2021-07556. 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 2022

AGENCY:

Centers for Medicare & Medicaid Services (CMS), HHS.

ACTION:

Proposed rule.

SUMMARY:

This proposed rule would update the payment rates used under the prospective payment system (PPS) for skilled nursing facilities (SNFs) for fiscal year (FY) 2022. In addition, the proposed rule includes a proposed forecast error adjustment for FY 2022, proposes updates to the diagnosis code mappings used under the Patient Driven Payment Model (PDPM), proposes to rebase and revise the SNF market basket, proposes to implement a recently-enacted SNF consolidated billing exclusion along with the required proportional reduction in the SNF PPS base rates, and includes a discussion of a methodology to recalibrate the PDPM parity adjustment. In addition, the proposed rule includes proposals for the SNF Quality Reporting Program (QRP) and the SNF Value-Based Purchasing (VBP) Program, including a proposal to suppress the use of the SNF readmission measure for scoring and payment adjustment purposes in the FY 2022 SNF VBP program because we have determined that circumstances caused by the public health emergency for COVID-19 have significantly affected the validity and reliability of the measure and resulting performance scores.

DATES:

To be assured consideration, comments must be received at one of the addresses provided below, no later than 5 p.m. on June 7, 2021.

ADDRESSES:

In commenting, please refer to file code CMS-1746-P.

Comments, including mass comment submissions, must be submitted in one of the following three ways (please choose only one of the ways listed):

1. Electronically. You may submit electronic comments on this regulation to http://www.regulations.gov. Follow the “Submit a comment” instructions.

2. By regular mail. You may mail written comments to the following address ONLY: Centers for Medicare & Medicaid Services, Department of Health and Human Services, Attention: CMS-1746-P, P.O. Box 8016, Baltimore, MD 21244-8016.

Please allow sufficient time for mailed comments to be received before the close of the comment period.

3. By express or overnight mail. You may send written comments to the following address ONLY: Centers for Medicare & Medicaid Services, Department of Health and Human Services, Attention: CMS-1746-P, Mail Stop C4-26-05, 7500 Security Boulevard, Baltimore, MD 21244-1850.

For information on viewing public comments, see the beginning of the SUPPLEMENTARY INFORMATION section.

FOR FURTHER INFORMATION CONTACT:

Penny Gershman, [phone], for information related to SNF PPS clinical issues.

Anthony Hodge, [phone], for information related to consolidated billing, and payment for SNF-level swing-bed services.

John Kane, [phone], for information related to the development of the payment rates and case-mix indexes, and general information.

Kia Burwell, [phone], for information related to the wage index.

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

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

SUPPLEMENTARY INFORMATION:

Inspection of Public Comments: All comments received before the close of the comment period are available for viewing by the public, including any personally identifiable or confidential business information that is included in a comment. We post all comments received before the close of the comment period on the following website as soon as possible after they have been received: http://www.regulations.gov. Follow the search instructions on that website to view public comments. CMS will not post on Regulations.gov public comments that make threats to individuals or institutions or suggest that the individual will take actions to harm the individual. CMS continues to encourage individuals not to submit duplicative comments. We will post acceptable comments from multiple unique commenters even if the content is identical or nearly identical to other comments.

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 proposed rule can be accessed on the SNF PPS Wage Index home page, at http://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. Proposed SNF PPS Rate Setting Methodology and FY 2022 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

IV. 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

V. Other SNF PPS Issues

A. Proposed Changes to SNF PPS Wage Index

B. Technical Updates to PDPM ICD-10 Mappings

C. Recalibrating the PDPM Parity Adjustment

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

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

VIII. Collection of Information Requirements

IX. Response to Comments

X. Economic Analyses

A. Regulatory Impact Analysis

B. Regulatory Flexibility Act Analysis

C. Unfunded Mandates Reform Act Analysis

D. Federalism Analysis

E. Reducing Regulation and Controlling Regulatory Costs

F. Congressional Review Act

G. Regulatory Review Costs

I. Executive Summary

A. Purpose

This proposed rule would update the SNF prospective payment rates for fiscal year (FY) 2022 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 proposed rule) in the Federal Register, before the August 1 that precedes the start of each FY. As discussed in section V.A. of this proposed rule, it would also rebase and revise the SNF market basket index, including updating the base year from 2014 to 2018. As discussed in section IV.D. of this proposed rule, it would also make revisions in the regulation text to exclude from SNF consolidated billing certain blood clotting factors and items and services related to the furnishing of such factors effective for items and services furnished on or after October 1, 2021, as required by the Consolidated Appropriations Act, 2021 (Pub. L. 116-260, enacted December 27, 2020), as well as certain other conforming revisions. In addition, as required under section 1888(e)(4)(G)(iii) of the Act, as added by section 103(b) of the BBRA 1999, we propose to provide for a proportional reduction in the Part A SNF PPS base rates to account for this exclusion, as described in section III.B.6. of this proposed rule. We also propose to make changes to the code mappings used under the SNF PPS for classifying patients into case-mix groups. Additionally, this proposed rule includes a proposed forecast error adjustment for FY 2022. This proposed rule also includes a discussion of a methodology to recalibrate the PDPM parity adjustment. Finally, this proposed rule would also update requirements for the Skilled Nursing Facility Quality Reporting Program (SNF QRP) and the Skilled Nursing Facility Value-Based Purchasing Program (SNF VBP), including a proposal to suppress the use of the SNF readmission measure for scoring and payment adjustment purposes in the FY 2022 SNF VBP program because we have determined that circumstances caused by the public health emergency for COVID-19 have significantly affected the validity and reliability of the measure and resulting performance scores.

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 proposed rule would reflect an update to the rates that we published in the SNF PPS final rule for FY 2021 (85 FR47594, August 5, 2020). We also propose to rebase and revise the SNF market basket index, including updating the base year from 2014 to 2018. This proposed rule proposes revisions to the regulation text to exclude from SNF consolidated billing certain blood clotting factors and items and services related to the furnishing of such factors effective for items and services furnished on or after October 1, 2021, as required by the Consolidated Appropriations Act, 2021, as well as certain conforming revisions. We also propose to make a required reduction in the SNF PPS base rates to account for this new exclusion. This proposed rule also proposes revisions to the International Classification of Diseases, Version 10 (ICD-10) code mappings used under PDPM to classify patients into case-mix groups. Additionally, this proposed rule includes a proposed forecast error adjustment for FY 2022. This proposed rule also includes a discussion of a methodology to recalibrate the PDPM parity adjustment, used to implement PDPM in a budget neutral manner.

This proposed rule proposes to update requirements for the SNF QRP, including the proposal of two new quality measures beginning with the FY 2023 SNF QRP: The SNF Healthcare Associated Infections (HAI) Requiring Hospitalization measure; and the COVID-19 Vaccination Coverage among Healthcare Personnel (HCP) measure. We are proposing that SNFs use the Centers for Disease Control and Prevention (CDC)/National Healthcare Safety Network (NHSN) as the method of data submission for the proposed COVID-19 Vaccination Coverage among Healthcare Personnel (HCP) measure. We are also proposing to modify the denominator for the Transfer of Health Information to the Patient—Post Acute Care (PAC) Measure. We are proposing a revision to the number of quarters used for publicly reporting certain SNF QRP measures due to the public health emergency (PHE). Finally, we are seeking comment on the use of Health Level Seven International (HL7®) Fast Healthcare Interoperability Resources® (FHIR) in quality programs, specifically the SNF QRP, and on our continued efforts to close the health equity gap.

Additionally, we are proposing several updates for the SNF VBP Program including a proposal to suppress the Skilled Nursing Facility 30-Day All-Cause Readmission Measure (SNFRM) for the FY 2022 SNF VBP Program Year and other proposals for scoring and adjusting payments to SNFs for that program year if the SNFRM is suppressed. We are also proposing to update the Phase One Review and Corrections policy to implement a claims “snapshot” policy which would align the review and corrections policy for the SNF VBP Program with the review and corrections policy we use in other value-based purchasing programs and to codify the proposed policy at § 413.338(e)(1) of our regulations. We are further proposing to make a technical update to the instructions for a SNF to request an extraordinary circumstance exception and to codify that update at § 413.338(d)(4)(ii) of our regulations. Finally, we are seeking comments on measures and measure concepts we are considering for an expanded SNF VBP Program measure set.

C. Summary of Cost and Benefits

Table 1—Cost and Benefits

Provision description	Total transfers/costs
Proposed FY 2022 SNF PPS payment rate update	The overall economic impact of this proposed rule is an estimated increase of $444 million in aggregate payments to SNFs during FY 2022.
Proposed FY 2022 SNF QRP changes	The overall economic impact of this proposed rule is an estimated increase in cost to SNFs of $6.63 million.
Proposed FY 2022 SNF VBP changes	The overall economic impact of the SNF VBP Program is an estimated reduction of $191.64 million in aggregate payments to SNFs during FY 2022.

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 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) (https://pacioproject.org/​) to facilitate collaboration with industry stakeholders to develop FHIR standards. These standards could support the exchange and reuse of patient assessment data derived from the minimum data set (MDS), inpatient rehabilitation facility patient assessment instrument (IRF-PAI), long term care hospital continuity assessment record and evaluation (LCDS), outcome and assessment information set (OASIS), and other sources. The PACIO Project has focused on FHIR implementation guides for functional status, cognitive status and new use cases on advance directives and speech, and language pathology. We encourage post-acute care (PAC) provider and health information technology (IT) vendor participation as these efforts advance.

The CMS Data Element Library (DEL) continues to be updated and serves as the authoritative 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). The DEL furthers CMS' goal of data standardization and interoperability. When combined with digital information systems that capture and maintain these coded elements, their standardized clinical content can reduce provider burden by supporting and exchange of standardized healthcare data; supporting provider exchange of electronic health information for care coordination, person-centered care; and supporting real-time, data driven, clinical decision making. Standards in the Data Element Library (https://del.cms.gov/​DELWeb/​pubHome) can be referenced on the CMS website and in the ONC Interoperability Standards Advisory (ISA). The 2021 ISA is available at https://www.healthit.gov/​isa.

The 21st Century Cures Act (Cures Act) (Pub. L. 114-255, enacted December 13, 2016) requires HHS to take new steps to enable the electronic sharing of health information ensuring interoperability for providers and settings across the care continuum. The Cures Act includes a trusted exchange framework and common agreement (TEFCA) provision ^[1] that will enable the nationwide exchange of electronic health information across health information networks and provide an important way to enable bi-directional health information exchange in the future. For more information on current developments related to TEFCA, we refer readers to https://www.healthit.gov/​topic/​interoperability/​trusted-exchange-framework-and-common-agreement and https://rce.sequoiaproject.org/​.

The ONC final rule entitled “21st Century Cures Act: Interoperability, Information Blocking, and the ONC Health IT Certification Program” (85 FR 25642) published in the May 1, 2020 Federal Register (hereinafter referred to as “ONC Cures Act Final Rule”) established policies related to information blocking as authorized under section 4004 of the 21st Century Cures Act. Information blocking is generally defined as a practice by a health IT developer of certified health IT, health information network, health information exchange, or health care provider that, except as required by law or specified by the HHS Secretary as a reasonable and necessary activity, is likely to interfere with access, exchange, or use of electronic health information. The definition of information blocking includes a knowledge standard, which is different for health care providers than for health IT developers of certified health IT and health information networks or health information exchanges. A healthcare provider must know that the practice is unreasonable, as well as likely to interfere with access, exchange, or use of electronic health information. To deter information blocking, health IT developers of certified health IT, health information networks and health information exchanges whom the HHS Inspector General determines, following an investigation, have committed information blocking, are subject to civil monetary penalties of up to $1 million per violation. Appropriate disincentives for health care providers are expected to be established by the Secretary through future rulemaking. Stakeholders can learn more about information blocking at https://www.healthit.gov/​curesrule/​final-rule-policy/​information-blocking. ONC has posted information resources including fact sheets (https://www.healthit.gov/​curesrule/​resources/​fact-sheets), frequently asked questions (https://www.healthit.gov/​curesrule/​resources/​information-blocking-faqs), and recorded webinars (https://www.healthit.gov/​curesrule/​resources/​webinars).

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

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.

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 2021 (85 FR 47594, August 5, 2020).

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 proposed rule provides the required annual updates to the per diem payment rates for SNFs for FY 2022.

III. Proposed SNF PPS Rate Setting Methodology and FY 2022 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 this year's rule, we propose to rebase and revise the market basket index and update the base year from 2014 to 2018. See section V.A. of this proposed rule for more information.

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 III.B.2.d. of this proposed rule. In the FY 2021 SNF PPS final rule (85 FR 47597), the SNF market basket percentage was estimated to be 2.2 percent for FY 2021 based on IHS Global Inc's (IGI's) second quarter 2020 forecast of the 2014-based SNF market basket with historical data through first quarter 2020.

For this proposed rule, we propose a FY 2022 SNF market basket percentage of 2.3 percent based on IGI's fourth quarter 2020 forecast of the proposed 2018-based SNF market basket (before application of the forecast error adjustment and multifactor productivity (MFP) adjustment). We also propose that if more recent data subsequently become available (for example, a more recent estimate of the market basket and/or the MFP), we would use such data, if appropriate, to determine the FY 2022 SNF market basket percentage change, labor-related share relative importance, forecast error adjustment, or MFP adjustment in the SNF PPS final rule.

In section III.B.2.e. of this proposed rule, we discuss 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 set forth in this proposed rule, we use the percentage change in the SNF market basket index to compute the update factor for FY 2022. This factor is based on the FY 2022 percentage increase in the proposed 2018-based SNF market basket index reflecting routine, ancillary, and capital-related expenses. As stated previously, in this proposed rule, the SNF market basket percentage update is estimated to be 2.3 percent for FY 2022 based on IGI's fourth quarter 2020 forecast.

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 2020 (the most recently available FY for which there is final data), the forecasted or estimated increase in the SNF market basket index was 2.8 percentage points, and the actual increase for FY 2020 is 2.0 percentage points, resulting in the actual increase being 0.8 percentage point lower 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 2022 market basket percentage change of 2.3 percent would be adjusted downward to account for the forecast error correction of 0.8 percentage point, resulting in a SNF market basket percentage change of 1.5 percent.

We note that we may consider modifying this forecast error methodology in future rulemaking. We invite comments and feedback on this issue, in particular on the possibility of, in future rulemaking, either eliminating the forecast error adjustment, or raising the threshold for the forecast error from 0.5 percent to 1.0 percent.

Table 2 shows the forecasted and actual market basket increases for FY 2020.

Table 2—Difference Between the Actual and Forecasted Market Basket Increases for FY 2020

Index	Forecasted FY 2020 Increase*	Actual FY 2020 Increase**	FY 2020 difference
SNF	2.8	2.0	−0.8
* Published in Federal Register; based on second quarter 2019 IGI forecast (2014-based index).
** Based on the fourth quarter 2020 IGI forecast (2014-based index).

4. Multifactor 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 MFP 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 MFP adjustment to be equal to the 10-year moving average of changes in annual economy-wide private nonfarm business multi-factor productivity (as projected by the Secretary for the 10-year period ending with the applicable FY, year, cost-reporting period, or other annual period). The Bureau of Labor Statistics (BLS) is the agency that publishes the official measure of private nonfarm business MFP. We refer readers to the BLS website at http://www.bls.gov/​mfp for the BLS historical published MFP data.

MFP is derived by subtracting the contribution of labor and capital inputs growth from output growth. The projections of the components of MFP are currently produced by IGI, a nationally recognized economic forecasting firm with which CMS contracts to forecast the components of the market baskets and MFP. To generate a forecast of MFP, IGI replicates the MFP measure calculated by the BLS, using a series of proxy variables derived from IGI's U.S. macroeconomic models. For a discussion of the MFP projection methodology, we refer readers to the FY 2012 SNF PPS final rule (76 FR 48527 through 48529) and the FY 2016 SNF PPS final rule (80 FR 46395). A complete description of the MFP projection methodology is available on our website at http://www.cms.gov/​Research-Statistics-Data-and-Systems/​Statistics-Trends-and-Reports/​MedicareProgramRatesStats/​MarketBasketResearch.html.

a. Incorporating the MFP 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 (which we refer to as the MFP adjustment). Section 1888(e)(5)(B)(ii) of the Act further states that the reduction of the market basket percentage by the MFP 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 MFP adjustment to the market basket percentage calculated under section 1888(e)(5)(B)(i) of the Act results in an MFP-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 this FY 2022 SNF PPS proposed rule, the current estimate of the 10-year moving average of changes in MFP for the period ending September 30, 2022 would be 0.2 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 2022 for the SNF PPS is based on IGI's fourth quarter 2020 forecast of the SNF market basket percentage, which is estimated to be 2.3 percent. As discussed above, we are applying a 0.2 percentage point MFP adjustment to the FY 2022 SNF market basket percentage. The resulting MFP-adjusted FY 2022 SNF market basket update is, therefore, equal to 2.1 percent, or 2.3 percent less 0.2 percentage point.

5. Market Basket Update Factor for FY 2022

Sections 1888(e)(4)(E)(ii)(IV) and (e)(5)(i) of the Act require that the update factor used to establish the FY 2022 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, 2020 through September 30, 2021 to the average market basket level for the period of October 1, 2021 through September 30, 2022. This process yields a percentage change in the proposed 2018-based SNF market basket of 2.3 percent.

As further explained in section III.B.2.c. of this proposed 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. Since the forecasted FY 2020 SNF market basket percentage change exceeded the actual FY 2020 SNF market basket percentage change (FY 2020 is the most recently available FY for which there is historical data) by more than the 0.5 percentage point threshold, we propose to adjust the FY 2022 market basket percentage change downward by the forecast error correction. Applying the −0.8 percent forecast error correction results in an adjusted FY 2022 SNF market basket percentage change of 1.5 percent (2.3 percent market basket update less 0.8 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 MFP adjustment (10-year moving average of changes in MFP for the period ending September 30, 2022) which is estimated to be 0.2 percent, as described in section III.B.2.d. of this proposed rule. Thus, we propose to apply a net SNF market basket update factor of 1.3 percent in our determination of the FY 2022 SNF PPS unadjusted Federal per diem rates, which reflects a market basket increase factor of 2.3 percent, less the 0.8 percent forecast error correction and less the projected 0.2 percentage point MFP adjustment.

We note that if more recent data become available (for example, a more recent estimate of the SNF market basket and/or MFP), we would use such data, if appropriate, to determine the FY 2022 SNF market basket percentage change, labor-related share relative importance, forecast error adjustment, or MFP adjustment in the FY 2022 SNF PPS final rule.

We also note 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 MFP 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.

6. Unadjusted Federal per Diem Rates for FY 2022

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. of that final rule, 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 propose 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 2022 from the average prices for FY 2021. We propose 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 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.

For FY 2022, we note an additional adjustment to the unadjusted per diem base rates. Specifically, section 134 in Division CC of the Consolidated Appropriations Act, 2021 included a provision amending section 1888(e)(2)(A)(iii) of the Act so as to add “blood clotting factors indicated for the treatment of patients with hemophilia and other bleeding disorders . . . and items and services related to the furnishing of such factors under section 1842(o)(5)(C)” to the list of items and services excludable from the Part A SNF PPS per diem payment, effective for items and services furnished on or after October 1, 2021. We discuss this provision further in section IV.B. of this proposed rule.

Section 1888(e)(4)(G)(iii) of the Act further requires that the Secretary “provide for an appropriate proportional reduction in payments so that . . . the aggregate amount of such reductions is equal to the aggregate increase in payments attributable to the exclusion” of the services from the Part A PPS per diem rates under section 1888(e)(2)(A)(iii) of the Act.

In the FY 2001 rulemaking cycle (65 FR 19202 and 46792), we established a methodology for computing such offsets in response to similar targeted consolidated billing exclusions added to section 1888(e)(2)(A)(iii) Act by section 103 of BBRA 1999. This methodology resulted in a reduction of 5 cents ($0.05) in the unadjusted urban and rural rates, using the identical data as used to establish the Part B add-on for a sample of approximately 1,500 SNFs from the 1995 base period. However, because this methodology relied on data from 1995, we propose a new methodology based on updated data (as discussed below) to apply the offsets required for the exclusion of the blood clotting factors and items and services related to the furnishing of such factors under section 1842(o)(5)(C) of the Act (referred to collectively as the blood clotting factor exclusion), as specified under the Consolidated Appropriations Act, 2021. We believe the use of the updated data will more accurately capture the actual cost of these factors, as using updated utilization data would reflect new types of blood clotting factors introduced in recent years and changes in utilization patterns of blood clotting factors since 1995.

The proposed methodology for calculating the blood clotting factor exclusion offset consists of five steps. In the first step, we begin with the total number of SNF utilization days for beneficiaries who have any amount of blood clotting factor (BCF) use in FY 2020. While we recognize the potential effects of the PHE for COVID-19 on SNF utilization during 2020, we believe we should use FY 2020 data because it is the most recent data available, and thus would best reflect the latest types of blood clotting factors and the most recent changes in utilization patterns; also, the FY 2020 data is the only data available that reflects utilization under the PDPM model rather than the RUG-IV model. However, in light of the potential impact of the PHE for COVID-19 on SNF utilization, particularly as it relates to those patients admitted with COVID-19 or whose stays utilized a PHE-related waiver (for example, the waiver which removes the requirement for a three-day prior inpatient hospital stay in order to receive SNF Part A coverage), we believe it would be appropriate to use a subset of the full FY 2020 SNF population which excludes patients diagnosed with COVID-19 and those stays which utilized a PHE-related waiver. We discuss this concept in more detail in relation to the recalibration of the PDPM parity adjustment, discussed in section V.C. of this proposed rule. As further explained below, we would note that using this subset population has very little impact on the result of the methodology described below. Throughout the discussion below, the term “SNF beneficiary” refers to beneficiaries in the FY 2020 subset population described above.

Since BCF use has historically been subject to SNF consolidated billing and its usage cannot be observed on billed SNF claims, this methodology resorts to claims from other settings to approximate BCF utilization in SNFs. Specifically, BCF use as well as items and services related to the furnishing of such factors under section 1842(o)(5)(C) of the Act are identified by checking if any of the HCPCS codes listed in the Act, including J7170, J7175, J7177-J7183, J7185-J7190, J7192-J7195, J7198-J7203, J7205, and J7207-J7211, are recorded on outpatient claims, which are claims submitted by institutional outpatient providers (such as a hospital outpatient department), or carrier claims, which are fee-for-service claims submitted by professional practitioners, such as physicians, physician assistants, clinical social workers, and nurse practitioners, and by some organizational providers, such as free-standing facilities. A SNF beneficiary with any BCF use is defined as a SNF beneficiary with at least one matched outpatient or carrier claim for blood clotting factors in FY 2020. To calculate the number of SNF utilization days for beneficiaries who have any amount of BCF use in FY 2020, we sum up the corresponding SNF utilization days of SNF beneficiaries with BCF use in FY 2020 (84 beneficiaries), which is 3,317 total utilization days.

In the second step, we estimate the BCF payment per day per SNF beneficiary with any BCF use in FY 2020, which would include payment for the BCFs and items and services related to the furnishing of such factors under section 1842(o)(5)(C) of the Act. There is no direct payment data to track BCF use in SNFs since BCF use is bundled within the Part A per diem payment. Therefore, we rely on payment in outpatient and carrier claims as a proxy for this step. Instead of calculating BCF payment per day for SNF beneficiaries in a SNF stay, we estimate the BCF payment per day for SNF beneficiaries outside of their SNF and inpatient stays, under the assumption that BCF payment per day for SNF beneficiaries is similar during and outside of SNF stays. Outpatient or carrier claims for BCF use that overlap with a SNF stay or an inpatient stay of a SNF beneficiary are excluded to ensure that BCF-related payment is fully captured in Part B claims instead of partially paid through Part A. Overlapping claims are identified when the outpatient claim “From” date or the carrier claim expense date fall within a SNF or inpatient stay's admission and discharge date window. The total BCF payment for SNF beneficiaries' BCF use observed through Part B claims in FY 2020 was $4,843,551. Next, to determine the corresponding utilizations days for SNF beneficiaries' BCF use, we need to carve out their utilization days in a SNF or inpatient setting for these target beneficiaries. We first determine the total SNF and inpatient utilization days for these beneficiaries in FY 2020, which totals 5,408. Next, we determine the total days that the beneficiaries with BCF use were not in a SNF or inpatient stay, which is 365 (for days in the year) multiplied by the number of SNF beneficiaries with BCF use (84), less the total SNF and inpatient utilization days for these beneficiaries (5,408), which is 20,142. Finally, we estimated the BCF payment per day, which is the total BCF payment observed in outpatient and carrier claims ($4,843,551) divided by the total days the beneficiaries were not in a SNF or inpatient (20,142). Thus, we calculate the BCF payment per day per SNF beneficiary to be $240.

In the third step, we calculate the percentage of SNF payment associated with BCF usage. We multiply the estimated BCF payment per day ($240 as determined in step 2) by the total SNF utilization days for SNF beneficiaries with BCF use in FY 2020 (3,317 as determined in step 1). This yields an estimated BCF payment for SNF beneficiaries in the study population of $797,640. Next, we divide this by the total SNF payment for the study population during FY 2020 ($22,636,345,868) to yield the percentage of SNF payment associated with BCF use, which we estimate to be 0.00352 percent.

