropriate for the SNF HAI measure and provides sufficient time to calculate and announce performance standards prior to the start of the performance period.

For these reasons, we proposed to adopt a 1-year baseline period for the SNF HAI measure. In addition, we proposed to adopt FY 2022 (October 1, 2021 through September 30, 2022) as the baseline period for the SNF HAI measure for the FY 2026 SNF VBP Program.

In alignment with the current Program measure, we also proposed that for the SNF HAI measure, we would automatically adopt the baseline period for a SNF VBP program year by advancing the beginning of the baseline period by 1 year from the previous program year's baseline period.

We invited public comment on these proposals related to the baseline period for the SNF HAI measure for the FY 2026 program year and subsequent years. We received the following comment related to the SNF HAI measure performance and baseline periods and provide our response:

Comment: One commenter supported the performance and baseline periods for the SNF HAI measure as proposed.

Response: We thank the commenter for its support of the proposed performance and baseline periods for the SNF HAI measure.

After considering the public comment, we are finalizing our proposal to adopt FY 2024 (October 1, 2023 through September 30, 2024) as the performance period for the SNF HAI measure for the FY 2026 program year and finalizing our proposal to adopt performance periods for the SNF HAI measure for subsequent program years by advancing the beginning of the performance period by 1 year from the previous program year's performance period. Additionally, we are finalizing our proposal to adopt FY 2022 (October 1, 2021 through September 30, 2022) as the baseline period for the SNF HAI measure for the FY 2026 program year and finalizing our policy to adopt baseline periods for the SNF HAI measure for subsequent program years by advancing the beginning of the baseline period by 1 year from the previous program year's baseline period.

4. Performance Periods and Baseline Periods for the Total Nursing Hours per Resident Day Staffing Measure Beginning With the FY 2026 SNF VBP Program

a. Performance Period for the Total Nursing Hours per Resident Day Staffing Measure for the FY 2026 SNF VBP Program and Subsequent Years

As stated in the proposed rule, in considering the appropriate performance period for the Total Nurse Staffing measure for the FY 2026 SNF VBP Program, we recognized that we must balance the length of the performance period with our need to calculate valid and reliable performance scores and announce the resulting payment adjustments no later than 60 days prior to the program year involved, in accordance with section 1888(h)(7) of the Act. The Total Nurse Staffing measure is currently reported on a quarterly basis for the Nursing Home Five-Star Quality Rating System. For purposes of inclusion in the SNF VBP Program, we proposed that the measure rate would be calculated on an annual basis. To do so, we proposed to aggregate the quarterly measure rates using a simple mean of the available quarterly case-mix adjusted scores in a 1-year performance period. We conducted testing of the measure and found that the quarterly measure rate and resident census are stable across quarters. Further, an unweighted yearly measure aligns the SNF VBP Program rates with rates reported on the Provider Data Catalog website currently hosted by HHS, available at https://data.cms.gov/​provider-data/​. It can also be easily understood by, and is transparent to, the public. In addition, we refer readers to the FY 2017 SNF PPS final rule (81 FR 51998 through 51999) for discussion of the factors we should consider when specifying performance periods for the SNF VBP Program as well as our preference for 1-year performance periods. Based on these considerations, we believed that a 1-year performance period for the Total Nurse Staffing measure is operationally feasible under the SNF VBP Program and provides sufficiently accurate and reliable Total Nurse Staffing measure rates and resulting performance scores.

We also recognized that we must balance our desire to specify a performance period for a fiscal year as close to the fiscal year's start date as possible to ensure clear connections between quality measurement and value-based payment with our need to announce the net results of the Program's adjustments to Medicare payments not later than 60 days prior to the fiscal year involved, in accordance with section 1888(h)(7) of the Act. In considering these constraints, and in alignment with the SNFRM, we believed that a performance period that occurs 2 fiscal years prior to the applicable fiscal program year is most appropriate for the Total Nurse Staffing measure.

For these reasons, we proposed to adopt a 1-year performance period for the Total Nurse Staffing measure. In addition, we proposed to adopt FY 2024 (October 1, 2023 through September 30, 2024) as the performance period for the Total Nurse Staffing measure for the FY 2026 SNF VBP program year.

In alignment with the current Program measure, we also proposed that, for the Total Nurse Staffing measure, we would automatically adopt the performance period for a SNF VBP program year by advancing the beginning of the performance period by 1 year from the previous program year's performance period.

We invited public comment on these proposals related to the performance period for the Total Nurse Staffing measure for the FY 2026 program year and subsequent years. We received the following comment and provide our response:

Comment: One commenter recommended that we use the calendar year rather than the fiscal year for the Total Nurse Staffing measure's performance period. The commenter stated that because data for this measure are collected and reported quarterly starting 45 days after the end of the quarter, a calendar year schedule provides CMS with enough time to announce the Program's adjustments to Medicare payments not later than 60 days prior to the fiscal year involved.

Response: We believe that using the fiscal year as the performance period for the Total Nurse Staffing measure is important to maintain consistency with our other measures in the SNF VBP Program that use fiscal year performance and baseline periods. All of the measures proposed thus far for the SNF VBP program rely on fiscal year measurement periods, and we intend to use measures relying on fiscal year periods in the Program in the future to the extent such alignment is feasible and practical. We believe that this type of alignment, where possible, helps stakeholders understand their quality measurement obligations and reporting periods more easily.

After considering the public comments, we are finalizing our proposal to adopt FY 2024 (October 1, 2023 through September 30, 2024) as the performance period for the Total Nurse Staffing measure for the FY 2026 program year. We are also finalizing our proposal to adopt 1-year performance periods for the Total Nurse Staffing measure for subsequent program years as proposed by advancing the beginning of the performance period by 1 year from the previous program year's performance period.

b. Baseline Period for the Total Nursing Hours per Resident Day Staffing Measure for the FY 2026 SNF VBP Program and Subsequent Years

We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, as with other Medicare quality programs, we generally adopt a baseline period for a fiscal year that occurs prior to the performance period for that fiscal year to establish measure performance standards. In the FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent to adopt baseline periods that are as close as possible in duration as the performance period for a fiscal year, as well as our intent to seasonally align baseline periods with the performance period to avoid any effects on quality measurement that may result from tracking SNF performance during different times in a year. Therefore, to align with the proposed performance period length for the Total Nurse Staffing measure, we believed a 1-year baseline period is most appropriate.

We also recognized that we are required to calculate and announce performance standards no later than 60 days prior to the start of the performance period, as required by section 1888(h)(3)(C) of the Act. Therefore, in alignment with the SNFRM baseline period, we believed that a baseline period that occurs 4 fiscal years prior to the applicable fiscal program year, and 2 fiscal years prior to the performance period, is most appropriate for the Total Nurse Staffing measure and provides sufficient time to calculate and announce performance standards prior to the start of the performance period.

For these reasons, we proposed to adopt a 1-year baseline period for the Total Nurse Staffing measure. In addition, we proposed to adopt FY 2022 (October 1, 2021 through September 30, 2022) as the baseline period for the Total Nurse Staffing measure for the FY 2026 SNF VBP Program.

In alignment with the current Program measure, we also proposed that for the Total Nurse Staffing measure, we would automatically adopt the baseline period for a SNF VBP program year by advancing the beginning of the baseline period by 1 year from the previous program year's baseline period.

We invited public comment on these proposals related to the baseline period for the Total Nurse Staffing measure for the FY 2026 program year and subsequent years. We received the following comments and provide our responses:

Comment: One commenter supported our proposal to use FY 2022 as the baseline period for the Total Nurse Staffing measure.

Response: We thank the commenter for their support of the proposed baseline period for the Total Nurse Staffing measure.

Comment: One commenter expressed concern about using any FY 2021 data for the Total Nurse Staffing measure, stating that during the PHE for COVID-19, many nursing facilities reported severe staffing shortages. The commenter suggested that we adopt a different baseline period focusing on the year with the highest staffing levels nationally, on average.

Response: We clarify that we proposed to adopt FY 2022 as the baseline period for the Total Nurse Staffing measure for the FY 2026 SNF VBP Program. We also believe that adopting a baseline period for a fiscal year that occurs prior to the performance period for that fiscal year gives us enough time to establish the measure's performance standards in our quality programs. Further, we note that we are required to calculate and announce performance standards no later than 60 days prior to the start of the performance period, as required by section 1888(h)(3)(C) of the Act.

Comment: One commenter opposed our proposal to use FY 2022 as the baseline period for the Total Nurse Staffing measure, stating that we should instead use FY 2019 to assess performance from prior to the COVID-19 pandemic.

Response: We believe that additional policies we adopted in response to the challenges presented by the COVID-19 pandemic, including quality measure suppression, sufficiently mitigate the effects of the PHE on quality measurements and allow us to adopt FY 2022 as the baseline period.

After considering the public comments, we are finalizing our proposal to adopt FY 2022 (October 1, 2021 through September 30, 2022) as the baseline period for the Total Nurse Staffing measure for the FY 2026 program year. We are also finalizing our proposal to adopt 1-year baseline periods for the Total Nurse Staffing measure for subsequent program years as proposed by advancing the beginning of the baseline period by 1 year from the previous program year's baseline period.

5. Performance Periods and Baseline Periods for the DTC PAC Measure for SNFs for the FY 2027 SNF VBP Program and Subsequent Years

a. Performance Period for the DTC PAC SNF Measure for the FY 2027 SNF VBP Program and Subsequent Years

Under the SNF QRP, The Discharge to Community—PAC SNF QRP measure has a reporting period that uses 2 consecutive years to calculate the measure (83 FR 39217 through 39272). In alignment with the reporting period that applies to the measure under the SNF QRP, we proposed to adopt a 2-year performance period for the DTC PAC SNF measure under the SNF VBP Program.

We proposed to align our performance period with the performance period for the measure used by the SNF QRP to maintain streamlined data requirements and reduce any confusion for participating SNFs. In addition, we proposed to adopt FY 2024 through FY 2025 (October 1, 2023 through September 30, 2025) as the performance period for the DTC PAC SNF measure for the FY 2027 SNF VBP Program.

We also proposed that for the DTC PAC SNF measure, we would automatically adopt the performance period for a SNF VBP program year by advancing the beginning of the performance period by 1 year from the previous program year's performance period.

We invited public comment on our proposals related to the performance period for the DTC PAC SNF measure for FY 2027 program year and subsequent years. We received the following comment and provide our response:

Comment: One commenter supported the proposed performance period for the DTC PAC SNF measure.

Response: We thank the commenter for their support of the proposed performance period for the DTC PAC SNF measure.

After considering the public comment, we are finalizing our proposal to adopt FY 2024 through FY 2025 (October 1, 2023 through September 30, 2025) as the performance period for the DTC PAC SNF measure for the FY 2027 program year. We are also finalizing our proposal to adopt performance periods for the DTC PAC SNF measure for subsequent program years by advancing the beginning of the performance period by 1 year from the previous program year's performance period.

b. Baseline Period for the DTC PAC SNF Measure for the FY 2027 SNF VBP Program Year and Subsequent Years

We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, as with other Medicare quality programs, we generally adopt a baseline period for a fiscal year that occurs prior to the performance period for that fiscal year to establish measure performance standards. In the FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent to adopt baseline periods that are as close as possible in duration as the performance period for a fiscal year, as well as our intent to seasonally align baseline periods with the performance period to avoid any effects on quality measurement that may result from tracking SNF performance during different times in a year. Therefore, to align with the proposed performance period length for the DTC PAC SNF measure, we believed a 2-year baseline period is most appropriate for this measure.

We also recognized that we are required to calculate and announce performance standards no later than 60 days prior to the start of the performance period, as required by section 1888(h)(3)(C) of the Act. Therefore, we believed that a baseline period that begins 6 fiscal years prior to the applicable fiscal program year, and 3 fiscal years prior to the performance period, is most appropriate for the DTC PAC SNF measure and provides sufficient time to calculate and announce performance standards prior to the start of the performance period.

For these reasons, we proposed to calculate the performance period for the DTC PAC SNF measure using 2 consecutive years of data. In addition, we proposed to adopt FY 2021 through FY 2022 (October 1, 2020 through September 30, 2022) as the baseline period for the DTC PAC SNF measure for the FY 2027 SNF VBP Program.

In alignment with the current Program measure, we also proposed that for the DTC PAC SNF measure, we would automatically adopt the baseline period for a SNF VBP program year by advancing the beginning of the baseline period by 1 year from the previous program year's baseline period.

We invited public comment on these proposals related to the baseline period for the DTC PAC SNF measure for FY 2027 program year and subsequent years. We received the following comment and provide our response:

Comment: One commenter expressed concern about adopting a baseline period for the DTC PAC SNF measure that includes FY 2021 through FY 2022 data, stating that many beneficiaries discharged during those years may have been discharged early due to COVID-19 fears. The commenter noted that the associated census declines compared to pre-PHE practices may adversely affect facilities' outcomes. The commenter also encouraged us to delay implementation of the DTC PAC SNF measure until the baseline period does not include quality data from other measures that have been suppressed.

Response: We continue to believe that using FY 2021 through FY 2022 as the baseline period for the DTC PAC SNF measure for the FY 2027 program year is most appropriate and would help ensure clear connections between the quality measurement and value-based incentive payments. As stated in the proposed rule, we note that the continuation of the PHE for COVID-19 did not necessarily impact all measures in the SNF setting specifically, but measures related to hospital care, including the SNFRM, may be impacted because of how closely the surge in COVID-19 cases was related to the surge in COVID-19 related hospital admissions. We do not believe the DTC PAC SNF measure data has been affected in this way. In addition, we believe the additional policies we adopted in response to the challenges presented by the PHE for COVID-19, including quality measure suppression, sufficiently mitigate the effects of the PHE on quality measurement. As we have done with the SNFRM, we will continue to assess whether the PHE has impacted the DTC PAC SNF measure data. Further, we note that SNFs that do not meet the case minimum for the DTC PAC SNF measure during the baseline period due to potential census declines associated with the PHE for COVID-19 will continue to have the opportunity to be scored on achievement during the applicable performance period.

After considering the public comment, we are finalizing our proposal to adopt FY 2021 through FY 2022 (October 1, 2020 through September 30, 2022) as the baseline period for the DTC PAC SNF measure for the FY 2027 program year. We are also finalizing our proposal to adopt baseline periods for the DTC PAC SNF measure for subsequent program years by advancing the beginning of the baseline period by 1 year from the previous program year's baseline period.

D. Performance Standards

1. Background

We refer readers to the FY 2017 SNF PPS final rule (81 FR 51995 through 51998) for a summary of the statutory provisions governing performance standards under the SNF VBP Program and our finalized performance standards policy. We adopted the final numerical values for the FY 2023 performance standards in the FY 2021 SNF PPS final rule (85 FR 47625) and adopted the final numerical values for the FY 2024 performance standards in the FY 2022 SNF PPS final rule (86 FR 42513). We also adopted a policy allowing us to correct the numerical values of the performance standards in the FY 2019 SNF PPS final rule (83 FR 39276 through 39277).

We did not propose any changes to these performance standard policies in the proposed rule.

2. SNF VBP Performance Standards Correction Policy

In the FY 2019 SNF PPS final rule (83 FR 39276 through 39277), we finalized a policy to correct numerical values of performance standards for a program year in cases of errors. We also finalized that we will only update the numerical values for a program year one time, even if we identify a second error, because we believe that a one-time correction will allow us to incorporate new information into the calculations without subjecting SNFs to multiple updates. We stated that any update we make to the numerical values based on a calculation error will be announced via the CMS website, listservs, and other available channels to ensure that SNFs are made fully aware of the update. In the FY 2021 SNF PPS final rule (85 FR 47625), we amended the definition of “Performance standards” at § 413.338(a)(9), consistent with these policies finalized in the FY 2019 SNF PPS final rule, to reflect our ability to update the numerical values of performance standards if we determine there is an error that affects the achievement threshold or benchmark. To improve the clarity of this policy, we proposed to amend the definition of “Performance standards” and redesignate it as § 413.338(a)(12), then add additional detail about the correction policy at § 413.338(d)(6).

We invited public comment on our changes to the text at § 413.338(a)(12) and (d)(6). However, we did not receive any public comments on this topic. Accordingly, we are finalizing our proposal to update the performance standards correction policy in our regulations.

3. Performance Standards for the FY 2025 Program Year

As discussed in section VIII.C.2. of this final rule, we are finalizing our proposal to use FY 2019 data as the baseline period for the FY 2025 program year. Based on this updated baseline period and our previously finalized methodology for calculating performance standards (81 FR 51996 through 51998), the final numerical values for the FY 2025 program year performance standards are shown in Table 17.

E. SNF VBP Performance Scoring

1. Background

We refer readers to the FY 2017 SNF PPS final rule (81 FR 52000 through 52005) for a detailed discussion of the scoring methodology that we have finalized for the Program. We also refer readers to the FY 2018 SNF PPS final rule (82 FR 36614 through 36616) for discussion of the rounding policy we adopted. We also refer readers to the FY 2019 SNF PPS final rule (83 FR 39278 through 39281), where we adopted: (1) a scoring policy for SNFs without sufficient baseline period data, (2) a scoring adjustment for low-volume SNFs, and (3) an ECE policy. Finally, we refer readers to the FY 2022 SNF PPS final rule (86 FR 42513 through 42515), where we adopted for FY 2022 a special scoring and payment policy due to the impact of the PHE for COVID-19.

2. Special Scoring Policy for the FY 2023 SNF VBP Program Due to the Impact of the PHE for COVID-19

In the FY 2023 SNF PPS proposed rule, we proposed to suppress the SNFRM for the FY 2023 program year due to the impacts of the PHE for COVID-19. Specifically, for FY 2023 scoring, we proposed that, for all SNFs participating in the FY 2023 SNF VBP Program, we will use data from the previously finalized performance period (FY 2021) and baseline period (FY 2019) to calculate each SNF's RSRR for the SNFRM. Then, we will assign all SNFs a performance score of zero. This will result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. We also proposed that SNFs that do not meet the case minimum for the SNFRM for FY 2023 (see VIII.E.3.b. of this final rule) will be excluded from the Program for FY 2023. SNFs will not be ranked for the FY 2023 SNF VBP Program. We also proposed to update our regulation text at § 413.338(i) to codify this scoring policy for FY 2023. As we noted in section VIII.B.1. of this final rule, our goal is to continue the use of measure data for scoring and payment adjustment purposes beginning with the FY 2024 program year.

We invited public comment on our proposal to use a special scoring policy for the FY 2023 Program year. We received the following comments and provide our responses:

Comment: Some commenters supported our proposals to adopt special scoring and payment policies for FY 2023.

Response: We thank the commenters for their support.

Comment: Some commenters opposed our proposal to adopt a special scoring and payment policy for FY 2023. Some commenters noted that awarding all SNFs a performance score of zero does not create a value-based incentive payment as required by statute and further stated that CMS is required to rank SNFs for the fiscal year. Another commenter stated that the special scoring and payment policy will cause all SNFs to experience a payment reduction, which they believed is inconsistent with the statute. One commenter recommended that we give all SNFs an exemption from the payment reduction for FY 2023, while other commenters recommended that we adopt a 70 percent payback percentage for the FY 2023 Program year. One commenter suggested that we grant a full exemption from the adjusted Federal per diem rate reduction required by section 1888(h)(6) of the Act.

Response: We stated in the proposed rule our belief that for purposes of scoring and payment adjustments under the SNF VBP Program, the SNFRM as impacted by the COVID-19 PHE should not be attributed to the participating facility positively or negatively. We believe that using SNFRM data that has been impacted by the PHE due to COVID-19 could result in performance scores that do not accurately reflect SNF performance for making national comparisons and ranking purposes. Due to the SNFRM being the only quality measure currently authorized for use in the FY 2023 SNF VBP, suppression of the SNFRM would mean we would not be able to calculate SNF performance scores for any SNF nor to differentially rank SNFs. Therefore, we are finalizing a change to the scoring methodology to assign all SNFs a performance score of zero and effectively rank all SNFs equally in the FY 2023 SNF VBP program year.

After considering the public comments, we are finalizing our proposal to adopt a special scoring policy for the FY 2023 program year as proposed and codifying it at § 413.338(i) of our regulations.

3. Case Minimum and Measure Minimum Policies

a. Background

Section 111(a)(1) of Division CC of the CAA amended section 1888(h)(1) of the Act by adding paragraph (h)(1)(C), which established criteria for excluding SNFs from the SNF VBP Program. Specifically, with respect to payments for services furnished on or after October 1, 2022, paragraph (h)(1)(C) precludes the SNF VBP Program from applying to a SNF for which there are not a minimum number of cases (as determined by the Secretary) for the measures that apply to the SNF for the performance period for the applicable fiscal year, or a minimum number of measures (as determined by the Secretary) that apply to the SNF for the performance period for the applicable fiscal year.

To implement this provision, we proposed to establish case and measure minimums that SNFs must meet to be included in the Program for a given program year. These case and measure minimum requirements will serve as eligibility criteria for determining whether a SNF is included in, or excluded from, the Program for a given program year. Inclusion in the Program for a program year means that a SNF would receive a SNF performance score and would be eligible to receive a value-based incentive payment. Exclusion from the Program for a program year means that, for the applicable fiscal year, a SNF would not be subject to the requirements under § 413.338 and would also not be subject to a payment reduction under § 413.337(f). Instead, the SNF would receive its full Federal per diem rate under § 413.337 for the applicable fiscal year.

We proposed to establish a case minimum for each SNF VBP measure that SNFs must meet during the performance period for the program year. We also proposed that SNFs must have a minimum number of measures during the performance period for the applicable program year in order to be eligible to participate in the SNF VBP Program for that program year. We proposed to codify these changes to the applicability of the SNF VBP Program beginning with FY 2023 at § 413.338(b).

We proposed that the case and measure minimums would be based on statistical accuracy and reliability, such that only SNFs that have sufficient data are included in the SNF VBP Program for a program year. The purpose of these restrictions is to apply program requirements only to SNFs for which we can calculate reliable measure rates and SNF performance scores.

Because the case and measure minimum policies will ensure that SNFs participate in the Program for a program year only if they have sufficient data for calculating accurate and reliable measure rates and SNF performance scores, we do not believe there is a continuing need to apply the low-volume adjustment (LVA) policy beginning with FY 2023. Accordingly, in the FY 2023 SNF PPS proposed rule (87 FR 22783), we proposed to remove the LVA policy from the Program beginning with the FY 2023 program year. As discussed further in section VIII.E.5. of this final rule, we are finalizing our proposal to remove the LVA policy.

We did not receive any public comments on our proposal to codify the changes to the applicability of the SNF VBP Program beginning with FY 2023 at § 413.338(b), and therefore, we are finalizing this proposal.

b. Case Minimum During a Performance Period for the SNFRM Beginning With the FY 2023 SNF VBP Program Year

We proposed that beginning with the FY 2023 program year, SNFs must have a minimum of 25 eligible stays for the SNFRM during the applicable 1-year performance period in order to be eligible to receive a score on that measure in the SNF VBP Program.