In the fourth step, we calculate the urban and rural base rate reductions, by multiplying the proposed FY 2022 urban/rural base rates by the percentage of SNF payment associated with clotting factor use determined in step 3 (0.00352 percent). In the case of the proposed urban base rate of $434.79, this yields an urban base rate deduction of $0.02, which we would apply as a $0.01 reduction to the proposed FY 2022 NTA base rate and a $0.01 reduction to the proposed FY 2022 nursing base rate. In the case of the proposed rural base rate of $444.79, this yields a rural base rate deduction of $0.02, which we would apply as a $0.01 reduction to the proposed FY 2022 NTA base rates and a $0.01 reduction to the proposed FY 2022 nursing base rate. We would apply the reduction to the NTA and nursing base rates because BCF is a type of NTA and nursing resources are required to furnish this medication.

In step five, for purposes of impact analysis, we calculate the budget impact of the base rate reductions to be $782,785. We estimate the budget impact by multiplying the total FY2022 SNF baseline ($34,211,000,000) by the percentage of SNF payment for clotting factor (0.00352 percent). This results in a total reduction in SNF spending of $1.2 million. To compare the result of this proposed methodology to that which would have resulted from using the full FY 2020 SNF population, we note that if we had used the full FY 2020 SNF population, the resultant impact would be a reduction in SNF spending of $1.5 million, which represents 0.004551 percent of total payments made under the SNF PPS. Given that these figures are so close as to result in the same two cent reduction in the FY 2022 SNF PPS unadjusted per diem rates, and given the reasons for using the subset population discussed in section V.C. of this proposed rule, we believe it is appropriate to use this subset population as the basis for the calculations described throughout this section.

We apply these rate reductions to the NTA and nursing components of the unadjusted Federal urban and rural per diem rate as shown in Tables 4 and 5.

Table 3 displays the methodology and figures used to calculate these rate reductions.

Table 3—Estimation of Blood Clotting Factor on Base Rate Reduction

Step 1: SNF Utilization Days of Benes with Any BCF Use:
FY2020 # SNF Benes with Any BCF Use	84
FY2020 Total SNF Util Days for Benes with Any BCF Use	3,317
Step 2: Clotting Factor Payment per Day per SNF Bene with Any BCF Use:
FY2020 Total Part B Clotting Factor Payment for Benes with Any BCF Use Outside of SNF or Inpatient Stay	$4,843,551
FY2020 Total SNF and Inpatient Util Days for Benes with Any BCF Use	5,408
FY2020 Total Days Not in SNF or Inpatient Stay for Benes with Any BCF Use	20,142
FY2020 Clotting Factor Payment per Day	$240
Step 3: % of SNF Payment Associated with Clotting Factor Use:
FY2020 Estimated Clotting Factor Payment in SNF	$797,640
FY2020 Total SNF Payment	$22,636,345,868
% of SNF Payment Associated with Clotting Factor Use	0.00352%

Tables 4 and 5 reflect the updated unadjusted Federal rates for FY 2022, prior to adjustment for case-mix. The rates in Tables 4 and 5 include the reductions calculated in Table 3 for blood clotting factor use.

Table 4—FY 2022 Unadjusted Federal Rate Per Diem—Urban

Rate component	PT	OT	SLP	Nursing	NTA	Non-case-mix
Per Diem Amount	$62.84	$58.49	$23.46	$109.55	$82.64	$98.10

Table 5—FY 2022 Unadjusted Federal Rate Per Diem—Rural

Rate component	PT	OT	SLP	Nursing	NTA	Non-case-mix
Per Diem Amount	$71.63	$65.79	$29.56	$104.66	$78.96	$99.91

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.

As we noted in the FY 2021 SNF PPS final rule (85 FR 47600), we continue to monitor the impact of PDPM implementation on patient outcomes and program outlays. We hope to release information in the future that relates to these issues, though we provide some of this information in section V.C. of this proposed rule. We also continue to monitor the impact of PDPM implementation as it relates to our intention to ensure that PDPM is implemented in a budget neutral manner, as discussed in the FY 2020 SNF PPS final rule (84 FR 38734). In section V.C. of this proposed rule, we discuss and solicit comments on a methodology to recalibrate the PDPM parity adjustment as appropriate to ensure budget neutrality, as we did after the implementation of RUG-IV in FY 2011.

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 proposed 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 http://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 proposed FY 2022 payment rates set forth in this proposed rule reflect the use of the PDPM case-mix classification system from October 1, 2021, through September 30, 2022. We list the proposed case-mix adjusted PDPM payment rates for FY 2022 separately for urban and rural SNFs, in Tables 6 and 7 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 6 and 7 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 6 and 7 reflect the PDPM's structure. Accordingly, Column 1 of Tables 6 and 7 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 6 and 7 do not reflect adjustments which may be made to the SNF PPS rates as a result of the SNF VBP program, discussed in section III.D. of this proposed 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.

Table 6—PDPM Case-Mix Adjusted Federal Rates and Associated Indexes—URBAN

PDPM group	PT CMI	PT rate	OT CMI	OT rate	SLP CMI	SLP rate	Nursing CMG	Nursing CMI	Nursing rate	NTA CMI	NTA rate
A	1.53	$96.15	1.49	$87.15	0.68	$15.95	ES3	4.06	$444.77	3.24	$267.75
B	1.70	106.83	1.63	95.34	1.82	42.70	ES2	3.07	336.32	2.53	209.08
C	1.88	118.14	1.69	98.85	2.67	62.64	ES1	2.93	320.98	1.84	152.06
D	1.92	120.65	1.53	89.49	1.46	34.25	HDE2	2.40	262.92	1.33	109.91
E	1.42	89.23	1.41	82.47	2.34	54.90	HDE1	1.99	218.00	0.96	79.33
F	1.61	101.17	1.60	93.58	2.98	69.91	HBC2	2.24	245.39	0.72	59.50
G	1.67	104.94	1.64	95.92	2.04	47.86	HBC1	1.86	203.76
H	1.16	72.89	1.15	67.26	2.86	67.10	LDE2	2.08	227.86
I	1.13	71.01	1.18	69.02	3.53	82.81	LDE1	1.73	189.52
J	1.42	89.23	1.45	84.81	2.99	70.15	LBC2	1.72	188.43
K	1.52	95.52	1.54	90.07	3.7	86.80	LBC1	1.43	156.66
L	1.09	68.50	1.11	64.92	4.21	98.77	CDE2	1.87	204.86
M	1.27	79.81	1.30	76.04			CDE1	1.62	177.47
N	1.48	93.00	1.50	87.74			CBC2	1.55	169.80
O	1.55	97.40	1.55	90.66			CA2	1.09	119.41
P	1.08	67.87	1.09	63.75			CBC1	1.34	146.80
Q							CA1	0.94	102.98
R							BAB2	1.04	113.93
S							BAB1	0.99	108.45
T							PDE2	1.57	171.99
U							PDE1	1.47	161.04
V							PBC2	1.22	133.65
W							PA2	0.71	77.78
X							PBC1	1.13	123.79
Y							PA1	0.66	72.30

Table 7—PDPM Case-Mix Adjusted Federal Rates and Associated Indexes—RURAL

PDPM Group	PT CMI	PT rate	OT CMI	OT rate	SLP CMI	SLP rate	Nursing CMG	Nursing CMI	Nursing rate	NTA CMI	NTA rate
A	1.53	$109.59	1.49	$98.03	0.68	$20.10	ES3	4.06	$424.92	3.24	$255.83
B	1.70	121.77	1.63	107.24	1.82	53.80	ES2	3.07	321.31	2.53	199.77
C	1.88	134.66	1.69	111.19	2.67	78.93	ES1	2.93	306.65	1.84	145.29
D	1.92	137.53	1.53	100.66	1.46	43.16	HDE2	2.40	251.18	1.33	105.02
E	1.42	101.71	1.41	92.76	2.34	69.17	HDE1	1.99	208.27	0.96	75.80
F	1.61	115.32	1.60	105.26	2.98	88.09	HBC2	2.24	234.44	0.72	56.85
G	1.67	119.62	1.64	107.90	2.04	60.30	HBC1	1.86	194.67
H	1.16	83.09	1.15	75.66	2.86	84.54	LDE2	2.08	217.69
I	1.13	80.94	1.18	77.63	3.53	104.35	LDE1	1.73	181.06
J	1.42	101.71	1.45	95.40	2.99	88.38	LBC2	1.72	180.02
K	1.52	108.88	1.54	101.32	3.7	109.37	LBC1	1.43	149.66
L	1.09	78.08	1.11	73.03	4.21	124.45	CDE2	1.87	195.71
M	1.27	90.97	1.30	85.53			CDE1	1.62	169.55
N	1.48	106.01	1.50	98.69			CBC2	1.55	162.22
O	1.55	111.03	1.55	101.97			CA2	1.09	114.08
P	1.08	77.36	1.09	71.71			CBC1	1.34	140.24
Q							CA1	0.94	98.38
R							BAB2	1.04	108.85
S							BAB1	0.99	103.61
T							PDE2	1.57	164.32
U							PDE1	1.47	153.85
V							PBC2	1.22	127.69
W							PA2	0.71	74.31
X							PBC1	1.13	118.27
Y							PA1	0.66	69.08

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 propose to continue this practice for FY 2022, 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 2022, the updated wage data are for hospital cost reporting periods beginning on or after October 1, 2017 and before October 1, 2018 (FY 2018 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. Therefore, 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.

In addition, we propose 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 propose to continue to use the average wage index from all contiguous Core-Based Statistical Areas (CBSAs) as a reasonable proxy. For FY 2022, there are no rural geographic areas that do not have hospitals, and thus, this methodology will not be applied. For rural Puerto Rico, we propose not to apply this methodology due to the distinct economic circumstances that exist there (for example, due to the close proximity to one another 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 propose that we would continue to use the most recent wage index previously available for that area. For urban areas without specific hospital wage index data, we propose that we would use the average wage indexes of all of the urban areas within the state to serve as a reasonable proxy for the wage index of that urban CBSA. For FY 2022, the only urban area without wage index data available is CBSA 25980, Hinesville-Fort Stewart, GA.

The wage index applicable to FY 2022 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.

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.

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 are proposing to adopt the updates set forth in OMB Bulletin No. 20-01 consistent with our longstanding policy of adopting OMB delineation updates, we note that specific wage index updates would not be necessary for FY 2022 as a result of adopting these OMB updates.

The proposed wage index applicable to FY 2022 is set forth in Tables A and B and is 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, as discussed in section V.A.4. of this proposed rule, for FY 2022, we are proposing to rebase and revise the labor-related share to reflect the relative importance of the proposed 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 proposed methodology for calculating the labor-related portion for FY 2022 is discussed in section V.A. of this proposed rule.

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 2022. 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 2022 than the base year weights from the SNF market basket. We calculate the labor-related relative importance for FY 2022 in four steps. First, we compute the FY 2022 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 2022 price index level for that cost category by the total market basket price index level. Third, we determine the FY 2022 relative importance for each cost category by multiplying this ratio by the base year (2018) weight. Finally, we add the FY 2022 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 2022 labor-related relative importance. Table 8 summarizes the proposed labor-related share for FY 2022, based on IGI's fourth quarter 2020 forecast of the proposed 2018-based SNF market basket with historical data through third quarter 2020, compared to the labor-related share that was used for the FY 2021 SNF PPS final rule.

Table 8—Labor-Related Relative Importance, FY 2021 and FY 2022

	Relative importance, labor-related share, FY 2021 20:2 forecast 1	Relative importance, labor-related share, FY 2022 20:4 forecast 2
Wages and salaries	51.1	51.2
Employee benefits	9.9	9.5
Professional fees: Labor-related	3.7	3.5
Administrative & facilities support services	0.5	0.6
Installation, maintenance & repair services	0.6	0.4
All other: Labor-related services	2.6	1.9
Capital-related (.391)	2.9	3.0
Total	71.3	70.1
1 Published in the Federal Register (85 FR 47605); based on the second quarter 2020 IHS Global Inc. forecast of the 2014-based SNF market basket, with historical data through first quarter 2020.
2 Based on the fourth quarter 2020 IHS Global Inc. forecast of the proposed 2018-based SNF market basket.

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 2022 labor-related share percentage provided in Table 8. 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 stakeholders 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 2022 (Federal rates effective October 1, 2021), we would apply an adjustment to fulfill the budget neutrality requirement. We would 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 2021 to the weighted average wage adjustment factor for FY 2022. For this calculation, we would use the same FY 2020 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 2022 would be 0.9999.

We note that if more recent data become 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.

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 VII. of this proposed rule for a further discussion of our policies for the SNF VBP Program.

F. Adjusted Rate Computation Example

Tables 9, 10, and 11 provide examples generally illustrating payment calculations during FY 2022 under PDPM for a hypothetical 30-day SNF stay, involving the hypothetical SNF XYZ, located in Frederick, MD (Urban CBSA 23244), for a hypothetical patient who is classified into such groups that the patient's HIPPS code is NHNC1. Table 9 shows the adjustments made to the Federal per diem rates (prior to application of any adjustments under the SNF VBP program as discussed previously) to compute the provider's case-mix adjusted per diem rate for FY 2022, 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 10 shows the adjustments made to the case-mix adjusted per diem rate from Table 9 to account for the provider's wage index. The wage index used in this example is based on the FY 2022 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 11 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 11 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 9, SNF XYZ's total PPS payment for this particular patient's stay would equal $20,571.17.

Table 9—PDPM Case-Mix Adjusted Rate Computation Example

Per Diem Rate Calculation
Component	Component group	Component rate	VPD adjustment factor	VPD adj. rate
PT	N	$93.00	1.00	$93.00
OT	N	87.74	1.00	87.74
SLP	H	67.10	1.00	67.10
Nursing	N	169.80	1.00	169.80
NTA	C	152.06	3.00	456.18
Non-Case-Mix		98.10		98.10
Total PDPM Case-Mix Adj. Per Diem				$971.92

Table 10—Wage Index Adjusted Rate Computation Example

PDPM wage index adjustment calculation
HIPPS code	PDPM case-mix adjusted per diem	Labor portion	Wage index	Wage index adjusted rate	Non-labor portion	Total case mix and wage index adj. rate
NHNC1	$971.92	$681.32	0.9776	$666.06	$290.60	$956.66

Table 11—Adjusted Rate Computation Example

Day of stay	NTA VPD adjustment factor	PT/OT VPD adjustment factor	Case mix and wage index adjusted per diem rate
1	3.0	1.0	$956.66
2	3.0	1.0	956.66
3	3.0	1.0	956.66
4	1.0	1.0	657.31
5	1.0	1.0	657.31
6	1.0	1.0	657.31
7	1.0	1.0	657.31
8	1.0	1.0	657.31
9	1.0	1.0	657.31
10	1.0	1.0	657.31
11	1.0	1.0	657.31
12	1.0	1.0	657.31
13	1.0	1.0	657.31
14	1.0	1.0	657.31
15	1.0	1.0	657.31
16	1.0	1.0	657.31
17	1.0	1.0	657.31
18	1.0	1.0	657.31
19	1.0	1.0	657.31
20	1.0	1.0	657.31
21	1.0	0.98	653.76
22	1.0	0.98	653.76
23	1.0	0.98	653.76
24	1.0	0.98	653.76
25	1.0	0.98	653.76
26	1.0	0.98	653.76
27	1.0	0.98	653.76
28	1.0	0.96	650.20
29	1.0	0.96	650.20
30	1.0	0.96	650.20
Total Payment			20,571.17

IV. 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 III.B.3. of this proposed 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 BBRA 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 Healthcare Common Procedure Coding System (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 Consolidated Appropriations Act, 2021 (Pub. L. 116-260) has 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. The specific factors, and items and services related to the furnishing of such factors, excluded under this provision are those identified, as of July 1, 2020, by HCPCS codes J7170, J7175, J7177-J7183, J7185-J7190, J7192-J7195, J7198-J7203, J7205, and J7207-J7211. 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. Section 1888(e)(4)(G)(iii) of the Act further requires that for any services that are unbundled from consolidated billing under section 1888(e)(2)(A)(iii) of the Act (and, thus, become qualified for separate payment under Part B), there must also be a corresponding proportional reduction made in aggregate SNF payments under Part A. Accordingly, using the methodology described in section III.B.6. of this proposed rule, we propose to make a proportional reduction of $0.02 in the unadjusted urban and rural rates to reflect these new exclusions, effective for items and services furnished on or after October 1, 2021.

In this proposed rule, we specifically invite 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. We may consider excluding a particular service if it meets our criteria for exclusion as specified previously. We request 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.

We note that the original BBRA amendment and the Consolidated Appropriations Act, 2021 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 Consolidated Appropriations Act, 2021, July 1, 2020), as subsequently modified by the Secretary. In addition, as noted above, the statute (section 1888(e)(2)(A)(iii)(II)-(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 (for example, J7212, “factor viia (antihemophilic factor, recombinant)-jncw (sevenfact), 1 microgram”, which became effective on January 1, 2021 and would fall in the blood clotting factor exclusion category).

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, 2021). 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.

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 proposed 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 http://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​index.html.

D. Revisions to the Regulation Text

We propose to make certain revisions in the regulation text itself. Specifically, we propose to redesignate current 42 CFR 411.15(p)(2)(xvii) and 489.20(s)(17) to § 411.15(p)(2)(xviii) and 489.20(s)(18), and update the regulation text at §§ 411.15(p)(2)(xvii) and 489.20(s)(17) to reflect the recently-enacted exclusion from SNF consolidated billing at section 1888(e)(2)(A)(iii)(VI) of the Act effective for items and services furnished on or after October 1, 2021. Specifically, proposed revised §§ 411.15(p)(2)(xvii) and 489.20(s)(17) would reflect the exclusion of certain blood clotting factors for the treatment of patients with hemophilia and other bleeding disorders (identified by designated HCPCS codes in effect as of July 1, 2020, as subsequently modified by CMS), and items and services related to the furnishing of such factors, and would allow for the exclusion of any additional blood clotting factors identified by CMS and items and services related to the furnishing of such factors. In addition, we are proposing to make conforming changes to the regulation text at §§ 411.15(p)(2)(xiii) through (xvi) and 489.20(s)(13) through (16) to reflect the authority that has always existed for CMS to make updates to the list of excluded codes as provided in section 1888(e)(2)(A)(iii)(II) through (V) of the Act, and as discussed in section IV. C. of this proposed rule.

V. Other SNF PPS Issues

A. Rebasing and Revising the SNF Market Basket

Section 1888(e)(5)(A) of the Act requires the Secretary to establish a market basket index that reflects the 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. We use the SNF market basket index, adjusted in the manner described in section III.B. of this proposed rule, to update the SNF PPS per diem rates and to determine the labor-related share on an annual basis.

The SNF market basket is a fixed-weight, Laspeyres-type price index. A Laspeyres price index 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.

The index itself is constructed in three steps. First, a base period is selected (the proposed base period is 2018) and total base period expenditures are estimated for a set of mutually exclusive and exhaustive spending categories and the proportion of total costs that each category represents is calculated. These proportions are called cost or expenditure weights. Second, each expenditure category is matched to an appropriate price or wage variable, referred to as a price proxy. In nearly every instance, these price proxies are derived from publicly available statistical series that are published on a consistent schedule (preferably at least on a quarterly basis). Finally, the expenditure weight for each cost category is multiplied by the level of its respective price proxy. The sum of these products (that is, the expenditure weights multiplied by their price levels) for all cost categories yields the composite index level of the market basket in a given period. Repeating this step for other periods produces a series of market basket levels over time. Dividing an index level for a given period by an index level for an earlier period produces a rate of growth in the input price index over that timeframe.

Effective for cost reporting periods beginning on or after July 1, 1998, we revised and rebased our 1977 routine costs input price index and adopted a total expenses SNF input price index using FY 1992 as the base year. In the FY 2002 SNF PPS final rule (66 FR 39582), we rebased and revised the market basket to a base year of FY 1997. In the FY 2008 SNF PPS final rule (72 FR 43425), we rebased and revised the market basket to a base year of FY 2004. In the FY 2014 SNF PPS final rule (78 FR 47939), we revised and rebased the SNF market basket, which included updating the base year from FY 2004 to FY 2010. Lastly, in the FY 2018 SNF PPS final rule (82 FR 36548), we revised and rebased the SNF market basket, which included updating the base year from FY 2010 to FY 2014. For FY 2022 and subsequent fiscal years, we are proposing to rebase the market basket to reflect 2018 Medicare-allowable total cost data (routine, ancillary, and capital-related) from freestanding SNFs and to revise applicable cost categories and price proxies used to determine the market basket. Medicare-allowable costs are those costs that are eligible to be paid under the SNF PPS. For example, the SNF market basket excludes home health agency (HHA) costs as these costs would be paid under the HHA PPS and, therefore, these costs are not SNF PPS Medicare-allowable costs. We propose to maintain our policy of using data from freestanding SNFs, which represent 93 percent of the total SNFs shown in Table 12. We believe using freestanding Medicare cost report (MCR) data, as opposed to the hospital-based SNF MCR data, for the proposed cost weight calculation is most appropriate because of the complexity of hospital-based data and the representativeness of the freestanding data. Because hospital-based SNF expenses are embedded in the hospital cost report, any attempt to incorporate data from hospital-based facilities requires more complex calculations and assumptions regarding the ancillary costs related to the hospital-based SNF unit. We believe the use of freestanding SNF cost report data is technically appropriate for reflecting the cost structures of SNFs serving Medicare beneficiaries.

We are proposing to use 2018 as the base year as we believe that the 2018 MCRs represent the most recent, complete set of MCR data available to develop cost weights for SNFs at the time of rulemaking. We believe it is important to regularly rebase and revise the SNF market to reflect more recent data. Historically, the cost weights change minimally from year to year as they represent percent of total costs rather than cost levels; however, given the PHE for COVID-19, we will continue to monitor the upcoming MCR data to see if a more frequent rebasing schedule is necessary than our recent historical precedent of about every 4 years. The 2018 Medicare cost reports are for cost reporting periods beginning on and after October 1, 2017 and before October 1, 2018. While these dates appear to reflect fiscal year data, we note that a Medicare cost report that begins in this timeframe is generally classified as a “2018 cost report”. For example, we found that of the available 2018 Medicare cost reports for SNFs, approximately 7 percent had an October 1, 2017 begin date, approximately 70 percent of the reports had a January 1, 2018 begin date, and approximately 12 percent had a July 1, 2018 begin date. For this reason, we are defining the base year of the market basket as “2018-based” instead of “FY 2018-based”.

Specifically, we are proposing to develop cost category weights for the 2018-based SNF market basket in two stages. First, we are proposing to derive eight major expenditures or cost weights from the 2018 MCR data (CMS Form 2540-10, OMB NO. 0938-0463) for freestanding SNFs: Wages and Salaries; Employee Benefits; Contract Labor; Pharmaceuticals; Professional Liability Insurance; Home Office/Related Organization Contract Labor; Capital-related; and a residual “All Other”. These are the same cost categories calculated using the 2014 MCR data for the 2014-based SNF market basket. The residual “All Other” category would reflect all remaining costs that are not captured in the other seven cost categories. Second, we are proposing to divide the residual “All Other” cost category into more detailed subcategories, using U.S. Department of Commerce Bureau of Economic Analysis' (BEA) 2012 Benchmark Input-Output (I-O) “use table before redefinitions, purchaser's value” for the Nursing and Community Care Facilities industry (NAICS 623A00) aged to 2018 using applicable price proxy growth for each category of costs. Furthermore, we are proposing to continue to use the same overall methodology as was used for the 2014-based SNF market basket to develop the capital related cost weights of the proposed 2018-based SNF market basket.

1. Development of Cost Categories and Weights

a. Use of Medicare Cost Report Data To Develop Major Cost Weights

In order to create a market basket that is representative of freestanding SNF providers serving Medicare patients and to help ensure accurate major cost weights (which is the percent of total Medicare-allowable costs, as defined below), we propose to apply edits to remove reporting errors and outliers. Specifically, the SNF MCRs used to calculate the market basket cost weights exclude any providers that reported costs less than or equal to zero for the following categories: Total facility costs (Worksheet B, part 1, column 18, line 100); total operating costs (Worksheet B, part 1, column 18, line 100 less Worksheet B, part 2, column 18, line 100); Medicare general inpatient routine service costs (Worksheet D, part 1, column 1, line 1); and Medicare PPS payments (Worksheet E, part 3, column 1, line 1). We also limited our sample to providers that had a MCR reporting period that was between 10 and 14 months. The final sample used included roughly 13,500 MCRs (about 90 percent of the universe of SNF MCRs for 2018). The sample of providers is representative of the national universe of providers by region, by ownership-type (proprietary, nonprofit, and government), and by urban/rural status.

Additionally, for all of the major cost weights, except Home Office/Related Organization Contract Labor costs, the data are trimmed to remove outliers (a standard statistical process) by: (1) Requiring that major expenses (such as Wages and Salaries costs) and total Medicare-allowable costs are greater than zero; and (2) excluding the top and bottom five percent of the major cost weight (for example, Wages and Salaries costs as a percent of total Medicare-allowable costs). We note that missing values are assumed to be zero, consistent with the methodology for how missing values are treated in the 2014-based market basket methodology.

For the Home Office/Related Organization Contract Labor cost weight, we propose to first exclude providers whose Home Office/Related Organization Contract Labor costs are greater than Medicare-allowable total costs and then apply a trim that excludes those reporters with a Home Office/Related Organization Contract Labor cost weight above the 99th percentile. This allows providers with no Home Office/Related Organization Contract Labor costs to be included in the Home Office/Related Organization Contract Labor cost weight calculation . If we were to trim the top and bottom Home Office/Related Organization Contract Labor cost weight, we would exclude providers with a zero cost weight and the MCR data (Worksheet S-2 line 45) indicate that not all SNF providers have a home office. Providers without a home office would report administrative costs that might typically be associated with a home office in the Wages and Salaries and Employee Benefits cost weights, or in the residual “All-Other” cost weight if they purchased these types of services from external contractors. We believe the trimming methodology that excludes those who report Home Office costs above the 99th percentile is appropriate as it removes extreme outliers while also allowing providers with zero Home Office/Related Organization Contract Labor costs to be included in the Home Office/Related Organization Contract Labor cost weight calculation.

The trimming process is done individually for each cost category so that providers excluded from one cost weight calculation are not automatically excluded from another cost weight calculation. We note that these proposed trimming methods are the same types of edits performed for the 2014-based SNF market basket, as well as other PPS market baskets (including but not limited to the IPPS market basket and HHA market basket). We believe this trimming process improves the accuracy of the data used to compute the major cost weights by removing possible data misreporting.

The final weights of the proposed 2018-based SNF market basket are based on weighted means. For example, the aggregate Wages and Salaries cost weight, after trimming, is equal to the sum of total Medicare-allowable wages and salaries of all providers divided by the sum of total Medicare-allowable costs for all providers in the sample. This methodology is consistent with the methodology used to calculate the 2014-based SNF market basket cost weights and other PPS market basket cost weights. We note that for each of the cost weights, we evaluated the distribution of providers and costs by region, by ownership-type, and by urban/rural status. For all of the cost weights, with the exception of the PLI (which is discussed in more detail later), the trimmed sample was nationally representative.

For all of the cost weights, we use Medicare-allowable total costs as the denominator (for example, Wages and Salaries cost weight = Wages and Salaries costs divided by Medicare-allowable total costs). Medicare-allowable total costs were equal to total costs (after overhead allocation) from Worksheet B part I, column 18, for lines 30, 40 through 49, 51, 52, and 71 plus estimated Medicaid drug costs, as defined below. We included estimated Medicaid drug costs in the pharmacy cost weight, as well as the denominator for total Medicare-allowable costs. This is the same methodology used for the 2014-based SNF market basket. The inclusion of Medicaid drug costs was finalized in the FY 2008 SNF PPS final rule (72 FR 43425 through 43430), and for the same reasons set forth in that final rule, we are proposing to continue to use this methodology in the proposed 2018-based SNF market basket.

We describe the detailed methodology for obtaining costs for each of the eight cost categories determined from the Medicare Cost Report below. The methodology used in the 2014-based SNF market basket can be found in the FY 2018 SNF PPS final rule (82 FR 36548 through 36555).

(1) Wages and Salaries: To derive Wages and Salaries costs for the Medicare-allowable cost centers, we are proposing first to calculate total facility wages and salaries costs as reported on Worksheet S-3, part II, column 3, line 1. We are then proposing to remove the wages and salaries attributable to non-Medicare-allowable cost centers (that is, excluded areas), as well as a portion of overhead wages and salaries attributable to these excluded areas. Excluded area wages and salaries are equal to wages and salaries as reported on Worksheet S-3, part II, column 3, lines 3, 4, and 7 through 11 plus nursing facility and non-reimbursable salaries from Worksheet A, column 1, lines 31, 32, 50, and 60 through 63.

Overhead wages and salaries are attributable to the entire SNF facility; therefore, we are proposing to include only the proportion attributable to the Medicare-allowable cost centers. We are proposing to estimate the proportion of overhead wages and salaries attributable to the non-Medicare-allowable costs centers in two steps. First, we propose to estimate the ratio of excluded area wages and salaries (as defined above) to non-overhead total facility wages and salaries (total facility wages and salaries (Worksheet S-3, part II, column 3, line 1) less total overhead wages and salaries (Worksheet S-3, Part III, column 3, line 14)). Next, we propose to multiply total overhead wages and salaries by the ratio computed in step 1. We excluded providers whose excluded areas wages and salaries were greater than total facility wages and salaries and/or their excluded area overhead wages and salaries were greater than total facility wages and salaries (about 50 providers). This is similar to the methodology used to derive Wages and Salaries costs in the 2014-based SNF market basket. For the 2014-based SNF market basket, we estimated the proportion of overhead wages and salaries that is attributable to the non-Medicare allowable costs centers (that is, excluded areas) by multiplying the ratio of excluded area wages and salaries (as defined above) to total wages and salaries as reported on Worksheet S-3, Part II, column 3, line 1 by total overhead wages and salaries as reported on Worksheet S-3, Part III, column 3, line 14.

(2) Employee Benefits: Medicare-allowable employee benefits are equal to total facility benefits as reported on Worksheet S-3, part II, column 3, lines 17 through 19 minus non-Medicare-allowable (that is, excluded area) employee benefits and minus a portion of overhead benefits attributable to these excluded areas. Excluded area employee benefits are derived by multiplying total excluded area wages and salaries (as defined above in the `Wages and Salaries' section) times the ratio of total facility benefits to total facility wages and salaries. This ratio of benefits to wages and salaries is defined as total facility benefit costs to total facility wages and salary costs (as reported on Worksheet S-3, part II, column 3, line 1). Likewise, the portion of overhead benefits attributable to the excluded areas is derived by multiplying overhead wages and salaries attributable to the excluded areas (as defined in the `Wages and Salaries' section) times the ratio of total facility benefit costs to total facility wages and salary costs (as defined above). Similar to the Wages and Salaries cost weight, we excluded providers whose excluded areas benefits were greater than total facility benefits and/or their excluded area overhead benefits were greater than total facility benefits (zero providers were excluded because of this edit). This is similar to the methodology used to derive Employee Benefits costs in the 2014-based SNF market basket.

(3) Contract Labor: We are proposing to derive Medicare-allowable contract labor costs from Worksheet S-3, part II, column 3, line 14, which reflects costs for contracted direct patient care services (that is, nursing, therapeutic, rehabilitative, or diagnostic services furnished under contract rather than by employees and management contract services). This is the same methodology used to derive the Contract Labor costs in the 2014-based SNF market basket.

(4) Pharmaceuticals: We are proposing to calculate pharmaceuticals costs using the non-salary costs from the Pharmacy cost center (Worksheet B, part I, column 0, line 11 less Worksheet A, column 1, line 11) and the Drugs Charged to Patients' cost center (Worksheet B, part I, column 0, line 49 less Worksheet A, column 1, line 49). Since these drug costs were attributable to the entire SNF and not limited to Medicare-allowable services, we propose to adjust the drug costs by the ratio of Medicare-allowable pharmacy total costs (Worksheet B, part I, column 11, for lines 30, 40 through 49, 51, 52, and 71) to total pharmacy costs from Worksheet B, part I, column 11, line 11. Worksheet B, part I allocates the general service cost centers, which are often referred to as “overhead costs” (in which pharmacy costs are included) to the Medicare-allowable and non-Medicare-allowable cost centers. This adjustment was made for those providers who reported Pharmacy cost center expenses. Otherwise, we assumed the non-salary Drugs Charged to Patients costs were Medicare-allowable. Since drug costs for Medicare patients are included in the SNF PPS per diem rate, a provider with Medicare days should have also reported costs in the Drugs Charged to Patient cost center. We found a small number of providers (roughly 60) did not report Drugs Charged to Patients' costs despite reporting Medicare days (an average of about 2,600 Medicare days per provider) and, therefore, these providers were excluded from the Pharmaceuticals cost weight calculations. This is similar to the methodology used for the 2014-based SNF market basket.

Second, as was done for the 2014-based SNF market basket, we propose to continue to adjust the drug expenses reported on the MCR to include an estimate of total Medicaid drug costs, which are not represented in the Medicare-allowable drug cost weight. As stated previously in this section, the proposed 2018-based SNF market basket reflects total Medicare-allowable costs (that is, total costs for all payers for those services reimbursable under the SNF PPS). For the FY 2006-based SNF market basket (72 FR 43426), commenters noted that the total pharmaceutical costs reported on the MCR did not include pharmaceutical costs for dual-eligible Medicaid patients as these were directly reimbursed by Medicaid. Since all of the other cost category weights reflect expenses associated with treating Medicaid patients (including the compensation costs for dispensing these drugs), we made an adjustment to include these Medicaid drug expenses so the market basket cost weights would be calculated consistently.

Similar to the 2014-based SNF market basket, we propose to estimate Medicaid drug costs based on data representing dual-eligible Medicaid beneficiaries. Medicaid drug costs are estimated by multiplying Medicaid dual-eligible drug costs per day times the number of Medicaid days as reported in the Medicare-allowable skilled nursing cost center (Worksheet S-3, part I, column 5, line 1) in the SNF MCR. Medicaid dual-eligible drug costs per day (where the day represents an unduplicated drug supply day) were estimated using 2018 Part D claims for those dual-eligible beneficiaries who had a Medicare SNF stay during the year. The total drug costs per unduplicated day for 2018 of $24.48 represented all drug costs (including the drug ingredient cost, the dispensing fee, vaccine administration fee and sales tax) incurred during the 2018 calendar year for those dual-eligible beneficiaries who had a SNF Medicare stay during that 2018 calendar year. Therefore, they include drug costs incurred during a Medicaid SNF stay occurring in the 2018 calendar year. By comparison, the 2014-based SNF market basket also relied on data from the Part D claims, which yielded a dual-eligible Medicaid drug cost per day of $19.62 for 2014.

We continue to believe that Medicaid dual-eligible beneficiaries are a reasonable proxy for the estimated drug costs per day incurred by Medicaid patients staying in a skilled nursing unit under a Medicaid stay. The skilled nursing unit is the Medicare-allowable unit in a SNF, which encompasses more skilled nursing and rehabilitative care compared to a nursing facility or long-term care unit. We believe that Medicaid patients receiving this skilled nursing care would on average have similar drug costs per day to dual-eligible Medicare beneficiaries who have received Medicare skilled nursing care in the skilled nursing care unit during the year. We note that our previous analysis of the Part D claims data showed that Medicare beneficiaries with a SNF stay during the year have higher drug costs than Medicare patients without a SNF stay during the year. Also, in 2018, dual-eligible beneficiaries with a SNF stay during the year had drug costs per day of $24.48, which were approximately two times higher than the drug costs per day of $13.19 for nondual-eligible beneficiaries with a SNF Part A stay during the year.

The Pharmaceuticals cost weight using only 2018 MCR data (without the inclusion of the Medicaid dual-eligible drug costs) is 2.6 percent, compared to the proposed Pharmaceuticals cost weight (including the adjustment for Medicaid dual-eligible drug costs) of 7.5 percent. The 2014-based SNF market basket had a Pharmaceuticals cost weight using only 2014 MCR data without the inclusion of the Medicaid dual-eligible drug costs of 2.9 percent and a total Pharmaceuticals cost weight of 7.3 percent. Therefore, the 0.2 percentage point increase in the Pharmaceuticals cost weight is a result of a 0.5-percentage point increase in the Medicaid dual-eligible drug cost weight (reflecting the 25 percent increase in the Medicaid dual-eligible drug costs per day between 2014 and 2018) and a 0.3-percentage point decrease in the MCR drug cost weight. The decrease in the MCR drug cost weight was consistent, in aggregate, across urban and rural status SNFs as well as across for-profit, government, and nonprofit ownership type SNFs.

(5) Professional Liability Insurance: We are proposing to calculate the professional liability insurance costs from Worksheet S-2 of the MCRs as the sum of premiums; paid losses; and self-insurance (Worksheet S-2, Part I, columns 1 through 3, line 41). This was the same methodology used to derive the Professional Liability costs for the 2014-based SNF market basket.

About 60 percent of SNFs (about 8,000) reported professional liability costs. After trimming, about 7,200 (reflecting about 850,000 Skilled Nursing unit beds) were included in the calculation of the Professional Liability Insurance (PLI) cost weight for the proposed 2018-based SNF market basket. These providers treated roughly 870,000 Medicare beneficiaries and had a Medicare length of stay (LOS) of 33 days, a skilled nursing unit occupancy rate of 80 percent, and an average skilled nursing unit bed size of 125 beds, which are all consistent with the national averages. We also verified that this sample of providers are representative of the national distribution of providers by ownership-type and urban/rural status. We note that the sample of providers is less consistent with the national distribution of providers by region; however, we performed a sensitivity analysis where the PLI cost weight was reweighted based on the national regional distribution and the impacts were less than a 0.1 percentage point on the cost weight.

We note that based on prior comments during the rebasing of the 2014-based SNF market basket, we reviewed in detail the AON 2018 Professional and General Liability Benchmark for Long Term Care Providers ^[2] that examines professional liability and general liability claim costs for long term care providers (including SNF beds, as well as independent living, assisted living, home health care, and rehabilitation facilities, representing about 186,000 long term care beds). This study, although informative, was not appropriate for calculating a PLI cost weight as it represents more than just SNFs serving Medicare patients and captures claim losses as opposed to PLI costs (premiums, paid losses, and self-insurance) incurred during a cost reporting year. We note that only 13 percent of providers reported PLI paid losses or PLI self-insurance costs on the MCR while over 90 percent of providers reported PLI premiums indicating that the majority of losses incurred by Medicare participating SNFs will be covered by insurance premiums paid over time. Our comparison of the MCR data to the AON study for those select states' data provided did show consistencies between the average state PLI costs per bed relative to the national average (as measured by the MCR) and AON's loss per occupied bed relative to national values indicating that states with higher losses per occupied bed have higher PLI costs per total bed.

We believe the MCR data continues to be the most appropriate data source to calculate the PLI cost weight for the proposed 2018-based SNF market basket as it is representative of SNFs serving Medicare beneficiaries and reflects PLI costs (premiums, paid losses, and self-insurance) incurred during the provider's cost reporting year.

(6) Capital-Related: We are proposing to derive the Medicare-allowable capital-related costs from Worksheet B, part II, column 18 for lines 30, 40 through 49, 51, 52, and 71. This is the same methodology to derive capital-related costs used in the 2014-based SNF market basket.

(7) Home Office/Related Organization Contract Labor Costs: We are proposing to calculate Medicare-allowable Home Office/Related Organization Contract Labor costs to be equal to data reported on Worksheet S-3, part II, column 3, line 16. We note that for the 2014-based SNF market basket we also used Worksheet S-3, part II, column 3, line 16 (Home office salaries & wage related costs) to determine these expenses; however, we referred to this category as Home Office Contract Labor Costs. The instructions for this data state “enter the salaries and wage related costs (as defined on lines 17 and 18 below) paid to personnel who are affiliated with a home office and/or related organization, who provide services to the SNF and/or NF, and whose salaries are not included on Worksheet A, column 1,” and therefore, we are referring to this cost category as Home Office/Related Organization Contract Labor costs. Furthermore, for this rebasing we are no longer adjusting these expenses by the ratio of Medicare allowable operating costs to total facility operating costs as done for the 2014-based SNF market basket as the instructions indicate these expenses are for the SNF and NF units.

About 7,000 providers (about 53 percent) in 2018 reported having a home office (as reported on Worksheet S-2, part I, line 45); a lower share of providers than those in the 2014-based SNF market basket. As discussed in section VI.A.1. of this proposed rule, providers without a home office can incur these expenses directly by having their own staff, for which the costs would be included in the Wages and Salaries and Employee Benefits cost weights. Alternatively, providers without a home office could also purchase related services from external contractors for which these expenses would be captured in the residual “All-Other” cost weight. For this reason, unlike the other major cost weights described previously, we did not exclude providers that did not report Home Office/Related Organization Contract Labor costs. We note that this is similar to the methodology that was used for other PPS market baskets such as the 2017-based LTCH market basket (85 FR 58911).

(8) All Other (residual): The “All Other” cost weight is a residual, calculated by subtracting the major cost weights (Wages and Salaries, Employee Benefits, Contract Labor, Pharmaceuticals, Professional Liability Insurance, Capital-Related, and Home Office/Related Organization Contract Labor) from 100.

Table 12 shows the proposed major cost categories and their respective cost weights as derived from the 2018 Medicare cost reports.

Table 12—Major Cost Categories Derived From the SNF Medicare Cost Reports *

Major cost categories	Proposed 2018-based	2014-based
Wages and Salaries	44.1	44.3
Employee Benefits	8.6	9.3
Contract Labor	7.5	6.8
Pharmaceuticals	7.5	7.3
Professional Liability Insurance	1.1	1.1
Capital-related	8.2	7.9
Home Office/Related Organization Contract Labor	0.7	0.7
All other (residual)	22.3	22.6
* Total may not sum to 100 due to rounding.

Compared to the 2014-based SNF market basket, the Wages and Salaries cost weight and the Employee Benefits cost weight as calculated directly from the Medicare cost reports decreased by 0.2 percentage point and 0.7 percentage point, respectively. The Contract Labor cost weight increased 0.7 percentage point and so in aggregate, the Compensation cost weight decreased 0.2 percentage point.

As we did for the 2014-based SNF market basket (82 FR 36555), we are proposing to allocate contract labor costs to the Wages and Salaries and Employee Benefits cost weights based on their relative proportions under the assumption that contract labor costs are comprised of both wages and salaries and employee benefits. The contract labor allocation proportion for wages and salaries is equal to the Wages and Salaries cost weight as a percent of the sum of the Wages and Salaries cost weight and the Employee Benefits cost weight. Using the 2018 Medicare cost report data, this percentage is 84 percent (1 percentage point higher than the percent in the 2014-based SNF market basket); therefore, we are proposing to allocate approximately 84 percent of the Contract Labor cost weight to the Wages and Salaries cost weight and 16 percent to the Employee Benefits cost weight.

Table 13 shows the Wages and Salaries and Employee Benefits cost weights after contract labor allocation for the proposed 2018-based SNF market basket and the 2014-based SNF market basket.

Table 13—Wages and Salaries and Employee Benefits Cost Weights After Contract Labor Allocation

Major cost categories	Proposed 2018-based market basket	2014-based market basket
Wages and Salaries	50.4	50.0
Employee Benefits	9.9	10.5

b. Derivation of the Detailed Operating Cost Weights

To further divide the “All Other” residual cost weight estimated from the 2018 Medicare cost report data into more detailed cost categories, we are proposing to use the 2012 Benchmark I-O “Use Tables/Before Redefinitions/Purchaser Value” for Nursing and Community Care Facilities industry (NAICS 623A00), published by the Census Bureau's, Bureau of Economic Analysis (BEA). These data are publicly available at the following website at http://www.bea.gov/​industry/​io_​annual.htm. The BEA Benchmark I-O data are generally scheduled for publication every 5 years with 2012 being the most recent year for which data is available. The 2012 Benchmark I-O data are derived from the 2012 Economic Census and are the building blocks for BEA's economic accounts; therefore, they represent the most comprehensive and complete set of data on the economic processes or mechanisms by which output is produced and distributed.^[3] BEA also produces Annual I-O estimates. However, while based on a similar methodology, these estimates are less comprehensive and provide less detail than benchmark data. Additionally, the annual I-O data are subject to revision once benchmark data become available. For these reasons, we propose to inflate the 2012 Benchmark I-O data aged forward to 2018 by applying the annual price changes from the respective price proxies to the appropriate market basket cost categories that are obtained from the 2012 Benchmark I-O data. Next, the relative shares of the cost shares that each cost category represents to the total residual I-O costs are calculated. These resulting 2018 cost shares of the I-O data are applied to the “All Other” residual cost weight to obtain detailed cost weights for the residual costs for the proposed 2018-based SNF market basket. For example, the cost for Food: Direct Purchases represents 11.3 percent of the sum of the “All Other” 2012 Benchmark I-O Expenditures inflated to 2018. Therefore, the Food: Direct Purchases cost weight is 2.5 percent of the proposed 2018-based SNF market basket (11.3 percent × 22.3 percent = 2.5 percent). For the 2014-based SNF market basket (82 FR 36553), we used a similar methodology utilizing the 2007 Benchmark I-O data (aged to 2014).

Using this methodology, we are proposing to derive 19 detailed SNF market basket cost category weights from the proposed 2018-based SNF market basket “All Other” residual cost weight (22.3 percent). These categories are: (1) Fuel: Oil and Gas; (2) Electricity and Other Non-Fuel Utilities; (3) Food: Direct Purchases; (4) Food: Contract Services; (5) Chemicals; (6) Medical Instruments and Supplies; (7) Rubber and Plastics; (8) Paper and Printing Products; (9) Apparel; (10) Machinery and Equipment; (11) Miscellaneous Products; (12) Professional Fees: Labor-Related; (13) Administrative and Facilities Support Services; (14) Installation, Maintenance, and Repair Services; (15) All Other: Labor-Related Services; (16) Professional Fees: Nonlabor-Related; (17) Financial Services; (18) Telephone Services; and (19) All Other: Nonlabor-Related Services. The 2014-based SNF market basket had separate cost categories for Postage services and Water and Sewerage. Due to the small weights (less than 0.1 percentage point), we are proposing that Postage costs be included in the All Other: Non-labor-Related Services and Water and Sewerage costs be included in the Electricity and Other Non-Fuel Utilities category.

We note that the machinery and equipment expenses are for equipment that is paid for in a given year and not depreciated over the asset's useful life. Depreciation expenses for moveable equipment are accounted for in the capital component of the proposed 2018-based SNF market basket (described in section IV.A.1.c. of this proposed rule).

c. Derivation of the Detailed Capital Cost Weights

Similar to the 2014-based SNF market basket, we further divided the Capital-related cost weight into: Depreciation, Interest, Lease and Other Capital-related cost weights.

We calculated the depreciation cost weight (that is, depreciation costs excluding leasing costs) using depreciation costs from Worksheet S-2, column 1, lines 20 and 21. Since the depreciation costs reflect the entire SNF facility (Medicare and non-Medicare-allowable units), we used total facility capital costs (Worksheet B, Part I, Column 18, line 100) as the denominator. This methodology assumes that the depreciation of an asset is the same regardless of whether the asset was used for Medicare or non-Medicare patients. This methodology yielded depreciation costs as a percent of capital costs of 25.3 percent for 2018. We then apply this percentage to the proposed 2018-based SNF market basket Medicare-allowable Capital-related cost weight of 8.2 percent, yielding a Medicare-allowable depreciation cost weight (excluding leasing expenses, which is described in more detail below) of 2.1 percent. To further disaggregate the Medicare-allowable depreciation cost weight into fixed and moveable depreciation, we are proposing to use the 2018 SNF MCR data for end-of-the-year capital asset balances as reported on Worksheet A-7. The 2018 SNF MCR data showed a fixed/moveable split of 86/14. The 2014-based SNF market basket, which utilized the same data from the 2014 MCRs, had a fixed/moveable split of 83/17.

We also derived the interest expense share of capital-related expenses from 2018 SNF MCR data, specifically from Worksheet A, column 2, line 81. Similar to the depreciation cost weight, we calculated the interest cost weight using total facility capital costs. This methodology yielded interest costs as a percent of capital costs of 22.8 percent for 2018. We then apply this percentage to the proposed 2018-based SNF market basket Medicare-allowable Capital-related cost weight of 8.2 percent, yielding a Medicare-allowable interest cost weight (excluding leasing expenses) of 1.9 percent. As done with the last rebasing (82 FR 36556), we are proposing to determine the split of interest expense between for-profit and not-for-profit facilities based on the distribution of long-term debt outstanding by type of SNF (for-profit or not-for-profit/government) from the 2018 SNF MCR data. We estimated the split between for-profit and not-for-profit interest expense to be 25/75 percent compared to the 2014-based SNF market basket with 27/73 percent.

Because the detailed data were not available in the MCRs, we used the most recent 2017 Census Bureau Service Annual Survey (SAS) data to derive the capital-related expenses attributable to leasing and other capital-related expenses. The 2014-based SNF market basket used the 2014 SAS data. We note that we are proposing to use the 2017 SAS data because the Census Bureau no longer publishes these detailed capital-related expenses effective for 2018.

Based on the 2017 SAS data, we determined that leasing expenses are 62 percent of total leasing and capital-related expenses costs. In the 2014-based SNF market basket, leasing costs represent 63 percent of total leasing and capital-related expenses costs. We then apply this percentage to the proposed 2018-based SNF market basket residual Medicare-allowable capital costs of 4.2 percent derived from subtracting the Medicare-allowable depreciation cost weight and Medicare-allowable interest cost weight from the 2018-based SNF market basket of total Medicare-allowable capital cost weight (8.2 percent−2.1 percent−1.9 percent = 4.2 percent). This produces the proposed 2018-based SNF Medicare-allowable leasing cost weight of 2.6 percent and all-other capital-related cost weight of 1.6 percent.