As stated in the proposed rule, we believed this case minimum requirement for the SNFRM is appropriate and consistent with the findings of reliability tests conducted for the SNFRM, and it is also consistent with the case threshold we have applied under the LVA policy. The reliability testing results, which combined CY 2014 and 2015 SNFRM files, indicated that a minimum of 25 eligible stays for the SNFRM produced sufficiently reliable measure rates. In addition, the testing results found that approximately 85 percent of all SNFs met the 25 eligible stay minimum during the CY 2015 testing period. While excluding 15 percent of SNFs may seem high, we continue to believe that the 25 eligible stay minimum for the SNFRM appropriately balances quality measure reliability with our desire to allow as many SNFs as possible to participate in the Program. For further details on the measure testing, we refer readers to the minimum eligible stay threshold analysis for the SNFRM available at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​Other-VBPs/​SNFRM-Reliability-Testing-Memo.pdf.

We also believed this case minimum requirement for the SNFRM ensures that those SNFs included in the Program receive a sufficiently accurate and reliable SNF performance score. However, we also proposed changes to our scoring and payment policies for the FY 2023 SNF VBP Program in the proposed rule. If finalized, beginning with the FY 2023 SNF VBP program year, any SNF that does not meet this case minimum requirement for the SNFRM during the applicable performance period will be excluded from the Program for the affected program year, provided there are no other measures specified for the affected program year. Those SNFs will not be subject to any payment reductions under the Program and instead will receive their full Federal per diem rate.

We invited public comment on our proposal to adopt a case minimum requirement for the SNFRM beginning with the FY 2023 SNF VBP program year. We received the following comments and provide our responses:

Comment: One commenter supported the proposed case minimum for the SNFRM based on the evidence and rationale provided.

Response: We thank the commenter for support of the case minimum for the SNFRM.

Comment: Some commenters urged CMS to increase the case minimums adopted in the Program to reach a reliability standard of 0.7, which they stated could be achieved with a case minimum of 60. The commenters stated that adopting longer performance and baseline periods would mitigate the effects of this recommendation on excluded SNFs based on the higher minimum number of cases.

Response: Our reliability testing results demonstrated that increasing the case minimum threshold to 50 eligible stays would slightly increase the measure's reliability but would approximately double the number of SNFs that would not meet this higher case minimum.^[272] Therefore, we continue to believe that a 25-eligible stay minimum for the SNFRM best balances quality measure reliability with our desire to allow as many SNFs as possible to participate in the Program. As we discussed in the FY 2023 SNF PPS proposed rule (87 FR 22781), reliability testing for the SNFRM indicated that a 25 eligible stay minimum produces sufficiently reliable measure rates. In addition, our analyses found that approximately 85 percent of all SNFs met the 25 eligible stay minimum during the CY 2015 testing period.

We also disagree with the commenters' suggestion to adopt longer performance and baseline periods as a method for increasing measure reliability. As we discussed in the FY 2016 SNF PPS final rule (80 FR 46422) and the FY 2017 SNF PPS final rule (81 FR 51998 through 51999), we continue to believe that 1-year performance and baseline periods provide sufficient levels of data accuracy and reliability for scoring performance on the SNFRM, while also allowing us to link SNF performance on the measure as closely as possible to the payment year to ensure clear connections between quality measurement and value-based payment. We also believe that adopting longer performance and baseline periods would create a time gap that would hinder our ability to clearly connect the quality data with SNFs' value-based payment, as well as limit the actionability of such quality data for SNFs to make quality improvements.

After considering the public comments, we are finalizing our proposal to adopt a 25 eligible stay minimum requirement during a performance period for the SNFRM beginning with the FY 2023 program year.

c. Case Minimums During a Performance Period for the SNF HAI, Total Nurse Staffing, and DTC PAC SNF Measures

In the FY 2023 SNF PPS proposed rule (87 FR 22767 through 22777), we proposed to adopt the SNF HAI and Total Nurse Staffing measures beginning with the FY 2026 program year, as well as the DTC PAC SNF measure beginning with the FY 2027 program year.

For the SNF HAI measure, we proposed that SNFs must have a minimum of 25 eligible stays during the applicable 1-year performance period in order to be eligible to receive a score on the measure. As stated in the proposed rule, we believed this case minimum requirement for the SNF HAI measure is appropriate and consistent with the findings of measure testing analyses. For example, testing results indicated that a 25 eligible stay minimum produced moderately reliable measure rates for purposes of public reporting under the SNF QRP. In addition, testing results found that 85 percent of SNFs met the 25 eligible stay minimum for public reporting under the SNF QRP. We believed these case minimum standards for public reporting purposes are also appropriate standards for establishing a case minimum for this measure under the SNF VBP Program. In addition, we believed these testing results for the 25 eligible stay minimum support our objective, which is to establish case minimums that appropriately balance quality measure reliability with our continuing desire to score as many SNFs as possible on this measure. For further details on SNF HAI measure testing for the SNF QRP, we refer readers to the SNF HAI Measure Technical Report available at https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf.

For the Total Nurse Staffing measure, we proposed that SNFs must have a minimum of 25 residents, on average, across all available quarters during the applicable 1-year performance period in order to be eligible to receive a score on the measure. As discussed in the proposed rule, we tested three potential case minimums for this measure: a 25-resident minimum, a minimum of one quarter of PBJ data, and a minimum of two quarters of PBJ data. Over 94 percent of SNFs satisfied the case minimum under all three alternatives tested. There were very minimal differences observed between the case minimums tested, and this finding held for most subgroups tested as well, including rural SNFs, large SNFs, and those SNFs serving the highest proportion of dually eligible beneficiaries. The only notable observed difference occurred within small SNFs, defined as those with fewer than 46 beds as a proxy for size. About 90 percent of small SNFs reported two quarters of PBJ data, and about 92 percent of small SNFs reported one quarter of PBJ data, but only about 63 percent of small SNFs satisfied the 25-resident minimum, indicating that even after two quarters of successful PBJ reporting there was a substantial proportion of small SNFs (about 27 percent) reporting minimal numbers of residents, calling into question the utility of their limited staffing data. After considering these alternatives, we determined that the proposed 25-resident minimum best balances quality measure reliability with our desire to score as many SNFs as possible on this measure. We also noted that the 25-resident minimum for this measure aligns with the case minimums we are proposing for the other proposed measures.

Further, for the DTC PAC SNF measure, we proposed that SNFs must have a minimum of 25 eligible stays during the applicable 2-year performance period in order to be eligible to receive a score on the measure. As stated in the proposed rule, we believed this case minimum requirement for the DTC PAC SNF measure is appropriate and consistent with the findings of measure testing analyses. Analyses conducted by CMS contractors found that a 25 eligible stay minimum produced good to excellent measure score reliability. In addition, analyses using 2015 through 2016 Medicare FFS claims data found that 94 percent of SNFs met the 25 eligible stay minimum during the 2-year performance period. We believed these testing results for the 25 eligible stay minimum support our objective, which is to establish case minimums that appropriately balance quality measure reliability with our continuing desire to score as many SNFs as possible on this measure. The complete measure testing results conducted by our contractors that we included as part of the documentation supporting our request for NQF to endorse the measure are available at https://www.qualityforum.org/​QPS/​3481.

We invited public comment on our proposal to adopt case minimums for the SNF HAI, Total Nurse Staffing, and DTC PAC SNF measures. We received the following comments and provide our responses:

Comment: One commenter supported the proposed case minimums for the SNF HAI, DTC PAC SNF, and Total Nurse Staffing measures as proposed.

Response: We thank the commenter for support of the case minimums for the SNF HAI, DTC PAC SNF, and Total Nurse Staffing measures.

Comment: One commenter recommended increasing the proposed minimum number of stays to at least 60 to mitigate the effects of a larger Medicare Advantage population and nursing homes that have had to limit or reduce admissions due to staff shortages.

Response: We continue to believe that a 25 eligible stay minimum for the SNF HAI measure; a 25-resident minimum, on average, across all available quarters for the Total Nurse Staffing measure; and a 25 eligible stay minimum for the DTC PAC SNF measure best balance quality measure reliability with our desire to score as many SNFs as possible on these measures. We recognize the growing Medicare Advantage population as well as the impact of staff shortages on the ability of a SNF to admit residents and we intend to continue assessing these topics in the future.

After considering the public comments, we are finalizing our proposal to adopt a 25 eligible stay minimum for the SNF HAI measure; a 25-resident minimum, on average, across all available quarters for the Total Nurse Staffing measure; and a 25 eligible stay minimum for the DTC PAC SNF measure.

d. Measure Minimums for the FY 2026 and FY 2027 Program Years

We proposed to adopt measure minimums for the FY 2026 and FY 2027 program years. Under these policies, only SNFs that have the minimum number of measures applicable to the program year would be eligible for inclusion in the Program for that program year.

In the proposed rule, we proposed to adopt two new quality measures (SNF HAI and Total Nurse Staffing measures) beginning with the FY 2026 Program. If finalized, the SNF VBP Program would consist of three quality measures in FY 2026 (SNF Readmission Measure, SNF HAI, and Total Nurse Staffing measures). We proposed that for FY 2026, SNFs must have the minimum number of cases for two of these three measures during the performance period to receive a performance score and value-based incentive payment. SNFs that do not meet these minimum requirements will be excluded from the FY 2026 program and will receive their full Federal per diem rate for that fiscal year. Under these minimum requirements, we estimated that approximately 14 percent of SNFs would be excluded from the FY 2026 Program. Alternatively, if we required SNFs to have the minimum number of cases for all three measures during the performance period, approximately 21 percent of SNFs would be excluded from the FY 2026 Program. We also assessed the consistency of value-based incentive payment adjustment factors, or incentive payment multipliers (IPMs), between time periods as a proxy for performance score reliability under the different measure minimum options. The testing results indicated that the reliability of the SNF performance score would be relatively consistent across the different measure minimum requirements. Based on these testing results, we believed the minimum of two out of three measures for FY 2026 best balances SNF performance score reliability with our desire to ensure that as many SNFs as possible can receive a performance score and value-based incentive payment.

We also proposed to adopt an additional quality measure (DTC PAC SNF measure) beginning with the FY 2027 Program. If finalized, the SNF VBP Program would consist of four quality measures in FY 2027 (SNF Readmission Measure, SNF HAI, Total Nurse Staffing, and DTC PAC SNF measures). We proposed that for FY 2027, SNFs must have the minimum number of cases for three of the four measures during a performance period to receive a performance score and value-based incentive payment. SNFs that do not meet these minimum requirements will be excluded from the FY 2027 program and will receive their full Federal per diem rate for that fiscal year. Under these minimum requirements, we estimated that approximately 16 percent of SNFs would be excluded from the FY 2027 Program. Alternatively, if we required SNFs to have the minimum number of cases for all four measures, we estimated that approximately 24 percent of SNFs would be excluded from the FY 2027 Program. We also assessed the consistency of incentive payment multipliers (IPMs) between time periods as a proxy for performance score reliability under the different measure minimum options. The testing results indicated that the reliability of the SNF performance score for the FY 2027 program year would be relatively consistent across the different measure minimum requirements. Based on these testing results, we believed the minimum of three out of four measures for FY 2027 best balances SNF performance score reliability with our desire to ensure that as many SNFs as possible can receive a performance score and value-based incentive payment.

Under these measure minimums, we estimated that 14 percent of SNFs would be excluded from the Program for the FY 2026 program year, but that the excluded SNFs would, as a whole, provide care to approximately 2 percent of the total number of eligible SNF stays. Similarly, for the FY 2027 Program, we estimated that 16 percent of SNFs would be excluded from the Program, but that the excluded SNFs, as a whole, provide care to approximately 2 percent of the total number of eligible SNF stays.

We invited public comment on our proposal to adopt measure minimums for the FY 2026 and FY 2027 SNF VBP program years. We received the following comment and provide our response:

Comment: One commenter supported the measure minimums for FY 2026 and FY 2027 as proposed.

Response: We thank the commenter for support of the measure minimums for the FY 2026 and FY 2027 program years.

After considering the public comment, we are finalizing our proposal for FY 2026 that SNFs must have the minimum number of cases for two of the three measures during the performance period to receive a performance score and value-based incentive payment, and finalizing our proposal for FY 2027 that SNFs must have the minimum number of cases for three of the four measures during a performance period to receive a performance score and value-based incentive payment.

4. Updated Scoring Policy for SNFs Without Sufficient Baseline Period Data Beginning With the FY 2026 Program Year

In the FY 2019 SNF PPS final rule (83 FR 39278), we finalized a policy to score SNFs based only on their achievement during the performance period for any program year for which they do not have sufficient baseline period data, which we defined as SNFs with fewer than 25 eligible stays during the baseline period for a fiscal year. We codified this policy at § 413.338(d)(1)(iv) of our regulations.

We continue to be concerned that measuring SNF performance on a given measure for which the SNF does not have sufficient baseline period data may result in unreliable improvement scores for that measure and, as a result, unreliable SNF performance scores. However, the current policy was designed for a SNF VBP Program with only one measure. As we continue to add measures to the Program, we aim to maintain the reliability of our SNF performance scoring. Therefore, we proposed to update our policy beginning with the FY 2026 program year. Under this updated policy, we will not award improvement points to a SNF on a measure for a program year if the SNF has not met the case minimum for that measure during the baseline period that applies to the measure for the program year. That is, if a SNF does not meet a case minimum threshold for a given measure during the applicable baseline period, that SNF will only be eligible to be scored on achievement for that measure during the performance period for that measure for the applicable fiscal year.

For example, if a SNF has fewer than the minimum of 25 eligible stays during the applicable 1-year baseline period for the SNF HAI measure for FY 2026, that SNF would only be scored on achievement during the performance period for the SNF HAI measure for FY 2026, so long as that SNF meets the case minimum for that measure during the applicable performance period.

We proposed to codify this update in our regulation text at § 413.338(e)(1)(iv).

We invited public comment on this proposal to update the policy for scoring SNFs that do not have sufficient baseline period data. We received the following comment and provide our response:

Comment: One commenter supported our proposal to not award improvement points to SNFs that do not meet the case minimums during the applicable baseline periods.

Response: We thank the commenter for support of this proposal.

After considering the public comment, we are finalizing our proposal to update the policy for scoring SNFs that do not have sufficient baseline period data such that we would not award improvement points to a SNF on a measure for a program year if that SNF does not meet the case minimum for that measure during the baseline period that applies to the measure for the program year. We are also finalizing our proposal to codify this update at § 413.338(e)(1)(iv) of our regulations.

5. Removal of the LVA Policy From the SNF VBP Program Beginning With the FY 2023 Program Year

In the FY 2019 SNF PPS final rule (83 FR 39278 through 39280), we finalized our LVA policy, which provides an adjustment to the Program's scoring methodology to ensure low-volume SNFs receive sufficiently reliable performance scores for the SNF readmission measure. In that final rule, we also codified the LVA policy in § 413.338(d)(3) of our regulations. As we discussed in the FY 2019 SNF PPS final rule, we found that the reliability of the SNFRM measure rates and resulting performance scores were adversely affected if SNFs had fewer than 25 eligible stays during the performance period for a program year (83 FR 39279). Therefore, we believed that assigning a performance score that results in a value-based incentive payment amount that is equal to the adjusted Federal per diem rate that the SNF would have received in the absence of the Program, to any SNF with fewer than 25 eligible stays for the SNFRM during the performance period, was the most appropriate adjustment for ensuring reliable performance scores.

However, as discussed in the proposed rule, we no longer believe the LVA policy is necessary because we are now required under the statute to have case and measure minimum policies for the SNF VBP Program, and those policies will achieve the same payment objective as the LVA policy. Therefore, we proposed to remove the LVA Policy from the SNF VBP Program's scoring methodology beginning with the FY 2023 program year. With the removal of the LVA policy, the total amount available for a fiscal year will no longer be increased as appropriate for each fiscal year to account for the assignment of a performance score to low-volume SNFs. We proposed to update the total amount available for a fiscal year to 60 percent of the total amount of the reduction to the adjusted SNF PPS payments for that fiscal year, as estimated by us, in our regulations at§ 413.338(c)(2)(i). We proposed to update the LVA policy at § 413.338(d)(3) to reflect its removal from the Program.

We invited public comment on our proposal to remove the LVA policy from the SNF VBP Program beginning with the FY 2023 program year. We received the following comment and provide our response:

Comment: One commenter supported our proposed removal of the LVA policy.

Response: We thank the commenter for their support of this proposal.

After considering the public comment, we are finalizing our proposal to remove the LVA policy from the SNF VBP Program beginning with the FY 2023 program year and finalizing our proposal to update our regulations at § 413.338(d)(3) to reflect its removal from the Program.

6. Updates to the SNF VBP Scoring Methodology Beginning in the FY 2026 Program Year

a. Background

In the FY 2017 SNF PPS final rule (81 FR 52000 through 52005), we adopted a scoring methodology for the SNF VBP Program where we score SNFs on their performance on the SNFRM, award between zero and 100 points to each SNF (with up to 90 points available for improvement) and award each SNF a SNF performance score consisting of the higher of its scores for achievement and improvement. The SNF performance score is then translated into a value-based incentive payment multiplier that can be applied to each SNF's Medicare claims during the SNF VBP Program year using an exchange function. Additionally, in the FY 2018 SNF PPS final rule (82 FR 36615), we adopted a clarification of our rounding policy in SNF VBP scoring to award SNF performance scores that are rounded to the nearest ten-thousandth of a point, or with no more than five significant digits to the right of the decimal point. We have also codified numerous aspects of the SNF VBP Program's policies in our regulations at § 413.338, and our scoring policies appear in paragraph (d) of that section.

We refer readers to the FY 2017 rule cited above for a detailed discussion of the SNF VBP Program's scoring methodology, public comments on the proposed policies, and examples of our scoring calculations.

b. Measure-Level Scoring Update

We proposed to update our achievement and improvement scoring methodology to allow a SNF to earn a maximum of 10 points on each measure for achievement, and a maximum of nine points on each measure for improvement. For purposes of determining these points, we proposed to define the benchmark as the mean of the top decile of SNF performance on a measure during the baseline period and the achievement threshold as the 25th percentile of national SNF performance on a measure during the baseline period.

We proposed to award achievement points to SNFs based on their performance period measure rate for each measure according to the following:

• If a SNF's performance period measure rate was equal to or greater than the benchmark, the SNF would be awarded 10 points for achievement.
• If a SNF's performance period measure rate was less than the achievement threshold, the SNF would receive zero points for achievement.
• If a SNF's performance period measure rate was equal to or greater than the achievement threshold, but less than the benchmark, we would award between zero and 10 points according to the following formula:

We also proposed to award improvement points to SNFs based on their performance period measure rate according to the following:

• If a SNF's performance period measure rate was equal to or lower than its baseline period measure rate, the SNF would be awarded zero points for improvement.
• If a SNF's performance period measure rate was equal to or higher than the benchmark, the SNF would be awarded nine points for improvement.
• If a SNF's performance period measure rate was greater than its baseline period measure rate but less than the benchmark, we would award between zero and nine points according to the following formula:

As proposed, we will score SNFs' performance on achievement and improvement for each measure and award them the higher of the two scores for each measure to be included in the SNF performance score, except in the instance that the SNF does not meet the case minimum threshold for the measure during the applicable baseline period, in which case we proposed that the SNF would only be scored on achievement, as discussed in section VIII.E.4. of this final rule. As discussed in the following section of this final rule, we will then sum each SNFs' measure points and normalize them to arrive at a SNF performance score that ranges between zero and 100 points. We believe that this policy appropriately recognizes the best performers on each measure and reserves the maximum points for their performance levels while also recognizing that improvement over time is important and should also be rewarded.

We further proposed that this change would apply beginning with the FY 2026 SNF VBP program year. As proposed, all measures in the expanded SNF VBP Program would be weighted equally, as we believe that an equal weighting approach is simple for participating SNFs to understand and assigns significant scoring weight (that is, 33.33 percentage points if a SNF has sufficient data on all three measures proposed for FY 2026) to each measure topic covered by the expanded SNF VBP Program. However, as we consider whether we should propose to adopt additional measures, we also intend to consider whether we should group the measures into domains and weight them, similar to what we do under the Hospital VBP Program scoring methodology.

We view this change to the measure-level scoring as a necessary update to the SNF VBP Program's scoring methodology to incorporate additional quality measures and to allow us to add more measures in the future. We also proposed to codify these updates to our scoring methodology in our regulation text by revising the heading for paragraph (d) and adding paragraph (e)(1) at § 413.338.

We invited public comment on this proposal. We received the following comments and provide our responses:

Comment: Some commenters supported our proposed measure-level scoring updates. One commenter recommended adding decimal gradations to the nine and 10-point scales to allow additional variation and ensure that providers are not being disadvantaged by the scoring methodology.

Response: We did not propose to round the measure-level scores that result from use of the scoring formulas specified earlier in this section, and we will award measure-level scores with decimal gradations as the commenter suggested.

Comment: One commenter opposed the use of the mean of the top decile of SNFs' performance during the baseline period as the benchmark, stating that only about 5 percent of SNFs can meet such performance levels. The commenter argued that this methodology discriminates against certain types of SNFs, such as urban SNFs and those that provide care to larger minority populations. The commenter recommended placing the benchmark at the 10th decile of SNFs' performance and presenting analytical findings to a TEP for review and connection to clinical goals.

Response: We thank the commenter for this feedback. While the commenter is correct that only a small percentage of SNFs are likely to qualify for the maximum number of points available on any given measure in a SNF VBP Program year, we believe this policy appropriately rewards top performers on the Program's quality measures. In our view, a value-based purchasing program correctly provides incentives to all participating providers to achieve the best performance possible on the Program's measures. We note further that all SNFs whose performance on a quality measure exceeds the 25th percentile of performance from the baseline period can receive achievement points on a quality measure under the Program's scoring methodology. Further, all SNFs whose performance improves between the baseline and performance period can quality for improvement points under the Program's methodology. We therefore do not agree with the commenter's view that our performance standards policy discriminates against any SNFs, and we continue to believe that the performance standards policy, including the definition of the term “benchmark,” appropriately balances our desire to reward top performers while also recognizing SNFs whose performance improves over time.

Comment: One commenter stated that we should consider adopting a form of risk-adjustment for SNF VBP scores, noting that some facilities do not have enough data to calculate some quality measures.

Response: We thank the commenter for this suggestion. However, we are finalizing policies in this final rule that are designed to accommodate SNFs that do not have enough data to calculate some quality measures, specifically including a minimum number of measures required to receive a SNF performance score. We believe that this policy appropriately balances our desire to allow as much participation in the Program as possible while ensuring that those SNFs' performance scores are based on sufficiently reliable data.

Comment: One commenter stated that we should review adjustments and incentives for clinically complex residents, stating that capturing multiple diagnoses and residents' overarching socioeconomic needs is important for care coordination.

Response: We agree with the commenter that clinically complex residents may present challenges to SNFs attempting to provide the best possible care, and we will continue examining this topic as part of our monitoring and evaluation efforts. However, we would like to clarify that we already incorporate clinical risk adjustment and certain exclusions in the specifications for many of our quality measures. The SNFRM accounts for variation across SNFs in both case mix and patient characteristics.^[273] The SNF HAI measure incorporates risk adjustment that estimates both the average predictive effect of resident characteristics across all SNFs, and the degree to which each SNF has an effect on the outcome that differs from that of the average SNF.^[274] Finally, the DTC PAC measure includes a statistical model for risk adjustment that estimates both the average predictive effect of the resident characteristics across all facilities and the degree to which each facility has an effect on discharge to community that differs from that of the average facility, as well as exclusions from the measure's calculations for situations where discharge to the community may not be clinically appropriate.^[275] We also refer readers to the FY 2023 SNF PPS proposed rule for our discussion of risk-adjustments for the SNF HAI measure (87 FR 22770), the DTC PAC SNF measure (87 FR 22776), and case-mix adjustment for the Total Nurse Staffing measure (87 FR 22774).

After considering the public comments, we are finalizing our proposal to adopt a measure-level scoring policy beginning with the FY 2026 program year as described above, and to update our regulations at § 413.338 to reflect the new policy.

c. Normalization Policy

We continue to believe that awarding SNF performance scores out of a total of 100 points helps interested parties more easily understand the performance evaluation that we provide through the SNF VBP Program. Therefore, we believe that continuing to award SNF performance scores out of 100 points would help interested parties understand the revised scoring methodology and would allow the scoring methodology to accommodate additional measures in the future without more methodological changes.

Therefore, we considered how we could construct the SNF performance score such that the scores continue to range between zero and 100 points. We considered our past experience in our VBP programs, specifically including our experience with the Hospital VBP Program, where we award between zero and 10 points to participating providers for their performance on each measure, and to arrive at a Total Performance Score that ranges between zero and 100 points regardless of the number of measures on which the hospital has sufficient data, we normalize hospitals' scores. We believe the Hospital VBP Program's success in comprehensible measure-level scoring provides a strong model for the expanded SNF VBP Program.

We proposed to adopt a “normalization” policy for SNF performance scores under the expanded SNF VBP Program, effective in the FY 2026 program year and subsequent years. As proposed, we will calculate a raw point total for each SNF by adding up the SNF's score on each of the measures. For example, a SNF that met the case minimum to receive a score on three quality measures would receive a score between zero to 30 points, while a SNF that met the case minimum to receive a score on two quality measures would receive a score between zero to 20 points. We will then normalize the raw point totals by converting them to a 100-point scale, with the normalized values being awarded as the SNF performance score. For example, we would normalize a SNF's raw point total of 27 points out of 30 by converting that total to a 100-point scale, with the result that the SNF would receive a SNF performance score of 90.

In addition to allowing us to maintain a 100-point total performance score scale, this policy enables us to adopt additional quality measures for the program without making further changes to the scoring methodology. If, for example, we proposed to adopt a total of seven quality measures in the future, the normalization policy would enable us to continue to award SNF performance scores on a 100-point scale, even though the maximum raw point total would be 70 points.

We view this normalization policy as a useful update to the SNF VBP Program's scoring methodology to accommodate additional quality measures and to ensure that the public understands the SNF performance scores that we award. We also proposed to codify these updates to our scoring methodology by adding paragraph (e)(2) to our regulation text at § 413.338.

We invited public comment on our proposal. However, we did not receive any comments specific to the normalization policy. Therefore, we are finalizing our proposal to adopt a normalization policy for SNF performance scores under the SNF VBP Program beginning with the FY 2026 program year, and to update our regulations at § 413.338 to reflect the new policy.

F. Adoption of a Validation Process for the SNF VBP Program Beginning With the FY 2023 Program Year

Section 1888(h)(12) of the Act (as added by Division CC, section 111(a)(4) of the Consolidated Appropriations Act, 2021 (Pub. L. 116-120)), requires the Secretary to apply a process to validate SNF VBP program measures and data, as appropriate. We proposed to adopt a validation process for the Program beginning with the FY 2023 program year.

For the SNFRM, we proposed that the process we currently use to ensure the accuracy of the SNFRM satisfies this statutory requirement. Information reported through claims for the SNFRM are validated for accuracy by Medicare Administrative Contractors (MACs) to ensure accurate Medicare payments. MACs use software to determine whether billed services are medically necessary and should be covered by Medicare, review claims to identify any ambiguities or irregularities, and use a quality assurance process to help ensure quality and consistency in claim review and processing. They conduct pre-payment and post-payment audits of Medicare claims, using both random selection and targeted reviews based on analyses of claims data. We proposed to codify these proposals for the FY 2023 SNF VBP in our regulation text at § 413.338(j).

We are considering additional validation methods that may be appropriate to include in the future for the SNF HAI, DTC PAC SNF, and Total Nurse Staffing measures, as well as for other new measures we may consider for the program, and for other SNF quality measures and assessment data. In the FY 2023 SNF PPS proposed rule (87 FR 22788 through 22789), we requested public comment on potential future approaches for data validation in the Request for Information on the Validation of SNF Measures and Assessment Data.

We invited public comment on our proposal to adopt a validation process for the SNF VBP Program beginning with the FY 2023 program year. We received the following comment and provide our response:

Comment: One commenter supported our proposed approach to SNFRM validation.

Response: We thank the commenter for their support.

After considering the public comment, we are finalizing our proposal to adopt a validation process for the SNF VBP Program beginning with the FY 2023 program year as proposed and codifying it at § 413.338(j) of our regulations.

G. SNF Value-Based Incentive Payments for FY 2023

We refer readers to the FY 2018 SNF PPS final rule (82 FR 36616 through 36621) for discussion of the exchange function methodology that we have adopted for the Program, as well as the specific form of the exchange function (logistic, or S-shaped curve) that we finalized, and the payback percentage of 60 percent. We adopted these policies for FY 2019 and subsequent fiscal years.

We also discussed the process that we undertake for reducing SNFs' adjusted Federal per diem rates under the Medicare SNF PPS and awarding value-based incentive payments in the FY 2019 SNF PPS final rule (83 FR 39281 through 39282).

As discussed in the FY 2023 SNF PPS proposed rule, we proposed to suppress the SNFRM for the FY 2023 program year and assign all SNFs a performance score of zero, which will result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. We also proposed that we will not rank SNFs for FY 2023. We also proposed to reduce each participating SNF's adjusted Federal per diem rate for FY 2023 by 2 percentage points and to award each participating SNF 60 percent of that 2 percent withhold, resulting in a 1.2 percent payback for the FY 2023 program year. We believe this continued application of the 2 percent withhold is required under section 1888(h)(5)(C)(ii)(III) of the Act and that a payback percentage that is spread evenly across all SNFs is the most equitable way to reduce the impact of the withhold considering our proposal to award a performance score of zero to all SNFs. We also proposed that those SNFs that do not meet the proposed case minimum for the SNFRM for FY 2023 will be excluded from the Program for FY 2023. We proposed to update § 413.338(i) to reflect that this special scoring and payment policy will apply for FY 2023 in addition to FY 2022. As noted in section VIII.B.1. of this final rule, our goal is to resume use of the scoring methodology we finalized for the program prior to the PHE beginning with the FY 2024 program year.

We invited public comment on this proposed change to the SNF VBP Program's payment policy for the FY 2023 program year. However, we did not receive any public comments on this policy. We are therefore finalizing our proposal to adopt a special payment policy for the FY 2023 program year and codifying it at § 413.338(i) of our regulations.

H. Public Reporting on the Provider Data Catalog Website

1. Background

Section 1888(g)(6) of the Act requires the Secretary to establish procedures to make SNFs' performance information on SNF VBP Program measures available to the public on the Nursing Home Compare website or a successor website, and to provide SNFs an opportunity to review and submit corrections to that information prior to its publication. We began publishing SNFs' performance information on the SNFRM in accordance with this directive and the statutory deadline of October 1, 2017. In December 2020, we retired the Nursing Home Compare website and are now using the Provider Data Catalog website ( https://data.cms.gov/​provider-data/​) to make quality data available to the public, including SNF VBP performance information.

Additionally, section 1888(h)(9)(A) of the Act requires the Secretary to make available to the public certain information on SNFs' performance under the SNF VBP Program, including SNF performance scores and their ranking. Section 1888(h)(9)(B) of the Act requires the Secretary to post aggregate information on the Program, including the range of SNF performance scores and the number of SNFs receiving value-based incentive payments, and the range and total amount of those payments.

In the FY 2017 SNF PPS final rule (81 FR 52009), we discussed the statutory requirements governing public reporting of SNFs' performance information under the SNF VBP Program. In the FY 2018 SNF PPS final rule (82 FR 36622 through 36623), we finalized our policy to publish SNF VBP Program performance information on the Nursing Home Compare or successor website after SNFs have had an opportunity to review and submit corrections to that information under the two-phase Review and Correction process that we adopted in the FY 2017 SNF PPS final rule (81 FR 52007 through 52009) and for which we adopted additional requirements in the FY 2018 SNF PPS final rule. In the FY 2018 SNF PPS final rule, we also adopted requirements to rank SNFs and adopted data elements that we will include in the ranking to provide consumers and interested parties with the necessary information to evaluate SNF's performance under the Program (82 FR 36623).

As discussed in section VIII.B.1. of this final rule, we are finalizing our proposal to suppress the SNFRM for the FY 2023 program year due to the impacts of the PHE for COVID-19. Under this finalized policy, for all SNFs participating in the FY 2023 SNF VBP Program, we will use the performance period (FY 2021, October 1, 2020 through September 30, 2021) we adopted in the FY 2021 SNF PPS final rule (85 FR 47624), as well as the previously finalized baseline period (FY 2019, October 1, 2018 through September 30, 2019) to calculate each SNF's RSRR for the SNFRM. We are also finalizing our proposal to assign all SNFs a performance score of zero. This will result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier.

While we will publicly report the SNFRM rates for the FY 2023 program year, we will make clear in the public presentation of those data that we are suppressing the use of those data for purposes of scoring and payment adjustments in the FY 2023 SNF VBP Program given the significant changes in SNF patient case volume and facility-level case-mix described earlier.

2. Changes to the Data Suppression Policy for Low-Volume SNFs Beginning With the FY 2023 SNF VBP Program Year

In the FY 2020 SNF PPS final rule (84 FR 38823 through 38824), we adopted a data suppression policy for low-volume SNF performance information. Specifically, we finalized that we will suppress the SNF performance information available to display as follows: (1) if a SNF has fewer than 25 eligible stays during the baseline period for a program year, we will not display the baseline risk-standardized readmission rate (RSRR) or improvement score, although we will still display the performance period RSRR, achievement score, and total performance score if the SNF had sufficient data during the performance period; (2) if a SNF has fewer than 25 eligible stays during the performance period for a program year and receives an assigned SNF performance score as a result, we will report the assigned SNF performance score and we will not display the performance period RSRR, the achievement score, or improvement score; and (3) if a SNF has zero eligible cases during the performance period for a program year, we will not display any information for that SNF. We codified this policy in the FY 2021 SNF PPS final rule (85 FR 47626) at § 413.338(e)(3)(i) through (iii).

As discussed in section VIII.B.1. of this final rule, we are finalizing our proposal to suppress the SNFRM for the FY 2023 program year, and we are finalizing a special scoring and payment policy for FY 2023. In addition, as discussed in section VIII.E.3.b. of this final rule, we are finalizing our proposal to adopt a new case minimum that will apply to the SNFRM beginning with FY 2023, new case minimums that will apply to the SNF HAI and Total Nurse Staffing measures and a measure minimum that will apply beginning with FY 2026, a new case minimum that will apply to the DTC PAC SNF measure and a new measure minimum that will apply beginning with FY 2027. As a result of these policies, and in order to implement them for purposes of clarity and transparency in our public reporting, we proposed revising the data suppression policy as follows:

(1) If a SNF does not have the minimum number of cases during the baseline period that applies to a measure for a program year, we would publicly report the SNF's measure rate and achievement score if the SNF had minimum number of cases for the measure during the performance period for the program year;

(2) If a SNF does not have the minimum number of cases during the performance period that applies to a measure for a program year, we would not publicly report any information on the SNF's performance on that measure for the program year;

(3) If a SNF does not have the minimum number of measures during the performance period for a program year, we would not publicly report any data for that SNF for the program year.

We proposed to codify this policy at § 413.338(f)(4).

We invited public comment on these proposals. However, we did not receive any public comments on this topic. We are therefore finalizing our proposal to revise our data suppression policy and codify those revisions at § 413.338(f)(4) of our regulations.

I. Requests for Comment Related to Future SNF VBP Program Expansion Policies

1. Requests for Comment on Additional SNF VBP Program Measure Considerations for Future Years

a. Request for Comment on Including a Staffing Turnover Measure in a Future SNF VBP Program Year

In the FY 2022 SNF PPS final rule (86 FR 42507 through 42511), we summarized feedback from interested parties on our RFI related to potential future measures for the SNF VBP Program, including a specific RFI on measures that focus on staffing turnover. Specifically, we noted that we have been developing measures of staff turnover with data that are required to be submitted under section 1128I(g)(4) of the Act, with the goal of making the information publicly available. We stated that, through our implementation of the PBJ staffing data collection program, we will be reporting rates of employee turnover in the future (for more information on this program, see CMS memorandum QSO-18-17-NH ^[276] ). We refer readers to the FY 2022 SNF PPS final rule for additional details on this RFI and a summary of the public comments we received (86 FR 42507 through 42511).

Nursing staff turnover has long been identified as a meaningful factor in nursing home quality of care.^[277] Studies have shown a relationship between staff turnover and quality outcomes; for example, higher staff turnover is associated with an increased likelihood of receiving an infection control citation.^[278] The collection of auditable payroll-based daily staffing data through the PBJ system has provided an opportunity to calculate, compare, and publicly report turnover rates; examine facility characteristics associated with higher or lower turnover rates; and further measure the relationship between turnover and quality outcomes. For example, a recent study using PBJ data found that nursing staff turnover is higher than previously understood, variable across facilities, and correlated with organizational characteristics such as for-profit status, chain ownership, and higher Medicaid census.^[279] In addition, we have found that higher overall star ratings are associated with lower average staff turnover rates, suggesting that lower staff turnover rates are associated with higher overall nursing home quality.^[280]

In January of 2022, we began publicly reporting a staffing turnover measure on the Compare tool currently hosted by HHS, available at https://www.medicare.gov/​care-compare, and this information will be included in the Nursing Home Five-Star Quality Rating System in July 2022. We refer readers to the Nursing Home Staff Turnover and Weekend Staffing Levels Memo for additional information related to this measure at https://www.cms.gov/​files/​document/​qso-22-08-nh.pdf. We believe staffing turnover is an important indicator of quality of care provided in nursing homes and SNFs. Additionally, in response to our RFI on a staffing turnover measure, interested parties strongly recommended that we consider measures of staffing turnover to assess patterns and consistency in staffing levels. As a part of our goals to build a robust and comprehensive measure set for the SNF VBP Program and in alignment with recommendations from interested parties, we stated our intent to propose to adopt a staffing turnover measure in the SNF VBP Program in the FY 2024 SNF PPS proposed rule. Specifically, the measure we intend to include in the SNF VBP Program is the percent of total nurse staff that have left the facility over the last year. Total nurse staff include RNs, LPNs, and nurse aides. More information on this measure, can be found in the Five-Star Rating Technical Users' Guide at https://www.cms.gov/​medicare/​provider-enrollment-and-certification/​certificationandcomplianc/​downloads/​usersguide.pdf.

The Biden-Harris Administration is committed to improving the quality of care in nursing homes. As stated in a fact sheet entitled “Protecting Seniors by Improving Safety and Quality of Care in the Nation's Nursing Homes,” we are committed to strengthening the SNF VBP Program and have begun to measure and publish staff turnover and weekend staffing levels, metrics which closely align with the quality of care provided in a nursing home. We stated our intent to propose new measures based on staffing adequacy, the resident experience, as well as how well facilities retain staff. Accordingly, we seek commenters' feedback on including the staff turnover measure that captures the percent of total nurse staff that have left the facility over the last year for the SNF VBP Program as currently specified or whether the measure should be revised before being proposed for inclusion in the SNF VBP Program.

In addition, we are interested in whether we should explore the development of a composite measure that would capture multiple aspects of staffing, including both total nurse hours and the staff turnover measure rather than having separate but related measures related to nursing home staffing, such a measure could potentially replace the initial measure we intend to propose to include in SNF VBP for FY 2024. Preliminary analyses using the staff turnover data on the Medicare.gov Care Compare website have indicated that as the lower average staff turnover decreases, the overall star ratings for facilities increases, suggesting that lower turnover is associated with higher overall quality,^[281] and research has indicated that staff turnover has been linked with increased infection control issues.^[282] We believe it is important to capture and tie aspects of both staffing levels and staffing turnover to quality payment and welcome commenter's feedback for how to balance those goals under the SNF VBP Program. We are also interested to hear about actions SNFs may take or have taken to reduce staff turnover in their facilities, and for SNFs that did reduce staff turnover, the reduction's observed impact on quality of care. In particular, we are interested in best practices for maintaining continuity of staffing among both nursing and nurse aide staff. Finally, we are interested in commenters feedback on any considerations we should take into account related to the impact that including a Nursing Home Staff Turnover measure may have on health equity. Before proposing to include this measure in the SNF VBP Program in the FY 2024 SNF PPS proposed rule, we would include the measure on a list of measures under consideration, as described in section 1890A of the Act.

We welcomed public comment on the potential future adoption of a staffing turnover measure. The following is a summary of the public comments we received on this RFI.

Comment: Many commenters supported a staffing turnover measure in the SNF VBP Program, citing growing evidence that staffing turnover affects quality of care for residents. One commenter suggested that we consider using a turnover measure from the Five-Star rating system rather than developing a new measure and suggested that we limit the Program's incentive payments to those facilities that achieve the lowest turnover rates. One commenter stated that we should assess both total nurse staff turnover and RN staff turnover and suggested that only nurses providing direct care should be included in the measure. Another commenter suggested that the measure make a distinction between voluntary and involuntary turnover, such as termination of staff that do not meet expectations. The commenter also suggested examining facility turnover by characteristics such as size and ownership. Some commenters suggested that CMS focus more on staff retention rather than turnover. Some commenters stated that facilities able to achieve lower levels of staff turnover have higher overall star ratings and better performance on Medicare's claims-based quality measures. One commenter noted that successfully reducing turnover is important to implementation of minimum staffing standards.

Some commenters opposed a staffing turnover measure on the basis that facilities face challenges when mitigating turnover. Some commenters stated that facilities have trouble maintaining staff due to the COVID-19 pandemic. Additionally, one commenter stated that cases where agency staff work assignments or where specialized teams travel to multiple facilities should not be counted as turnover. Another commenter similarly stated that short-term agency staff should not be included in a measure of staffing turnover and suggested that extended leaves of absence should also be excluded. The commenter also suggested that the resulting turnover does not indicate low quality of care and that measuring staffing turnover would result in payment cuts to facilities that are already struggling with staffing costs. Another commenter stated that many factors outside of SNFs' control affect turnover. Another commenter stated that all health care providers are struggling with staffing and suggested that we limit the number of staffing agencies that contribute to the problem. Another commenter stated that not all turnover is detrimental and that it may be beneficial to dismiss staff that do not have the patience or disposition to work in a nursing facility. One commenter suggested that we add administrative and facility turnover to reduce management turnover, which the commenter believed contributes to lower quality of care.

Some commenters expressed concern that a staffing turnover measure could impact the financial situation of SNFs with higher minority populations, which they believed tend to have higher turnover rates. One commenter worried that a staffing turnover measure would cause SNFs to focus narrowly on staff retention rather than care quality. One commenter recommended against a composite measure, stating that separate measures will provide consumers with clearer information and allow more stratification by facility type, staff members, and resident characteristics. One commenter expressed concern that the resources necessary for measure validation for the Total Nurse Staffing measure may shift facilities' efforts to those reviews rather than beneficiary care. The commenter also stated that both PBJ and MDS data are already reviewed for accuracy during health inspections.

Response: We will take this feedback into consideration as we develop our policies for the FY 2024 SNF PPS proposed rule. In addition, as previously indicated, we have been posting measures of staff turnover since January 2022 and including SNF employee turnover information as part of the staffing domain of the Nursing Home Five Star Quality Rating System on the Medicare.gov Care Compare website since July 2022.

b. Request for Comment on Including the National Healthcare Safety Network (NHSN) COVID-19 Vaccination Coverage Among Healthcare Personnel Measure in a Future SNF VBP Program Year

In addition to the staffing turnover measure and the other potential future measures listed in the FY 2022 SNF PPS final rule, we are also considering the inclusion of the NHSN COVID-19 Vaccination Coverage among Healthcare Personnel measure, which measures the percentage of healthcare personnel who receive a complete COVID-19 vaccination course. This measure data is collected by the CDC NHSN and the measure was finalized for use in the SNF QRP in the FY 2022 SNF PPS final rule (86 FR 42480 through 42489). We seek commenters' feedback on whether to propose to include this measure in a future SNF VBP program year. Before proposing to include any such measure, we would include the measure on a list of measures under consideration, as required by section 1890A of the Act.

We welcomed public comment on the potential future adoption of the NHSN COVID-19 Vaccination Coverage among Healthcare Personnel measure. The following is a summary of the public comments received on this RFI.

Comment: Some commenters supported a COVID-19 vaccination measure for healthcare personnel in the SNF VBP Program. One commenter stated that the measure is an important safety measure for beneficiaries and families. Another commenter suggested that the measure is best placed in the SNF QRP until long-term vaccination needs can be assessed.

Some commenters expressed concerns about a future COVID-19 vaccination measure for healthcare personnel in the SNF VBP Program. One commenter noted that the measure uses CDC processes and believed that may create interagency barriers and challenges. Another commenter stated that the measure specifications are likely to change as the definition of a completed COVID-19 vaccination course may change. One commenter stated that vaccination decisions are made by staffs' personal preferences, not the SNF. Another commenter noted that CMS already requires LTC facilities to report residents' and staffs' COVID-19 vaccination rates and suggested that such a measure in the SNF VBP Program would be duplicative. Another commenter stated that exemptions create variation in vaccination rates. One commenter stated that the measure is not a patient outcome measure and thus does not align with the Program's purpose.

Response: We will take this feedback into consideration as we develop our policies for future rulemaking.

2. Request for Comment on Updating the SNF VBP Program Exchange Function

In the FY 2018 SNF PPS final rule (82 FR 36616 through 36619), we adopted an exchange function methodology for translating SNFs' performance scores into value-based incentive payments. We illustrated four possibilities for the functional forms that we considered—linear, cube, cube root, and logistic—and discussed how we assessed how each of the four possible exchange function forms would affect SNFs' incentive payments under the Program. We also discussed several important factors that we considered when adopting an exchange function, including the numbers of SNFs that receive more in value-based incentive payments in each scenario compared to the number of SNFs for which a reduction is applied to their Medicare payments, as well as the resulting incentives for SNFs to reduce hospital readmissions. We also evaluated the distributions of value-based incentive payment adjustments and the functions' results for compliance with the Program's statutory requirements. We found that the logistic function maximized the number of SNFs with positive payment adjustments among SNFs measured using the SNFRM. We also found that the logistic function best fulfilled the requirement that SNFs in the lowest 40 percent of the Program's ranking receive a lower payment rate than would otherwise apply, resulted in an appropriate distribution of value-based incentive payment percentages, and otherwise fulfilled the Program's requirements specified in statute.

Additionally, we published a technical paper describing the analyses of the SNF VBP Program exchange function forms and payback percentages that informed the policies that we adopted in the FY 2018 SNF PPS final rule. The paper is available on our website at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​Other-VBPs/​SNF-VBP-exchange-function-analysis.pdf.

As discussed earlier, we proposed numerous policy changes to expand the SNF VBP Program's measure set based on authority provided by the Consolidated Appropriations Act, 2021, including additional quality measures and adjustments to the Program's scoring methodology to accommodate the presence of more than one quality measure. We are also considering whether we should propose a new form for the exchange function or modify the logistic exchange function in future years.