Lease expenses are not broken out as a separate cost category in the SNF market basket, but are distributed among the cost categories of depreciation, interest, and other capital-related expenses, reflecting the assumption that the underlying cost structure and price movement of leasing expenses is similar to capital costs in general. As was done with past SNF market baskets and other PPS market baskets, we assumed 10 percent of lease expenses are overhead and assigned them to the other capital-related expenses cost category. This is based on the assumption that leasing expenses include not only depreciation, interest, and other capital-related costs but also additional costs paid to the lessor. We distributed the remaining lease expenses to the three cost categories based on the proportion of depreciation, interest, and other capital-related expenses to total capital costs, excluding lease expenses.

Table 14 shows the capital-related expense distribution (including expenses from leases) in the proposed 2018-based SNF market basket and the 2014-based SNF market basket.

Table 14—Comparison of the Capital-Related Expense Distribution of the Proposed 2018-Based SNF Market Basket and the 2014-Based SNF Market Basket

Cost category	Proposed 2018-based SNF market basket	2014-based SNF market basket
Capital-related Expenses	8.2	7.9
Total Depreciation	3.0	2.9
Total Interest	2.7	3.0
Other Capital-related Expenses	2.6	2.0
Note: The cost weights are calculated using three decimal places. For presentational purposes, we are displaying one decimal and, therefore, the detail capital cost weights may not add to the total capital-related expenses cost weight due to rounding.

Table 15 presents the proposed 2018-based SNF market basket and the 2014-based SNF market basket.

Table 15—Proposed 2018-Based SNF Market Basket and 2014-Based SNF Market Basket

Cost category	Proposed 2018-based SNF market basket	2014-Based SNF market basket
Total	100.0	100.0
Compensation	60.2	60.4
Wages and Salaries 1	50.4	50.0
Employee Benefits 1	9.9	10.5
Utilities	1.5	2.6
Electricity and Other Non-Fuel Utilities	1.0	1.4
Fuel: Oil and Gas	0.4	1.3
Professional Liability Insurance	1.1	1.1
All Other	29.0	27.9
Other Products	17.6	14.3
Pharmaceuticals	7.5	7.3
Food: Direct Purchase	2.5	3.1
Food: Contract Purchase	4.3	0.7
Chemicals	0.2	0.2
Medical Instruments and Supplies	0.6	0.6
Rubber and Plastics	0.7	0.8
Paper and Printing Products	0.5	0.8
Apparel	0.5	0.3
Machinery and Equipment	0.5	0.3
Miscellaneous Products	0.3	0.3
All Other Services	11.5	13.6
Labor-Related Services	6.4	7.4
Professional Fees: Labor-related	3.5	3.8
Installation, Maintenance, and Repair Services	0.6	0.6
Administrative and Facilities Support	0.4	0.5
All Other: Labor-Related Services	1.9	2.5
Non Labor-Related Services	5.1	6.2
Professional Fees: Nonlabor-Related	2.0	1.8
Financial Services	1.3	2.0
Telephone Services	0.3	0.5
All Other: Nonlabor-Related Services 3	1.5	2.0
Capital-Related Expenses	8.2	7.9
Total Depreciation	3.0	2.9
Building and Fixed Equipment	2.5	2.5
Movable Equipment	0.4	0.4
Total Interest	2.7	3.0
For-Profit SNFs	0.7	0.8
Government and Nonprofit SNFs	2.0	2.1
Other Capital-Related Expenses	2.6	2.0
Note: The cost weights are calculated using three decimal places. For presentational purposes, we are displaying one decimal and, therefore, the detailed cost weights may not add to the aggregate cost weights or to 100.0 due to rounding.
1 Contract labor is distributed to wages and salaries and employee benefits based on the share of total compensation that each category represents.
2 Water and Sewerage costs are included in the Electricity and Other Non-Fuel Utilities cost category in the proposed 2018-based SNF market basket.
3 Postage costs are included in the All Other Non-labor-related cost category in the proposed 2018-based SNF market basket.

2. Price Proxies Used To Measure Operating Cost Category Growth

After developing the 27 cost weights for the proposed 2018-based SNF market basket, we selected the most appropriate wage and price proxies currently available to represent the rate of change for each expenditure category. With four exceptions (three for the capital-related expenses cost categories and one for Professional Liability Insurance (PLI)), we base the wage and price proxies on Bureau of Labor Statistics (BLS) data, and group them into one of the following BLS categories:

• Employment Cost Indexes. Employment Cost Indexes (ECIs) measure the rate of change in employment wage rates and employer costs for employee benefits per hour worked. These indexes are fixed-weight indexes and strictly measure the change in wage rates and employee benefits per hour. ECIs are superior to Average Hourly Earnings (AHE) as price proxies for input price indexes because they are not affected by shifts in occupation or industry mix, and because they measure pure price change and are available by both occupational group and by industry. The industry ECIs are based on the 2012 NAICS and the occupational ECIs are based on the 2000 and 2010 Standard Occupational Classification System (SOC).
• Producer Price Indexes. Producer Price Indexes (PPIs) measure the average change over time in the selling prices received by domestic producers for their output. The prices included in the PPI are from the first commercial transaction for many products and some services (https://www.bls.gov/​ppi/​).
• Consumer Price Indexes. Consumer Price Indexes (CPIs) measure the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services (https://www.bls.gov/​cpi/​). CPIs are only used when the purchases are similar to those of retail consumers rather than purchases at the producer level, or if no appropriate PPIs are available.

We evaluated the price proxies using the criteria of reliability, timeliness, availability, and relevance. Reliability indicates that the index is based on valid statistical methods and has low sampling variability. Widely accepted statistical methods ensure that the data were collected and aggregated in a way that can be replicated. Low sampling variability is desirable because it indicates that the sample reflects the typical members of the population. (Sampling variability is variation that occurs by chance because only a sample was surveyed rather than the entire population.) Timeliness implies that the proxy is published regularly, preferably at least once a quarter. The market baskets are updated quarterly, and therefore, it is important for the underlying price proxies to be up-to-date, reflecting the most recent data available. We believe that using proxies that are published regularly (at least quarterly, whenever possible) helps to ensure that we are using the most recent data available to update the market basket. We strive to use publications that are disseminated frequently, because we believe that this is an optimal way to stay abreast of the most current data available. Availability means that the proxy is publicly available. We prefer that our proxies are publicly available because this will help ensure that our market basket updates are as transparent to the public as possible. In addition, this enables the public to be able to obtain the price proxy data on a regular basis. Finally, relevance means that the proxy is applicable and representative of the cost category weight to which it is applied. The CPIs, PPIs, and ECIs that we have selected to propose in this regulation meet these criteria. Therefore, we believe that they continue to be the best measure of price changes for the cost categories to which they would be applied.

Table 20 lists all price proxies for the proposed 2018-based SNF market basket. Below is a detailed explanation of the price proxies used for each operating cost category.

• Wages and Salaries: We are proposing to use the ECI for Wages and Salaries for Private Industry Workers in Nursing Care Facilities (NAICS 6231; BLS series code CIU2026231000000I) to measure price growth of this category. NAICS 623 includes facilities that provide a mix of health and social services, with many of the health services being largely some level of nursing services. Within NAICS 623 is NAICS 6231, which includes nursing care facilities primarily engaged in providing inpatient nursing and rehabilitative services. These facilities, which are most comparable to Medicare-certified SNFs, provide skilled nursing and continuous personal care services for an extended period of time, and, therefore, have a permanent core staff of registered or licensed practical nurses. This is the same index used in the 2014-based SNF market basket.
• Employee Benefits: We are proposing to use the ECI for Benefits for Nursing Care Facilities (NAICS 6231) to measure price growth of this category. The ECI for Benefits for Nursing Care Facilities is calculated using BLS's total compensation (BLS series ID CIU2016231000000I) for nursing care facilities series and the relative importance of wages and salaries within total compensation. We believe this constructed ECI series is technically appropriate for the reason stated above in the Wages and Salaries price proxy section. This is the same index used in the 2014-based SNF market basket.
• Electricity and Other Non-Fuel Utilities: We are proposing to use the PPI Commodity for Commercial Electric Power (BLS series code WPU0542) to measure the price growth of this cost category as Electricity costs account for 93 percent of these expenses. This is the same index used for the Electricity cost category in the 2014-based SNF market basket. As previously noted, we are proposing to include Water and Sewerage costs within the Electricity and Other Non-Fuel Utilities cost category, and to no longer use the CPI All Urban for Water and Sewerage Maintenance as we did for the 2014-based SNF market basket, due to the small size of this estimated cost weight (less than 0.1 percent).
• Fuel: Oil and Gas: We are proposing to change the proxy used for the Fuel: Oil and Gas cost category. Our analysis of the Bureau of Economic Analysis' 2012 Benchmark I-O data for Nursing and Community Care Facilities shows approximately 96 percent of SNF Fuel: Oil and Gas expenses are for Petroleum Refineries (NAICS 324110), Natural gas (NAICS 221200), and Other Petroleum and Coal Products Manufacturing (NAICS 324190). We are proposing to create a blended index based on those three NAICS chemical expenses listed above that account for 96 percent of SNF chemical expenses. We are proposing to create this blend based on each NAICS' expenses as a share of their sum. Therefore, we are proposing a blended proxy of 61 percent of the PPI Industry for Petroleum Refineries (BLS series code PCU32411-32411), 7 percent of the PPI Commodity for Natural Gas (BLS series code WPU0531), and 32 percent of the PPI for Other Petroleum and Coal Products manufacturing (BLS series code PCU32419-32419).

The 2014-based SNF market basket also used a blended chemical proxy that was based on 2007 Benchmark I-O data. We believe our proposed Fuel: Oil and Gas blended index for the 2018-based SNF market basket is technically appropriate as it reflects more recent data on SNFs purchasing patterns. Table 16 provides the weights for the proposed 2018-based blended chemical index and the 2014-based blended chemical index.

Table 16—Proposed Fuel: Oil and Gas Blended Index Weights

NAICS	Price proxy	Proposed 2018-based index (%)	2014-based index (%)
221200	PPI Commodity for Natural Gas	7	35
324110	PPI Industry for Petroleum Refineries	61	65
324190	PPI for Other Petroleum and Coal Products manufacturing	32	n/a
Total		100	100

• Professional Liability Insurance: We are proposing to use the CMS Hospital Professional Liability Insurance Index to measure price growth of this category. We were unable to find a reliable data source that collects SNF-specific PLI data. Therefore, we are proposing to use the CMS Hospital Professional Liability Index, which tracks price changes for commercial insurance premiums for a fixed level of coverage, holding non-price factors constant (such as a change in the level of coverage). This is the same index used in the 2014-based SNF market basket. We believe this is an appropriate proxy to measure the price growth associated of SNF professional liability insurance as it captures the price inflation associated with other medical institutions that serve Medicare patients.
• Pharmaceuticals: We are proposing to use the PPI Commodity for Pharmaceuticals for Human Use, Prescription (BLS series code WPUSI07003) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.
• Food: Wholesale Purchases: We are proposing to use the PPI Commodity for Processed Foods and Feeds (BLS series code WPU02) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.
• Food: Retail Purchase: We are proposing to use the CPI All Urban for Food Away From Home (All Urban Consumers) (BLS series code CUUR0000SEFV) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.
• Chemicals: For measuring price change in the Chemicals cost category, we are proposing to use a blended PPI composed of the Industry PPIs for Other Basic Organic Chemical Manufacturing (NAICS 325190) (BLS series code PCU32519-32519), Soap and Cleaning Compound Manufacturing (NAICS 325610) (BLS series code PCU32561-32561), and Other Miscellaneous Chemical Product Manufacturing (NAICS 325998) (BLS series code PCU325998325998).

Using the 2012 Benchmark I-O data, we found that these three NAICS industries accounted for approximately 96 percent of SNF chemical expenses. The remaining four percent of SNF chemical expenses are for three other incidental NAICS chemicals industries such as Paint and Coating Manufacturing. We are proposing to create a blended index based on those three NAICS chemical expenses listed above that account for 96 percent of SNF chemical expenses. We are proposing to create this blend based on each NAICS' expenses as a share of their sum. These expenses as a share of their sum are listed in Table 17.

The 2014-based SNF market basket also used a blended chemical proxy that was based on 2007 Benchmark I-O data. We believe our proposed chemical blended index for the 2018-based SNF market basket is technically appropriate as it reflects more recent data on SNFs purchasing patterns. Table 17 provides the weights for the proposed 2018-based blended chemical index and the 2014-based blended chemical index.

Table 17—Proposed Chemical Blended Index Weights

NAICS	Price proxy	Proposed 2018-based index (%)	2014-based index (%)
325190	PPI for Other Basic Organic Chemical Manufacturing	34	22
325610	PPI for Soap and Cleaning Compound Manufacturing	21	37
325998	PPI for Other Miscellaneous Chemical Product Manufacturing	45	41
Total		100	100

• Medical Instruments and Supplies: We are proposing to change the proxy used for the Medical Instruments and Supplies cost weight. The 2012 Benchmark I-O data shows 46 percent of medical instruments and supply costs are for Surgical and medical instrument manufacturing costs (NAICS 339112) and 54 percent are for Surgical appliance and supplies manufacturing costs (NAICS 339113). To proxy the price changes associated with NAICS 339112, we propose using the PPI—Commodity—Surgical and medical instruments (BLS series code WPU1562). This the same price proxy we used in the 2014-based SNF market basket. To proxy the price changes associated with NAICS 339113, we are proposing to use 50 percent for the PPI—Commodity—Medical and surgical appliances and supplies (BLS series code WPU1563) and 50 percent for the PPI Commodity data for Miscellaneous products-Personal safety equipment and clothing (BLS series code WPU1571). The latter price proxy would reflect personal protective equipment including but not limited to face shields and protective clothing. The 2012 Benchmark I-O data does not provide specific expenses for personal protective equipment (which would be reflected in the NAICS 339113 expenses); however, we recognize that this category reflects costs faced by SNFs. In absence of any specific cost data on personal protective equipment, we are proposing to include the PPI Commodity data for Miscellaneous products-Personal safety equipment and clothing (BLS series code WPU1571) in the blended proxy for Medical Instruments and Supplies cost category with a weight of 27 percent (that is, 50 percent of the NAICS 339113 expenses as a percent of the sum of NAICS 339113 and NAICS 339112 expenses from the I-O).

The 2014-based SNF market basket used a blend composed of 60 percent of the PPI Commodity for Medical and Surgical Appliances and Supplies (BLS series code WPU1563) and 40 percent of the PPI Commodity for Surgical and Medical Instruments (BLS series code WPU1562). Table 18 provides the proposed Medical Instruments and Supplies cost weight blended price proxy.

Table 18—Proposed Medical Instruments and Supplies Blended Index Weights

NAICS	Price proxy	Proposed 2018-based index (%)	2014-based index (%)
339112	PPI—Commodity—Surgical and medical instruments (WUI1562)	46	40
339113	PPI—Commodity—Medical and surgical appliances and supplies (WPU1563)	27	60
	PPI Commodity data for Miscellaneous products—Personal safety equipment and clothing (WPU1571)	27	n/a
Total		100	100

• Rubber and Plastics: We are proposing to use the PPI Commodity for Rubber and Plastic Products (BLS series code WPU07) to measure price growth of this cost category. This is the same index used in the 2014-based SNF market basket.
• Paper and Printing Products: We are proposing to use the PPI Commodity for Converted Paper and Paperboard Products (BLS series code WPU0915) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.
• Apparel: We are proposing to use the PPI Commodity for Apparel (BLS series code WPU0381) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.
• Machinery and Equipment: We are proposing to use the PPI Commodity for Machinery and Equipment (BLS series code WPU11) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.
• Miscellaneous Products: For measuring price change in the Miscellaneous Products cost category, we are proposing to use the PPI Commodity for Finished Goods less Food and Energy (BLS series code WPUFD4131). Both food and energy are already adequately represented in separate cost categories and should not also be reflected in this cost category. This is the same index used in the 2014-based SNF market basket.
• Professional Fees: Labor-Related: We are proposing to use the ECI for Total Compensation for Private Industry Workers in Professional and Related (BLS series code CIU2010000120000I) to measure the price growth of this category. This is the same index used in the 2014-based SNF market basket.
• Administrative and Facilities Support Services: We are proposing to use the ECI for Total Compensation for Private Industry Workers in Office and Administrative Support (BLS series code CIU2010000220000I) to measure the price growth of this category. This is the same index used in the 2014-based SNF market basket.
• Installation, Maintenance and Repair Services: We are proposing to use the ECI for Total Compensation for All Civilian Workers in Installation, Maintenance, and Repair (BLS series code CIU1010000430000I) to measure the price growth of this new cost category. This is the same index used in the 2014-based SNF market basket.
• All Other: Labor-Related Services: We are proposing to use the ECI for Total Compensation for Private Industry Workers in Service Occupations (BLS series code CIU2010000300000I) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.

Professional Fees: NonLabor-Related: We are proposing to use the ECI for Total Compensation for Private Industry Workers in Professional and Related (BLS series code CIU2010000120000I) to measure the price growth of this category. This is the same index used in the 2014-based SNF market basket.

Financial Services: We are proposing to use the ECI for Total Compensation for Private Industry Workers in Financial Activities (BLS series code CIU201520A000000I) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.

Telephone Services: We are proposing to use the CPI All Urban for Telephone Services (BLS series code CUUR0000SEED) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket.

• All Other: NonLabor-Related Services: We are proposing to use the CPI All Urban for All Items Less Food and Energy (BLS series code CUUR0000SA0L1E) to measure the price growth of this cost category. This is the same index used in the 2014-based SNF market basket. As previously noted, we are proposing to include Postage costs within the All Other: NonLabor-Related Services cost category, and to no longer use the CPI All Urban for Postage as we did for the 2014-based SNF market basket, due to the small size of this estimated cost weight (less than 0.1 percent).

3. Price Proxies Used To Measure Capital Cost Category Growth

We are proposing to apply the same capital price proxies as were used in the 2014-based SNF market basket, with the exception of the For-profit interest cost category, and below is a detailed explanation of the price proxies used for each capital cost category. We also are proposing to continue to vintage weight the capital price proxies for Depreciation and Interest to capture the long-term consumption of capital. This vintage weighting method is the same method that was used for the 2014-based SNF market basket and is described below.

• Depreciation—Building and Fixed Equipment: We are proposing to use the BEA Chained Price Index for Private Fixed Investment in Structures, Nonresidential, Hospitals and Special Care (BEA Table 5.4.4. Price Indexes for Private Fixed Investment in Structures by Type). This BEA index is intended to capture prices for construction of facilities such as hospitals, nursing homes, hospices, and rehabilitation centers. This is the same index used in the 2014-based SNF market basket.
• Depreciation—Movable Equipment: We are proposing to use the PPI Commodity for Machinery and Equipment (BLS series code WPU11). This price index reflects price inflation associated with a variety of machinery and equipment that would be utilized by SNFs including but not limited to medical equipment, communication equipment, and computers. This is the same index used in the 2014-based SNF market basket.
• Nonprofit Interest: We are proposing to use the average yield on Municipal Bonds (Bond Buyer 20-bond index). This is the same index used in the 2014-based SNF market basket.
• For-Profit Interest: For the For-Profit Interest cost category, we are proposing to use the iBoxx AAA Corporate Bond Yield index instead of the Moody's AAA Corporate Bond Yield index that was used for the 2014-based SNF market basket. Effective for December 2020, the Moody's AAA Corporate Bond series is no longer available for use under license to IGI, the nationally-recognized economic and financial forecasting firm with whom we contract to forecast the components of the market baskets and MFP. Therefore, we are proposing to replace the price proxy for the For-Profit interest cost category. We compared the iBoxx AAA Corporate Bond Yield index with the Moody's AAA Corporate Bond Yield index and found that the average growth rates in the two series were similar. Over the historical time period of FY 2000 to FY 2020, the 4-quarter percent change moving average growth in the iBoxx series was approximately 0.1 percentage point higher, on average, than the Moody's AAA corporate Bond Yield index.
• Other Capital: Since this category includes fees for insurances, taxes, and other capital-related costs, we are proposing to use the CPI for Rent of Primary Residence (BLS series code CUUS0000SEHA), which would reflect the price growth of these costs. This is the same index used in the 2014-based SNF market basket.

We believe that these price proxies are the most appropriate proxies for SNF capital costs that meet our selection criteria of relevance, timeliness, availability, and reliability.

As stated above, we are proposing to continue to vintage weight the capital price proxies for Depreciation and Interest to capture the long-term consumption of capital. To capture the long-term nature, the price proxies are vintage-weighted; and the vintage weights are calculated using a two-step process. First, we determine the expected useful life of capital and debt instruments held by SNFs. Second, we identify the proportion of expenditures within a cost category that is attributable to each individual year over the useful life of the relevant capital assets, or the vintage weights.

We rely on Bureau of Economic Analysis (BEA) fixed asset data to derive the useful lives of both fixed and movable capital, which is the same data source used to derive the useful lives for the 2014-based SNF market basket. The specifics of the data sources used are explained below.

a. Calculating Useful Lives for Moveable and Fixed Assets

Estimates of useful lives for movable and fixed assets for the proposed 2018-based SNF market basket are 9 and 26 years, respectively. These estimates are based on three data sources from the BEA: (1) Current-cost average age; (2) historical-cost average age; and (3) industry-specific current cost net stocks of assets.

BEA current-cost and historical-cost average age data by asset type are not available by industry but are published at the aggregate level for all industries. The BEA does publish current-cost net capital stocks at the detailed asset level for specific industries. There are 64 detailed movable assets (including intellectual property) and there are 32 detailed fixed assets in the BEA estimates. Since we seek aggregate useful life estimates applicable to SNFs, we developed a methodology to approximate movable and fixed asset ages for nursing and residential care services (NAICS 623) using the published BEA data. For the proposed 2018 SNF market basket, we use the current-cost average age for each asset type from the BEA fixed assets Table 2.9 for all assets and weight them using current-cost net stock levels for each of these asset types in the nursing and residential care services industry, NAICS 6230. (For example, nonelectro medical equipment current-cost net stock (accounting for about 35 percent of total moveable equipment current-cost net stock in 2018) is multiplied by an average age of 4.7 years. Current-cost net stock levels are available for download from the BEA website at https://apps.bea.gov/​iTable/​index_​FA.cfm. We then aggregate the “weighted” current-cost net stock levels (average age multiplied by current-cost net stock) into moveable and fixed assets for NAICS 6230. We then adjust the average ages for moveable and fixed assets by the ratio of historical-cost average age (Table 2.10) to current-cost average age (Table 2.9).

This produces historical cost average age data for movable (equipment and intellectual property) and fixed (structures) assets specific to NAICS 6230 of 4.7 and 13.1 years for 2018, respectively. The average age reflects the average age of an asset at a given point in time, whereas we want to estimate a useful life of the asset, which would reflect the average over all periods an asset is used. To do this, we multiply each of the average age estimates by two to convert to average useful lives with the assumption that the average age is normally distributed (about half of the assets are below the average at a given point in time, and half above the average at a given point in time). This produces estimates of likely useful lives of 9.49 and 26.19 years for movable and fixed assets, which we round to 9 and 26 years, respectively. We are proposing an interest vintage weight time span of 24 years, obtained by weighting the fixed and movable vintage weights (26 years and 9 years, respectively) by the fixed and movable split (86 percent and 14 percent, respectively). This is the same methodology used for the 2014-based SNF market basket, which had useful lives of 23 years and 10 years for fixed and moveable assets, respectively. We estimate that the impact of revising the useful lives had a minor impact on the average historical growth rate of the proposed 2018-based SNF market basket total aggregate capital cost price proxy. Over the FY 2016 to FY 2020 time period, the percent change moving average in the total aggregate capital cost price proxy was about 0.06 percentage point higher, on average, based on the proposed 2018-based SNF market basket compared to the 2014-based SNF market basket.

b. Constructing Vintage Weights

Given the expected useful life of capital (fixed and moveable assets) and debt instruments, we must determine the proportion of capital expenditures attributable to each year of the expected useful life for each of the three asset types: Building and fixed equipment, moveable equipment, and interest. These proportions represent the vintage weights. We were not able to find a historical time series of capital expenditures by SNFs. Therefore, we approximated the capital expenditure patterns of SNFs over time, using alternative SNF data sources. For building and fixed equipment, we used the stock of beds in nursing homes from the National Nursing Home Survey (NNHS) conducted by the National Center for Health Statistics (NCHS) for 1962 through 1999. For 2000 through 2010, we extrapolated the 1999 bed data forward using a 5-year moving average of growth in the number of beds from the SNF MCR data. For 2011 to 2014, we extrapolate the 2010 bed data forward using the average growth in the number of beds over the 2011 to 2014 time period. For 2015 to 2018, we propose to extrapolate the 2014 bed data forward using the average growth in the number of beds over the 2015 to 2018 time period. We then used the change in the stock of beds each year to approximate building and fixed equipment purchases for that year. This procedure assumes that bed growth reflects the growth in capital-related costs in SNFs for building and fixed equipment. We believe that this assumption is reasonable because the number of beds reflects the size of a SNF, and as a SNF adds beds, it also likely adds fixed capital.

As was done for the 2014-based SNF market basket (as well as prior market baskets), we are proposing to estimate moveable equipment purchases based on the ratio of ancillary costs to routine costs. The time series of the ratio of ancillary costs to routine costs for SNFs measures changes in intensity in SNF services, which are assumed to be associated with movable equipment purchase patterns. The assumption here is that as ancillary costs increase compared to routine costs, the SNF caseload becomes more complex and would require more movable equipment. The lack of movable equipment purchase data for SNFs over time required us to use alternative SNF data sources. A more detailed discussion of this methodology was published in the FY 2008 SNF final rule (72 FR 43428). We believe the resulting two time series, determined from beds and the ratio of ancillary to routine costs, reflect real capital purchases of building and fixed equipment and movable equipment over time.