When we adopted the logistic function for the SNF VBP Program, we focused on that function's ability, coupled with the 60 percent payback percentage, to provide net-positive value-based incentive payments to as many top-performing SNFs as possible. We believed that structuring the Program's incentive payments in this manner enabled us to reward the Program's top-performing participants and provide significant incentives for SNFs that were not performing as well to improve over time.

We continue to believe that these considerations are important and that net-positive incentive payments help drive quality improvement in the SNF VBP Program. However, in the context of a value-based purchasing program employing multiple measures, we are considering whether a new functional form or modifications to the existing logistic exchange function may provide the best incentives to SNFs to improve on the Program's measures.

If finalized, the additional measures that we are proposing for the SNF VBP Program would align the Program more closely with the Hospital VBP Program, on which some of SNF VBP's policies, like the exchange function methodology, are based. The Hospital VBP Program employs a linear exchange function to translate its Total Performance Scores into value-based incentive payment percentages that can be applied to hospitals' Medicare claims. A linear exchange function is somewhat simpler for interested parties to understand but presents less of an opportunity to reward top performers than the logistic form that we currently employ in the SNF VBP Program at https://data.cms.gov/​provider-data/​ or https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​SNF-VBP-Page.

We requested feedback from interested parties on whether we should consider proposing either a new functional form or modified logistic exchange function for the SNF VBP Program. Specifically, we requested comments on whether the proposed addition of new quality measures in the Program should weigh in favor of a new exchange function form, a modified logistic exchange function, or no change to the existing exchange function, whether interested parties believe that the increased incentive payment percentages for top performers offered by the logistic function should outweigh the simplicity of the linear function, and whether we should further consider either the cube, cube root, or other functional forms.

We welcomed public comment on potential future updates to the Program exchange function. The following is a summary of the public comments we received on this RFI.

Comment: One commenter recommended providing more information to SNFs on how their value-based incentive payments would change with an updated exchange function. The commenter also noted that the current system may disadvantage smaller SNFs, as well as those that treat sicker patients and a higher proportion of dual-eligible patients. The commenter requested that CMS explore how the SNF VBP Program could ensure more equitable opportunity for these SNFs to achieve a positive value-based incentive payment, including utilizing peer groups. One commenter recommended that any change to the exchange function should be consistent with the rationale used for adopting the logistic function. The commenter also recommended that all options be further evaluated to ensure a potential exchange function does not create incentives at the higher end of performance to deny needed care. One commenter stated that, based on quality measures' typical distribution in a bell curve, the Program's exchange function methodology prevents many facilities from reaching top performance. The commenter stated that every facility should have the opportunity to be a top performer if they meet measure requirements.

Response: We will take this feedback into consideration as we develop our policies for future rulemaking.

3. Request for Comment on the Validation of SNF Measures and Assessment Data

We have proposed to adopt measures for the SNF VBP Program that are calculated using data from a variety of sources, including Medicare FFS claims, the minimum data set (MDS), and the PBJ system, and we are seeking feedback on the adoption of additional validation procedures. In addition, section 1888(h)(12) of the Act requires the Secretary to apply a process to validate SNF VBP program measures, quality measure data, and assessment data as appropriate. MDS information is transmitted electronically by nursing homes to the national MDS database at CMS. The data set was updated in 2010 from MDS 2.0 to MDS 3.0 to address concerns about the quality and validity of the MDS 2.0 data. Final testing of MDS 3.0 showed strong results, with the updated database outperforming MDS 2.0 in terms of accuracy, validity for cognitive and mood items, and clinical relevance.^[283] Research has also shown that MDS 3.0 discharge data match Medicare enrollment and hospitalization claims data with a high degree of accuracy.^[284]

Although the MDS data sets are assessed for accuracy, as described above, we are interested in ensuring the validity of the data reported by skilled nursing facilities because use of this data would have payment implications under the SNF VBP Program. Accordingly, we requested feedback from interested parties on the feasibility and need to select SNFs for validation via a chart review to determine the accuracy of elements entered into MDS 3.0 and PBJ. Additionally, we requested feedback on data validation methods and procedures that could be utilized to ensure data element validity and accuracy.

We noted that other programs, including the Hospital IQR (85 FR 58946) and Hospital OQR programs (76 FR 74485), have developed validation processes for chart-abstracted measures and electronic clinical quality measures (eCQMs), data sources not utilized for the SNF VBP Program. However, there are other elements of existing programs' validation procedures that may be considered for a future SNF VBP Program validation effort. For example, we request feedback on the volume of facilities to select for validation under the SNF VBP Program. We estimate that 3,300 hospitals report data under the Hospital OQR (86 FR 63961) and Hospital IQR (86 FR 45508) Programs. We estimate that over 15,000 SNFs are eligible for the SNF VBP Program. The Hospital OQR Program randomly selects the majority of hospitals (450 hospitals) for validation and additionally select a subset of targeted hospitals (50 hospitals) (86 FR 63872). Under the Hospital IQR Program, 400 hospitals are selected randomly and up to 200 hospitals are targeted for chart-abstracted data validation and up to 200 hospitals are randomly selected for eCQM data validation (86 FR 45424). We sample approximately 10 records from 300 randomly selected facilities under the ESRD QIP Program (82 FR 50766).

We also requested feedback from interested parties on the use of both random and targeted selection of facilities for validation. The Hospital OQR program identifies hospitals for targeted validation based on whether they have previously failed validation or have reported an outlier value deviating markedly from the measure values for other hospitals (more than 3 standard deviations of the mean) (76 FR 74485). Validation targeting criteria utilized by the Hospital IQR Program include factors such as: (1) abnormal, conflicting or rapidly changing data patterns; (2) facilities which have joined the program within the previous 3 years, and which have not been previously validated or facilities which have not been randomly selected for validation in any of the previous 3 years; and (3) any hospital that passed validation in the previous year, but had a two-tailed confidence interval that included 75 percent (85 FR 58946).

Finally, we requested feedback from interested parties on the implementation timeline for additional SNF VBP Program validation processes, as well as validation processes for other quality measures and assessment data. We believe it may be feasible to implement additional validation procedures beginning with data from the FY 2026 program year, at the earliest. Additionally, we may consider the adoption of a pilot of additional data validation processes; such an approach would be consistent with the implementation of the ESRD QIP data validation procedures, which began with a pilot in CY 2014 (82 FR 50766).

We welcomed public comments on the data validation considerations for the SNF VBP Program discussed previously in this section. The following is a summary of the public comments we received on this RFI.

Comment: Some commenters supported adopting a chart review process for SNF VBP validation. One commenter specifically recommended that we assess how MDS coding is equated with medical review. Another commenter noted MDS reviews could be included in a SNF VBP validation program structured similarly to hospital validation processes. Another commenter recommended that we consider the burden placed on SNFs, particularly chart reviews, that may take staff away from patient care. One commenter recommended that we consider the HVBP Program's experience with validation. The commenter also urged us to involve patients and families when developing validation to ensure that results are meaningful to consumers. Another commenter recommended that we adopt a pilot validation program first. One commenter suggested that we adopt the same types of validation procedures for the DTC and HAI measures as we proposed for the SNFRM. Another commenter requested that we work with relevant interested parties to develop and make available evidence-based practices on validation processes. Another commenter requested that we confirm whether a multidisciplinary care team can participate in MDS completion. Some commenters stated that additional validation processes are unnecessary because measures or data collection processes already include methods to ensure their accuracy.

One commenter supported additional validation of SNF VBP measures, including auditing measures based on MDS data. The commenter was concerned that facilities may report inaccurate or inflated MDS data to increase their Five-Star measure ratings. One commenter stated that MDS data have already been shown to be accurate. One commenter suggested that we consider a mix of random and targeted selection of providers in the validation process, and one commenter supported both random and targeted facility selection for validation. One commenter supported implementing a validation program beginning with FY 2026 data.

Response: We will take this feedback into consideration as we develop our policies for future rulemaking.

4. Request for Comment on a SNF VBP Program Approach To Measuring and Improving Health Equity

Significant and persistent inequities in healthcare outcomes exist in the U.S. Belonging to a racial or ethnic minority group; living with a disability; being a member of the lesbian, gay, bisexual, transgender, and queer (LGBTQ+) community; living in a rural area; being a member of a religious minority; or being near or below the poverty level, is often associated with worse health outcomes.^{[285 286 287 288 289 290 291 292 293]} In accordance with Executive Order 13985 of January 20, 2021 on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government, equity is defined as consistent and systematic fair, just, and impartial treatment of all individuals, including individuals who belong to underserved communities that have been denied such treatment, such as Black, Latino, and Indigenous and Native American persons, Asian Americans and Pacific Islanders and other persons of color; members of religious minorities; lesbian, gay, bisexual, transgender, and queer (LGBTQ+) persons; persons with disabilities; persons who live in rural areas; and persons otherwise adversely affected by persistent poverty or inequality (86 FR 7009). In February 2022, we further expanded on this definition by defining health equity as the attainment of the highest level of health for all people, where everyone has a fair and just opportunity to attain their optimal health regardless of race, ethnicity, disability, sexual orientation, gender identity, sex, socioeconomic status, geography, preferred language, or other factors that affect access to care and health outcomes. We are working to advance health equity by designing, implementing, and operationalizing policies and programs that support health for all the people served by our programs, eliminating avoidable differences in health outcomes experienced by people who are disadvantaged or underserved, and providing the care and support that our enrollees need to thrive. Over the past decade we have enacted a suite of programs and policies aimed at reducing health care disparities including the CMS Mapping Medicare Disparities Tool,^[294] the CMS Innovation Center's Accountable Health Communities Model,^[295] the CMS Disparity Methods stratified reporting program,^[296] and efforts to expand social risk factor data collection, such as the collection of Standardized Patient Assessment Data Elements in the post-acute care setting.^[297]

As we continue to leverage our value-based purchasing programs to improve quality of care across settings, we are interested in exploring the role of health equity in creating better health outcomes for all populations in these programs. As the March 2020 ASPE Report to Congress on Social Risk Factors and Performance in Medicare's VBP Program notes, it is important to implement strategies that cut across all programs and health care settings to create aligned incentives that drive providers to improve health outcomes for all beneficiaries.^[298] Therefore, in the proposed rule, we requested feedback from interested parties on guiding principles for a general framework that could be utilized across our quality programs to assess disparities in healthcare quality in a broader RFI in section VI.E. of the proposed rule. We refer readers to this RFI titled, “Overarching Principles for Measuring Healthcare Quality Disparities Across CMS Quality Programs—A Request for Information,” which includes a complete discussion on the key considerations that we intend to consider when determining how to address healthcare disparities and advance health equity across all of our quality programs. Additionally, we are interested in feedback from interested parties on specific actions the SNF VBP Program can take to align with other value-based purchasing and quality programs to address healthcare disparities and advance health equity.

As we continue assessing the SNF VBP Program's policies in light of its operation and its expansion as directed by the CAA, we requested public comments on policy changes that we should consider on the topic of health equity. We specifically requested comments on whether we should consider incorporating adjustments into the SNF VBP Program to reflect the varied patient populations that SNFs serve around the country and tie health equity outcomes to SNF payments under the Program. These adjustments could occur at the measure level in forms such as stratification (for example, based on dual status or other metrics) or including measures of social determinants of health (SDOH). These adjustments could also be incorporated at the scoring or incentive payment level in forms such as modified benchmarks, points adjustments, or modified incentive payment multipliers (for example, peer comparison groups based on whether the facility includes a high proportion of dual eligible beneficiaries or other metrics). We requested commenters' views on which of these adjustments, if any, would be most effective for the SNF VBP Program at accounting for any health equity issues that we may observe in the SNF population.

We welcomed public comment on potential approaches to measuring and improving health equity in the SNF VBP Program. The following is a summary of the public comments we received on this RFI.

Comment: Many commenters supported our commitment to health equity for SNF residents. Some commenters suggested that we examine factors that may lead to care inequities and suggested that we incorporated patient-reported outcomes and experiences in shaping our equity strategies. Another commenter suggested that we consider balancing short-stay and long-stay residents' needs when developing equity adjustments. Some commenters recommended that we report quality data stratified by race and ethnicity to assess health equity issues in the SNF sector. Another commenter suggested that we adopt a risk-adjustment or incentive payment policy for facilities that accept residents that other facilities will not. Another commenter recommended that we engage with interested parties throughout any health equity policy development so that facilities can implement proper data collection. One commenter recommended that we pair clinical data measures with social risk metrics to help providers deliver more comprehensive care. One commenter recommended against tying quality measures involving race and ethnicity to payment, stating that such policies may be unconstitutional and could lead to ineffective or biased clinical care. The commenter stated that categories such as dual eligibility status or social determinants of health would be better ways to stratify measures than racial or ethnic categories. One commenter supported measures emphasizing and incorporating social determinants of health but recommended delaying their implementation on the basis that additional administrative burden on providers is inappropriate at this time.

Response: We will take this feedback into consideration as we develop our policies for future rulemaking.

IX. Changes to the Requirements for the Director of Food and Nutrition Services and Physical Environment Requirements in Long-Term (LTC) Facilities and Summary of Public Comments and Responses to the Request for Information on Revising the Requirements for Long-Term Care Facilities To Establish Mandatory Minimum Staffing Levels

A. Changes to the Requirements for the Director of Food and Nutrition Services and Physical Environment Requirements in Long-Term (LTC) Facilities

On July 18, 2019, we published a proposed rule entitled, “Requirements for Long-Term Care (LTC) Facilities: Provisions to Promote Efficiency and Transparency” (84 FR 34737). In combination with our internal review of the existing regulations, we used feedback from interested parties to inform our policy decisions about the proposals we set forth. We specifically considered how each recommendation could potentially reduce burden or increase flexibility for providers without impinging on the health and safety of residents. In the proposed rule, we included a detailed discussion regarding interested parties' response to our solicitations for suggestions to reduce provider burden. In response to the proposed rule, we received a total of 1,503 public comments. In this final rule, we are finalizing two of the proposals, which we believe will have a significant impact on a facility's ability to recruit and retain qualified staff as well as, allowing older existing nursing homes to remain in compliance without having to completely rebuild their facility or have to use the Fire Safety Evaluation System (FSES). On July 14, 2022, we published a notice to extend the timeframe allowed to finalize the remaining proposals in the July 18, 2019 rule (87 FR 42137). We are continuing to evaluate those proposals and will issue an additional final rule if we choose to proceed with further rulemaking.

Responses to Public Comments and Provisions of the Final Rule

1. Food and Nutrition Services (§ 483.60)

Dietary standards for residents of LTC facilities are critical to both quality of care and quality of life. LTC interested parties have shared concerns regarding the current requirement that existing dietary staff include certified dietary managers or food service managers. Specifically, interested parties have concerns regarding the need for existing dietary staff, who are experienced in the duties of a dietary manager and currently operate in the position, to obtain new or additional training to become qualified under the current regulatory requirements. We believe that effective management and oversight of the food and nutrition service is critical to the safety and well-being of all residents of a nursing facility. Therefore, we continue to believe that it is important that there are standards for the individuals who will lead this service. However, to address concerns from interested parties we proposed to revise the standards at § 483.60(a)(2) to increase flexibility, while providing that the director of food and nutrition services is an individual who has the appropriate competencies and skills necessary to oversee the functions of the food and nutrition services. Specifically, we proposed to revise the standards at § 483.60(a)(2)(i) and (ii) to provide that at a minimum an individual designated as the director of food and nutrition services would have 2 or more years of experience in the position of a director of food and nutrition services, or have completed a minimum course of study in food safety that would include topics integral to managing dietary operations such as, but not limited to, foodborne illness, sanitation procedures, and food purchasing/receiving. We are retaining the existing requirement at § 483.60(a)(2)(iii) which specifies that the director of food and nutrition services must receive frequently scheduled consultations from a qualified dietitian or other clinically qualified nutrition professional. We noted in the proposed rule that these revisions will maintain established standards for the director of food and nutrition services given the critical aspects of their job function, while addressing concerns related to costs associated with training existing staff and the potential need to hire new staff.

We received public comments on these proposals. The following is a summary of the comments we received and our responses.

Comment: Some commenters supported the proposal stating that the changes would increase flexibility for providers to be able to recruit and retain important staff members, and also allow experienced professionals to remain in their roles. Other commenters had significant concerns and stated that the proposed qualification requirements were insufficient since some knowledge necessary for the position could not be gained through experience alone. For example, commenters noted that the knowledge and expertise received during the Certified Dietary Manager (CDM) certification required courses are not necessarily skills staff would learn from experience. These commenters encouraged CMS to retain the current requirements for the director of food and nutrition services.

Response: We appreciate the feedback and agree that increased flexibility for recruitment and staff retention is important. However, we also acknowledge that some knowledge obtained through education may not be easily gained through experience alone. We agree with the commenters that certain training/education should be required for anyone seeking to qualify as the director of food and nutrition services, including those experienced staff. Therefore, we are revising the proposal to allow a person who has 2 or more years of experience in the position and has completed a minimum course of study in food safety to meet the requirement by October 1, 2023, to qualify. These modifications to the requirements at § 483.60 will allow for more flexibility and will help providers with recruiting and retaining qualified staff, while also providing for an adequate minimum standard of education for the position. We believe that there are many paths to obtaining the knowledge and skills necessary to meet these requirements. Therefore, the experience qualifier is only one option for meeting the requirements for the director of food and nutrition services.

Therefore, the director of food and nutrition services must meet the following requirements, some of which remain unchanged from our current regulations:

• In States that have established standards for food service managers or dietary managers, meets State requirements for food service managers or dietary managers (existing § 483.60(a)(2)(ii)); and
• Receive frequently scheduled consultations from a qualified dietitian or other clinically qualified nutrition professional (existing § 483.60(a)(2)(iii)).

In addition, the director will need to meet the conditions of one of the following five options, four of which are retained from the existing rule:

• Have 2 or more years of experience in the position of a director of food and nutrition services, and have completed a minimum course of study in food safety, by no later than 1 year following the effective date of this rule, that includes topics integral to managing dietary operations such as, but not limited to, foodborne illness, sanitation procedures, food purchasing/receiving, etc. (new § 483.60(a)(2)(i)(E)) (we note that this would essentially be the equivalent of a ServSafe Food Manager certification); or

• Be a certified dietary manager (existing § 483.60(a)(2)(i)(A)); or
• Be a certified food service manager (existing § 483.60(a)(2)(i)(B)); or
• Have similar national certification for food service management and safety from a national certifying body(existing § 483.60(a)(2)(i)(C)); or
• Have an associate's or higher degree in food service management or in hospitality, if the course study includes food service or restaurant management, from an accredited institution of higher learning (existing § 483.60(a)(2)(i)(D)).

We believe that maintaining qualified and trained food and nutrition personnel is critical to the health and safety of residents in LTC facilities. We note that issues with food and nutrition requirements are the 3rd most frequently cited deficiencies in LTC facilities. We believe that these requirements will help ensure resident safety while also allowing facilities the flexibility to staff according to their unique needs and resources.

Comment: Many commenters recommended this requirement be phased in over 3 years to allow providers and professionals the time they need to obtain the necessary certifications, which require 15 to 18 months and an investment of more than $2,000 for the course, textbooks, fees, and to sit for the exam.

Response: We do not agree that a phase-in is necessary. As discussed in detail in the previous response, we have revised the requirements to allow 1 year for an experienced director of food and nutrition services to obtain training necessary to qualify for the position. Experience plus a minimum course of study is one of five ways to qualify for the position of the director of food and nutrition services. Given the many options available to qualify as well as the importance of food and safety in nursing homes, we do not believe that a 3-year delay in implementing the requirements is necessary or in the best interest of resident health and safety. We believe that all required staff will be able to meet the requirements.

After consideration of public comments, we are finalizing our proposal with the following changes—

• We are withdrawing our proposal at § 483.60(a)(2) to replace the existing qualifications for the director of food and nutrition services with an experience qualification and minimum course of study exclusively.
• We are revising § 483.60(a)(2)(i), to add experience in the position as one of the ways to qualify for the position of the director of food and nutrition services. Specifically, an individual who, on the effective date of this final rule, has 2 or more years of experience in the position of director of food and nutrition services in a nursing facility setting and has completed a course of study in food safety and management by no later than October 1, 2023, along with the other requirements set out at § 483.60(a)(2), is qualified to be the director of food and nutrition services.

2. Physical Environment (§ 483.90)

a. Life Safety Code

On May 4, 2016, we published a final rule entitled, “Medicare and Medicaid; Fire Safety Requirements for Certain Health Care Facilities,” adopting the 2012 edition of the National Fire Protection Association (NFPA) 101 (81 FR 26871), also known as the Life Safety Code (LSC). One of the references in the LSC is NFPA 101A, Guide on Alternative Approaches to Life Safety, also known as the Fire Safety Evaluation System (FSES). The FSES was developed as a means of achieving and documenting an equivalent level of life safety without requiring literal compliance with the Life Safety Code. The FSES is a point score system which establishes the general overall level of fire safety for health care facilities as compared to explicit conformance to individual requirements outlined in the Life Safety Code. The system uses combinations of widely accepted fire safety systems and arrangements to provide a level of fire safety which has been judged to be at least equivalent to the level achieved through strict compliance with the Life Safety Code. Some LTC facilities that utilized the FSES in order to determine compliance with the containment, extinguishment and people movement requirements of the LSC were no longer able to achieve a passing score, on the FSES, because of a change in scoring.

To address this need, in the July 2019 rule, we proposed to allow those existing LTC facilities (those that were Medicare or Medicaid certified before July 5, 2016) that have previously used the FSES to determine equivalent fire protection levels, to use an alternate scoring methodology to meet the requirements. Specifically, we proposed to have facilities use the mandatory values provided in the proposed regulations text at § 483.90(a)(1)(iii) when determining compliance for containment, extinguishment and people movement requirements. In the proposed rule, we noted that allowing the use of the provided mandatory scoring values will continue to provide the same amount of safety for residents and staff as has been provided since we began utilizing the score values set out in the FSES. We also indicated that the proposed values would allow existing LTC facilities that previously met the FSES requirements to continue to do so without incurring great expense to change their construction types. We proposed to use the mandatory scoring values as shown in Table 18.

We proposed to include Table 18 at § 483.90(a)(1)(iii).

We received public comments on these proposals. The following is a summary of the comments we received and our responses.

Comment: Many commenters supported the proposed changes to allow LTC facilities to use the provided mandatory values found at § 483.90(a)(1)(iii) when determining compliance for containment, extinguishment and people movement requirements, especially the LTC facilities that are currently affected by this issue. Commenters stated that using the 2013 NFPA 101A (FSES) values create substantial and unnecessary hardships for providers, residents and staff. Since the adoption of the 2013 NFPA 101A several nursing homes have struggled to remain in compliance, and using the provided mandatory values is a much-needed change. Many facilities stated that they meet the 2001 FSES, but the 2013 FSES would require retrofitting and essentially put them out of business due to financial hardship. Using the FSES mandatory values would allow existing facilities that previously met the FSES requirements to continue to do so without incurring great expense to change construction type that will not substantially improve the safety of residents.

Response: We agree that using the proposed mandatory values at § 483.90(a)(1)(iii) would allow existing facilities to continue to operate without incurring additional expenses that might otherwise be necessary to achieve compliance. All of the affected facilities are completely sprinklered and would not be lowering their safety standards at all. We agree that using the mandatory values set forth in the chart at § 483.90(a)(1)(iii) would allow us to resolve the scoring issue immediately for the affected providers. Therefore, this fix will remain in place until CMS adopts a newer version of the LSC.

Comment: One commenter stated that revisions to the construction limits for existing nursing homes were proposed for the 2021 edition of NFPA 101 based on input from the long-term care industry and believe that the effectiveness and dependability of automatic sprinkler systems could allow facilities to continue to operate. The commenter stated that existing facilities installed automatic sprinklers in good faith to compensate for construction deficiencies and demonstrate equivalency via NFPA 101A-2001 prior to the adoption of the 2012 edition of the NFPA 101. The commenters stated that since facilities would be in compliance with the revised construction requirements of the 2021 edition of the NFPA 101, equivalency would not need to be demonstrated via an FSES. The commenter suggested that we not finalize this proposal, and instead institute a categorical waiver process for the affected facilities until CMS incorporated by reference the standards of the 2021 edition of the NFPA 101.

Response: We are aware that revisions to the NFPA 101 were finalized and issued August 11, 2021. We will need to go through notice and comment rulemaking in order to adopt the 2021 edition or a newer edition of the LSC, which could take up to 3 additional years. Using the values found in the chart at § 483.90(a)(1)(iii) will allow us to address the problem immediately and will remain in place until we adopt a newer version of the LSC.

Comment: Many commenters agreed that the FSES chart resulting from adoption of the 2012 Life Safety Code has created a huge unanticipated negative effect on certain types of existing building construction, which may result in such buildings being forced to relocate residents and close within the next 2 years without any reduction in the overall fire safety features such as smoke detectors, sprinklers, fire alarm systems and building construction. Modifying the FSES mandatory scoring values as proposed by CMS solves this problem.

Response: We do not want any facilities to potentially have to close or completely reconstruct their building because of the scoring system for the FSES. LTC facilities are currently required to meet the required health and safety standards based on the 2012 edition of the LSC and Health Care Facilities Code (NFPA 99). By using the FSES these facilities can demonstrate that although they may not meet a certain requirement such as the construction type for the current LSC requirements, they are able to demonstrate that they have other measures in place to provide the same or higher level of safety for residents and staff. We also know that all LTC facilities are fully sprinklered, which helps them maintain this higher level of safety. We are finalizing this provision as proposed to avoid any facility closures or displacement for residents and to avoid significant facility expenditures that may not be necessary.

After consideration of public comments, we are finalizing our proposed changes without modifications.

B. Summary of Public Comments and Responses to the Request for Information on Revising the Requirements for Long-Term Care Facilities To Establish Mandatory Minimum Staffing Levels

The COVID-19 Public Health Emergency has highlighted and exacerbated longstanding concerns with inadequate staffing in long-term care (LTC) facilities. The Biden-Harris Administration is committed to improving the quality of U.S. nursing homes so that seniors and others living in nursing homes get the reliable, high-quality care they deserve. As a result, we intend to propose in future rulemaking the minimum standards for staffing adequacy that nursing homes would be required to meet. We will conduct a new research study to help inform policy decisions related to determining the level and type of staffing needed to ensure safe and quality care and expect to issue proposed rules within one year. In the Medicare Program; Prospective Payment System and Consolidated Billing for Skilled Nursing Facilities; Updates to the Quality Reporting Program and Value-Based Purchasing Program for Federal Fiscal Year 2023; Request for Information on Revising the Requirements for Long-Term Care Facilities To Establish Mandatory Minimum Staffing Levels proposed rule (87 FR 22720), we solicited public comments on opportunities to improve our health and safety standards to promote thoughtful, informed staffing plans and decisions within LTC facilities that aim to meet resident needs, including maintaining or improving resident function and quality of life. We stated that such an approach is essential to effective person-centered care and that we are considering policy options for future rulemaking to establish specific minimum direct care staffing standards and are seeking stakeholder input to inform our policy decisions.

Specifically, we solicited stakeholder input on options for future rulemaking regarding adequate staffing levels and we asked questions that we should consider as we evaluate future policy options (87 FR 22794 through 22795).

Comment: We received 3,129 comments from a variety of interested parties involved in long-term care issues, including advocacy groups, long-term care ombudsmen, industry associations (providers), labor unions and organizations, nursing home staff and administrators, industry experts and other researchers, family members and caretakers of nursing home residents. Overall, commenters were generally supportive of establishing a minimum staffing requirement, whereas other commenters were opposed. Commenters supporting the establishment of a minimum staffing requirement voiced safety concerns regarding residents not receiving adequate care due to chronic understaffing in facilities. Commenters offered examples of residents going entire shifts without receiving toileting assistance, which can lead to an increase in falls or presence of pressure ulcers. Other commenters shared stories of residents wearing the same outfit for a week without a change of clothing or a shower. These commenters highlighted the contributions of facility staff and greatly attributed these incidences and lack of quality care to insufficient staffing levels. Commenters offered recommendations for implementing minimum staffing requirements, with some commenters suggesting that CMS focus on implementing an acuity staffing model per shift instead of a minimum staffing requirement, while others recommended that minimum staffing levels be established for residents with the lowest care needs, assessed using the MDS 3.0 assessment forms, citing concerns that acuity-based minimums will be more susceptible to gaming. Commenters also provided information on several resident and facility factors for consideration when assessing a facility's ability to meet any mandated staffing standard, including whether or not the facility may have a higher Medicaid census, larger bed size, for profit ownership, higher county SNF competition, and, for staffing RNs specifically, higher community poverty and lower Medicare census. Other commenters stated that resident acuity should be a primary determinant in establishing minimum staffing standards, noting that CMS pays nursing homes based on resident acuity level.

We also received comments on factors impacting facilities' ability to recruit and retain staff, with most commenters in support of creating avenues for competitive wages for nursing home staff to address issues of recruitment and retention and other commenters suggesting that skilled nursing facility payments are continuing to be cut, complicating facilities ability to increase staff wages and benefits.

Finally, we received comments on the cost impacts of establishing staffing standards, payment, and study design. Some commenters pointed to the variability of Medicaid labor reimbursement amounts and how many States' Medicaid rates do not keep pace with rising labor costs while others noted that evidence shows most facilities have adequate resources to increase their staffing levels without additional Medicaid resources and pointed to a recent study documenting that most major publicly traded nursing home companies were highly profitable, even during the COVID pandemic. Commenters provided robust feedback on the action design and method for implementing a nurse staffing requirement, with some noting that resident acuity could change on a daily basis and recommended that CMS establish benchmarks rather than absolute values in staffing requirements. Other commenters recommended using both minimum nursing hours per resident day (hprd) and nurse to resident ratios.

Response: We appreciate the robust response we received on this RFI. As noted, staff levels in nursing homes have a substantial impact on the quality of care and outcomes residents experience. The input received will be used in conjunction with a new research study being conducted by CMS to determine the level and type of nursing home staffing needed to ensure safe and quality care. CMS intends to issue proposed rules on a minimum staffing level measure within one year. We will consider the feedback that we have received on this RFI for the upcoming rulemaking and changes to the LTC facility requirements for participation. This feedback from a wide range of interested parties will help to establish minimum staffing requirements that ensure all residents are provided safe, quality care, and that workers have the support they need to provide high-quality care.

X. Collection of Information Requirements

As explained below, this final rule will not impose any new or revised “collection of information” requirements or burden. Consequently, this final rule is not subject to the requirements of the Paperwork Reduction Act of 1995 (PRA) (44 U.S.C. 3501 et seq.). For the purpose of this section, collection of information is defined under 5 CFR 1320.3(c) of the PRA's implementing regulations.

With regard to the SNF QRP, in section VI.C.1. of this final rule, we are finalizing our proposal that SNFs submit data on the Influenza Vaccination Coverage among HCP measure beginning with the FY 2024 SNF QRP. We noted in the proposed rule that the CDC has a PRA waiver for the collection and reporting of vaccination data under section 321 of the National Childhood Vaccine Injury Act (NCVIA) (Pub. L. 99-660, enacted November 14, 1986).^[299] Since the burden is exempt from the requirements of the PRA, we set out such burden under the economic analysis section (see section X.A.5.) of the proposed rule. While the waiver is specific to the PRA's requirements (“Chapter 35 of Title 44, United States Code”), our economic analysis requirements are not waived by any such statutes. We refer readers to section X.A.5. of the proposed rule, where we provided an estimate of the burden to SNFs.

In section VI.C.2. of this final rule, we are finalizing our proposal to revise the compliance date for certain SNF QRP reporting requirements including the Transfer of Health information measures and certain standardized patient assessment data elements (including race, ethnicity, preferred language, need for interpreter, health literacy, and social isolation). The finalized change in compliance date will have no impact on any requirements or burden estimates; both proposals are active and accounted for under OMB control number 0938-1140 (CMS-10387). Consequently, we did not finalize any changes under that control number.

In section VI.C.3. of this final rule, we are finalizing our proposed revisions to the regulatory text. The finalized revisions will have no collection of information implications.

With regard to the SNF VBP Program, in section VIII.B.1.b. of this final rule, we are finalizing our proposal to suppress the SNFRM for scoring and payment purposes for the FY 2023 SNF VBP program year. This measure is calculated using Medicare FFS claims data, and our suppression of data on this measure for the FY 2023 program year will not create any new reporting burden for SNFs. We will publicly report the SNFRM rates for the FY 2023 program year, and we will make clear in the public presentation of those data that we are suppressing the use of those data for purposes of scoring and payment adjustments in the FY 2023 SNF VBP Program given the significant changes in SNF patient case volume and facility-level case mix, as described in section VIII.H.1. of this final rule. In sections VIII.B.3.b. and VIII.B.3.c. of this final rule, we are finalizing the adoption of two additional measures (the SNF Healthcare-Associated Infections (HAI) Requiring Hospitalization and the Total Nursing Hours per Resident Day/Payroll-Based Journal (Total Nurse Staffing) measures) beginning with the FY 2026 Program. The SNF HAI measure is calculated using Medicare FFS claims data, therefore, this measure will not create any new reporting burden for SNFs. The Total Nurse Staffing measure is calculated using data that SNFs currently report to CMS under the Nursing Home Five-Star Quality Rating System, and therefore, this will not create new reporting burden for SNFs.

In section VIII.B.3.d. of this final rule, we are finalizing the adoption of the DTC PAC Measure for SNFs beginning with the FY 2027 Program. The DTC PAC SNF measure is calculated using Medicare FFS claims data; therefore, this measure will not create a new reporting burden for SNFs.

The aforementioned FFS-related claims submission requirements and burden are active and approved by OMB under control number 0938-1140 (CMS-10387). This rule's changes will have no impact on the requirements and burden that are currently approved under that control number.

XI. Economic Analyses

A. Regulatory Impact Analysis

1. Statement of Need

a. Statutory Provisions

This final rule updates the FY 2023 SNF prospective payment rates as required under section 1888(e)(4)(E) of the Act. It also responds to section 1888(e)(4)(H) of the Act, which requires the Secretary to provide for publication in the Federal Register before the August 1 that precedes the start of each FY, the unadjusted Federal per diem rates, the case-mix classification system, and the factors to be applied in making the area wage adjustment. These are statutory provisions that prescribe a detailed methodology for calculating and disseminating payment rates under the SNF PPS, and we do not have the discretion to adopt an alternative approach on these issues.

With respect to the SNF QRP, this final rule updates the FY 2024 SNF QRP requirements. Section 1888(e)(6) of the Act authorizes the SNF QRP and applies to freestanding SNFs, SNFs affiliated with acute care facilities, and all non-critical access hospital (CAH) swing-bed rural hospitals. We finalize one new measure which we believe will encourage healthcare personnel to receive the influenza vaccine, resulting in fewer cases, less hospitalizations, and lower mortality associated with the virus. We finalize a revision to the compliance date for certain SNF QRP reporting requirements to improve data collection to allow for better measurement and reporting on equity across post-acute care programs and policies. For consistency in our regulations, we are also finalizing conforming revisions to the Requirements under the SNF QRP at § 413.360.

With respect to the SNF VBP Program, this final rule updates SNF VBP Program requirements for FY 2023 and subsequent years, including a policy to suppress the Skilled Nursing Facility 30-Day All-Cause Readmission Measure (SNFRM) for the FY 2023 SNF VBP Program Year for scoring and payment adjustment purposes. In addition, section 1888(h)(3) of the Act requires the Secretary to establish and announce performance standards for SNF VBP Program measures no later than 60 days before the performance period, and this final rule finalizes numerical values of the performance standards for the all-cause, all-condition hospital readmission measure. Section 1888(h)(2)(A)(ii) of the Act (as amended by section 111(a)(2)(C) of the Consolidated Appropriations Act, 2021 (Pub. L. 116-120)) allows the Secretary to add up to nine new measures to the SNF VBP Program, and in this final rule we are also adding two new measures to the SNF VBP Program beginning with the FY 2026 SNF VBP program year and one new measure beginning with the FY 2027 program year and finalizing several updates to the scoring methodology beginning with the FY 2026 program year. We have updated regulations at § 413.338 in accordance with these updates.

With respect to LTC physical environment changes and the changes to the requirements for the Director of Food and Nutrition Services in LTC facilities, sections 1819 and 1919 of the Act, authorize the Secretary to issue requirements for participation in Medicare and Medicaid, including such regulations as may be necessary to protect the health and safety of residents (sections 1819(d)(4)(B) and 1919(d)(4)(B) of the Act). Such regulations are codified in the implementing regulations at 42 CFR part 483, subpart B.

b. Discretionary Provisions

In addition, this final rule includes the following discretionary provisions:

(1) Recalibrating the Patient Driven Payment Model (PDPM) Parity Adjustment

As a policy decision to ensure on-going budget neutral implementation of the new case mix system, the PDPM, we proposed a recalibration of the PDPM parity adjustment. Since October 1, 2019, we have been monitoring the implementation of PDPM and our analysis of FY 2020 and FY 2021 data reveals that the PDPM implementation led to an increase in Medicare Part A SNF spending, even after accounting for the effects of the COVID-19 PHE. We noted that recalibrating the PDPM parity adjustment and reducing SNF spending by 4.6 percent, or $1.7 billion, in FY 2023 with no delayed implementation or phase-in period would allow for the most rapid establishment of payments at the appropriate level. This would work to ensure that PDPM will be budget-neutral as intended and prevent continuing accumulation of excess SNF payments, which we cannot recoup. However, while we received few comments on the methodology used to calculate the PDPM parity adjustment, we received a significant number of comments recommending that CMS use a phased approach in implementing the recalibration of the parity adjustment. These comments, and our responses, are discussed in section VI.C of this final rule. Considering these comments, in this final rule, we are finalizing the proposed recalibration of the PDPM parity adjustment with a 2-year phase-in, resulting in a reduction in FY 2023 of 2.3 percent, or $780 million, and a reduction in FY 2024 of 2.3 percent.

(2) SNF Forecast Error Adjustment

Each year, we evaluate the market basket forecast error for the most recent year for which historical data is available. The forecast error is determined by comparing the projected market basket increase in a given year with the actual market basket increase in that year. In evaluating the data for FY 2021, we found that the forecast error for FY 2021 was 1.5 percentage point, exceeding the 0.5 percentage point threshold we established in regulation for proposing adjustments to correct for forecast error. Given that the forecast error exceeds the 0.5 percentage threshold, current regulations require that the SNF market basket percentage change for FY 2023 be increased by 1.5 percentage point.

(3) Proposed Permanent Cap on Wage Index Decreases

The Secretary has broad authority to establish appropriate payment adjustments under the SNF PPS, including the wage index adjustment. As discussed earlier in this section, the SNF PPS regulations require us to use an appropriate wage index based on the best available data. For the reasons discussed earlier in this section, we believe that a 5-percent cap on wage index decreases would be appropriate for the SNF PPS. Therefore, for FY 2023 and subsequent years, we proposed to apply a permanent 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, regardless of the circumstances causing the decline. In this final rule, we are finalizing this proposed cap, as proposed.

(4) Technical Updates to ICD-10 Mappings

Each year, the ICD-10 Coordination and Maintenance Committee, a Federal interdepartmental committee that is chaired by representatives from the National Center for Health Statistics (NCHS) and by representatives from CMS, meets biannually and publishes updates to the ICD-10 medical code data sets in June of each year. These changes become effective October 1 of the year in which these updates are issued by the committee. The ICD-10 Coordination and Maintenance Committee also has the ability to make changes to the ICD-10 medical code data sets effective on April 1 of each year. In the proposed rule, we proposed several changes to the ICD-10 code mappings and lists. In this final rule, we are finalizing these proposed changes to the PDPM ICD-10 mappings, as proposed.

2. Introduction

We have examined the impacts of this final rule as required by Executive Order 12866 on Regulatory Planning and Review (September 30, 1993), Executive Order 13563 on Improving Regulation and Regulatory Review (January 18, 2011), the Regulatory Flexibility Act (RFA, September 19, 1980, Pub. L. 96-354), section 1102(b) of the Act, section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA, March 22, 1995; Pub. L. 104-4), Executive Order 13132 on Federalism (August 4, 1999), and the Congressional Review Act (5 U.S.C. 804(2)).

Executive Orders 12866 and 13563 direct agencies to assess all costs and benefits of available regulatory alternatives and, if regulation is necessary, to select regulatory approaches that maximize net benefits (including potential economic, environmental, public health and safety effects, distributive impacts, and equity). Executive Order 13563 emphasizes the importance of quantifying both costs and benefits, of reducing costs, of harmonizing rules, and of promoting flexibility. Based on our estimates, OMB's Office of Information and Regulatory Affairs has determined this rulemaking is “economically significant” as measured by the $100 million threshold. Accordingly, we have prepared a regulatory impact analysis (RIA) as further discussed below.

3. Overall Impacts

This rule updates the SNF PPS rates contained in the SNF PPS final rule for FY 2022 (86 FR 42424). We estimated in the proposed rule that the aggregate impact would be a decrease of approximately $320 million (0.9 percent) in Part A payments to SNFs in FY 2023. This reflected a $1.4 billion (3.9 percent) increase from the proposed update to the payment rates and a $1.7 billion (4.6 percent) decrease from the proposed reduction to the SNF payment rates to account for the recalibrated parity adjustment. We noted in the proposed rule that these impact numbers do not incorporate the SNF VBP Program reductions that we estimated would total $185.55 million in FY 2023. We noted in the proposed rule that events may occur to limit the scope or accuracy of our impact analysis, as this analysis is future-oriented, and thus, very susceptible to forecasting errors due to events that may occur within the assessed impact time period.

For this final rule, as noted in section IV.B. of this final rule, we have updated the productivity-adjusted market basket increase factor for FY 2023 based on a more recent forecast. Additionally, as discussed in section VI.C of this final rule, we are finalizing a 2-year phase-in for recalibrating the PDPM parity adjustment. As a result, we estimate that the aggregate impact of the provisions in this final rule will result in an estimated net increase in SNF payments of 2.7 percent, or $904 million, for FY 2023. This reflects a 5.1 percent increase from the final update to the payment rates and a 2.3 percent decrease from the reduction to the SNF payment rates to account for the recalibrated parity adjustment, using the formula to multiply the percentage change described in section X.A.4. of this final rule.

In accordance with sections 1888(e)(4)(E) and (e)(5) of the Act and implementing regulations at § 413.337(d), we are updating the FY 2022 payment rates by a factor equal to the market basket index percentage change increased by the forecast error adjustment and reduced by the productivity adjustment to determine the payment rates for FY 2023. The impact to Medicare is included in the total column of Table 19. When we proposed the SNF PPS rates for FY 2023, we proposed a number of standard annual revisions and clarifications as mentioned in the proposed rule.

The annual update in this rule applies to SNF PPS payments in FY 2023. Accordingly, the analysis of the impact of the annual update that follows only describes the impact of this single year. Furthermore, in accordance with the requirements of the Act, we will publish a rule or notice for each subsequent FY that will provide for an update to the payment rates and include an associated impact analysis.

4. Detailed Economic Analysis

The FY 2023 SNF PPS payment impacts appear in Table 19. Using the most recently available data, in this case FY 2021 we apply the current FY 2022 CMIs, wage index and labor-related share value to the number of payment days to simulate FY 2022 payments. Then, using the same FY 2021 data, we apply the FY 2023 CMIs, wage index and labor-related share value to simulate FY 2023 payments. We noted in the proposed rule that, given that this same data is being used for both parts of this calculation, as compared to other analyses discussed in the proposed rule which compare data from FY 2020 to data from other fiscal years, any issues discussed throughout this rule with regard to data collected in FY 2020 will not cause any difference in this economic analysis. We tabulate the resulting payments according to the classifications in Table 19 (for example, facility type, geographic region, facility ownership), and compare the simulated FY 2022 payments to the simulated FY 2023 payments to determine the overall impact. The breakdown of the various categories of data in Table 19 is as follows:

• The first column shows the breakdown of all SNFs by urban or rural status, hospital-based or freestanding status, census region, and ownership.
• The first row of figures describes the estimated effects of the various proposed changes on all facilities. The next six rows show the effects on facilities split by hospital-based, freestanding, urban, and rural categories. The next nineteen rows show the effects on facilities by urban versus rural status by census region. The last three rows show the effects on facilities by ownership (that is, government, profit, and non-profit status).
• The second column shows the number of facilities in the impact database.
• The third column shows the effect of the proposed parity adjustment recalibration discussed in section V.C. of this final rule.
• The fourth column shows the effect of the annual update to the wage index. This represents the effect of using the most recent wage data available as well as accounts for the 5 percent cap on wage index transitions, discussed in section VI.A. of this final rule. The total impact of this change is 0.0 percent; however, there are distributional effects of the proposed change.
• The fifth column shows the effect of all of the changes on the FY 2023 payments. The update of 5.1 percent is constant for all providers and, though not shown individually, is included in the total column. It is projected that aggregate payments would increase by 5.1 percent, assuming facilities do not change their care delivery and billing practices in response.

As illustrated in Table 19, the combined effects of all of the changes vary by specific types of providers and by location. For example, due to changes in this final rule, rural providers would experience a 2.5 percent increase in FY 2023 total payments.

In this chart and throughout the rule, we use a multiplicative formula to derive total percentage change. This formula is:

(1 + Parity Adjustment Percentage) * (1 + Wage Index Update Percentage) * (1 + Payment Rate Update Percentage)−1 = Total Percentage Change

For example, the figures shown in Column 5 of Table 19 are calculated by multiplying the percentage changes using this formula. Thus, the Total Change figure for the Total Group Category is 2.7 percent, which is (1−2.3%) * (1 + 0.0%) * (1 + 5.1%)−1.

As a result of rounding and the use of this multiplicative formula based on percentage, derived dollar estimates may not sum.

5. Impacts for the Skilled Nursing Facility Quality Reporting Program (SNF QRP) for FY 2023

Estimated impacts for the SNF QRP are based on analysis discussed in section IX.B. of the proposed rule.

In accordance with section 1888(e)(6)(A)(i) of the Act, the Secretary must reduce by 2 percentage points the annual payment update applicable to a SNF for a fiscal year if the SNF does not comply with the requirements of the SNF QRP for that fiscal year. In section VI.A. of the proposed rule, we discussed the method for applying the 2-percentage point reduction to SNFs that fail to meet the SNF QRP requirements.