To obtain nominal purchases, which are used to determine the vintage weights for interest, we converted the two real capital purchase series from 1963 through 2018 determined above to nominal capital purchase series using their respective price proxies (the BEA Chained Price Index for Nonresidential Construction for Hospitals & Special Care Facilities and the PPI for Machinery and Equipment). We then combined the two nominal series into one nominal capital purchase series for 1963 through 2018. Nominal capital purchases are needed for interest vintage weights to capture the value of debt instruments.

Once we created these capital purchase time series for 1963 through 2018, we averaged different periods to obtain an average capital purchase pattern over time: (1) For building and fixed equipment, we averaged 31, 26-year periods; (2) for movable equipment, we averaged 48, 9-year periods; and (3) for interest, we averaged 33, 24-year periods. We calculate the vintage weight for a given year by dividing the capital purchase amount in any given year by the total amount of purchases during the expected useful life of the equipment or debt instrument. To provide greater transparency, we posted on the CMS market basket website at http://www.cms.gov/​Research-Statistics-Data-and-Systems/​Statistics-Trends-and-Reports/​MedicareProgramRatesStats/​MarketBasketResearch.html,, an illustrative spreadsheet that contains an example of how the vintage-weighted price indexes are calculated.

The vintage weights for the proposed 2018-based SNF market basket and the 2014-based SNF market basket are presented in Table 19.

Table 19—Proposed 2018-Based Vintage Weights and 2014-Based Vintage Weights

Year 1	Building and fixed equipment		Movable equipment		Interest
	Proposed 2018-based 26 years	2014-Based 23 years	Proposed 2018-based 9 years	2014-Based 10 years	Proposed 2018-based 24 years	2014-Based 21 years
1	0.049	0.056	0.135	0.085	0.027	0.032
2	0.050	0.055	0.140	0.087	0.028	0.033
3	0.049	0.054	0.128	0.091	0.029	0.034
4	0.047	0.052	0.112	0.097	0.031	0.036
5	0.045	0.049	0.119	0.099	0.032	0.037
6	0.043	0.046	0.111	0.102	0.034	0.039
7	0.041	0.044	0.084	0.108	0.036	0.041
8	0.040	0.043	0.080	0.109	0.037	0.043
9	0.037	0.040	0.091	0.110	0.038	0.044
10	0.035	0.038		0.112	0.040	0.045
11	0.036	0.038			0.043	0.048
12	0.036	0.039			0.047	0.052
13	0.036	0.039			0.049	0.056
14	0.036	0.039			0.051	0.058
15	0.035	0.039			0.050	0.060
16	0.036	0.039			0.048	0.059
17	0.036	0.040			0.048	0.057
18	0.038	0.041			0.048	0.057
19	0.037	0.043			0.048	0.056
20	0.036	0.042			0.048	0.056
21	0.035	0.042			0.047	0.057
22	0.035	0.042			0.047
23	0.035	0.042			0.047
24	0.033				0.049
25	0.032
26	0.032
Total	1.000	1.000	1.000	1.000	1.000	1.000
Note: The vintage weights are calculated using thirteen decimals. For presentation purposes, we are displaying three decimals and therefore, the detail vintage weights may not add to 1.000 due to rounding.
1 Year 1 represents the vintage weight applied to the farthest year while the vintage weight for year 26, for example, would apply to the most recent year.

Table 20 shows all the price proxies for the proposed 2018-based SNF market basket.

Table 20—Proposed Price Proxies for the Proposed 2018-Based SNF Market Basket

Cost category	Weight	Proposed price proxy
Total	100.0
Compensation	60.2
Wages and Salaries 1	50.4	ECI for Wages and Salaries for Private Industry Workers in Nursing Care Facilities.
Employee Benefits 1	9.9	ECI for Total Benefits for Private Industry Workers in Nursing Care Facilities.
Utilities	1.5
Electricity and Other Non-Fuel Utilities	1.0	PPI Commodity for Commercial Electric Power.
Fuel: Oil and Gas	0.4	Blend of Fuel PPIs.
Professional Liability Insurance	1.1	CMS Professional Liability Insurance Premium Index.
All Other	29.0
Other Products	17.6
Pharmaceuticals	7.5	PPI Commodity for Pharmaceuticals for Human Use, Prescription.
Food: Direct Purchase	2.5	PPI Commodity for Processed Foods and Feeds.
Food: Contract Purchase	4.3	CPI for Food Away From Home (All Urban Consumers).
Chemicals	0.2	Blend of Chemical PPIs.
Medical Instruments and Supplies	0.6	Blend of Medical Instruments and Supplies PPIs.
Rubber and Plastics	0.7	PPI Commodity for Rubber and Plastic Products.
Paper and Printing Products	0.5	PPI Commodity for Converted Paper and Paperboard Products.
Apparel	0.5	PPI Commodity for Apparel.
Machinery and Equipment	0.5	PPI Commodity for Machinery and Equipment.
Miscellaneous Products	0.3	PPI Commodity for Finished Goods Less Food and Energy.
All Other Services	11.5
Labor-Related Services	6.4
Professional Fees: Labor-related	3.5	ECI for Total Compensation for Private Industry Workers in Professional and Related.
Installation, Maintenance, and Repair Services	0.6	ECI for Total Compensation for All Civilian workers in Installation, Maintenance, and Repair.
Administrative and Facilities Support	0.4	ECI for Total Compensation for Private Industry Workers in Office and Administrative Support.
All Other: Labor-Related Services	1.9	ECI for Total Compensation for Private Industry Workers in Service Occupations.
Non Labor-Related Services	5.1
Professional Fees: Nonlabor-Related	2.0	ECI for Total Compensation for Private Industry Workers in Professional and Related.
Financial Services	1.3	ECI for Total Compensation for Private Industry Workers in Financial Activities.
Telephone Services	0.3	CPI for Telephone Services.
All Other: Nonlabor-Related Services	1.5	CPI for All Items Less Food and Energy.
Capital-Related Expenses	8.2
Total Depreciation	3.0
Building and Fixed Equipment	2.5	BEA's Chained Price Index for Private Fixed Investment in Structures, Nonresidential, Hospitals and Special Care—vintage weighted 26 years.
Movable Equipment	0.4	PPI Commodity for Machinery and Equipment—vintage weighted 9 years.
Total Interest	2.7
For-Profit SNFs	0.7	iBoxx—Average yield on Aaa bond—vintage weighted 24 years.
Government and Nonprofit SNFs	2.0	Bond Buyer—Average yield on Domestic Municipal Bonds—vintage weighted 24 years.
Other Capital-Related Expenses	2.6	CPI for Rent of Primary Residence.
Note: The cost weights are calculated using three decimal places. For presentation purposes, we are displaying one decimal and, therefore, the detailed cost weights may not add to the aggregate cost weights or to 100.0 due to rounding.
1 Contract labor is distributed to wages and salaries and employee benefits based on the share of total compensation that each category represents.

4. Labor-Related Share

We define the labor-related share (LRS) as those expenses that are labor-intensive and vary with, or are influenced by, the local labor market. Each year, we calculate a revised labor-related share based on the relative importance of labor-related cost categories in the input price index. Effective for FY 2022, we are proposing to revise and update the labor-related share to reflect the relative importance of the proposed 2018-based SNF market basket cost categories that we believe are labor-intensive and vary with, or are influenced by, the local labor market. For the proposed 2018-based SNF market basket these are: (1) Wages and Salaries (including allocated contract labor costs as described above); (2) Employee Benefits (including allocated contract labor costs as described above); (3) Professional fees: Labor-related; (4) Administrative and Facilities Support Services; (5) Installation, Maintenance, and Repair Services; (6) All Other: Labor-Related Services; and (7) a proportion of capital-related expenses. We propose to continue to include a proportion of capital-related expenses because a portion of these expenses are deemed to be labor-intensive and vary with, or are influenced by, the local labor market. For example, a proportion of construction costs for a medical building would be attributable to local construction workers' compensation expenses.

Consistent with previous SNF market basket revisions and rebasings, the All Other: Labor-related services cost category is mostly comprised of building maintenance and security services (including, but not limited to, landscaping services, janitorial services, waste management services services) and dry cleaning and laundry services. Because these services tend to be labor-intensive and are mostly performed at the SNF facility or in the local area (and therefore, unlikely to be purchased in the national market), we believe that they meet our definition of labor-related services.

These are the same cost categories we have included in the LRS for the 2014-based SNF market basket rebasing (82 FR 36563) as well as the same categories included in the LRS for the 2016-based IRF market basket (84 FR 39087), 2016-based IPF market basket (84 FR 38445), and 2017-based LTCH market basket (85 FR 58910).

As discussed in the FY 2018 SNF PPS proposed rule (82 FR 21040), in an effort to determine more accurately the share of nonmedical professional fees (included in the proposed 2018-based SNF market basket Professional Fees cost categories) that should be included in the labor-related share, we surveyed SNFs regarding the proportion of those fees that are attributable to local firms and the proportion that are purchased from national firms. Based on these weighted results, we determined that SNFs purchase, on average, the following portions of contracted professional services inside their local labor market:

• 78 percent of legal services.
• 86 percent of accounting and auditing services.
• 89 percent of architectural, engineering services.
• 87 percent of management consulting services.

Together, these four categories represent 3.5 percentage points of the total costs for the proposed 2018-based SNF market basket. We applied the percentages from this special survey to their respective SNF market basket weights to separate them into labor-related and nonlabor-related costs. As a result, we are designating 2.9 of the 3.5 percentage points total to the labor-related share, with the remaining 0.6 percentage point categorized as nonlabor-related.

In addition to the professional services as previously listed, for the 2018-based SNF market basket, we propose to allocate a proportion of the Home Office/Related Organization Contract Labor cost weight, calculated using the Medicare cost reports as previously stated, into the Professional Fees: Labor-related and Professional Fees: Nonlabor-related cost categories. We propose to classify these expenses as labor-related and nonlabor-related as many facilities are not located in the same geographic area as their home office and, therefore, do not meet our definition for the labor-related share that requires the services to be purchased in the local labor market.

Similar to the 2014-based SNF market basket, we propose for the 2018-based SNF market basket to use the Medicare cost reports for SNFs to determine the home office labor-related percentages. The Medicare cost report requires a SNF to report information regarding their home office provider. Using information on the Medicare cost report, we compared the location of the SNF with the location of the SNF's home office. We propose to classify a SNF with a home office located in their respective labor market if the SNF and its home office are located in the same Metropolitan Statistical Area (MSA). Then we determine the proportion of the Home Office/Related Organization Contract Labor cost weight that should be allocated to the labor-related share based on the percent of total Home Office/Related Organization Contract Labor costs for those SNFs that had home offices located in their respective local labor markets of total Home Office/Related Organization Contract Labor costs for SNFs with a home office. We determined a SNF's and its home office's MSA using their zip code information from the Medicare cost report. Using this methodology, we determined that 21 percent of SNFs' Home Office/Related Organization Contract Labor costs were for home offices located in their respective local labor markets. Therefore, we propose to allocate 21 percent of the Home Office/Related Organization Contract Labor cost weight (0.14 percentage point = 0.69 percent × 21 percent) to the Professional Fees: Labor-related cost weight and 79 percent of the Home Office/Related Organization Contract Labor cost weight to the Professional Fees: Nonlabor-related cost weight (0.55 percentage point = 0.69 percent × 79 percent). The 2014-based SNF market basket used a similar methodology for allocating the Home Office/Related Organization Contract Labor cost weight to the labor-related share.

In summary, based on the two allocations mentioned earlier, we propose to apportion 3.0 percentage points of the Professional Fees (2.9 percentage points) and Home Office/Related Organization Contract Labor (0.1 percentage point) cost weights into the Professional Fees: Labor-Related cost category. This amount was added to the portion of professional fees that we already identified as labor-related using the I-O data such as contracted advertising and marketing costs (approximately 0.45 percentage point of total costs) resulting in a Professional Fees: Labor-Related cost weight of 3.5 percent.

Table 21 compares the FY 2022 labor-related share based on the proposed 2018-based SNF market basket relative importance and the FY 2021 labor-related share based on the 2014-based SNF market basket relative importance as finalized in the FY 2021 SNF final rule (85 FR 47605).

Table 21—FY 2021 and Proposed FY 2022 SNF Labor-Related Share

	Relative importance, labor-related share, FY 2021 20:2 forecast 1	Proposed relative importance, labor-related share, FY 2022 20:4 forecast 2
Wages and salaries3	51.1	51.2
Employee benefits*	9.9	9.5
Professional fees: Labor-related	3.7	3.5
Administrative & facilities support services	0.5	0.6
Installation, maintenance & repair services	0.6	0.4
All other: Labor-related services	2.6	1.9
Capital-related (.391)	2.9	3.0
Total	71.3	70.1
1 Published in the Federal Register (85 FR 47605); based on the second quarter 2020 IHS Global Inc. forecast of the 2014-based SNF market basket, with historical data through first quarter 2020.
2 Based on the fourth quarter 2020 IHS Global Inc. forecast of the proposed 2018-based SNF market basket.
3 The Wages and Salaries and Employee Benefits cost weight reflect contract labor costs as described above.

The proposed FY 2022 SNF labor-related share is 1.2 percentage points lower than the FY 2021 SNF labor-related share (based on the 2014-based SNF market basket). The major reason for the lower labor-related share is due to the incorporation of the 2012 Benchmark I-O data, primarily stemming from a decrease in the All Other: Labor-related services and Professional Fees: Labor-related services cost weights, and a decrease in the Compensation cost weight as a result of incorporating the 2018 MCR data.

5. Proposed Market Basket Estimate for the FY 2022 SNF PPS Update

As discussed previously in this proposed rule, beginning with the FY 2022 SNF PPS update, we are proposing to adopt the 2018-based SNF market basket as the appropriate market basket of goods and services for the SNF PPS. Consistent with historical practice, we estimate the market basket update for the SNF PPS based on IHS Global Inc.'s (IGI) forecast. IGI is a nationally recognized economic and financial forecasting firm that contracts with CMS to forecast the components of the market baskets and multifactor productivity (MFP). Based on IGI's fourth quarter 2020 forecast with historical data through the third quarter of 2020, the most recent estimate of the proposed 2018-based SNF market basket update for FY 2022 is 2.3 percent−0.1 percentage point lower (after rounding) than the FY 2022 percent change of the 2014-based SNF market basket. We are also proposing that if more recent data subsequently become available (for example, a more recent estimate of the market basket and/or the MFP), we would use such data, if appropriate, to determine the FY 2022 SNF market basket percentage change, labor-related share relative importance, forecast error adjustment, or MFP adjustment in the SNF PPS final rule.

Table 22 compares the proposed 2018-based SNF market basket and the 2014-based SNF market basket percent changes. For the historical period between FY 2017 and FY 2020, there is no difference in the average growth rates between the two market baskets. For the forecasted period between FY 2021 and FY 2023, the average difference between the two market baskets is −0.1 percentage point.

Table 22—Proposed 2018-Based SNF Market Basket and 2014-Based SNF Market Basket, Percent Changes: 2017-2023

Fiscal year (FY)	Proposed 2018-Based SNF market basket	2014-Based SNF market basket
Historical data:
FY 2017	2.5	2.7
FY 2018	2.6	2.6
FY 2019	2.4	2.3
FY 2020	2.1	2.0
Average FY 2017-2020	2.4	2.4
Forecast:
FY 2021	2.4	2.4
FY 2022	2.3	2.4
FY 2023	2.6	2.7
Average FY 2021-2023	2.4	2.5
Source: IHS Global, Inc. 4th quarter 2020 forecast with historical data through 3rd quarter 2020.

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 used for categorization 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/​MedicareFee-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 has the ability to make changes to the ICD-10 medical code data sets effective on April 1.

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, so as 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-ServicePayment/​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 are proposing several changes to the PDPM ICD-10 code mappings and lists. Our proposed changes are as follows:

On October 1, 2020 two ICD-10 codes representing types of sickle-cell disease; D57.42 “Sickle-cell thalassemia beta zero without crisis” and D57.44 “Sickle-cell thalassemia beta plus without crisis” took effect and were clinically mapped to the category of “Medical Management”. However, there are more specific codes to indicate why a patient with sickle-cell disease would require SNF care, and if the patient is not in crisis, this most likely indicates that SNF care is not required. For this reason, we propose to change the assignment of D57.42 and D57.44 to “Return to Provider”.

On October 1, 2020, three new ICD-10 codes representing types of esophageal conditions; K20.81 “Other esophagitis with bleeding”, K20.91, “Esophagitis, unspecified with bleeding, and K21.01 “Gastro-esophageal reflux disease with esophagitis, with bleeding” took effect and were clinically mapped to “Return to Provider”. Upon review of these codes, we recognize 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. Therefore, we propose to change the assignment of K20.81, K20.91, and K21.01 to “Medical Management” in order to promote more accurate clinical category assignment.

In December 2020, the CDC announced several additions to the ICD-10 Classification related to COVID-19 that became effective on January 1, 2021. One such code, M35.81 “Multisystem inflammatory syndrome”, was assigned to “Non-Surgical Orthopedic/Musculoskeletal”. However, Multisystem inflammatory syndrome can involve more than the musculoskeletal system. It can also involve the gastrointestinal tract, heart, central nervous system, and kidneys. For this reason, we propose to change the assignment of M35.81 to “Medical Management” in order to promote more accurate clinical category assignment.

On October 1, 2020, three new ICD-10 codes representing types of neonatal cerebral infarction were classified as “Return to Provider.” These codes were P91.821 “Neonatal cerebral infarction, right side of brain,” P91.822, “Neonatal cerebral infarction, left side of brain,” and P91.823, “Neonatal cerebral infarction, bilateral.” While a neonate is unlikely to be a Medicare beneficiary, this diagnosis could continue to be used later in life hence placing those with this condition in the acute neurologic category. Therefore, we propose to change the assignment of P91.821, P91.822, and P91.823 to “Acute Neurologic” in order to promote more accurate clinical category assignment.

On April 1, 2020, U07.0, “Vaping-related disorder,” took effect and was classified as a “Return to Provider” code because at the time, “Vaping-related disorder” was not considered a code that would be a primary diagnosis during a SNF stay. However, upon further review, we believe that many patients who exhibit this diagnosis require steroids, empiric antibiotics and oxygen for care which could carry over to the post-acute setting. For this reason, we propose to change the assignment of U07.0 to “Pulmonary” classification in order to promote more accurate clinical category assignment.

In the FY 2021 proposed rule (85 FR 20939), we sought comments on additional substantive and nonsubstantive changes that commenters believed were necessary. We received three comments suggesting several changes to the ICD-10 to clinical category mappings. One of those changes was substantive, requiring notice and comment rulemaking. The commenter suggested that the FY 2020 ICD-10 to clinical category mapping of G93.1 “Anoxic brain damage, not elsewhere classified” be changed to “Acute Neurologic” from “Return to Provider,” which we would consider a substantive change. Codes that result in “Return to Provider” are codes that cannot be used in I0020B of the MDS because item I0020B is used to establish the primary medical condition that a patient presents with during a SNF stay. Although some codes are considered “Return to Provider” for payment purposes, they are still used to support the care and services used for secondary and co-morbidity diagnoses. The ICD-10 code, G93.1 was initially clinically mapped to “Return to provider” because “Anoxic brain damage, not elsewhere classified” was non-specific and did not fully describe a patient's deficits and may not have been an acute condition. However, upon further review, our clinicians determined that although this may not be an acute condition, “Anoxic brain damage, not elsewhere classified” would still likely result in a need for SNF care and is similar to conditions such as “Compression of the brain”, “Cerebral edema”, and “encephalopathy”, which are mapped into the “Acute Neurologic” category. Therefore, we propose to change the assignment of G93.1 “Anoxic brain damage, not elsewhere classified” to “Acute Neurologic”.

We invite comments on the proposed substantive changes to the ICD-10 code mappings discussed previously, as well as comments on additional substantive and non-substantive changes that commenters believe are necessary.

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 to implement 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 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 (CMI) 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), and what we expected total payments would be under the then proposed PDPM based on that same FY 2017 claims and assessment data. In the FY 2020 SNF PPS final rule (84 FR 38734-38735), CMS finalized an updated standardization multiplier and parity adjustment based on FY 2018 claims and assessment data. Through this comparison, and as discussed in the FY 2020 SNF PPS final rule, this analysis resulted in an adjustment factor of 1.46, by which 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 the CMIs by 1.46, the CMIs were inflated by 46 percent in order to achieve budget neutrality.

A similar type of adjustment was used when we transitioned from RUG-III to RUG-IV in FY 2011. However, 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. As a result, in the FY 2012 SNF PPS final rule, we used actual FY 2011 RUG-IV utilization data to recalibrate the RUG-IV parity adjustment. Based on the use of FY 2011 RUG-IV utilization data, we decreased the RUG-IV parity adjustment applied to 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 PDPM utilization data to ascertain, among other things, if the PDPM parity adjustment provided for a budget neutral transition to this new case-mix classification model. Similar to what occurred in FY 2011 with RUG-IV implementation, we have observed significant differences between expected SNF PPS payments and case-mix utilization, based on historical data, and the actual SNF PPS payments and case-mix utilization under the PDPM, based on FY 2020 data. As a result, it would appear that rather than simply achieving parity, the FY 2020 parity adjustment may have inadvertently triggered a significant increase in overall payment levels under the SNF PPS. We believe that, based on the data from this initial phase of PDPM, a recalibration of the PDPM parity adjustment is warranted to ensure that the adjustment serves its intended purpose to make the transition between RUG-IV and PDPM budget neutral.

However, we also acknowledge that the pandemic-related PHE for COVID-19, which began during the first year of PDPM and has continued into at least part of FY 2021, has had a likely impact on SNF PPS utilization data. Further, following the methodology utilized in calculating the initial parity adjustment, we typically would 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 between the prior system and new system and determine an appropriate adjustment factor to achieve parity. When we performed a similar recalibration of the RUG-IV parity adjustment, for example, we used data from FY 2011, the first year of RUG-IV implementation, as the basis for recalibrating the RUG-IV parity adjustment. However, in addition to the aforementioned potential issues with the FY 2020 SNF utilization data arising from the PHE for COVID-19, we are concerned that given the significant differences in both patient assessment requirements and payment incentives between RUG-IV and PDPM, using the same methodology we have used in the past to calculate a recalibrated PDPM parity adjustment could lead to a potentially inaccurate recalibration.

Therefore, given these issues, and for the reasons below, we are taking this opportunity to present some of the results of our PDPM data monitoring efforts and a potential recalibration methodology intended to address the issues presented above. First, it is important to provide transparency on the observed impacts of PDPM implementation, as we do believe there have been significant changes observed in SNF utilization that are tied strictly to PDPM and not the PHE for COVID-19. Second, we wish to make clear why we believe that the typical methodology for recalibrating the parity adjustment may not provide an accurate recalibration under PDPM. Finally, we view this as an opportunity to seek comment on a path forward for recalibrating the PDPM parity adjustment to ensure that PDPM is implemented in a budget neutral manner, as intended.

2. FY 2020 Changes in SNF Case-Mix Utilization

FY 2020 was a year of significant change under the SNF PPS. In addition to implementing PDPM, a national PHE for COVID-19 was declared. With the announcement of the PHE for COVID-19, we also announced a number of waivers which impacted SNF operations and the population of Medicare beneficiaries who were able to access the Part A SNF benefit. Most notably, under authority granted us by section 1812(f) of the Act, we issued a waiver of section 1861(i) of the Act, specifically the requirement that in order for a SNF stay to be covered by Medicare, a beneficiary must have a prior inpatient hospital stay of not less than 3 consecutive days before being admitted to the Part A SNF stay. Additionally, this waiver also allowed certain beneficiaries renewed SNF coverage without first having to start a new benefit period. The section 1812(f) waiver, particularly the component which permits beneficiaries to access the Part A SNF benefit without a prior hospitalization, allowed beneficiaries who would not typically be able to access the Part A SNF benefit to receive a Part A covered SNF stay (for example, long term care nursing home patients without any prior hospitalization). A key aspect of our methodology for recalibrating the PDPM parity adjustment involves parsing out the impact of these waivers and the different population of beneficiaries that had access to the SNF benefit as result of these waivers from the population of beneficiaries that would have been admitted to SNFs subsequent to PDPM implementation without these waivers, as well as differences in the type of care these patients received. We would note that while the PHE for COVID-19 clearly had impacts on nursing home care protocols and many other aspects of SNF operations, the relevant issue for pursuing a recalibration of the PDPM parity adjustment is whether or not these changes caused the SNF case-mix distribution to be distinctly different from what it would have been were it not for the PHE for COVID-19. In other words, while different people were able to access the Part A SNF benefit than would typically be able to do so, the issue is whether or not the relative percentage of beneficiaries in each PDPM group is different than what those percentages would have been were it not for the PHE for COVID-19 and related waivers. We solicit comments on whether stakeholders believe that the PHE for COVID-19 impacted on the distribution of patient case-mix.