As discussed in section VI.C.1. of the proposed rule, we proposed the adoption of one new measure to the SNF QRP beginning with the FY 2024 SNF QRP, the Influenza Vaccination Coverage among HCP (NQF #0431) measure. We believe that the burden associated with the SNF QRP is the time and effort associated with complying with the non-claims-based measures requirements of the SNF QRP. Although the burden associated with the Influenza Vaccination Coverage among HCP (NQF #0431) measure is not accounted for under the Centers for Diseases Control and Prevention Paperwork Reduction Act (CDC PRA) package due to the NCVIA waiver discussed in section IX. of this final rule, the cost and burden are discussed here.

Consistent with the CDC's experience of collecting data using the NHSN, we estimated that it would take each SNF an average of 15 minutes per year to collect data for the Influenza Vaccination Coverage among HCP (NQF #0431) measure and enter it into NHSN. We did not estimate that it will take SNFs additional time to input their data into NHSN, once they have logged onto the system for the purpose of submitting their monthly COVID-19 vaccine report. We believe it would take an administrative assistant 15 minutes to enter this data into NHSN. For the purposes of calculating the costs associated with the collection of information requirements, we obtained mean hourly wages from the U.S. Bureau of Labor Statistics' May 2020 National Occupational Employment and Wage Estimates.^[300] To account for overhead and fringe benefits, we have doubled the hourly wage. These amounts are detailed in Table 20.

Based on this time range, it would cost each SNF an average cost of $9.38 each year. We believe the data submission for the Influenza Vaccination Coverage among HCP (NQF #0431) measure would cause SNFs to incur additional average burden of 15 minutes per year for each SNF and a total annual burden of 3,868 hours across all SNFs. The estimated annual cost across all 15,472 SNFs in the U.S. for the submission of the Influenza Vaccination Coverage among HCP (NQF #0431) measure would be an average of $145,127.36.

As discussed in section VII.C.2. of the proposed rule, we proposed that SNFs would begin collecting data on two quality measures and certain standardized patient assessment data elements beginning with discharges on October 1, 2023. CMS estimated the impacts for collecting the new data elements in the FY 2020 SNF PPS final rule (84 FR 38829). When we delayed the compliance date for certain reporting requirements under the SNF QRP in the May 8th COVID-19 IFC, we did not remove the impacts for the new reporting requirements. However, we are providing updated impact information.

For these two quality measures, we are adding 4 data elements on discharge which would require an additional 1.2 minutes of nursing staff time per discharge. We estimate these data elements for these quality measures would be completed by registered nurses (25 percent of the time or 0.30 minutes) and by licensed practical and vocational nurses (75 percent of the time or 0.90 minutes). For the purposes of calculating the costs associated with the collection of information requirements, we obtained mean hourly wages from the U.S. Bureau of Labor Statistics' May 2020 National Occupational Employment and Wage Estimates.^[301] To account for overhead and fringe benefits, we have doubled the hourly wage. These amounts are detailed in Table 21.

With 2,406,401 discharges from 15,472 SNFs annually, we estimate an annual burden of 48,128 additional hours (2,406,401 discharges × 1.2 min/60) at a cost of $2,664,127 (2,406,401 × [(0.30/60 × $76.94/hr) + (0.90/60 × $48.16/hr)]). For each SNF we estimate an annual burden of 3.11 hours (48,128 hr/15,472 SNFs) at a cost of $172.19 ($2,664,127/15,472 SNFs).

We also proposed SNFs would begin collecting data on certain standardized patient assessment data elements, beginning with admissions and discharges (except for the preferred language, need for interpreter services, hearing, vision, race, and ethnicity standardized patient assessment data elements, which would be collected at admission only) on October 1, 2023. If finalized as proposed, SNFs would use the MDS 3.0 V1.18.11 to submit SNF QRP data. We are finalizing requirements to collect 55.5 standardized patient assessment data elements consisting of 8 data elements on admission and 47.5 data elements on discharge beginning with the FY 2024 SNF QRP. We estimate that the data elements would take an additional 12.675 minutes of nursing staff time consisting of 1.725 minutes to report on each admission and 10.95 minutes to report on each discharge. We assume the added data elements would be performed by both registered nurses (25 percent of the time or 3.169 minutes) and licensed practical and vocational (75 percent of the time or 9.506 minutes). We estimate the reporting of these assessment items will impose an annual burden of 508,352 total hours (2,406,401 discharges × 12.675 min/60) at a cost of $28,139,825 ((508,352 hr × 0.25 × $76.94/hr) + (508,352 hr × 0.75 × $48.16/hr)). For each SNF the annual burden is 32.86 hours (508,352 hr/15,472 SNFs) at a cost of $1,818.76 ($28,139,825/15,472 SNFs). The overall annual cost of the finalized changes associated with the newly added 59.5 assessment items is estimated at $1,990.95 per SNF annually ($172.19 + $1,818.76), or $30,803,952 ($2,664,127 + $28,139,825) for all 15,472 SNFs annually.

We proposed in section VI.C.3. of the proposed rule to make certain revisions in the regulation text itself at § 413.360 to include new paragraph (f) to reflect all the data completion thresholds required for SNFs to meet the compliance threshold for the annual payment update, as well as certain conforming revisions. As discussed in section IX. of the final rule, this change would not affect the information collection burden for the SNF QRP.

We welcomed comments on the estimated time to collect influenza vaccination data and enter it into NHSN. We received public comments on this issue. The following is a summary of the comments we received and our responses.

Comment: One commenter expressed concern with respect to CMS' 15-minute burden estimate for reporting the measure, noting it may be an underestimation.

Response: The burden associated with the proposed measure is the time it takes to sign into the NHSN, complete the required NHSN forms and submit the data. We estimate that data collection and reporting of the measure into the NHSN should take approximately 15-minutes annually, and can be completed once they have logged onto the system for the purpose of submitting their monthly COVID-19 vaccine report. The commenter did not provide additional information to support why CMS' estimate did not capture the full burden for the reporting requirements. We are confident with this estimation since the measure has been reported in the IRF and LTCH quality reporting programs for several years. Additionally, all SNF providers have been using the NHSN for data submission for approximately 15 months, and therefore, have familiarity with it. Without additional information, we are unable to respond further.

Although we did not seek comment on the proposal to Revise the Compliance Date for the Transition of Health (TOH) information measures and certain standardized patient assessment data elements beginning with the FY 2024 QRP, we did receive one comment.

Comment: A commenter expressed concern with CMS' burden estimate of 3.11 hours annually for reporting of the TOH Information measures and 32.86 hours annually for the collection of the standardized patient assessment data elements, noting that it may not capture the full actual burden of the new reporting requirements.

Response: We interpret the commenter to be referring to CMS' estimated impacts for collecting the new data elements published in the FY 2020 SNF PPS final rule (84 FR 38829). However, the commenter did not provide additional information to support why CMS' estimate did not capture the full burden for the reporting requirements. The estimate is based on CMS' assumption that the data elements would be performed by both Registered Nurses and Licensed Practical Nurses. Without additional information, we are unable to respond further.

After consideration of public comments, we are finalizing our burden estimate for the data submission for the Influenza Vaccination Coverage among HCP (NQF #0431) measure. The burden estimate for the reporting of the TOH Information measures and collection of the standardized patient assessment data elements was finalized in the FY 2020 SNF PPS final rule (84 FR 38829).

6. Impacts for the SNF VBP Program

The estimated impacts of the FY 2023 SNF VBP Program are based on historical data and appear in Table 22. We modeled SNF performance in the Program using SNFRM data from FY 2018 as the baseline period and April 1st through December 1st, 2019 as the performance period. Additionally, we modeled a logistic exchange function with a payback percentage of 60 percent, as we finalized in the FY 2018 SNF PPS final rule (82 FR 36619 through 36621).

However, in section VIII.B.1 of this final rule, we discuss the suppression of the SNFRM for the FY 2023 program year. As finalized, we will award each participating SNF 60 percent of their 2 percent withhold. Additionally, we finalized our proposal to apply a case minimum requirement for the SNFRM in section VIII.E.3.b. of this final rule. In section VIII.E.5. of this final rule, we also finalized our proposal to remove the Low-Volume Adjustment policy beginning with the FY 2023 Program year. As a result of these provisions, SNFs that do not meet the case minimum specified for the FY 2023 program year will be excluded from the Program and will receive their full Federal per diem rate for that fiscal year. As finalized, this policy will maintain the overall payback percentage at 60 percent.

Based on the 60 percent payback percentage, we estimated that we will redistribute approximately $278.32 million (of the estimated $463.86 million in withheld funds) in value-based incentive payments to SNFs in FY 2023, which means that the SNF VBP Program is estimated to result in approximately $185.55 million in savings to the Medicare Program in FY 2023.

Our detailed analysis of the impacts of the FY 2023 SNF VBP Program is shown in Table 22.

In section VIII.B.2. of this final rule, we are adopting two additional measures (the SNF HAI and Total Nurse Staffing measures) beginning with the FY 2026 program year. Additionally, we finalized our proposal to apply a case minimum requirement for the SNF HAI and Total Nurse Staffing measures in section VIII.E.3.c. of this final rule. In section VIII.E.3.d. of this final rule, we also finalized our proposal to adopt a measure minimum policy for the FY 2026 program year. Therefore, we are providing estimated impacts of the FY 2026 SNF VBP Program, which are based on historical data and appear in Table 23. We modeled SNF performance in the Program using measure data from FY 2018 as the baseline period and FY 2019 as the performance period for the SNFRM, SNF HAI, and Total Nurse Staffing measures. Additionally, we modeled a logistic exchange function with a payback percentage of 60 percent, as we finalized in the FY 2018 SNF PPS final rule (82 FR 36619 through 36621), though we noted that the logistic exchange function and payback percentage policies could be reconsidered in a future rulemaking. Based on the 60 percent payback percentage, we estimated that we will redistribute approximately $296.44 million (of the estimated $494.07 million in withheld funds) in value-based incentive payments to SNFs in FY 2026, which means that the SNF VBP Program is estimated to result in approximately $197.63 million in savings to the Medicare Program in FY 2026.

Our detailed analysis of the impacts of the FY 2026 SNF VBP Program is shown in Table 23.

In section VIII.B.2. of this final rule, we are adopting one additional measure (the DTC PAC SNF measure) beginning with the FY 2027 program year. Additionally, we finalized our proposal to apply a case minimum requirement for the DTC PAC SNF measure in section VIII.E.3.c. of this final rule. In section VIII.E.3.d, of this final rule, we also finalized our proposal to adopt a measure minimum policy for the FY 2027 program year. Therefore, we are providing estimated impacts of the FY 2027 SNF VBP Program, which are based on historical data and appear in Table 24. We modeled SNF performance in the Program using measure data from FY 2018 (the SNFRM, SNF HAI, and Total Nurse Staffing measures) and FY 2017 through FY 2018 (the DTC PAC SNF measure) as the baseline period and FY 2019 (the SNFRM, SNF HAI, and Total Nurse Staffing measures) and FY 2019 through FY 2020 (the DTC PAC SNF measure) as the performance period. Additionally, we modeled a logistic exchange function with a payback percentage of 60 percent, as we finalized in the FY 2018 SNF PPS final rule (82 FR 36619 through 36621), though we noted that the logistic exchange function and payback percentage policies could be reconsidered in a future rule. Based on the 60 percent payback percentage, we estimated that we will redistribute approximately $294.67 million (of the estimated $491.12 million in withheld funds) in value-based incentive payments to SNFs in FY 2027, which means that the SNF VBP Program is estimated to result in approximately $196.45 million in savings to the Medicare Program in FY 2027.

Our detailed analysis of the impacts of the FY 2027 SNF VBP Program is shown in Table 24.

7. Impacts for LTC Physical Environment Changes

As discussed at section IX. of this rule, we are finalizing our proposal at § 483.90(a)(1)(iii) based on public comments. We are allowing those existing LTC facilities (those that were Medicare or Medicaid certified before July 5, 2016) that have previously used the FSES to determine equivalent fire protection levels, to continue to use the 2001 FSES mandatory values when determining compliance for containment, extinguishment and people movement requirements. This will allow existing LTC facilities that previously met the FSES requirements to continue to do so without incurring great expense to change construction type—essentially undertake an effort to completely rebuild.

While we do not have information on the number of facilities that undertake reconstruction in a given year, we can estimate the number of facilities placed at risk of a deficiency citation by these requirements, and thus the risk of being required to rebuild the structure in order to update the building's construction type, by considering the age of the facility and the building methodologies used in given time periods. We consulted with CMS Regional Office survey staff, and based on information received from them, we estimate that 50 facilities are directly impacted by the change in the scoring of the FSES and would no longer achieve a passing score on the FSES. We estimate the average size of the affected nursing homes to be roughly 25,000 sq. ft. The cost of construction per sq. ft. is estimated at $180 in 2013 dollars ( https://www.rsmeans.com/​model-pages/​nursing-home.aspx). Assuming a construction cost increase over this period of 10.33 percent using GDP deflator, the 2019 construction cost per square foot would be about $199 a square foot. The total savings from this proposal in 2019 dollars would be approximately $248,750,000 (25,000 sq. ft. × $199 per sq. ft. × 50 facilities).

This estimate assumes that essentially all these facilities would be replaced. Based on our research, we assume that there are two major and offsetting trends affecting the nursing home care market in coming decades: the increasing preference and ability of elderly and disabled adults to finance and obtain long term nursing care in their own homes; and the increasing number of elderly and disabled adults as the baby boom population ages.^[302 303] Assuming, absent specific evidence, that these two trends roughly offset each other, the preceding estimates are a reasonable projection of likely investment costs in new (or totally reconstructed) facilities. For purposes of annual cost estimates, we assume that those costs would be spread over 5 years, and would therefore be approximately $49,750,000 million annually in those years ($248,750,000 million/5 years). There are additional uncertainties in these estimates and we therefore provide estimates that are 25 percent lower and higher in Table 28.

8. Impacts for Changes to the Requirements for the Director of Food and Nutrition Services in LTC Facilities

As discussed in section IX. of this final rule, we are revising our proposal to revise the required qualifications for a director of food and nutrition services to provide that those with several years of experience performing as the director of food and nutrition services in a facility can continue to do so. In addition to the existing credentialing requirements for the director of food and nutrition services to include being a “certified food service manager,” or “certified dietary manager,” or “has similar national certification from a national certifying body,” or “has an associate's or higher degree in food service or restaurant management”, we have added that an individual with 2 or more years of experience and completion of a course in food safety and management may also meet the required qualifications. Under the October 2016 final rule, a significant fraction of current directors of food and nutrition services would have had to be replaced or, at great expense, have had to attend an institution of higher education to obtain required credentials.

The current annual cost for the director of food and nutrition services is an estimated $122,400 annually (updated to reflect current salary information and including fringe benefits and overhead costs). We previously estimated that 10 percent of facilities would need to pursue additional candidates that meet the new qualifications for a director of food and nutrition services. Assuming that, on average, there is a 10 percent wage differential between those with experience but no further credentials, and those who would have met the standards of the October 2016 final rule for director of food and nutrition services either as specified in that rule, or by meeting the even higher standards for “qualified dietician,” this means that removing those standards would reduce costs to facilities by $18,929,840.00 (10 percent of 15,266 facilities × $12,400). In this calculation, the wage differential is assumed to be about 10 percent because there are offsetting costs to the facility for retaining staff who are qualified by experience but who may need expert help, such as the proposed requirement for frequently scheduled consultation with a qualified dietician.

We are requiring that an individual may also be designated as the director of food and nutrition services if they have 2 or more years of experience in the position and has completed a minimum course of study in food safety. These revisions will provide an experience qualifier that will likely eliminate the need for many facilities to hire additional or higher salaried staff.

9. Alternatives Considered

As described in this section, we estimate that the aggregate impact of the provisions in this final rule will result in an estimated net increase in SNF payments of 2.7 percent, or $904 million, for FY 2023. This reflects a 5.1 percent increase from the final update to the payment rates and a 2.3 percent decrease from the reduction to the SNF payment rates to account for the recalibrated parity adjustment, using the formula to multiply the percentage change described in section X.A.4. of this final rule.

Section 1888(e) of the Act establishes the SNF PPS for the payment of Medicare SNF services for cost reporting periods beginning on or after July 1, 1998. This section of the statute prescribes a detailed formula for calculating base payment rates under the SNF PPS, and does not provide for the use of any alternative methodology. It specifies that the base year cost data to be used for computing the SNF PPS payment rates must be from FY 1995 (October 1, 1994, through September 30, 1995). In accordance with the statute, we also incorporated a number of elements into the SNF PPS (for example, case-mix classification methodology, a market basket index, a wage index, and the urban and rural distinction used in the development or adjustment of the Federal rates). Further, section 1888(e)(4)(H) of the Act specifically requires us to disseminate the payment rates for each new FY through the Federal Register , and to do so before the August 1 that precedes the start of the new FY; accordingly, we are not pursuing alternatives for this process.

With regard to the alternatives considered related to the methodology for calculating the proposed parity adjustment to the rates, we considered numerous alternative approaches to the methodology, including alternative data sets, applying the parity adjustment to targeted components of the payment system, and delaying or phasing-in the parity adjustment. These alternatives were described in full detail in section V.C. of the proposed rule.

With regard to the proposal to add the HCP Influenza Vaccine measure to the SNF QRP Program, the COVID-19 pandemic has exposed the importance of implementing infection prevention strategies, including the promotion of HCP influenza vaccination. We believe this measure will encourage healthcare personnel to receive the influenza vaccine, resulting in fewer cases, less hospitalizations, and lower mortality associated with the virus, but were unable to identify any alternative methods for collecting the data. A compelling public need exists to target quality improvement among SNF providers and this proposed measure has the potential to generate actionable data on HCP vaccination rates.

With regard to the proposal to revise the compliance date for the MDS v1.18.11, section 1888(d)(6)(B)(i)(III) of the Act requires that, for fiscal years 2019 and each subsequent year, SNFs must report standardized patient assessment data required under section 1899B(b)(1) of the Act. Section 1899(a)(1)(C) of the Act requires, in part, the Secretary to modify the PAC assessment instruments in order for PAC providers, including SNFs, to submit standardized patient assessment data under the Medicare program. Further delay of collecting this data would delay compliance with the current regulations.

As discussed previously the burden for these proposals is minimal, and we believe the importance of the information necessitates these provisions.

With regard to the proposals for the SNF VBP Program, we discussed alternatives considered within those sections. In section VIII.B.2. of this final rule, we considered 4 options to adjust for COVID-19 in a technical update to the SNFRM. None of the alternatives will change the analysis of the impacts of the FY 2023 SNF VBP Program described in section VIII.B.2. of this final rule. In section VIII.C.2. of this final rule, we finalized our proposal to revise the baseline period for the FY 2025 SNF VBP Program to FY 2019. We considered using alternative baseline periods, including FY 2020 and FY 2022, but these options are operationally infeasible.

In section VIII.E.3.c. of this final rule, we finalized our proposal that SNFs must have a minimum of 25 residents, on average, across all available quarters during the applicable 1-year performance period in order to be eligible to receive a score on the Total Nurse Staffing measure. We tested three alternative case minimums for this measure: a 25-resident minimum, a minimum of one quarter of PBJ data, and a minimum of two quarters of PBJ data. After considering these alternatives, we determined that the proposed 25-resident minimum best balances quality measure reliability with our desire to score as many SNFs as possible on this measure.

In section VIII.E.3.d. of this final rule, we finalized our proposed measure minimums for the FY 2026 and FY 2027 SNF VBP Programs. SNFs that do not meet these minimum requirements would be excluded from the Program and would receive their full Federal per diem rate for that fiscal year. We also discussed alternatives, which are detailed below, that would result in more SNFs being excluded from the Program.

We finalized that for FY 2026, SNFs must have the minimum number of cases for two of the three measures during the performance period to receive a performance score and value-based incentive payment. Under these minimum requirements for the FY 2026 program year, we estimated that approximately 14 percent of SNFs would be excluded from the FY 2026 Program. Alternatively, if we required SNFs to have the minimum number of cases for all three measures during the performance period, approximately 21 percent of SNFs would be excluded from the FY 2026 Program. We also assessed the consistency of incentive payment multipliers (IPMs) between time periods as a proxy for performance score reliability under the different measure minimum options. The testing results indicated that the reliability of the SNF performance score would be relatively consistent across the different measure minimum requirements. Specifically, for the FY 2026 program year, we estimated that under the proposed minimum of two measures, 82 percent of SNFs receiving a net-negative IPM in the first testing period also received a net-negative IPM in the second testing period. Alternatively, under a minimum of three measures for the FY 2026 program year, we found that the consistency was 81 percent. Based on these testing results, we believe the minimum of two out of three measures for FY 2026 best balances SNF performance score reliability with our desire to ensure that as many SNFs as possible can receive a performance score and value-based incentive payment.

We finalized that for FY 2027, SNFs must have the minimum number of cases for three of the four measures during a performance period to receive a performance score and value-based incentive payment. Under these minimum requirements, we estimated that approximately 16 percent of SNFs would be excluded from the FY 2027 Program. Alternatively, if we required SNFs to report the minimum number of cases for all four measures, we estimated that approximately 24 percent of SNFs would be excluded from the FY 2027 Program. We also assessed the consistency of incentive payment multipliers (IPMs) between time periods as a proxy for performance score reliability under the different measure minimum options. The testing results indicated that the reliability of the SNF performance score for the FY 2027 program year would be relatively consistent across the different measure minimum requirements. That is, among the different measure minimums for the FY 2027 program year, a strong majority (between 85 and 87 percent) of the SNFs receiving a net-negative IPM for the first testing period also received a net-negative IPM for the second testing period. These findings indicated that increasing the measure minimum requirements did not meaningfully increase the consistency of the performance score. Based on these testing results, we believe the minimum of three out of four measures for FY 2027 best balances SNF performance score reliability with our desire to ensure that as many SNFs as possible can receive a performance score and value-based incentive payment.

10. Accounting Statement

As required by OMB Circular A-4 (available online at https://obamawhitehouse.archives.gov/​omb/​circulars_​a004_​a-4/​), in Tables 25 through 27, we have prepared an accounting statement showing the classification of the expenditures associated with the provisions of this final rule for FY 2023. Tables 19 and 25 provide our best estimate of the possible changes in Medicare payments under the SNF PPS as a result of the policies in this final rule, based on the data for 15,541 SNFs in our database. Table 26 provides our best estimate of the possible changes in Medicare payments under the SNF VBP as a result of the policies for this program. Tables 20 and 27 provide our best estimate of the additional cost to SNFs to submit the data for the SNF QRP as a result of the policies in this final rule. Table 28 provides our best estimate of the costs avoided by Medicare and Medicaid SNFs/NFs. This is our estimate of the aggregate costs of SNFs nationwide to rebuild facility structures for compliance for fire protection or LTC Physical Environment Changes. These costs will be avoided as a result of the policies in this final rule. Table 29 provides our best estimate of the amount saved by Medicare and Medicaid-participating SNFs/NFs to designate a director of Food and Nutrition (F&N) Services as a result of the policies in this final rule.

11. Conclusion

This rule updates the SNF PPS rates contained in the SNF PPS final rule for FY 2022 (86 FR 42424). Based on the above, we estimate that the overall payments for SNFs under the SNF PPS in FY 2023 are projected to increase by approximately $904 million, or 2.7 percent, compared with those in FY 2022. We estimate that in FY 2023, SNFs in urban and rural areas would experience, on average, a 2.7 percent increase and 2.5 percent increase, respectively, in estimated payments compared with FY 2022. Providers in the urban Pacific region would experience the largest estimated increase in payments of approximately 3.6 percent. Providers in the urban Outlying region would experience the smallest estimated increase in payments of 1.4 percent.

B. Regulatory Flexibility Act Analysis

The RFA requires agencies to analyze options for regulatory relief of small entities, if a rule has a significant impact on a substantial number of small entities. For purposes of the RFA, small entities include small businesses, non-profit organizations, and small governmental jurisdictions. Most SNFs and most other providers and suppliers are small entities, either by reason of their non-profit status or by having revenues of $30 million or less in any 1 year. We utilized the revenues of individual SNF providers (from recent Medicare Cost Reports) to classify a small business, and not the revenue of a larger firm with which they may be affiliated. As a result, for the purposes of the RFA, we estimate that almost all SNFs are small entities as that term is used in the RFA, according to the Small Business Administration's latest size standards (NAICS 623110), with total revenues of $30 million or less in any 1 year. (For details, see the Small Business Administration's website at https://www.sba.gov/​category/​navigation-structure/​contracting/​contracting-officials/​eligibility-size-standards.) In addition, approximately 20 percent of SNFs classified as small entities are non-profit organizations. Finally, individuals and states are not included in the definition of a small entity.

This rule updates the SNF PPS rates contained in the SNF PPS final rule for FY 2022 (86 FR 42424). Based on the above, we estimate that the aggregate impact for FY 2023 will be an increase of $904 million in payments to SNFs, resulting from the final SNF market basket update to the payment rates, reduced by the parity adjustment discussed in section VI.C. of this final rule, using the formula described in section X.A.4. of this rule. While it is projected in Table 19 that all providers would experience a net increase in payments, we note that some individual providers within the same region or group may experience different impacts on payments than others due to the distributional impact of the FY 2023 wage indexes and the degree of Medicare utilization.

Guidance issued by the Department of Health and Human Services on the proper assessment of the impact on small entities in rulemakings, utilizes a cost or revenue impact of 3 to 5 percent as a significance threshold under the RFA. In their March 2022 Report to Congress (available at https://www.medpac.gov/​wp-content/​uploads/​2022/​03/​Mar22_​MedPAC_​ReportToCongress_​Ch7_​SEC.pdf), MedPAC states that Medicare covers approximately 10 percent of total patient days in freestanding facilities and 17 percent of facility revenue (March 2022 MedPAC Report to Congress, 238). As indicated in Table 19, the effect on facilities is projected to be an aggregate positive impact of 2.7 percent for FY 2023. As the overall impact on the industry as a whole, and thus on small entities specifically, is less than the 3 to 5 percent threshold discussed previously, the Secretary has determined that this final rule will not have a significant impact on a substantial number of small entities for FY 2023.

In addition, section 1102(b) of the Act requires us to prepare a regulatory impact analysis if a rule may have a significant impact on the operations of a substantial number of small rural hospitals. This analysis must conform to the provisions of section 604 of the RFA. For purposes of section 1102(b) of the Act, we define a small rural hospital as a hospital that is located outside of an MSA and has fewer than 100 beds. This final rule will affect small rural hospitals that: (1) furnish SNF services under a swing-bed agreement or (2) have a hospital-based SNF. We anticipate that the impact on small rural hospitals would be similar to the impact on SNF providers overall. Moreover, as noted in previous SNF PPS final rules (most recently, the one for FY 2022 (86 FR 42424)), the category of small rural hospitals is included within the analysis of the impact of this final rule on small entities in general. As indicated in Table 19, the effect on facilities for FY 2023 is projected to be an aggregate positive impact of 2.7 percent. As the overall impact on the industry as a whole is less than the 3 to 5 percent threshold discussed above, the Secretary has determined that this final rule will not have a significant impact on a substantial number of small rural hospitals for FY 2023.

C. Unfunded Mandates Reform Act Analysis

Section 202 of the Unfunded Mandates Reform Act of 1995 also requires that agencies assess anticipated costs and benefits before issuing any rule whose mandates require spending in any 1 year of $100 million in 1995 dollars, updated annually for inflation. In 2022, that threshold is approximately $165 million. This final rule will impose no mandates on State, local, or tribal governments or on the private sector.

D. Federalism Analysis

Executive Order 13132 establishes certain requirements that an agency must meet when it issues a proposed rule (and subsequent final rule) that imposes substantial direct requirement costs on State and local governments, preempts State law, or otherwise has federalism implications. This final rule will have no substantial direct effect on State and local governments, preempt State law, or otherwise have federalism implications.

E. Regulatory Review Costs

If regulations impose administrative costs on private entities, such as the time needed to read and interpret this final rule, we should estimate the cost associated with regulatory review. Due to the uncertainty involved with accurately quantifying the number of entities that will review the rule, we assume that the total number of unique commenters on this year's proposed rule will be the number of reviewers of this year's final rule. We acknowledge that this assumption may understate or overstate the costs of reviewing this rule. It is possible that not all commenters reviewed this year's proposed rule in detail, and it is also possible that some reviewers chose not to comment on that proposed rule. For these reasons, we believe that the number of commenters on this year's proposed rule is a fair estimate of the number of reviewers of this year's final rule.

We also recognize that different types of entities are in many cases affected by mutually exclusive sections of this final rule, and therefore, for the purposes of our estimate we assume that each reviewer reads approximately 50 percent of the rule.

Using the national mean hourly wage data from the May 2020 BLS Occupational Employment Statistics (OES) for medical and health service managers (SOC 11-9111), we estimate that the cost of reviewing this rule is $114.24 per hour, including overhead and fringe benefits https://www.bls.gov/​oes/​current/​oes_​nat.htm. Assuming an average reading speed, we estimate that it would take approximately 4 hours for the staff to review half of the final rule. For each SNF that reviews the rule, the estimated cost is $456.96 (4 hours × $114.24). Therefore, we estimate that the total cost of reviewing this regulation is $3,185,011.20 ($456.96 × 6,970 reviewers).

In accordance with the provisions of Executive Order 12866, this final rule was reviewed by the Office of Management and Budget.

Chiquita Brooks-LaSure,

Administrator of the Centers for Medicare & Medicaid Services, approved this document on July 25, 2022.

List of Subjects

42 CFR Part 413

• Diseases
• Health facilities
• Medicare
• Puerto Rico
• Reporting and recordkeeping requirements

42 CFR Part 483

• Grant programs—health
• Health facilities
• Health professions
• Health records
• Medicaid
• Medicare
• Nursing homes
• Nutrition
• Reporting and recordkeeping requirements
• Safety

For the reasons set forth in the preamble, the Centers for Medicare & Medicaid Services amends 42 CFR chapter IV as set forth below:

PART 413—PRINCIPLES OF REASONABLE COST REIMBURSEMENT; PAYMENT FOR END-STAGE RENAL DISEASE SERVICES; PROSPECTIVELY DETERMINED PAYMENT RATES FOR SKILLED NURSING FACILITIES; PAYMENT FOR ACUTE KIDNEY INJURY DIALYSIS

1. The authority citation for part 413 continues to read as follows:

Authority: 42 U.S.C. 1302, 1395d(d), 1395f(b), 1395g, 1395I(a), (i), and (n), 1395x(v), 1395hh, 1395rr, 1395tt, and 1395ww.

2. Amend § 413.337 by revising paragraph (b)(4) to read as follows:

§ 413.337

Methodology for calculating the prospective payment rates.

* * * * *

(b) * * *

(4) Standardization of data for variation in area wage levels and case-mix. The cost data described in paragraph (b)(2) of this section are standardized to remove the effects of geographic variation in wage levels and facility variation in case-mix.

(i) The cost data are standardized for geographic variation in wage levels using the wage index. The application of the wage index is made on the basis of the location of the facility in an urban or rural area as defined in § 413.333.

(ii) Starting on October 1, 2022, CMS applies a cap on decreases to the wage index such that the wage index applied to a SNF is not less than 95 percent of the wage index applied to that SNF in the prior FY.

(iii) The cost data are standardized for facility variation in case-mix using the case-mix indices and other data that indicate facility case-mix.

* * * * *

3. Amend § 413.338 by—

a. Revising paragraphs (a)(1) and (4) through (17);

b. Revising paragraphs (b) and (c)(2)(i), paragraph (d) paragraph heading, and paragraph (d)(3);

c. Adding paragraphs (d)(5) and (6);

d. Redesignating paragraphs (e) through (g) as paragraphs (f) through (h);

e. Adding a new paragraph (e);

f. Revising newly redesignated paragraph (f)(1) and paragraph (f)(3) introductory text; and

g. Adding paragraphs (f)(4), (i), and (j).

The revisions and additions read as follows:

§ 413.338

Skilled nursing facility value-based purchasing program.

(a) * * *

(1) Achievement threshold (or achievement performance standard) means the 25th percentile of SNF performance on a measure during the baseline period for a fiscal year.

* * * * *

(4) Baseline period means the time period used to calculate the achievement threshold, benchmark, and improvement threshold that apply to a measure for a fiscal year.

(5) Benchmark means, for a fiscal year, the arithmetic mean of the top decile of SNF performance on a measure during the baseline period for that fiscal year.

(6) Eligible stay means, for purposes of the SNF readmission measure, an index SNF admission that would be included in the denominator of that measure.

(7) Improvement threshold (or improvement performance standard) means an individual SNF's performance on a measure during the applicable baseline period for that fiscal year.

(8) Logistic exchange function means the function used to translate a SNF's performance score into a value-based incentive payment percentage.

(9) Low-volume SNF means a SNF with fewer than 25 eligible stays included in the SNF readmission measure denominator during the performance period for each of fiscal years 2019 through 2022.

(10) Performance period means the time period during which SNF performance on a measure is calculated for a fiscal year.

(11) Performance score means the numeric score ranging from 0 to 100 awarded to each SNF based on its performance under the SNF VBP Program for a fiscal year.

(12) Performance standards are the levels of performance that SNFs must meet or exceed to earn points on a measure under the SNF VBP Program for a fiscal year.

(13) Ranking means the ordering of SNFs based on each SNF's performance score under the SNF VBP Program for a fiscal year.

(14) SNF readmission measure means, prior to October 1, 2019, the all-cause all-condition hospital readmission measure (SNFRM) or the all-condition risk-adjusted potentially preventable hospital readmission rate (SNFPPR) specified by CMS for application in the SNF Value-Based Purchasing Program. Beginning October 1, 2019, the term SNF readmission measure means the all-cause all-condition hospital readmission measure (SNFRM) or the all-condition risk-adjusted potentially preventable hospital readmission rate (Skilled Nursing Facility Potentially Preventable Readmissions after Hospital Discharge measure) specified by CMS for application in the SNF VBP Program.

(15) SNF Value-Based Purchasing (VBP) Program means the program required under section 1888(h) of the Act.

(16) Value-based incentive payment adjustment factor is the number that will be multiplied by the adjusted Federal per diem rate for services furnished by a SNF during a fiscal year, based on its performance score for that fiscal year, and after such rate is reduced by the applicable percent.

(17) Value-based incentive payment amount is the portion of a SNF's adjusted Federal per diem rate that is attributable to the SNF VBP Program.

(b) Applicability of the SNF VBP Program. The SNF VBP Program applies to SNFs, including facilities described in section 1888(e)(7)(B) of the Act. Beginning with fiscal year 2023, the SNF VBP Program does not include a SNF, with respect to a fiscal year, if:

(1) The SNF does not have the minimum number of cases that applies to each measure for the fiscal year, as specified by CMS; or

(2) The SNF does not have the minimum number of measures for the fiscal year, as specified by CMS.

(c) * * *

(2) * * *

(i) Total amount available for a fiscal year. The total amount available for value-based incentive payments for a fiscal year is at least 60 percent of the total amount of the reduction to the adjusted SNF PPS payments for that fiscal year, as estimated by CMS, and will be increased as appropriate for each fiscal year to account for the assignment of a performance score to low-volume SNFs under paragraph (d)(3) of this section. Beginning with the FY 2023 SNF VBP, the total amount for value-based incentive payments for a fiscal year is 60 percent of the total amount of the reduction to the adjusted SNF PPS payments for that fiscal year, as estimated by CMS.

* * * * *

(d) Performance scoring under the SNF VBP Program (applicable, as described in this paragraph, to fiscal year 2019 through and including fiscal year 2025).

* * * * *

(3) If, with respect to a fiscal year beginning with fiscal year 2019 through and including fiscal year 2022, CMS determines that a SNF is a low-volume SNF, CMS will assign a performance score to the SNF for the fiscal year that, when used to calculate the value-based incentive payment amount (as defined in paragraph (a)(17) of this section), results in a value-based incentive payment amount that is equal to the adjusted Federal per diem rate (as defined in paragraph (a)(2) of this section) that would apply to the SNF for the fiscal year without application of § 413.337(f).

* * * * *

(5) CMS will specify the measures for application in the SNF VBP Program for a given fiscal year.

(6)(i) Performance standards are announced no later than 60 days prior to the start of the performance period that applies to that measure for that fiscal year.

(ii) Beginning with the performance standards that apply to FY 2021, if CMS discovers an error in the performance standard calculations subsequent to publishing their numerical values for a fiscal year, CMS will update the numerical values to correct the error. If CMS subsequently discovers one or more other errors with respect to the same fiscal year, CMS will not further update the numerical values for that fiscal year.

(e) Performance scoring under the SNF VBP Program beginning with fiscal year 2026. (1) Points awarded based on SNF performance. CMS will award points to SNFs based on their performance on each measure for which the SNF reports the applicable minimum number of cases during the performance period applicable to that fiscal year as follows:

(i) CMS will award from 1 to 9 points for achievement to each SNF whose performance on a measure during the applicable performance period meets or exceeds the achievement threshold for that measure but is less than the benchmark for that measure.

(ii) CMS will award 10 points for achievement to a SNF whose performance on a measure during the applicable performance period meets or exceeds the benchmark for that measure.

(iii) CMS will award from 0 to 9 points for improvement to each SNF whose performance on a measure during the applicable performance period exceeds the improvement threshold but is less than the benchmark for that measure.

(iv) CMS will not award points for improvement to a SNF that does not meet the case minimum for a measure for the applicable baseline period.

(v) The highest of the SNF's achievement and improvement score for a given measure will be the SNF's score on that measure for the applicable fiscal year.

(2) Calculation of the SNF performance score. The SNF performance score for a fiscal year is calculated as follows:

(i) CMS will sum all points awarded to a SNF as described in paragraph (e)(1) of this section for each measure applicable to a fiscal year to calculate the SNF's point total.

(ii) CMS will normalize the point total such that the resulting SNF performance score is expressed as a number of points earned out of a total of 100.

(f) * * *

(1) CMS will provide quarterly confidential feedback reports to SNFs on their performance on each measure specified for the fiscal year. Beginning with the baseline period and performance period quality measure quarterly reports issued on or after October 1, 2021, which contain the baseline period and performance period measure rates, respectively, SNFs will have 30 days following the date CMS provides each of these reports to review and submit corrections to the measure rates contained in that report. The administrative claims data used to calculate measure rates are not subject to review and correction under paragraph (f)(1) of this section. All correction requests must be accompanied by appropriate evidence showing the basis for the correction to each of the applicable measure rates.

* * * * *

(3) CMS will publicly report the information described in paragraphs (f)(1) and (2) of this section on the Nursing Home Compare website or a successor website. Beginning with information publicly reported on or after October 1, 2019, and ending with information publicly reported on September 30, 2022 the following exceptions apply:

* * * * *

(4) Beginning with the information publicly reported on or after October 1, 2022, the following exceptions apply:

(i) If a SNF does not have the minimum number of cases during the baseline period that applies to a measure for a fiscal year, CMS will not publicly report the SNF's baseline period measure rate for that particular measure, although CMS will publicly report the SNF's performance period measure rate and achievement score if the SNF had the minimum number of cases for the measure during the performance period of the same program year;

(ii) If a SNF does not have the minimum number of cases during the performance period that applies to a measure for a fiscal year, CMS will not publicly report any information with respect to the SNF's performance on that measure for the fiscal year;

(iii) If a SNF does not have the minimum number of measures during the performance period for a fiscal year, CMS will not publicly report any data for that SNF for the fiscal year.

* * * * *

(i) Special rules for the FY 2023 SNF VBP Program. (1) CMS will calculate a SNF readmission measure rate for each SNF based on its performance on the SNF readmission measure during the performance period specified by CMS for fiscal year 2023, but CMS will not calculate a performance score for any SNF using the methodology described in paragraphs (d)(1) and (2) of this section. CMS will instead assign a performance score of zero to each SNF.

(2) CMS will calculate the value-based incentive payment adjustment factor for each SNF using a performance score of zero and will then calculate the value-based incentive payment amount for each SNF using the methodology described in paragraph (c)(2)(ii) of this section.

(3) CMS will provide confidential feedback reports to SNFs on their performance on the SNF readmission measure in accordance with paragraphs (f)(1) and (2) of this section.

(4) CMS will publicly report SNF performance on the SNF readmission measure in accordance with paragraph (f)(3) of this section.

(j) Validation. (1) Beginning with the FY 2023 Program year, for the SNFRM measure, information reported through claims for the SNFRM measure are validated for accuracy by Medicare Administrative Contractors (MACs) to ensure accurate Medicare payments.

(2) [Reserved]

4. Amend § 413.360 by—

a. Removing paragraph (b)(2);

b. Redesignating paragraph (b)(3) as paragraph (b)(2); and

c. Adding paragraph (f).

The addition reads as follows:

§ 413.360

Requirements under the Skilled Nursing Facility (SNF) Quality Reporting Program (QRP).

* * * * *

(f) Data completion threshold. (1) SNFs must meet or exceed two separate data completeness thresholds: One threshold set at 80 percent for completion of required quality measures data and standardized patient assessment data collected using the MDS submitted through the CMS designated data submission system; beginning with FY 2018 and for all subsequent payment updates; and a second threshold set at 100 percent for measures data collected and submitted using the CDC NHSN, beginning with FY 2023 and for all subsequent payment updates.

(2) These thresholds (80 percent for completion of required quality measures data and standardized patient assessment data on the MDS; 100 percent for CDC NHSN data) will apply to all measures and standardized patient assessment data requirements adopted into the SNF QRP.

(3) A SNF must meet or exceed both thresholds to avoid receiving a 2-percentage point reduction to their annual payment update for a given fiscal year.

PART 483—REQUIREMENTS FOR STATES AND LONG TERM CARE FACILITIES

5. The authority citation for part 483 continues to read as follows:

Authority: 42 U.S.C. 1302, 1320a-7, 1395i, 1395hh and 1396r.

6. Amend § 483.60 by—

a. Revising paragraphs (a)(2) introductory text, and (a)(2)(i) introductory text;

b. Removing the word “or” at the end of paragraphs (a)(2)(i)(C);

c. Revising paragraph (a)(2)(i)(D); and

d. Adding paragraph (a)(2)(i)(E).

The revisions and addition read as follows:

§ 483.60

Food and nutrition services.

* * * * *

(a) * * *

(2) If a qualified dietitian or other clinically qualified nutrition professional is not employed full-time, the facility must designate a person to serve as the director of food and nutrition services.

(i) The director of food and nutrition services must at a minimum meet one of the following qualifications—

* * * * *

(D) Has an associate's or higher degree in food service management or in hospitality, if the course study includes food service or restaurant management, from an accredited institution of higher learning; or

(E) Has 2 or more years of experience in the position of director of food and nutrition services in a nursing facility setting and has completed a course of study in food safety and management, by no later than October 1, 2023, that includes topics integral to managing dietary operations including, but not limited to, foodborne illness, sanitation procedures, and food purchasing/receiving; and

* * * * *

7. Amend § 483.90 by adding paragraph (a)(1)(iii) to read as follows:

§ 483.90

Physical environment.

(a) * * *

(1) * * *

(iii) If a facility is Medicare- or Medicaid-certified before July 5, 2016 and the facility has previously used the Fire Safety Evaluation System for compliance, the facility may use the scoring values in the following Mandatory Values Chart:

* * * * *

Xavier Becerra,

Secretary, Department of Health and Human Services.

Footnotes

1.  HL7 FHIR Release 4. Available at https://www.hl7.org/​fhir/​.

2.  HL7 FHIR. PACIO Functional Status Implementation Guide. Available at https://paciowg.github.io/​functional-status-ig/​.

3.  PACIO Project. Available at http://pacioproject.org/​about/​.

4.  Centers for Medicare & Medicaid Services. Newsroom. Fact sheet: CMS Data Element Library Fact Sheet. June 21, 2018. Available at https://www.cms.gov/​newsroom/​fact-sheets/​cms-data-element-library-fact-sheet.

5.  Sections 4001 through 4008 of Public Law 114-255. Available at https://www.govinfo.gov/​content/​pkg/​PLAW-114publ255/​html/​PLAW-114publ255.htm.

6.  The Trusted Exchange Framework (TEF): Principles for Trusted Exchange (Jan. 2022). Available at https://www.healthit.gov/​sites/​default/​files/​page/​2022-01/​Trusted_​Exchange_​Framework_​0122.pdf.

7.  Common Agreement for Nationwide Health Information Interoperability Version 1 (Jan. 2022). Available at https://www.healthit.gov/​sites/​default/​files/​page/​2022-01/​Common_​Agreement_​for_​Nationwide_​Health_​Information_​Interoperability_​Version_​1.pdf.

8.  The Common Agreement defines Individual Access Services (IAS) as “with respect to the Exchange Purposes definition, the services provided utilizing the Connectivity Services, to the extent consistent with Applicable Law, to an Individual with whom the QHIN, Participant, or Subparticipant has a Direct Relationship to satisfy that Individual's ability to access, inspect, or obtain a copy of that Individual's Required Information that is then maintained by or for any QHIN, Participant, or Subparticipant.” The Common Agreement defines “IAS Provider” as: “Each QHIN, Participant, and Subparticipant that offers Individual Access Services.” See Common Agreement for Nationwide Health Information Interoperability Version 1, at 7 (Jan. 2022), https://www.healthit.gov/​sites/​default/​files/​page/​2022-01/​Common_​Agreement_​for_​Nationwide_​Health_​Information_​Interoperability_​Version_​1.pdf.

9.  CMS Measures Inventory Tool. (2022). Influenza Vaccination Coverage among Healthcare Personnel. Retrieved from https://cmit.cms.gov/​CMIT_​public/​ReportMeasure?​measureId=​854.

10.  Grohskopf, L.A., Alyanak, E., Broder, K.R., Walter, E.B., Fry, A.M., & Jernigan, D.B. (2019). Prevention and Control of Seasonal Influenza with Vaccines: Recommendations of the Advisory Committee on Immunization Practices — United States, 2019-20 Influenza Season. MMWR Recommendations and Reports, 68(No. RR-3), 1-21. https://www.cdc.gov/​mmwr/​volumes/​68/​rr/​rr6803a1.htm?​s_​cid=​rr6803a1_​w.

11.  Centers for Disease Control and Prevention (CDC). (2021). Disease Burden of Flu. Retrieved from https://www.cdc.gov/​flu/​about/​burden/​index.html?​CDC_​AA_​refVal=​https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdisease%2Fus_​flu-related_​deaths.htm.