To understand the potential impact of the PHE for COVID-19 on SNF utilization data, we can begin by understanding the overall utilization of the waivers and the overall frequency of COVID-19 diagnoses among the SNF population. In FY 2020, only approximately 9.8 percent of SNF stays included a COVID-19 ICD-10 diagnosis code (either as a primary or secondary diagnosis), while 15.6 percent of SNF stays utilized a section 1812(f) waiver (with the majority of these cases using the prior hospitalization waiver), as identified by the presence of a “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 87 percent of SNF beneficiaries had a qualifying prior hospitalization in FY 2020. These general statistics are important, as they highlight that while the PHE for COVID-19 certainly impacted many aspects of nursing home operations, the overwhelming majority of SNF beneficiaries entered into Part A SNF stays in FY 2020 as they would have in any other year; that is, without using a PHE-related waiver, with a prior hospitalization, and without a COVID-19 diagnosis. In fact, as we discuss further below, even when removing those using a PHE-related waiver and those with a COVID-19 diagnosis from our dataset, the observed inadvertent increase in SNF payments since PDPM was implemented is approximately the same. This would seem to imply that this “new” population of SNF beneficiaries (that is, COVID-19 patients and those using a section 1812(f) waiver) does not appear to be the cause of the increase in SNF payments after implementation of PDPM, since we would expect a much greater impact on the calculation of the necessary recalibration from removing this population from our analysis if that were the case.

Moreover, we do believe that there is clear evidence that PDPM alone is impacting certain aspects of SNF patient classification and care provision. For example, through FY 2019, the average number of therapy minutes SNF patients received per day was approximately 91 minutes. Beginning almost immediately with PDPM implementation (and well before the onset of the pandemic), the average number of therapy minutes SNF patients received per day dropped to approximately 62, a decrease of over 30 percent. Given both the immediacy and ubiquity of this change in the SNF data, without any concurrent change in the SNF population, it is clear that this overall decrease in the amount of therapy services provided to SNF patients is a result of PDPM implementation and not other factors. A number of media articles further corroborated this finding, which identified significant changes in therapy staffing and care directives at the outset of PDPM. Similarly, we also observed an increase in non-individualized modes of therapy provision beginning with PDPM implementation. Specifically, while the percentage of SNF stays which included concurrent or group therapy was approximately 1 percent for each of these therapy modes prior to FY 2020, these numbers rose to approximately 32 percent and 29 percent, respectively, beginning in the first month of PDPM implementation. Coincidentally, these numbers then dropped to 8 percent and 4 percent, respectively, beginning in April 2020, close to when the PHE for COVID-19 was declared (highlighting at least one impact of the PHE for COVID-19 on SNF care provision and utilization). We also note that while these findings (increases in concurrent and group therapy utilization) were anticipated prior to PDPM implementation based on comments on the FY 2019 and FY 2020 SNF PPS proposed rules, we maintain the belief that the unique characteristics and goals of each SNF patient should drive patient care decisions. As we stated in the FY 2020 SNF PPS final rule (84 FR 38748), we believe that 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. We would also note that, despite these changes in therapy provision, we did not identify any significant changes in health outcomes for SNF patients. For example, we observed no changes in the percentage of stays with falls with major injury, the percentage of stays ending with Stage 2-4 or unstageable pressure ulcers or deep tissue injury, the percentage of stays readmitted to an inpatient hospital setting within 30 days of SNF discharge, or other similar metrics. We will continue to monitor these and other metrics to identify any adverse trends that may have been caused by changes in care patterns that accompanied the implementation of PDPM.

These changes in therapy provision highlight the reasons we believe that the typical methodology for recalibrating a parity adjustment would not be appropriate in the context of PDPM. As discussed previously in this proposed rule and in the FY 2012 SNF PPS final rule (76 FR 26371), we would typically utilize claims and assessment data from a given period under the new payment system, classify patients under both the current and prior payment model using this same set of data, compare aggregate payments under each payment model, and calculate an appropriate adjustment factor to achieve budget neutrality. However, given the significant changes in therapy provision since PDPM implementation, we found that using patient assessment data collected under PDPM (for example, FY 2020 data) would lead to a drastic underestimation of RUG-IV case mix for purposes of determining what aggregate payments would have been under RUG-IV for the same period. In other words, given the significant reduction in the overall amount of therapy provided to SNF patients since PDPM implementation, as well as changes in the way that the therapy is provided (for example, increases in group and concurrent therapy), classifying SNF patients into RUG-IV payment groups using data collected under PDPM would lead to a RUG-IV case-mix distribution that contrasts significantly with historical trends under RUG-IV. This finding is precisely why we do not believe that the typical methodology for recalibrating the PDPM parity adjustment would result in an accurate calculation of the revised parity adjustment factor and may lead to an overcorrection. We invite comments on the information presented above, as well as on the potential impact of using the reported FY 2020 patient assessment data from the MDS to reclassify SNF beneficiaries under RUG-IV, consistent with the same type of recalibration methodology we have used after prior system transitions. Below, we discuss the methodology we are considering for recalibrating the PDPM parity adjustment, which we believe accounts for this change in therapy provision.

3. Methodology for Recalibrating the PDPM Parity Adjustment

As discussed above, we have identified an inadvertent increase in SNF spending since implementing PDPM. As in the past, identifying the scope and magnitude of this type of inadvertent increase begins with looking at the type of case-mix distribution that was expected under the new case-mix system and the actual case-mix distribution that occurs under the new case-mix system. In the FY 2012 SNF PPS proposed rule (76 FR 26371), we were able to provide a table which listed each of the RUG-IV payment groups with the projected and actual percentage of SNF days of service associated with each group. Due to the number of possible payment groups under PDPM, this type of table is not possible. However, Table 23 provides the average PDPM case-mix index expected for each of the PDPM rate components based on data from FY 2019. This average is calculated for each component by summing the expected PDPM case-mix index for each day of service and then dividing this number by the total number of FY 2019 days of service. Table 23 also provides the actual average PDPM case-mix index for each of these components in two different ways. First, we used FY 2020 data for the full SNF population and, following the same methodology described above to determine the expected average PDPM case-mix index, we summed the case-mix index for each day of service in FY 2020 and then divided this by the total number of FY 2020 days of service. Second, we used FY 2020 data for the SNF population excluding those SNF stays where either the patient was diagnosed with COVID-19 or the stay utilized a PHE for COVID-19 related waiver (for example, the waiver issued under authority granted by section 1812(f) of the Act to allow Part A coverage of a SNF stay without a qualifying prior hospital stay), as identified by the presence of a “DR” condition code on the associated SNF claim. We evaluated the average CMI using this subset of the SNF population as we believe it would provide a way to identify the effect of the PHE for COVID-19 on FY 2020 case mix and the recalibration calculation if we were to use FY 2020 data collected during the PHE for COVID-19. The results of this analysis are provided in Table 23.

Table 23—Average Case-Mix Index, Expected and Actual, by Component

Component	Expected CMI (FY 2019 Estimate)	Actual CMI (FY 2020)	Actual CMI (FY 2020 without DR or COVID)
	Average CMI	Average CMI	Average CMI
PT	1.53	1.50	1.52
OT	1.52	1.51	1.52
SLP	1.39	1.71	1.67
Nursing	1.43	1.67	1.62
NTA	1.14	1.20	1.21

According to this analysis, while we observed slight decreases in the average CMI for the PT and OT rate components for both the full and subset FY 2020 populations as compared to what was expected, we observed 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. We believe these significant increases in the average case-mix for these components is primarily responsible for the inadvertent increase in spending under PDPM. Further, given that we observe similar increases in the average CMI for these components even when using the subset of the FY 2020 SNF population that excludes those patients diagnosed with COVID-19 or who used a PHE-related waiver, we believe that these increases in average case-mix for these components are the result of PDPM and not the PHE for COVID-19. We invite comments on this approach and the extent to which commenters believe that the PHE for COVID-19 may have impacted on the PDPM case-mix distribution in ways not captured in Table 23 or in the discussion provided here.

Our basic methodology for recalibrating the parity adjustment has been to compare total payments under the new case-mix model with what total payments would have been under the prior case-mix model, were the new model not implemented. In the context of the PDPM, this means comparing total FY 2020 payments under PDPM to what FY 2020 payments would have been under RUG-IV if PDPM were not implemented. In order to calculate the actual total payments under PDPM for this proposed rule, 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 to compute the variable per diem adjustment, the presence of an HIV diagnosis on the claim to account for the PDPM AIDS add-on, and finally, we accounted for the provider's urban or rural status. As with the analysis that led to Table 23, we calculated total payments both for the full SNF population in FY 2020, as well as the subset of that population removing those with a COVID-19 diagnosis and those using a PHE-related waiver.

In order to calculate expected total payments under RUG-IV, in light of the discussion above (which describes why we believe it would not be appropriate simply to reclassify SNF patients under RUG-IV using the information reported in FY 2020), 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, as we would have were it not for the implementation of PDPM. The total payments under RUG-IV also account for the difference in how the AIDS add-on is calculated under RUG-IV, as compared to PDPM, and similarly accounts for a provider's FY 2020 urban or rural status.

We believe that this methodology provides a more accurate representation of what RUG-IV payments would have been in FY 2020 were it not for the change in payment incentives and care provision precipitated by PDPM implementation, than using data reported under PDPM to reclassify these patients under RUG-IV. In particular, given the reduction in therapy utilization under PDPM, as compared to RUG-IV, using the therapy utilization data reported under PDPM to reclassify SNF patients back into RUG-IV groups would produce a case-mix distribution that would be significantly different from the RUG-IV case-mix distribution we would have expected were it not for PDPM implementation. Since the reduction in therapy would lead to a reduction in the RUG-IV case-mix assignments (for example, Ultra-High and Very-High Rehabilitation assignments are not nearly as prevalent using PDPM-reported data as they are using data that existed prior to PDPM), this would lead to an underestimation of what RUG-IV payments would have been in FY 2020. This, in turn, would lead to an overcorrection in recalibrating the parity adjustment due to the low estimated total RUG-IV payments. Additionally, given the significant changes in the patient assessment schedule, specifically the removal of the Change of Therapy Other Medicare Required Assessment, we cannot know if the patient would continue to remain classified in the RUG-IV group into which the patient classified on the 5-day assessment beyond that assessment window. In other words, without having an interim assessment between the 5-day assessment and the patient's discharge from the facility, we would be 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 this initial RUG-IV classification. As a result, using reported data under PDPM could lead to a reclassification of patients under RUG-IV that is not consistent with how patients would have been classified under RUG-IV if PDPM had not been implemented. As such, we believe that using the FY 2019 RUG-IV case-mix distribution as a proxy for what the RUG-IV case-mix distribution would have been in FY 2020 were it not for PDPM implementation, provides a more accurate calculation of what total RUG-IV payments would have been during FY 2020 absent PDPM implementation.

The result of these analyses was that we identified 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 5.0 percent increase in aggregate spending under PDPM for FY 2020 when considering the subset population. Although these results are similar, in light of the potential differences in the PDPM case-mix distribution which may have been precipitated by the admission of patients diagnosed with COVID-19 and patients whose stays utilized a PHE-related waiver, we believe it would be more appropriate to pursue a recalibration using the subset population. We invite comments on our methodology, particularly on the use of the FY 2019 RUG-IV case-mix distribution to calculate expected FY 2020 SNF payments if PDPM were not implemented and on using the subset FY 2020 SNF population which excludes patients diagnosed with COVID-19 and those using a PHE-related waiver in our recalibration calculation rather than the full FY 2020 SNF population.

Based on the above discussion and analysis, we have described above a potential path towards a recalibration of the PDPM parity adjustment using a subset of the full FY 2020 SNF data set. Since the initial increase applied to the PDPM CMIs to achieve budget neutrality applied equally across all case-mix adjusted components, we believe it would be appropriate, in the event an adjustment is made, to adjust the CMIs across all such components in equal measure. Using the methodology described above, the resultant PDPM parity adjustment factor would be lowered from 46 percent to 37 percent for each of the PDPM case-mix adjusted components. If this were applied for FY 2022, we estimate that this methodology would result in a reduction in SNF spending of 5.0 percent, or approximately $1.7 billion.

Tables 24 and 25 set forth what the FY 2022 PDPM CMIs and case-mix adjusted rates would be if we applied the recalibration methodology described above in FY 2022.

Table 24—Recalibrated PDPM Case-Mix Adjusted Federal Rates and Associated Indexes—URBAN

PDPM group	PT CMI	PT rate	OT CMI	OT rate	SLP CMI	SLP rate	Nursing CMG	Nursing CMI	Nursing rate	NTA CMI	NTA rate
A	1.44	$90.49	1.40	$81.89	0.64	$15.01	ES3	3.82	$418.48	3.05	$252.05
B	1.60	100.54	1.53	89.49	1.71	40.12	ES2	2.89	316.60	2.38	196.68
C	1.77	111.23	1.59	93.00	2.51	58.88	ES1	2.76	302.36	1.73	142.97
D	1.81	113.74	1.44	84.23	1.37	32.14	HDE2	2.26	247.58	1.25	103.30
E	1.34	84.21	1.33	77.79	2.2	51.61	HDE1	1.87	204.86	0.9	74.38
F	1.52	95.52	1.51	88.32	2.80	65.69	HBC2	2.11	231.15	0.68	56.20
G	1.57	98.66	1.54	90.07	1.92	45.04	HBC1	1.75	191.71
H	1.09	68.50	1.08	63.17	2.69	63.11	LDE2	1.96	214.72
I	1.06	66.61	1.11	64.92	3.32	77.89	LDE1	1.63	178.57
J	1.34	84.21	1.36	79.55	2.81	65.92	LBC2	1.62	177.47
K	1.43	89.86	1.45	84.81	3.48	81.64	LBC1	1.35	147.89
L	1.03	64.73	1.04	60.83	3.96	92.90	CDE2	1.76	192.81
M	1.20	75.41	1.22	71.36			CDE1	1.52	166.52
N	1.39	87.35	1.41	82.47			CBC2	1.46	159.94
O	1.46	91.75	1.46	85.40			CA2	1.03	112.84
P	1.02	64.10	1.03	60.24			CBC1	1.26	138.03
Q							CA1	0.88	96.40
R							BAB2	0.98	107.36
S							BAB1	0.93	101.88
T							PDE2	1.48	162.13
U							PDE1	1.38	151.18
V							PBC2	1.15	125.98
W							PA2	0.67	73.40
X							PBC1	1.06	116.12
Y							PA1	0.62	67.92

Table 25: Recalibrated PDPM Case-Mix Adjusted Federal Rates and Associated Indexes—RURAL

PDPM group	PT CMI	PT rate	OT CMI	OT rate	SLP CMI	SLP rate	Nursing CMG	Nursing CMI	Nursing rate	NTA CMI	NTA rate
A	1.44	$103.15	1.40	$92.11	0.64	$18.92	ES3	3.82	$399.80	3.05	$240.83
B	1.60	114.61	1.53	100.66	1.71	50.55	ES2	2.89	302.47	2.38	187.92
C	1.77	126.79	1.59	104.61	2.51	74.20	ES1	2.76	288.86	1.73	136.60
D	1.81	129.65	1.44	94.74	1.37	40.50	HDE2	2.26	236.53	1.25	98.70
E	1.34	95.98	1.33	87.50	2.2	65.03	HDE1	1.87	195.71	0.9	71.06
F	1.52	108.88	1.51	99.34	2.8	82.77	HBC2	2.11	220.83	0.68	53.69
G	1.57	112.46	1.54	101.32	1.92	56.76	HBC1	1.75	183.16
H	1.09	78.08	1.08	71.05	2.69	79.52	LDE2	1.96	205.13
I	1.06	75.93	1.11	73.03	3.32	98.14	LDE1	1.63	170.60
J	1.34	95.98	1.36	89.47	2.81	83.06	LBC2	1.62	169.55
K	1.43	102.43	1.45	95.40	3.48	102.87	LBC1	1.35	141.29
L	1.03	73.78	1.04	68.42	3.96	117.06	CDE2	1.76	184.20
M	1.20	85.96	1.22	80.26			CDE1	1.52	159.08
N	1.39	99.57	1.41	92.76			CBC2	1.46	152.80
O	1.46	104.58	1.46	96.05			CA2	1.03	107.80
P	1.02	73.06	1.03	67.76			CBC1	1.26	131.87
Q							CA1	0.88	92.10
R							BAB2	0.98	102.57
S							BAB1	0.93	97.33
T							PDE2	1.48	154.90
U							PDE1	1.38	144.43
V							PBC2	1.15	120.36
W							PA2	0.67	70.12
X							PBC1	1.06	110.94
Y							PA1	0.62	64.89

We invite comments on the methodology described in this section of the proposed rule for recalibrating the PDPM parity adjustment, as well as the findings of our analysis described throughout this section. To assist commenters in providing comments on this issue, we have also posted a file on the CMS website, at https://www.cms.gov/​snfpps,, which provides 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. 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, in the event we confirm the finding that the current implementation of PDPM is not budget neutral and that a recalibration is appropriate, despite the importance of ensuring that PDPM is budget neutral going forward, we acknowledge the possibility that applying such a significant reduction in payments in a single year and without time to prepare for the reduction in revenue could create a financial burden for providers. In light of this possibility, we are also considering a number of potential mitigation strategies that would help to ease the transition to prospective budget neutrality in the event an adjustment is finalized. These strategies fall into two broad categories: Delayed implementation; and phased implementation.

With regard to a delayed implementation strategy, this would mean that we would implement the reduction in payment, or some portion of the reduction in payment if combined with a phased implementation approach described below, in a later year than the year in which the reduction is finalized. For example, considering the 5 percent reduction discussed above, if this reduction was finalized in FY 2022 with a 1 year delayed implementation, this would mean that the full 5 percent reduction would be prospectively applied to the PDPM CMIs in FY 2023. If the reduction was finalized in FY 2022 with a 2 year delayed implementation, then the reduction in the PDPM CMIs would be applied prospectively beginning in FY 2024. This type of strategy, on its own, does not serve to mitigate the overall amount of the reduction in a single year, but rather serves to provide facilities with time to prepare for the impending reduction in payments. We solicit comments on whether stakeholders believe that, in the event we finalize the parity adjustment recalibration, we should finalize this recalibration with a delayed implementation. Additionally, to the extent that stakeholders believe that a delayed implementation would be warranted, we solicit comments on the appropriate length of the delay.

With regard to a phased implementation strategy, this would mean that the amount of the reduction would be spread out over some number of years. Such an approach helps to mitigate the impact of the reduction in payments by applying only a portion of the reduction in a given year. For example, if we were to use a 2-year phased implementation approach to the 5 percent reduction discussed above, this would mean that the PDPM CMIs would be reduced by 2.5 percent in the first year of implementation and then reduced by the remaining 2.5 percent in the second and final year of implementation. So, for example, if this adjustment was finalized for FY 2022, then the PDPM CMIs would be reduced by 2.5 percent in FY 2022 and then reduced by an additional 2.5 percent in FY 2023. We note that the number of years for a phased implementation approach could be as little as 2 years but as long as necessary to appropriately mitigate the yearly impact of the reduction. For example, we could implement a 5-year phased approach for this reduction, which would apply a one percent reduction to the PDPM CMIs each year for 5 years. We solicit comments on the need for a phased implementation approach to recalibrating the PDPM parity adjustment, as well as on the appropriate length of such an approach.

We would, finally, note that these mitigation strategies may be used in combination with each other. For example, we could finalize a 2 year phased approach with a 1 year delayed implementation. Using FY 2022 as the hypothetical year in which such an approach could be finalized, this would mean that there would be no reduction to the PDPM CMIs in FY 2022, a 2.5 percent reduction to the PDPM CMIs in FY 2023 and then a 2.5 percent reduction in the PDPM CMIs in FY 2024. We solicit comments on the possibility of combining these approaches and what stakeholders believe would be appropriate, using these approaches, to appropriately mitigate the impact of the reduction in SNF PPS payments.

We note that in any of these options, the adjustment would be applied prospectively, and the case mix indexes would not be adjusted to account for deviations from budget neutrality in years before the payment adjustments were implemented.

We are considering these approaches as they may be warranted to mitigate potential negative impacts on providers resulting from implementation of such a reduction in the SNF PPS rates entirely within a single year in the event we determine that recalibrating the parity adjustment is necessary to achieve budget neutrality. However, we believe that these alternatives would continue to reimburse in amounts that significantly exceed our intended policy in excess of the rates that would have been paid had we maintained the prior payment classification system rather than in a budget neutral manner as intended, and as we stated above, we believe it is imperative that we act in a well-considered but appropriately expedient manner once excess payments are identified. In addition, as we move forward with programs designed to enhance and restructure our post-acute care payment systems, we believe that payments under the SNF PPS should be established at their intended and most appropriate levels as quickly as possible. Moreover, stabilizing the baseline is a necessary first step toward properly implementing and maintaining the integrity of the PDPM classification methodology and the SNF PPS as a whole as discussed above. We invite comments on the mitigation strategies described above for mitigating the impact of recalibrating the PDPM parity adjustment in the event we finalize a recalibration.

VI. Skilled Nursing Facility (SNF) Quality Reporting Program (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-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 multifactor productivity (MFP) 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 2022 SNF QRP

The SNF QRP currently has 13 measures for the FY 2022 SNF QRP, which are outlined in Table 26. For a discussion of the factors used to evaluate whether a measure should be removed from the SNF QRP, we refer readers to 42 CFR 413.360(b)(3).

Table 26—Quality Measures Currently Adopted for the FY 2022 SNF QRP

Short name	Measure name & data source
Resident Assessment Instrument Minimum Data Set (Assessment-Based)
Pressure Ulcer/Injury	Changes in Skin Integrity Post-Acute Care: Pressure Ulcer/Injury.
Application of Falls	Application of Percent of Residents Experiencing One or More Falls with Major Injury (Long Stay) (NQF #0674).
Application of Functional Assessment/Care Plan	Application of Percent of Long-Term Care Hospital (LTCH) Patients with an Admission and Discharge Functional Assessment and a Care Plan That Addresses Function (NQF #2631).
Change in Mobility Score	Application of IRF Functional Outcome Measure: Change in Mobility Score for Medical Rehabilitation Patients (NQF #2634).
Discharge Mobility Score	Application of IRF Functional Outcome Measure: Discharge Mobility Score for Medical Rehabilitation Patients (NQF #2636).
Change in Self-Care Score	Application of the IRF Functional Outcome Measure: Change in Self-Care Score for Medical Rehabilitation Patients (NQF #2633).
Discharge Self-Care Score	Application of IRF Functional Outcome Measure: Discharge Self-Care Score for Medical Rehabilitation Patients (NQF #2635).
DRR	Drug Regimen Review Conducted With Follow-Up for Identified Issues—Post Acute Care (PAC) Skilled Nursing Facility (SNF) Quality Reporting Program (QRP).
TOH-Provider *	Transfer of Health Information to the Provider Post-Acute Care (PAC).
TOH-Patient *	Transfer of Health Information to the Patient Post-Acute Care (PAC).
Claims-Based
MSPB SNF	Medicare Spending Per Beneficiary (MSPB)-Post Acute Care (PAC) Skilled Nursing Facility (SNF) Quality Reporting Program (QRP).
DTC	Discharge to Community (DTC)-Post Acute Care (PAC) Skilled Nursing Facility (SNF) Quality Reporting Program (QRP) (NQF #3481).
PPR	Potentially Preventable 30-Day Post-Discharge Readmission Measure for Skilled Nursing Facility (SNF) Quality Reporting Program (QRP).
* In response to the public health emergency (PHE) for the Coronavirus Disease 2019 (COVID-19), CMS released an Interim Final Rule (85 FR 27595 through 27597) which delayed the compliance date for collection and reporting of the Transfer of Health Information measures for at least two full fiscal years after the end of the PHE.

C. SNF QRP Quality Measure Proposals Beginning With the FY 2023 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)) 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 propose to adopt two new measures for the SNF QRP beginning with the FY 2023 SNF QRP: The SNF Healthcare-Associated Infections Requiring Hospitalization measure (SNF HAI) and the COVID-19 Vaccination Coverage among Healthcare Personnel (HCP) ^[4] measure as an “other measure” under section 1899B(d)(1) of the Act. The SNF HAI measure is an outcome measure. The data used to report the SNF HAI measure are standardized and interoperable and would allow providers to exchange this data and compare outcomes across the care continuum and PAC settings. Clinical data captured in every clinical setting informs a resident's current medical care plan, facilitates coordinated care, and improves Medicare beneficiary outcomes. We plan to develop HAI measures in other PAC settings, such as the Inpatient Rehabilitation Facility (IRF) Quality Reporting Program and the Long-Term Care Hospital (LTCH) Quality Reporting Program. The proposed measure supports the CMS Meaningful Measures Initiative through the Making Care Safer by Reducing Harm Caused in the Delivery of Care domain. We have previously solicited feedback on the SNF HAI measure as a future measure for the SNF QRP and received several comments of support as well as a few comments recommending suggestions (84 FR 38765). The measure is described in more detail below.

We are proposing the COVID-19 Vaccination Coverage among HCP measure as an “other” measure under section 1899B(d)(1) of the Act beginning with the FY 2023 SNF QRP. In accordance with section 1899B(a)(1)(B) of the Act, the data used to calculate this measure are standardized and interoperable. The proposed measure supports the Meaningful Measures domain of Promote Effective Prevention and Treatment of Chronic Disease. We identified the measure concept as a priority in response to the current public health crisis. This process measure was developed with the Centers for Disease Control and Prevention (CDC) to track COVID-19 vaccination coverage among HCP in the SNF setting. This measure is described in more detail below.