12.  Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & Infection Advisory Subcommittee of AMDA, The Society for Post-Acute and Long-Term Care Medicine (2020). Recommendations for Mandatory Influenza Vaccinations for Health Care Personnel From AMDA's Infection Advisory Subcommittee. Journal of the American Medical Directors Association, 21(1), 25-28.e2. https://doi.org/​10.1016/​j.jamda.2019.11.008.

13.  Centers for Disease Control and Prevention (CDC). (2021). Estimated Flu-Related Illnesses, Medical Visits, Hospitalizations, and Deaths in the United States—2019-2020 Flu Season. Retrieved from https://www.cdc.gov/​flu/​about/​burden/​2019-2020.html.

14.  Centers for Disease Control and Prevention (CDC). (2021). Retrieved from Flu & People 65 Years and Older: https://www.cdc.gov/​flu/​highrisk/​65over.htm?​CDC_​AA_​refVal=​https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdisease%2F65over.htm.

15.  Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & Infection Advisory Subcommittee of AMDA, The Society for Post-Acute and Long-Term Care Medicine (2020). Recommendations for Mandatory Influenza Vaccinations for Health Care Personnel From AMDA's Infection Advisory Subcommittee. Journal of the American Medical Directors Association, 21(1), 25-28.e2. https://doi.org/​10.1016/​j.jamda.2019.11.008.

16.  Centers for Disease Control and Prevention (CDC). (2021). Retrieved from Flu & People 65 Years and Older: https://www.cdc.gov/​flu/​highrisk/​65over.htm?​CDC_​AA_​refVal=​https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdisease%2F65over.htm.

17.  Lansbury, L.E., Brown, C.S., & Nguyen‐Van‐Tam, J.S. (2017). Influenza in long‐term care facilities. Influenza Other Respir Viruses, 11(5), 356-366. https://dx.doi.org/​10.1111%2Firv.12464.

18.  Pop-Vicas, A., & Gravenstein, S. (2011). Influenza in the elderly: a mini-review. Gerontology, 57(5), 397-404. https://doi.org/​10.1159/​000319033.

19.  Strausbaugh, L.J., Sukumar, S.R., & Joseph, C.L. (2003). Infectious disease outbreaks in nursing homes: an unappreciated hazard for frail elderly persons. Clinical Infectious Diseases, 36(7), 870-876. https://doi.org/​10.1086/​368197.

20.  Wilde, J.A., McMillan, J.A., Serwint, J., Butta, J., O'Riordan, M.A., & Steinhoff, M.C. (1999). Effectiveness of influenza vaccine in health care professionals: a randomized trial. JAMA, 281(10), 908-913. https://doi.org/​10.1001/​jama.281.10.908.

21.  Harriman, K., Rosenberg, J., Robinson, S., et al. (2009). Novel influenza A (H1N1) virus infections among health-care personnel—United States, April-May 2009. MMWR Morbidity and Mortality Weekly Report, 58(23), 641-645. Retrieved from https://www.cdc.gov/​mmwr/​preview/​mmwrhtml/​mm5823a2.htm.

22.  Black, C.L., Yue, X., Ball, S.W., Fink, R.V., de Perio, M.A., Laney, A.S., Williams, W.W., Graitcer, S.B., Fiebelkorn, A.P., Lu, P.J., & Devlin, R. (2018). Influenza Vaccination Coverage Among Health Care Personnel—United States, 2017-18 Influenza Season. MMWR Morbidity and Mortality Weekly Report, 67(38), 1050-1054. https://doi.org/​10.15585/​mmwr.mm6738a2.

23.  Jaklevic, M.C. (2020). Flu Vaccination Urged During COVID-19 Pandemic. JAMA. 324(10), 926-927. https://doi.org/​10.1001/​jama.2020.15444.

24.  Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & Infection Advisory Subcommittee of AMDA, The Society for Post-Acute and Long-Term Care Medicine (2020). Recommendations for Mandatory Influenza Vaccinations for Health Care Personnel From AMDA's Infection Advisory Subcommittee. Journal of the American Medical Directors Association, 21(1), 25-28.e2. https://doi.org/​10.1016/​j.jamda.2019.11.008.

25.  Kenny, E., McNamara, Á., Noone, C., & Byrne, M. (2020). Barriers to seasonal influenza vaccine uptake among health care workers in long-term care facilities: A cross-sectional analysis. British Journal of Health Psychology, 25(3), 519-539. https://doi.org/​10.1111/​bjhp.12419.

26.  Kose, S., Mandiracioglu, A., Sahin, S., Kaynar, T., Karbus, O., & Ozbel, Y. (2020). Vaccine hesitancy of the COVID-19 by health care personnel. International Journal of Clinical Practice, 75(5), e13917. https://doi.org/​10.1111/​ijcp.13917.

27.  Ofstead, C.L., Amelang, M.R., Wetzler, H.P., & Tan, L. (2017). Moving the needle on nursing staff influenza vaccination in long-term care: Results of an evidence-based intervention. Vaccine, 35(18), 2390-2395. https://doi.org/​10.1016/​j.vaccine.2017.03.041.

28.  Centers for Disease Control and Prevention (CDC). (2021). Retrieved from Vaccine Effectiveness: How Well Do Flu Vaccines Work?: https://www.cdc.gov/​flu/​vaccines-work/​vaccineeffect.htm.

29.  Centers for Disease Control and Prevention (CDC). (2021). Retrieved from Vaccine Effectiveness: How Well Do Flu Vaccines Work?: https://www.cdc.gov/​flu/​vaccines-work/​vaccineeffect.htm.

30.  Ferdinands, J.M., Thompson, M.G., Blanton, L., Spencer, S., Grant, L., & Fry, A.M. (2021). Does influenza vaccination attenuate the severity of breakthrough infections? A narrative review and recommendations for further research. Vaccine, 39(28), 3678-3695. https://doi.org/​10.1016/​j.vaccine.2021.05.011.

31.  Carman, W.F., Elder, A.G., Wallace, L.A., McAulay, K., Walker, A., Murray, G.D., & Stott, D.J. (2000). Effects of influenza vaccination of health-care workers on mortality of elderly people in long-term care: a randomised controlled trial. Lancet (London, England), 355 (9198), 93-97. https://doi.org/​10.1016/​S0140-6736(99)05190-9.

32.  Hayward, A.C., Harling, R., Wetten, S., Johnson, A.M., Munro, S., Smedley, J., Murad, S., & Watson, J.M. (2006). Effectiveness of an influenza vaccine programme for care home staff to prevent death, morbidity, and health service use among residents: cluster randomised controlled trial. BMJ (Clinical Research ed.), 333 (7581), 1241. https://doi.org/​10.1136/​bmj.39010.581354.55.

33.  Lemaitre, M., Meret, T., Rothan-Tondeur, M., Belmin, J., Lejonc, J.L., Luquel, L., Piette, F., Salom, M., Verny, M., Vetel, J.M., Veyssier, P., & Carrat, F. (2009). Effect of influenza vaccination of nursing home staff on mortality of residents: a cluster-randomized trial. Journal of the American Geriatrics Society, 57 (9), 1580-1586. https://doi.org/​10.1111/​j.1532-5415.2009.02402.x.

34.  Potter, J., Stott, D.J., Roberts, M.A., Elder, A.G., O'Donnell, B., Knight, P.V., & Carman, W.F. (1997). Influenza vaccination of health care workers in long-term-care hospitals reduces the mortality of elderly patients. Journal of Infectious Diseases, 175 (1), 1-6. https://doi.org/​10.1093/​infdis/​175.1.1.

35.  Bechini, A., Lorini, C., Zanobini, P., Mandò Tacconi, F., Boccalini, S., Grazzini, M., Bonanni, P., & Bonaccorsi, G. (2020). Utility of Healthcare System-Based Interventions in Improving the Uptake of Influenza Vaccination in Healthcare Workers at Long-Term Care Facilities: A Systematic Review. Vaccines (Basel), 8(2), 165. https://doi.org/​10.3390/​vaccines8020165.

36.  Ofstead, C.L., Amelang, M.R., Wetzler, H.P., & Tan, L. (2017). Moving the needle on nursing staff influenza vaccination in long-term care: Results of an evidence-based intervention. Vaccine, 35(18), 2390-2395. https://doi.org/​10.1016/​j.vaccine.2017.03.041.

37.  Yue, X., Black, C., Ball, S., Donahue, S., de Perio, M.A., Laney, A.S., & Greby, S. (2019). Workplace Interventions and Vaccination-Related Attitudes Associated With Influenza Vaccination Coverage Among Healthcare Personnel Working in Long-Term Care Facilities, 2015-2016 Influenza Season. Journal of the American Medical Directors Association, 20(6), 718-724. https://doi.org/​10.1016/​j.jamda.2018.11.029.

38.  Cai, S., Feng, Z., Fennell, M.L., & Mor, V. (2011). Despite small improvement, black nursing home residents remain less likely than whites to receive flu vaccine. Health Affairs (Project Hope), 30(10), 1939-1946. https://doi.org/​10.1377/​hlthaff.2011.0029.

39.  Luo, H., Zhang, X., Cook, B., Wu, B., & Wilson, M.R. (2014). Racial/Ethnic Disparities in Preventive Care Practice Among U.S. Nursing Home Residents. Journal of Aging and Health, 26(4), 519-539. https://doi.org/​10.1177/​0898264314524436.

40.  Mauldin, R.L., Sledge, S.L., Kinney, E.K., Herrera, S., & Lee, K. (2021). Addressing Systemic Factors Related to Racial and Ethnic Disparities among Older Adults in Long-Term Care Facilities. IntechOpen.

41.  Travers, J.L., Dick, A.W., & Stone, P.W. (2018). Racial/Ethnic Differences in Receipt of Influenza and Pneumococcal Vaccination among Long-Stay Nursing Home Residents. Health Services Research, 53(4), 2203-2226. https://doi.org/​10.1111/​1475-6773.12759.

42.  Riester, M.R., Bosco, E., Bardenheier, B.H., Moyo, P., Baier, R.R., Eliot, M., Silva, J.B., Gravenstein, S., van Aalst, R., Chit, A., Loiacono, M.M., & Zullo, A.R. (2021). Decomposing Racial and Ethnic Disparities in Nursing Home Influenza Vaccination. Journal of the American Medical Directors Association, 22(6), 1271-1278.e3. https://doi.org/​10.1016/​j.jamda.2021.03.003.

43.  Hall, L.L., Xu, L., Mahmud, S.M., Puckrein, G.A., Thommes, E.W., & Chit, A. (2020). A Map of Racial and Ethnic Disparities in Influenza Vaccine Uptake in the Medicare Fee-for-Service Program. Advances in Therapy, 37(5), 2224-2235. https://doi.org/​10.1007/​s12325-020-01324-y.

44.  Inactivated vaccines use the killed version of the germ that causes a disease. Inactivated vaccines usually don't provide immunity (protection) that is as strong as the live vaccines. For more information regarding inactivated vaccines we refer readers to the following web page: https://hhs.gov/​immunization/​basics/​types/​index.html.

45.  High-dose flu vaccines contain four times the amount of antigen (the inactivated virus that promotes a protective immune response) as a regular flu shot. They are associated with a stronger immune response following vaccination. For more information regarding high-dose flu vaccines, we refer readers to the following web page: https://www.cdc.gov/​flu/​highrisk/​65over.htm.

46.  Wang, X., Kulkarni, D., Dozier, M., Hartnup, K., Paget, J., Campbell, H., Nair, H., & Usher Network for COVID-19 Evidence Reviews (UNCOVER) group (2020). Influenza vaccination strategies for 2020-21 in the context of COVID-19. Journal of Global Health, 10(2), 021102. https://www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC7719353/​.

47.  Del Riccio, M., Lorini, C., Bonaccorsi, G., Paget, J., & Caini, S. (2020). The Association between Influenza Vaccination and the Risk of SARS-CoV-2 Infection, Severe Illness, and Death: A Systematic Review of the Literature. International Journal of Environmental Research and Public Health, 17(21), 7870. https://doi.org/​10.3390/​ijerph17217870.

48.  Libby, T.E., Lindley, M.C., Lorick, S.A., MacCannell, T., Lee, S.J., Smith, C., Geevarughese, A., Makvandi, M., Nace, D.A., & Ahmed, F. (2013). Reliability and validity of a standardized measure of influenza vaccination coverage among healthcare personnel. Infection Control and Hospital Epidemiology, 34(4), 335-345. https://doi.org/​10.1086/​669859.

49.  The Libby et al. (2013) article (preceding footnote) is referenced throughout the entirety of section VI.C.1.b. of this rule.

50.  For a full list of case study categorization results, please refer to the following study: Libby, T.E., Lindley, M.C., Lorick, S.A., MacCannell, T., Lee, S.J., Smith, C., Geevarughese, A., Makvandi, M., Nace, D.A., & Ahmed, F. (2013). Reliability and validity of a standardized measure of influenza vaccination coverage among healthcare personnel. Infection Control and Hospital Epidemiology, 34(4), 335-345. https://doi.org/​10.1086/​669859.

51.  Libby, T.E., Lindley, M.C., Lorick, S.A., MacCannell, T., Lee, S.J., Smith, C., Geevarughese, A., Makvandi, M., Nace, D.A., & Ahmed, F. (2013). Reliability and validity of a standardized measure of influenza vaccination coverage among healthcare personnel. Infection Control and Hospital Epidemiology, 34(4), 335-345. https://doi.org/​10.1086/​669859.

52.  Centers for Medicare & Medicaid Services. (2021). List of Measures Under Consideration for December 1, 2021. CMS.gov. https://www.cms.gov/​files/​document/​measures-under-consideration-list-2020-report.pdf.

53.  Refer to the proposed measure's specifications in The National Healthcare Safety Network (NSHN) Manual Healthcare Personnel Safety Component Protocol—Healthcare Personnel Vaccination Module: Influenza Vaccination Summary linked at https://www.cdc.gov/​nhsn/​pdfs/​hps-manual/​vaccination/​hps-flu-vaccine-protocol.pdf for an exhaustive list of those included in the licensed independent practitioners' definition.

54.  FY 2014 IRF PPS final rule. 78 FR 47906.

55.  Centers for Disease Control and Prevention (CDC). (2021). https://www.cdc.gov/​nhsn/​hps/​weekly-covid-vac/​index.html. Healthcare Personnel Safety Component (HPS). CDC.gov.

56.  Centers for Disease Control and Prevention (CDC). (2021). Measure Specification: NHSN COVID-19 Vaccination Coverage Updated August 2021. Retrieved from https://www.cdc.gov/​nhsn/​pdfs/​nqf/​covid-vax-hcpcoverage-508.pdf.

57.  Centers for Medicare & Medicaid Services (CMS). (2021). IMPACT Act of 2014 Data Standardization & Cross Setting Measures. Retrieved from https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Post-Acute-Care-Quality-Initiatives/​IMPACT-Act-of-2014/​IMPACT-Act-of-2014-Data-Standardization-and-Cross-Setting-Measures.

58.  86 FR 42424.

59.  Medicare and Medicaid Programs; COVID-19 Vaccine Requirements for Long-Term Care (LTC) Facilities and Intermediate Care Facilities for Individuals with Intellectual Disabilities (CFs-IID) Residents, Clients, and Staff. 86 FR 26306. May 13, 2021.

60.  86 FR 19990 through 20005.

61.  The MDS version referred to in IFC-2 was MDS 3.0 v1.18.1. This version number, MDS 3.0 v1.18.11, reflects the version that would be implemented if the proposal is finalized.

62.  Bhumbra, S., Malin, S., Kirkpatrick, L., et al. (2020). Clinical Features of Critical Coronavirus Disease 2019 in Children. Pediatric Critical Care Medicine, 02, 02. https://doi.org/​10.1097/​PCC.0000000000002511.

63.  Ebinger, J.E., Achamallah, N., Ji, H., Claggett, B.L., Sun, N., Botting, P., et al. (2020). Pre-existing Traits Associated with Covid-19 Illness Severity. PLoS ONE, 15(7), e0236240. https://doi.org/​10.1101/​2020.04.29.20084533.

64.  Gold, J.A.W., Wong, K.K., Szablewski, C.M., Patel, P.R., Rossow, J., da Silva, J., et al. (2020). Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19—Georgia, March 2020. MMWR Morbidity and Mortality Weekly Report, 69(18), 545-550. http://dx.doi.org/​10.15585/​mmwr.mm6918e1.

65.  Hsu, H.E., Ashe, E.M., Silverstein, M., Hofman, M., Lange, S.J., Razzaghi, H., et al. (2020). Race/Ethnicity, Underlying Medical Conditions, Homelessness, and Hospitalization Status of Adult Patients with COVID-19 at an Urban Safety-Net Medical Center—Boston, Massachusetts, 2020. MMWR Morbidity and Mortality Weekly Report, 69(27), 864-869. http://dx.doi.org/​10.15585/​mmwr.mm6927a3.

66.  Kim, L., Whitaker, M., O'Hallaran, A., et al. (2020). Hospitalization Rates and Characteristics of Children Aged <18 Years Hospitalized with Laboratory-confirmed COVID-19—COVID-NET, 14 states, March 1-July 25, 2020. MMWR Morbidity and Mortality Weekly Report, 69(32), 1081-1088. http://dx.doi.org/​10.15585/​mmwr.mm6932e3.

67.  Killerby, M.E., Link-Gelles, R., Haight, S.C., Schrodt, C.A., England, L., Gomes, D.J., et al. (2020). Characteristics Associated with Hospitalization Among Patients with COVID-19—Metropolitan Atlanta, Georgia, March-April 2020. MMWR Morbidity and Mortality Weekly Report, 69(25), 790-794. http://dx.doi.org/​10.15585/​mmwr.mm6925e1.

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106.  King, B.J., Gilmore-Bykovsky, A.L., Roiland, R.A., Polnaszek, B.E., Bowers, B.J., & Kind, A.J. (2013). The consequences of poor communication during transitions from hospital to skilled nursing facility: a qualitative study. Journal of the American Geriatrics Society, 61(7), 1095-1102.

107.  Calendar Year 2020 Home Health final rule (86 FR 62385 through 62390).

108.  Hood, C.M., Gennuso, K.P., Swain, G.R., & Catlin, B.B. (2016). County Health Rankings: Relationships Between Determinant Factors and Health Outcomes. American Journal of Preventive Medicine, 50(2), 129-135. Available at https://pubmed.ncbi.nlm.nih.gov/​26526164/​. Accessed 9/1/21.

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111.  Please see the Emergency Use Authorization web page for more details. This number includes 1 EUA authorizing both medical devices and a drug for emergency use.

112.  Medicare Program; Prospective Payment System and Consolidated Billing for Skilled Nursing Facilities; Updates to the Quality Reporting Program and Value-Based Purchasing Program for Federal Fiscal Year 2020. 84 FR 38728.

113.   https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Downloads/​Final-Specifications-for-SNF-QRP-Quality-Measures-and-SPADEs.pdf.

114.  Calendar Year 2020 Home Health final rule (86 FR 62385 through 62390).

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121.  Kwan, J.L., Lo, L., Sampson, M., & Shojania, K. G. (2013). Medication reconciliation during transitions of care as a patient safety strategy: a systematic review. Annals of Internal Medicine, 158(5), 397-403.

122.  Boockvar, K.S., Blum, S., Kugler, A., Livote, E., Mergenhagen, K.A., Nebeker, J.R., & Yeh, J. (2011). Effect of admission medication reconciliation on adverse drug events from admission medication changes. Archives of Internal Medicine, 171(9), 860-861.

123.  Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman, A.S., Scales, D.C., & Urbach, D.R. (2011). Association of ICU or hospital admission with unintentional discontinuation of medications for chronic diseases. JAMA, 306(8), 840-847.

124.  Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J. (2014). Prescribing errors on admission to hospital and their potential impact: a mixed-methods study. BMJ Quality & Safety, 23(1), 17-25.

125.  Desai, R., Williams, C.E., Greene, S.B., Pierson, S., & Hansen, R.A. (2011). Medication errors during patient transitions into nursing homes: characteristics and association with patient harm. American Journal of Geriatric Pharmacotherapy, 9(6), 413-422.

126.  Boling, P.A. (2009). Care transitions and home health care. Clinical Geriatric Medicine, 25(1), 135-148.

127.  Barnsteiner, J.H. (2005). Medication Reconciliation: Transfer of medication information across settings—keeping it free from error. American Journal of Nursing, 105(3 Suppl), 31-36.

128.  Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander, H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A. (2014). Regardless of age: incorporating principles from geriatric medicine to imp rove care transitions for patients with complex needs. Journal of General Internal Medicine, 29(6), 932-939.

129.  Jencks, S.F., Williams, M.V., & Coleman, E.A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418-1428.

130.  Institute of Medicine. Preventing medication errors: quality chasm series. Washington, DC: The National Academies Press 2007. Available at https://www.nap.edu/​read/​11623/​chapter/​1.

131.  Kitson, N. A., Price, M., Lau, F.Y., & Showler, G. (2013). Developing a medication communication framework across continuums of care using the Circle of Care Modeling approach. BMC Health Services Research, 13(1), 1-10.

132.  Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C. (2010). The revolving door of rehospitalization from skilled nursing facilities. Health Affairs, 29(1), 57-64.

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135.  Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi, T.K., & Bates, D.W. (2003). The incidence and severity of adverse events affecting patients after discharge from the hospital. Annals of Internal Medicine, 138(3), 161-167.

136.  King, B.J., Gilmore‐ Bykovsky, A.L., Roiland, R.A., Polnaszek, B.E., Bowers, B.J., & Kind, A.J. (2013). The consequences of poor communication during transitions from hospital to skilled nursing facility: a qualitative study. Journal of the American Geriatrics Society, 61(7), 1095-1102.

137.  Calendar Year 2020 Home Health final rule (86 FR 62385 through 62390).

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139.  Requirements for Long-Term Care Facilities. Part 483-Requriements for States and Long-Term Care Facilities; Subpart B—Requirements for Long Term Care Facilities; 42 CFR 483.15—Admission, transfer and discharge rights.

140.  Although there are new pain interference items, the current assessment item for Pain Effect on Function will be removed.

141.  The Disparity Methods Confidential Reporting refers to CMS's confidential reporting to educate hospitals about two disparity methods and allow hospitals to review their results and data related to readmission rates for patients with social risk factors. Available at https://qualitynet.cms.gov/​inpatient/​measures/​disparity-methods. Accessed 7/8/22.

142.  Calendar Year 2020 Home Health final rule (86 FR 62385 through 62390).

143.  Medicare and Medicaid Programs; COVID-19 Vaccine Requirements for Long-Term Care (LTC) Facilities and Intermediate Care Facilities for Individuals with Intellectual Disabilities (ICFs-IID) Residents, Clients, and Staff (86 FR 26315-26316). May 8, 2021.

144.  One threshold set at 80 percent for completion of required quality measures data and standardized patient assessment data collected using the MDS submitted through the CMS-designated data submission system, beginning with FY 2018 and for all subsequent payment updates; and a second threshold set at 100 percent for measures data collected and submitted using the CDC NHSN, beginning with FY 2023 and for all subsequent payment updates.

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148.  86 FR 42424.

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255.  We note that the SNF QRP refers to this measure as the “Discharge to Community—PAC SNF QRP” measure. Though we are using a different measure short name (“DTC PAC SNF”), we are proposing to adopt the same measure the SNF QRP uses for purposes of the SNF VBP program.

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