In addition, we propose to update the denominator for one measure, the Transfer of Health (TOH) Information to the Patient—Post-Acute Care (PAC) measure to exclude residents discharged home under the care of an organized home health service or hospice.

1. Proposed Skilled Nursing Facility (SNF) Healthcare-Associated Infections (HAI) Requiring Hospitalization Quality Measure Beginning With the FY 2023 SNF QRP

a. Background

Monitoring the occurrence of HAIs among SNF residents can provide valuable information about a SNF's quality of care. Although HAIs are not considered “never events”, or serious adverse errors in the provision of health care services that should never occur, most are preventable as they are often the result of poor processes and structures of care.^[5] Evidence suggests there is a wide variation in HAI rates among SNF providers. An analysis of FY 2018 SNF claims indicates a performance gap in HAI rates across SNFs. Among the 14,347 SNFs included in the sample for the analysis, risk-adjusted measure scores ranged from a minimum of 2.19 percent to a maximum of 19.83 percent. Further, a 2014 report from the Office of the Inspector General (OIG) estimated that one in four adverse events among SNF residents are due to HAIs, and more than half of all HAIs are potentially preventable.^[6] Typically, HAIs result from inadequate patient management following a medical intervention, such as surgery or device implementation, or poor adherence to protocol and antibiotic stewardship guidelines.^[7 8 9] Several provider characteristics are also related to HAIs including staffing levels (for example, high turnover, low staff-to-resident ratios, etc.), facility structure characteristics (for example, national chain membership, high occupancy rates, etc.), and adoption or lack thereof of infection surveillance and prevention policies.^{[10 11 12 13 14 15]} Inadequate prevention and treatment of HAIs is likely to result in poor health care outcomes for residents and wasteful resource use. For example, HAIs are associated with longer lengths of stay, use of higher-intensity care (for example, critical care services and hospital readmissions), increased mortality, and high health care costs.^{[16,17,18,19]} Monitoring SNF HAI rates would provide information about each facility's adeptness in infection prevention and management.

Addressing HAIs in SNFs is particularly important as several factors place SNF residents at high risk for infection, including increased age, cognitive and functional decline, use of indwelling devices, frequent care transitions, and close contact with other resident and healthcare workers.^[20 21] Furthermore, in SNFs, COVID-19 has a disproportionate impact on racial and ethnic minorities as well as people living with disabilities.^[22 23] Emerging COVID-19 studies reveal higher patient spread due to poor infection control, staff rotations between multiple SNFs, and poor patient COVID-19 screenings.^[24 25] An analysis comparing SNF HAI rates using FY 2019 data with the currently reported rates of COVID-19 in SNFs found that nursing homes with higher HAI rates in FY 2019 also have a higher number of COVID-19 cases.^[26] This analysis was presented to the PAC-LTC MAP Workgroup at the January 11th meeting (http://www.qualityforum.org/​WorkArea/​linkit.aspx?​LinkIdentifier=​id&​ItemID=​94559,, slide 134). We believe this finding supports a relationship not only between this measure and overall HAI prevention and control in SNFs, but also in predicting those SNFs more likely to have higher rates of infection in future pandemics. Several interventions may reduce HAI rates among SNFs, thus improving quality of care. These interventions include the adoption of infection surveillance and prevention policies, safety procedures, antibiotic stewardship, and staff education and training programs.^{[27 28 29 30 31 32 33]} Additionally, infection prevention and control programs with core components in education, monitoring, and feedback on infection rates from surveillance programs or feedback on infection control practices from audits have been found to be successful interventions for reducing HAIs.^[34] The effectiveness of these interventions suggests improvement of HAI rates among SNF residents is possible through modifying provider-led processes and interventions.

The proposed SNF HAI measure uses Medicare fee-for-service (FFS) claims data to estimate the risk-standardized rate of HAIs that are acquired during SNF care and result in hospitalization. Unlike other HAI measures that target specific infections, this measure would target all HAIs serious enough to require admission to an acute care hospital. Given the current COVID-19 public health emergency, we believe this measure would promote patient safety and increase the transparency of quality of care in the SNF setting. This measure also compares SNFs to their peers to statistically separate those that perform better than or worse than each other in infection prevention and management. We believe peer comparison would encourage SNFs to improve the quality of care they deliver.

b. Stakeholder and Technical Expert Panel (TEP) Input

In our development and specification of this measure, we employed a transparent process in which we sought input from stakeholders and national experts and engaged in a process that allowed for pre-rulemaking input, in accordance with section 1890A of the Act.

To meet this requirement, we provided the following opportunities for stakeholder input. Our measure development contractor for the SNF HAI measure convened a Technical Expert Panel (TEP) on May 9, 2019 to obtain expert input on the development of an HAI measure for use in the SNF QRP. The TEP consisted of stakeholders with a diverse range of expertise, including SNF and PAC subject matter knowledge, clinical and infectious disease expertise, patient and family perspectives, and measure development experience. The TEP supported the proposed measure concept and provided substantive input regarding the measure's specifications. Recommendations provided by the TEP included refining the measure's operational definition, exclusion criteria, and HAI ICD-10 diagnosis code list, among other considerations. All recommendations from the TEP were taken into consideration and applied appropriately where feasible. A summary of the TEP proceedings titled SNF HAI Final TEP Report is available on the SNF QRP Measures and Technical Information page at 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.

Following the TEP, our measure development contractor released draft quality measure specifications for public comment on the SNF HAI measure. Stakeholder feedback was solicited on the proposed measure by requesting comment on the CMS Measures Management System Blueprint site. The comment submission period was from September 14, 2020 to October 14, 2020. Comments on the measure varied. Many commenters supported the idea of adopting an HAI measure to improve prevention efforts; however, commenters also offered criticisms about the measure's specifications and implementation. The summary report of the September 14 to October 14, 2020 public comment period titled SNF HAI Public Comment Summary Report is available on the SNF QRP Measures and Technical Information page at 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.

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 on the list.

We included the SNF HAI measure under the SNF QRP Program in the publicly available “List of Measures under Consideration for December 21, 2020” (MUC List).^[35] The National Quality Forum (NQF)-convened Measure Applications Partnership (MAP) Post-Acute Care/Long-Term Care (PAC-LTC) workgroup met virtually on January 11, 2021 and provided input on the proposed measure. The MAP offered conditional support of the SNF HAI measure for rulemaking contingent upon NQF endorsement, noting that the measure adds value to the SNF QRP by presenting one overall measurement of all HAIs acquired during SNF care that result in hospitalizations, information that is not currently available. The MAP recognized that the proposed measure is intended to reflect global infection control for a facility, and may encourage SNFs to access processes and perform interventions to reduce adverse events among SNF residents that are due to HAIs. The MAP Rural Health

Workgroup also agreed that the SNF HAI measure is suitable for use with rural providers in the SNF QRP. The final MAP report is available at http://www.qualityforum.org/​Publications/​2021/​03/​MAP_​2020-2021_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx.

Additionally, measure testing was conducted on the SNF HAI measure. Split-half testing revealed the proposed measure's moderate reliability. Validity testing of the measure showed good model discrimination as the HAI model can accurately predict HAI cases while controlling for differences in resident case-mix. The SNF HAI TEP also showed strong support for the face validity of the proposed measure. For measure testing details, refer to the document titled, Skilled Nursing Facility Healthcare-Associated Infections Requiring Hospitalization for the Skilled Nursing Facility Quality Reporting Program Technical Report available on the SNF QRP Measures and Technical Information page at 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. This proposed measure is not currently NQF endorsed, but CMS plans to submit the measure for NQF endorsement in the future.

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, measures specified under section 1899B of the Act be endorsed by the entity with a contract under section 1890(a), currently the National Quality Forum (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) permits the Secretary to specify a measure that is not so endorsed, as long as due consideration is given to measures that have been endorsed or adopted by a consensus organization identified by the Secretary.

The proposed SNF HAI measure is not NQF endorsed, so we considered whether there are other available measures that assess HAIs in SNFs. After review of the NQF's consensus-endorsed measures, we were unable to identify any NQF endorsed measures for SNFs focused on capturing several types of severe infections attributable to the SNF setting in one composite score. For example, although the measures Percent of Residents with a Urinary Tract Infection (Long-Stay) (NQF #0684), National Healthcare Safety Network (NHSN) Catheter-Associated Urinary Tract Infections (NQF #0138), NHSN Central Line-Associated Bloodstream Infections (NQF #0139), and NHSN Facility-Wide Inpatient Hospital-onset Clostridium Difficile Infection (NQF #1717) are NQF endorsed and all report on specific types of infections, they do not provide an overall HAI rate and are not specific to the SNF setting. Additionally, although the Skilled Nursing Facility 30-Day All-Cause Readmission measure (NQF #2510), the Potentially Preventable 30-Day Post-Discharge Readmission measure for SNF QRP, and the Skilled Nursing Facility 30-Day Potentially Preventable Readmission after Hospital Discharge measure (SNFPPR) are all specific to the SNF setting, they are not solely focused on infections. We intend to submit this proposed measure to the NQF for consideration of endorsement when feasible.

Therefore, after consideration of other available measures, we find that the exception under section 1899B(e)(2)(B) of the Act applies and are proposing the measure, Skilled Nursing Facility (SNF) Healthcare-Associated Infections (HAI) Requiring Hospitalization measure beginning with the FY 2023 SNF QRP.

e. Quality Measure Calculation

The proposed measure 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.

Both the proposed measure numerator and denominator are risk-adjusted. The measure's adjusted numerator is the estimated number of SNF stays predicted to have an HAI that results in hospitalization. The estimate starts with the observed count of the measure outcome, which is then risk-adjusted for resident characteristics and a statistical estimate of the SNF effect beyond resident case mix. The term “SNF effect” represents provider-specific behaviors that result in facilities' HAI rates. These behaviors may include adherence to evidence-based infection control policies and procedures. The adjusted denominator is the expected number of SNF stays with the measure outcome. The adjusted denominator is calculated by risk-adjusting the total eligible SNF stays for resident characteristics excluding the SNF effect.

The proposed measure is calculated using a standardized risk ratio (SRR) in which the predicted number of HAIs for SNF stays per provider is divided by the expected number of HAIs. For each SNF, a risk-adjusted rate of HAIs that are acquired during SNF care and result in hospitalization is calculated by multiplying the SRR by the overall national observed rate of HAIs for all SNF stays. The measure is risk-adjusted for age and gender characteristics, original reason for Medicare Entitlement, principal diagnosis during the prior proximal inpatient (IP) stay, types of surgery or procedure from the prior proximal IP stay, length of stay and ICU/CCU utilization from the prior proximal IP stay, dialysis treatment from the prior proximal IP stay, and HCC comorbidities and number of prior IP stays within 1 year preceding the SNF stay. For technical information about this proposed measure, including information about the measure calculation, risk adjustment, and exclusions, refer to the document titled, Skilled Nursing Facility Healthcare-Associated Infections Requiring Hospitalization for the Skilled Nursing Facility Quality Reporting Program Technical Report available on the SNF QRP Measures and Technical Information page at 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. If this measure is finalized, we intend to publicly report this measure using four quarters of claims data. We refer readers to section VI.H.2. of this proposed rule for information regarding public reporting.

We invite public comment on our proposal to adopt the quality measure, the Skilled Nursing Facility (SNF) Healthcare-Associated Infections (HAIs) Requiring Hospitalization, beginning with the FY 2023 SNF QRP.

2. Proposed COVID-19 Vaccination Coverage Among Healthcare Personnel (HCP) Measure Beginning With the FY 2023 SNF QRP

a. Background

On January 31, 2020, the Secretary of the U.S. Department of Health and Human Services (HHS) declared a public health emergency (PHE) for the United States in response to the global outbreak of SARS-CoV-2, a novel (new) coronavirus that causes a disease named “coronavirus disease 2019” (COVID-19).^[36] COVID-19 is a contagious respiratory infection ^[37] that can cause serious illness and death. Older individuals, racial and ethnic minorities, and those with underlying medical conditions are considered to be at higher risk for more serious complications from COVID-19.^[38 39] As of April 4, 2021 the U.S. reported over 30 million cases of COVID-19 and over 553,000 COVID-19 deaths.^[40] Hospitals and health systems saw significant surges of COVID-19 patients as community infection levels increased.^[41] In December 2020 and January 2021, media outlets reported that more than 100,000 Americans were in the hospital with COVID-19.^[42]

Evidence indicates that COVID-19 primarily spreads when individuals are in close contact with one another.^[43] The virus is typically transmitted through respiratory droplets or small particles created when someone who is infected with the virus coughs, sneezes, sings, talks or breathes.^[44] Experts believe that COVID-19 spreads less commonly through contact with a contaminated surface ^[45] (although that is not thought to be a common way that COVID-19 spreads), and that in certain circumstances, infection can occur through airborne transmission.^[46] According to the CDC, those at greatest risk of infection are persons who have had prolonged, unprotected close contact (that is, within 6 feet for 15 minutes or longer) with an individual with confirmed SARS-CoV-2 infection, regardless of whether the individual has symptoms.^[47] Although personal protective equipment (PPE) and other infection-control precautions can reduce the likelihood of transmission in health care settings, COVID-19 can spread between healthcare personnel (HCP) and patients given the close contact that may occur during the provision of care.^[48] The CDC has emphasized that health care settings, including long-term care settings, can be high-risk places for COVID-19 exposure and transmission.^[49]

Vaccination is a critical part of the nation's strategy to effectively counter the spread of COVID-19 and ultimately help restore societal functioning.^[50]

On December 11, 2020, the Food and Drug Administration (FDA) issued the first Emergency Use Authorization (EUA) for a COVID-19 vaccine in the U.S.^[51] Subsequently, the FDA issued EUAs for additional COVID-19 vaccines. In issuing these EUAs, the FDA determined that it was reasonable to conclude that the known and potential benefits of each vaccine, when used as authorized to prevent COVID-19, outweighed its known and potential risks.^{[52 53 54]}

As part of its national strategy to address COVID-19, the current administration stated that it would work with states and the private sector to execute an aggressive vaccination strategy and has outlined a goal of administering 200 million shots in 100 days.^[55] Although the goal of the U.S. government is to ensure that every American who wants to receive a COVID-19 vaccine can receive one, Federal agencies recommended that early vaccination efforts focus on those critical to the PHE response, including healthcare personnel (HCP), and individuals at highest risk for developing severe illness from COVID-19.^[56] For example, the CDC's Advisory Committee on Immunization Practices (ACIP) recommended that HCP should be among those individuals prioritized to receive the initial, limited supply of the COVID-19 vaccination, given the potential for transmission in health care settings and the need to preserve health care system capacity.^[57] Research suggests most states followed this recommendation,^[58] and HCP began receiving the vaccine in mid-December of 2020.^[59]

HCP are at risk of carrying COVID-19 infection to patients, experiencing illness or death as a result of COVID-19 themselves, and transmitting it to their families, friends, and the general public. We believe it is important to require that SNFs report HCP vaccination in order to assess whether they are taking steps to limit the spread of COVID-19 among their HCP, reduce the risk of transmission of COVID-19 within their facilities, and to help sustain the ability of SNFs to continue serving their communities throughout the PHE and beyond. Currently, as required under the May 8, 2020 Interim final rule with comment period (85 FR 27601-27602), SNFs are required to submit COVID-19 data through the CDC's NHSN Long-term Care Facility COVID-19 Module of the NHSN. Examples of data reported in the module include: suspected and confirmed COVID-19 infections among residents and staff, including residents previously treated for COVID-19; total deaths and COVID-19 deaths among residents and staff; personal protective equipment and hand hygiene supplies in the facility; ventilator capacity and supplies available in the facility; resident beds and census; access to COVID-19 testing while the resident is in the facility; and staffing shortages. Although HCP and resident COVID-19 vaccination data reporting modules are currently available through the NHSN, the reporting of this data is voluntary. ^[60]

We also believe that publishing facility-level COVID-19 HCP vaccination rates on Care Compare would be helpful to many patients, including those who are at high-risk for developing serious complications from COVID-19, as they choose facilities from which to seek treatment. Under CMS' Meaningful Measures Framework, the COVID-19 Vaccination Coverage among Healthcare Personnel measure addresses the quality priority of “Promote Effective Prevention & Treatment of Chronic Disease” through the Meaningful Measures Area of “Preventive Care.”

Therefore, we are proposing a new measure, COVID-19 Vaccination Coverage among HCP to assess the proportion of a SNF's healthcare workforce that has been vaccinated against COVID-19.

b. Stakeholder Input

In the development and specification of the 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 on each measure, under section 1890A of the Act.^[61] To meet this requirement, the following opportunity was provided for stakeholder input.

The 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 Federal rulemaking process, for use in Medicare program(s). This allows multi-stakeholder groups to provide recommendations to the Secretary on the measures included on the list. The COVID-19 Vaccination Coverage among Healthcare Personnel measure was included on the publicly available “List of Measures under Consideration for December 21, 2020” (MUC List).^[62] Five comments were received from industry stakeholders during the pre-rulemaking process on the COVID-19 Vaccination Coverage among HCP measure, and support was mixed. Commenters generally supported the concept of the measure. However, there was concern about the availability of the vaccine and measure definition for HCP, and some commenters encouraged CMS to continue to update the measure as new evidence comes in.

c. Measure Applications Partnership (MAP) Review

When the Measure Applications Partnership (MAP) PAC-LTC Workgroup convened on January 11, 2021, it reviewed the MUC List and the COVID-19 Vaccination Coverage among HCP measure. The MAP recognized that the proposed measure represents a promising effort to advance measurement for an evolving national pandemic and that it would bring value to the SNF QRP measure set by providing transparency about an important COVID-19 intervention to help limit COVID-19 infections.^[63] The MAP also stated that collecting information on COVID-19 vaccination coverage among healthcare personnel and providing feedback to facilities would allow facilities to benchmark coverage rates and improve coverage in their facility, and that reducing rates of COVID-19 in healthcare personnel may reduce transmission among patients and reduce instances of staff shortages due to illness.^[64]

In its preliminary recommendations, the MAP PAC-LTC Workgroup did not support this measure for rulemaking, subject to potential for mitigation.^[65] To mitigate its concerns, the MAP believed that the measure needed well-documented evidence, finalized specifications, testing, and NQF endorsement prior to implementation.^[66] Subsequently, the MAP Coordinating Committee met on January 25, 2021, and reviewed the COVID-19 Vaccination Coverage among Healthcare Personnel measure. In the 2020-2021 MAP Final Recommendations, the MAP offered conditional support for rulemaking contingent on CMS bringing the measure back to the MAP once the specifications are further clarified. The final MAP report is available at http://www.qualityforum.org/​Publications/​2021/​03/​MAP_​2020-2021_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx.

In response to the MAP request for CMS to bring the measure back once the specifications were further clarified, CMS met with the MAP Coordinating Committee on March 15, 2021. First, CMS and CDC clarified the alignment of the COVID-19 Vaccination Coverage among HCP with the Influenza Vaccination Coverage among HCP (NQF #0431), an NQF-endorsed measure since 2012. The COVID-19 Vaccination Coverage among HCP measure is calculated using the same approach as the Influenza Vaccination Coverage among HCP measure.^[67] The approach to identifying HCPs eligible for the COVID-19 vaccination is analogous to those used in the NQF endorsed flu measure which underwent rigorous review from technical experts about the validity of that approach and for which ultimately received NQF endorsement. More recently, prospective cohorts of health care personnel, first responders, and other essential and frontline workers over 13 weeks in eight U.S. locations confirmed that authorized COVID-19 vaccines are highly effective in real-world conditions. Vaccine effectiveness of full immunization with two doses of vaccines was 90 percent.^[68]

Additionally, to support the measure's data element validity, CDC conducted testing of the COVID-19 vaccination numerator using data collected through the NHSN and independently reported through the Federal Pharmacy Partnership for Long-term Care Program for delivering vaccines to long-term care facilities. These are two completely independent data collection systems. In initial analyses of the first month of vaccination, the number of HCP vaccinated in approximately 1,200 facilities which had data from both systems, the number of HCP vaccinated was highly correlated between these two systems with a correlation coefficient of nearly 90 percent in the second two weeks of reporting. Of note, assessment of data element reliability may not be required by NQF if data element validity is demonstrated.^[69] To assess the validity of new performance measure score (in this case, percentage of COVID-19 vaccination coverage), NQF allows assessment by face validity (that is, subjective determination by experts that the measure appears to reflect quality of care, done through a systematic and transparent process),^[70] and the MAP concurred with the face validity of the COVID-19 Vaccination Coverage among HCP measure. Materials from the March 15, 2021 MAP Coordinating Committee meeting are on the NQF website at https://www.qualityforum.org/​ProjectMaterials.aspx?​projectID=​75367.

This measure is not NQF endorsed, but the CDC, in collaboration with CMS, plans to submit the measure for NQF endorsement in the future.

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 by the Secretary be endorsed by the entity with a contract under section 1890(a) of the Act, currently the National Quality Forum (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 COVID-19 Vaccination Coverage among HCP measure is not currently NQF endorsed and has not been submitted to the NQF for consideration, so we considered whether there are other available measures that assess COVID-19 vaccinations among HCP. After review of the NQF's consensus-endorsed measures, we were unable to identify any NQF endorsed measures for SNFs focused on capturing COVID-19 vaccination coverage of HCP, and we found no other feasible and practical measure on the topic of COVID-19 vaccination coverage among HCP. The only other vaccination coverage of HCP measure found was the Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431) measure which is NQF endorsed and was adopted in the IRF QRP in the FY 2014 IRF PPS Final Rule (78 FR 47905 through 47906), and in the LTCH QRP in the FY 2013 IPPS/LTCH PPS Final Rule (77 FR 53630 through 53631).

Given the novel nature of the SARS-CoV-2 virus, and the significant and immediate risk it poses in SNFs, we believe it is necessary to propose the measure as soon as possible. Therefore, after consideration of other available measures that assess COVID-19 vaccination rates among HCP, we believe the exception under section 1899B(e)(2)(B) of the Act applies. 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 COVID-19 Vaccination Coverage among Healthcare Personnel (HCP) measure is a process measure developed by the CDC to track COVID-19 vaccination coverage among HCP in facilities such as SNFs. Since this proposed measure is a process measure, rather than an outcome measure, it does not require risk-adjustment.

The denominator would be the number of HCP eligible to work in the facility for at least one day during the reporting period, excluding persons with contraindications to COVID-19 vaccination that are described by the CDC.^[71] While the SNF QRP applies to freestanding SNFs, SNFs affiliated with acute care facilities, and all non-CAH swing-bed rural hospitals, we believe it is necessary to include all HCP within the facility in the measure denominator because all HCP would have access to and may interact with SNF residents.

The numerator would be the cumulative number of HCP eligible to work in the facility for at least one day during the reporting period and who received a complete vaccination course against SARS-CoV-2. A complete vaccination course may require one or more doses depending on the specific vaccine used. The finalized measure specifications are on the CDC website at https://www.cdc.gov/​nhsn/​nqf/​index.html.

We propose that SNFs would submit data for the measure through the CDC/NHSN data collection and submission framework.^[72] SNFs would use the COVID-19 vaccination data reporting module in the NHSN Healthcare Personnel Safety (HPS) Component to report the number of HCP eligible who have worked at the facility that week (denominator) and the number of those HCP who have received a completed COVID-19 vaccination course (numerator). SNFs would submit COVID-19 vaccination data for at least 1 week each month. If SNFs submit more than 1 week of data in a month, the most recent week's data would be used for measure calculation purposes. Each quarter, the CDC would calculate a summary measure of COVID-19 vaccination coverage from the 3 monthly modules of data reported for the quarter. This quarterly rate would be publicly reported on the Care Compare website. Subsequent to the first refresh, one additional quarter of data would be added to the measure calculation during each advancing refresh, until the point four full quarters of data is reached. Thereafter, the measure would be reported using four rolling quarters of data on Care Compare.

For purposes of submitting data to CMS for the FY 2023 SNF QRP, SNFs would be required to submit data for the period October 1, 2021 through December 31, 2021. Following the initial data submission quarter for the FY 2023 SNF QRP, subsequent compliance for the SNF QRP would be based on four quarters of such data submission. For more information on the measure's proposed public reporting period, we refer readers to section VI.H.3. of this proposed rule.

We invite public comment on our proposal to add a new measure, COVID-19 Vaccination Coverage among Healthcare Personnel, to the SNF QRP beginning with the FY 2023 SNF QRP.

3. Proposed Update to the Transfer of Health (TOH) Information to the Patient—Post-Acute Care (PAC) Measure Beginning With the FY 2023 SNF QRP

We are proposing to update the Transfer of Health Information to the Patient—Post-Acute Care (PAC) measure denominator to exclude residents discharged home under the care of an organized home health service or hospice. This measure assesses for and reports on the timely transfer of health information, specifically transfer of a medication list. We adopted this measure in the FY 2020 SNF PPS final rule (84 FR 38761 through 38764) beginning with the FY 2022 SNF QRP. It is a process-based measure that evaluates for the transfer of information when a resident is discharged from his or her current PAC setting to a private home/apartment, board and care home, assisted living, group home, transitional living, or home under the care of an organized home health service organization or hospice.

This measure, adopted under section 1899B(c)(1)(E) of the Act, was developed to be a standardized measure for the IRF QRP, LTCH QRP, SNF QRP, and Home Health (HH) QRP. The measure is calculated by one standardized data element that asks, “At the time of discharge, did the facility provide the resident's current reconciled medication list to the resident, family, and/or caregiver?” The discharge location is captured by items on the Minimum Data Set (MDS).

Specifically, we are proposing to update the measure denominator. Currently, the measure denominators for both the TOH-Patient and the TOH-Provider measure assess the number of residents discharged home under the care of an organized home health service organization or hospice. In order to align the measure with the IRF QRP, LTCH QRP and HH QRP and avoid counting the resident in both TOH measures in the SNF QRP, we are proposing to remove this location from the definition of the denominator for the TOH-Patient measure. Therefore, we are proposing to update the denominator for the TOH-Patient measure to only discharges to a private home/apartment, board and care home, assisted living, group home, or transitional living. For additional technical information regarding the TOH-Patient measure, we refer readers to the document titled “Final Specifications for SNF QRP Quality Measures and Standardized Patient Assessment Data Elements (SPADEs)” 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 invite public comment on our proposal to update the denominator of the Transfer of Health (TOH) Information to the Patient—Post-Acute Care (PAC) measure beginning with the FY 2023 SNF QRP.

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

We are seeking input on the importance, relevance, appropriateness, and applicability of each of the measures and concepts under consideration listed in Table 27 for future years in the SNF QRP.

Assessment-based quality measures and measure concepts

Frailty.

Patient reported outcomes.

Shared decision making process.

Appropriate pain assessment and pain management processes.

Health equity.

While we will not be responding to specific comments submitted in response to this Request for Information (RFI) in the FY 2022 SNF PPS final rule, we intend to use this input to inform our future measure development efforts.

E. Fast Healthcare Interoperability Resources (FHIR) in Support of Digital Quality Measurement in Quality Programs—Request for Information (RFI)

1. Background

The SNF QRP is authorized by section 1888(e)(6) of the Act and furthers our mission to improve the quality of health care for beneficiaries through measurement, transparency, and public reporting of data. The SNF QRP and CMS's other quality programs are foundational for contributing to improvements in health care, enhancing patient outcomes, and informing consumer choice. In October 2017, we launched the Meaningful Measures Framework. This framework captures our vision to address health care quality priorities and gaps, including emphasizing digital quality measurement (dQM), reducing measurement burden, and promoting patient perspectives, while also focusing on modernization and innovation. The scope of the Meaningful Measures Framework has evolved to accommodate the changes in the health care environment, initially focusing on measure and burden reduction to include the promotion of innovation and modernization of all aspects of quality.^[73] There is a need to streamline our approach to data collection, calculation, and reporting to fully leverage clinical and patient-centered information for measurement, improvement, and learning.

In alignment with Meaningful Measures 2.0, we are seeking feedback on our future plans to define digital quality measures (dQMs) for the SNF QRP. We also are seeking feedback on the potential use of Fast Healthcare Interoperable Resources (FHIR) for dQMs within the SNF QRP aligning where possible with other quality programs. FHIR is a free and open source standards framework (in both commercial and government settings) created by Health Level Seven International (HL7®) that establishes a common language and process for all health information technology.

2. Definition of Digital Quality Measures

We are considering adopting a standardized definition of Digital Quality Measures (dQMs) in alignment across quality programs, including the SNF QRP. We are considering in the future to propose the adoption within the SNF QRP the following definition: Digital Quality Measures (dQMs) are quality measures that use one or more sources of health information that are captured and can be transmitted electronically via interoperable systems.^[74] A dQM includes a calculation that processes digital data to produce a measure score or measure scores. Data sources for dQMs may include administrative systems, electronically submitted clinical assessment data, case management systems, EHRs, instruments (for example, medical devices and wearable devices), patient portals or applications (for example, for collection of patient-generated health data), health information exchanges (HIEs) or registries, and other sources. As an example, the quality measures calculated from patient assessment data submitted electronically to CMS would be considered digital quality measures.

3. Use of FHIR for Future dQMs in the SNF QRP

One of the first areas CMS has identified relative to improving our digital strategy is through the use of Fast Healthcare Interoperability Resources (FHIR)-based standards to exchange clinical information through application programming interfaces (APIs), aligning with other programs where possible, to allow clinicians to digitally submit quality information one time that can then be used in many ways. We believe that in the future proposing such a standard within the SNF QRP could potentially enable collaboration and information sharing, which is essential for delivering high-quality care and better outcomes at a lower cost.

We are currently evaluating the use of FHIR based APIs to access assessment data collected and maintained through the Quality Improvement and Evaluation System (QIES) and internet QIES (iQIES) health information systems and are working with healthcare standards organizations to assure that their evolving standards fully support our assessment instrument content. Further, as more SNFs are adopting EHRs, we are evaluating using the FHIR interfaces for accessing patient data (including standard assessments) directly from SNF EHRs. Accessing data in this manner could also enable the exchange of data for purposes beyond data reporting to CMS, such as care coordination further increasing the value of EHR investments across the healthcare continuum. Once providers map their EHR data to a FHIR API in standard FHIR formats it could be possible to send and receive the data needed for measures and other uses from their EHRs through FHIR APIs.

4. Future Alignment of Measures Across Reporting Programs, Federal and State Agencies, and the Private Sector

We are committed to using policy levers and working with stakeholders to achieve interoperable data exchange and to transition to full digital quality measurement in our quality programs. We are considering the future potential development and staged implementation of a cohesive portfolio of dQMs across our quality programs (including the SNF QRP), agencies, and private payers. This cohesive portfolio would require, where possible, alignment of: (1) Measure concepts and specifications including narrative statements, measure logic, and value sets; and (2) the individual data elements used to build these measure specifications and calculate the measures. Further, the required data elements would be limited to standardized, interoperable elements to the fullest extent possible; hence, part of the alignment strategy will be the consideration and advancement of data standards and implementation guides for key data elements. We would coordinate closely with quality measure developers, Federal and state agencies, and private payers to develop and to maintain a cohesive dQM portfolio that meets our programmatic requirements and that fully aligns across Federal and state agencies and payers to the extent possible.

We intend this coordination to be ongoing and allow for continuous refinement to ensure quality measures remain aligned with evolving healthcare practices and priorities (for example, patient reported outcomes (PROs), disparities, care coordination), and track with the transformation of data collection. This includes conformance with standards and health IT module updates, future adoption of technologies incorporated within the ONC Health IT Certification Program and may also include standards adopted by ONC (for example, to enable standards-based APIs). The coordination would build on the principles outlined in HHS' Nation Health Quality Roadmap.^[75] It would focus on the quality domains of safety, timeliness, efficiency, effectiveness, equitability, and patient-centeredness. It would leverage several existing Federal and public-private efforts including our Meaningful Measures 2.0 Framework; the Federal Electronic Health Record Modernization (DoD/VA); the Core Quality Measure Collaborative, which convenes stakeholders from America's Health Insurance Plans (AHIP), CMS, NQF, provider organizations, private payers, and consumers and develops consensus on quality measures for provider specialties; and the NQF-convened Measure Applications Partnership (MAP), which recommends measures for use in public payment and reporting programs. We would coordinate with HL7's ongoing work to advance FHIR resources in critical areas to support patient care and measurement such as social determinants of health. Through this coordination, we would identify which existing measures could be used or evolved to be used as dQMs, in recognition of current healthcare practice and priorities.

This multi-stakeholder, joint Federal, state, and industry effort, made possible and enabled by the pending advances towards true interoperability, would yield a significantly improved quality measurement enterprise. The success of the dQM portfolio would be enhanced by the degree to which the measures achieve our programmatic requirements as well as the requirements of other agencies and payers.

5. Solicitation of Comments

We seek input on the following steps that would enable transformation of CMS' quality measurement enterprise to be fully digital:

• What EHR/IT systems do you use and do you participate in a health information exchange (HIE)?
• How do you currently share information with other providers?
• In what ways could we incentivize or reward innovative uses of health information technology (IT) that could reduce burden for post-acute care settings, including but not limited to SNFs?
• What additional resources or tools would post-acute care settings, including but not limited to SNFs, and health IT vendors find helpful to support the testing, implementation, collection, and reporting of all measures using FHIR standards via secure APIs to reinforce the sharing of patient health information between care settings?
• Would vendors, including those that service post-acute care settings, such as SNFs, be interested in or willing to participate in pilots or models of alternative approaches to quality measurement that would align standards for quality measure data collection across care settings to improve care coordination, such as sharing patient data via secure FHIR API as the basis for calculating and reporting digital measures?

We plan to continue working with other agencies and stakeholders to coordinate and to inform our transformation to dQMs leveraging health IT standards. While we will not be responding to specific comments submitted in response to this RFI in the FY 2022 SNF PPS final rule, we will actively consider all input as we develop future regulatory proposals or future subregulatory policy guidance. 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. Closing the Health Equity Gap in Post-Acute Care Quality Reporting Programs—Request for Information (RFI)

1. Background

Significant and persistent inequities in health outcomes exist in the United States. In recognition of persistent health disparities and the importance of closing the health equity gap, we request information on revising several CMS programs to make reporting of health disparities based on social risk factors and race and ethnicity more comprehensive and actionable for providers and patients. 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; or being near or below the poverty level is often associated with worse health outcomes.^{[76 77 78 79 80 81 82 83]} Such disparities in health outcomes are the result of a number of factors, but importantly for CMS programs, although not the sole determinant, poor access and provision of lower quality health care contribute to health disparities. For instance, numerous studies have shown that among Medicare beneficiaries, racial and ethnic minority individuals often receive lower quality of care, report lower experiences of care, and experience more frequent hospital readmissions and operative complications.^{[84 85 86 87 88 89]}

Readmission rates for common conditions in the Hospital Readmissions Reduction Program are higher for black Medicare beneficiaries and higher for Hispanic Medicare beneficiaries with Congestive Heart Failure and Acute Myocardial Infarction.^{[90 91 92 93 94]} Studies have also shown that African Americans are significantly more likely than white Americans to die prematurely from heart disease and stroke.^[95] The COVID-19 pandemic has further illustrated many of these longstanding health inequities with higher rates of infection, hospitalization, and mortality among black, Latino, and Indigenous and Native American persons relative to white persons.^[96 97] As noted by the Centers for Disease Control “long-standing systemic health and social inequities have put many people from racial and ethnic minority groups at increased risk of getting sick and dying from COVID-19”.^[98] 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.

We are also committed to achieving equity in health care outcomes for our beneficiaries by supporting providers in quality improvement activities to reduce health inequities, enabling them to make more informed decisions, and promoting provider accountability for health care disparities.^[99 100] For the purposes of this rule, we are using a definition of equity established in Executive Order 13985, as “the 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.” ^[101] We note that this definition was recently established by the current administration, and provides a useful, common definition for equity across different areas of government, although numerous other definitions of equity exist.

Our ongoing commitment to closing the equity gap in CMS quality programs is demonstrated by a portfolio of programs aimed at making information on the quality of health care providers and services, including disparities, more transparent to consumers and providers. The CMS Equity Plan for Improving Quality in Medicare outlines a path to equity which aims to support Quality Improvement Networks and Quality Improvement Organizations (QIN-QIOs); Federal, state, local, and tribal organizations; providers; researchers; policymakers; beneficiaries and their families; and other stakeholders in activities to achieve health equity. The CMS Equity Plan includes three core elements: (1) Increasing understanding and awareness of disparities; (2) developing and disseminating solutions to achieve health equity; and (3) implementing sustainable actions to achieve health equity.^[102] The CMS Quality Strategy and Meaningful Measures Framework ^[103] include elimination of racial and ethnic disparities as a central principle. Our ongoing commitment to closing the health equity gap in the SNF QRP is demonstrated by the adoption of standardized patient assessment data elements (SPADEs) which include several social determinants of health (SDOH) that were finalized in the FY 2020 SNF PPS final rule for the SNF QRP (84 FR 38805 through 38817).

We continue to work with Federal and private partners to better leverage data on social risk to improve our understanding of how these factors can be better measured in order to close the health equity gap. Among other things, we have developed an Inventory of Resources for Standardized Demographic and Language Data Collection ^[104] and supported collection of specialized International Classification of Disease, 10th Edition, Clinical Modification (ICD-10-CM) codes for describing the socioeconomic, cultural, and environmental determinants of health. We continue to work to improve our understanding of this important issue and to identify policy solutions that achieve the goals of attaining health equity for all patients.

2. Solicitation of Public Comment

Under authority of the IMPACT Act and section 1888(e)(6) of the Act, we are seeking comment on the possibility of revising measure development, and the collection of other SPADEs that address gaps in health equity in the SNF QRP. Any potential health equity data collection or measure reporting within a CMS program that might result from public comments received in response to this solicitation would be addressed through a separate notice-and-comment rulemaking in the future.

Specifically, we are inviting public comment on the following:

• Recommendations for quality measures, or measurement domains that address health equity, for use in the SNF QRP.
• As finalized in the FY 2020 SNF PPS final rule (84 FR 38805 through 38817), SNFs must report certain standardized patient assessment data elements (SPADEs) on SDOH, including race, ethnicity, preferred language, interpreter services, health literacy, transportation and social isolation.^[105] CMS is seeking guidance on any additional items, including SPADEs that could be used to assess health equity in the care of SNF residents, for use in the SNF QRP.

• Recommendations for how CMS can promote health equity in outcomes among SNF residents. For example, we are interested in feedback regarding whether including facility-level quality measure results stratified by social risk factors and social determinants of health (for example, dual eligibility for Medicare and Medicaid, race) in confidential feedback reports could allow facilities to identify gaps in the quality of care they provide. (For example, methods similar or analogous to the CMS Disparity Methods ^[106] which provide hospital-level confidential results stratified by dual eligibility for condition-specific readmission measures, which are currently included in the Hospital Readmission Reduction Program (see 84 FR 42496 through 42500)).

• Methods that commenters or their organizations use in employing data to reduce disparities and improve patient outcomes, including the source(s) of data used, as appropriate.
• Given the importance of structured data and health IT standards for the capture, use, and exchange of relevant health data for improving health equity, the existing challenges providers' encounter for effective capture, use, and exchange of health information, including data on race, ethnicity, and other social determinants of health, to support care delivery and decision making.

While we will not be responding to specific comments submitted in response to this RFI in the FY 2022 SNF PPS final rule, we intend to use this input to inform future policy development. We look forward to receiving feedback on these topics, and note for readers that responses to the RFI should focus on how they could be applied to the quality reporting program requirements. Please note that any responses provided will not impact payment decisions.

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

1. Background

We refer readers to the regulatory text at 42 CFR 413.360(b) for information regarding the current policies for reporting SNF QRP data.

2. Proposed Schedule for Data Submission of the SNF HAI Measure Beginning With the FY 2023 QRP

The SNF HAI measure, which we propose in section VI.C.1. of this proposed rule, is a Medicare FFS claims-based measure. Because claims-based measures can be calculated based on data that have already been submitted to the Medicare program for payment purposes, no additional information collection would be required from SNFs. We are proposing to use 1 year of FY 2019 claims data (October 1, 2018 through September 30, 2019) for the FY 2023 SNF QRP. We are proposing to use FY 2019 data to calculate this measure because it is the most recent fiscal year of data that has not been exempted due to the PHE. Beginning with the FY 2024 SNF QRP, compliance with APU reporting requirements would use FY 2021 claims data (October 1, 2020 through September 30, 2021) and advance by one FY with each annual refresh. Due to the fact that Q1 and Q2 2020 data were excepted by CMS related to the COVID-19 PHE, these quarters of data would not be used for purposes of the QRP. For information on public reporting of the SNF HAI measure, we refer you to Table 31 in section VI.H.4.c. of this proposed rule.

We invite public comment on this proposal.

3. Proposed Method of Data Submission for COVID-19 Vaccination Coverage Among Healthcare Personnel Measure

As discussed in section VI.C.2 of this proposed rule, we propose to require that SNFs submit data on the COVID-19 Vaccination Coverage among Healthcare Personnel Measure through the Centers for Disease Control and Prevention (CDC)/National Healthcare Safety Network (NHSN). The NHSN is a secure, internet-based surveillance system maintained by the CDC that can be utilized by all types of healthcare facilities in the United States, including acute care hospitals, long term acute care hospitals, psychiatric hospitals, rehabilitation hospitals, outpatient dialysis centers, ambulatory surgery centers, and SNFs. The NHSN enables healthcare facilities to collect and use vaccination data, and information on other adverse events. NHSN collects data via a Web-based tool hosted by the CDC (http://www.cdc.gov/​). The NHSN is provided free of charge. We propose for SNFs to submit the data needed to calculate the COVID-19 Vaccination Coverage among Healthcare Personnel measure using the NHSN's standard data submission requirements. CDC/NHSN requirements include adherence to training requirements, use of CDC measure specifications, data element definitions, data submission requirements and instructions, data submission timeframes, as well as NHSN participation forms and indications to CDC allowing CMS to access data for this measure for the SNF quality reporting program purposes. Detailed requirements for NHSN participation, measure specifications, and data collection can be found at http://www.cdc.gov/​nhsn/​. We propose to require SNFs to use the specifications and data collection tools for the proposed COVID-19 Vaccination Coverage among Healthcare Personnel measure as required by CDC as of the time that the data are submitted.

We invite public comment on this proposal.

4. Proposed Schedule for Data Submission of the COVID-19 Vaccination Coverage Among Healthcare Personnel Measure Beginning With the FY 2023 SNF QRP

As discussed in section VI.C.2. of this proposed rule, we are proposing to adopt the COVID-19 Vaccination Coverage among HCP quality measure beginning with the FY 2023 SNF QRP. Given the time-sensitive nature of this measure in light of the PHE, we propose an initial data submission period from October 1, 2021 through December 31, 2021. Starting in CY 2022, SNFs would be required to submit data for the entire calendar year beginning with the FY 2024 SNF QRP.

SNFs would submit data for the measure through the CDC/NHSN web-based surveillance system. SNFs would use the COVID-19 vaccination data collection module in the NHSN Long-term Care (LTC) Component to report the cumulative number of HCP eligible to work in the healthcare facility for at least 1 day during the reporting period, excluding persons with contraindications to COVID-19 vaccination (denominator) and the cumulative number of HCP eligible to work in the SNF for at least 1 day during the reporting period and who received a complete vaccination course against COVID-19 (numerator). SNFs would submit COVID-19 vaccination data through the NHSN for at least 1 week each month and the CDC would report to CMS quarterly.

We invite public comment on this proposal.

5. Consolidated Appropriations Act and the SNF QRP

On December 27, 2020, Congress enacted the Consolidated Appropriations Act, 2021 (CAA) (Pub. L. 116-260). Section 111(a)(3) of Division CC of the CAA amends section 1888 of the Act by adding a new paragraph (h)(12), which requires the Secretary to apply a process to validate the measures submitted under the SNF VBP and the measures and data submitted under the SNF QRP as appropriate, which may be similar to the process specified under the Hospital Inpatient Quality Reporting (IQR) Program for validating inpatient hospital measures. We plan to develop a process for validating the SNF QRP measures and data and implement this policy as soon as technically feasible. We will provide more details and seek public comment in future rulemaking. For more information on the SNF VBP please refer to section VII. of this rule.

H. Proposed 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. Proposal to Publicly Report the Skilled Nursing Facility Healthcare-Associated Infections Requiring Hospitalization Measure Beginning With the FY 2023 SNF QRP

We propose public reporting for the SNF HAI measure beginning with the April 2022 Care Compare refresh or as soon as technically feasible using data collected from discharges in FY 2019 beginning October 1, 2018 through September 30, 2019. Provider preview reports would be distributed in January 2022. A SNF's HAI rates would be displayed based on 1 fiscal year of data. Since we cannot publicly report data from Q1 and Q2 of 2020 due to the PHE, we are proposing to use data collected from discharges in FY 2021 (October 1, 2020 through September 30, 2021) for public reporting of the SNF HAI measure in the October 2022 Care Compare refresh. Thereafter, the SNF HAI measure would be calculated using four quarters of FY data for the annual refresh on Care Compare. Claims-based measures are only refreshed on Care Compare annually. To ensure statistical reliability of the data, we propose assigning SNFs with fewer than 25 eligible stays during a performance period to a separate category: “The number of resident stays is too small to report.” Eligible stays meet the measure's denominator inclusion criteria, and we refer readers to the Skilled Nursing Facility Healthcare-Associated Infections Requiring Hospitalization for the Skilled Nursing Facility Quality Reporting Program Technical Report available at https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf/​ for more details. If a SNF had fewer than 25 eligible stays, the SNF's performance would not be publicly reported for the measure for that performance period. We refer readers to CMS's SNF QRP Public Reporting web page for more information available at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Skilled-Nursing-Facility-Quality-Reporting-Program/​SNF-Quality-Reporting-Program-Public-Reporting.

We invite public comment on this proposal for the public display of the SNF HAI measure on Care Compare.

3. Proposal to Publicly Report the COVID-19 Vaccination Coverage Among Healthcare Personnel (HCP) Measure Beginning With the FY 2023 SNF QRP

We propose to publicly report the COVID-19 Vaccination Coverage among Healthcare Personnel measure beginning with the October 2022 Care Compare refresh or as soon as technically feasible using data collected for Q4 2021 (October 1, 2021 through December 31, 2021). If finalized as proposed, a SNF's HCP COVID-19 vaccination coverage rate would be displayed based on one quarter of data. Provider preview reports would be distributed in July 2022. Thereafter, HCP COVID-19 vaccination coverage rates would be displayed based on one quarter of data updated quarterly. Subsequent to this, one additional quarter of data would be added to the measure calculation during each advancing refresh, until the point four full quarters of data is reached. Thereafter, the measure would be reported using four rolling quarters of data.

We invite public comment on this proposal for the public display of the COVID-19 Vaccination Coverage among HCP measure on Care Compare.

4. Proposals for Public Reporting of Quality Measures in the SNF QRP With Fewer Quarters Due to COVID-19 Public Health Emergency (PHE) Exemptions

a. COVID-19 Public Health Emergency Temporary Exemptions

Under the authority of section 319 of the Public Health Service Act, the Secretary of Health and Human Services declared a public health emergency (PHE) effective as of January 27, 2020. On March 13, 2020, subsequent to a presidential declaration of national emergency under the Stafford Act, the Secretary invoked section 1135(b) of the Act (42 U.S.C. 1320b-5) to waive or modify the requirements of titles XVIII, XIX, and XXI of the Act and regulations related to the PHE for COVID-19, effective as of March 1, 2020.^[107] On March 27, 2020, we sent a guidance memorandum under the subject title, “Exceptions and Extensions for Quality Reporting Requirements for Acute Care Hospitals, PPS-Exempt Cancer Hospitals, Inpatient Psychiatric Facilities, Skilled Nursing Facilities, Home Health Agencies, Hospices, Inpatient Rehabilitation Facilities, Long-Term Care Hospitals, Ambulatory Surgical Centers, Renal Dialysis Facilities, and MIPS Eligible Clinicians Affected by COVID-19” to the Medicare Learning Network (MLN) Connects Newsletter and Other Program-Specific Listserv Recipients,^[108] hereafter referred to as the March 27, 2020 CMS Guidance Memo. In that memo we granted an exception to the SNF QRP reporting requirements from Q4 2019 (October 1, 2019-December 31, 2019), Q1 2020 (January 1, 2020-March 31, 2020), and Q2 2020 (April 1, 2020-June 30, 2020). We also stated that we would not publicly report any SNF QRP data that might be greatly impacted by the exceptions from Q1 and Q2 of 2020. This exception impacted the schedule for public reporting that would have included those two quarters of data.

SNF quality measures are publicly reported on Care Compare. Care Compare uses four quarters of data for MDS assessment-based measures and eight quarters for claims-based measures. Table 28 displays the original schedule for public reporting of SNF QRP measures.^[109]

Table 28—SNF Quarters in Care Compare Original Schedule for Refreshes Affected by COVID-19 PEH Exemptions—Assessment and Claims Based Measures

Quarter refresh	SNF quarters in original schedule for care compare
January 2021	MDS: Q2 2019—Q1 2020 (4 quarters). Claims: Q4 2017—Q3 2019 (8 quarters).
April 2021	MDS: Q3 2019—Q2 2020 (4 quarters). Claims: Q4 2017—Q3 2019 (8 quarters).
July 2021	MDS: Q4 2019—Q3 2020 (4 quarters). Claims: Q4 2017—Q3 2019 (8 quarters).
October 2021	MDS: Q1 2020—Q4 2020 (4 quarters). Claims: Q4 2018—Q3 2020 (8 quarters).
January 2022	MDS: Q2 2020—Q1 2021 (4 quarters). Claims: Q4 2018—Q3 2020 (8 quarters).
April 2022	MDS: Q3 2020—Q2 2021 (4 quarters). Claims: Q4 2018—Q3 2020 (8 quarters).
July 2022	MDS: Q4 2020—Q3 2021 (4 quarters). Claims: Q4 2018—Q3 2020 (8 quarters).
October 2022	MDS: Q1 2021—Q4 2021 (4 quarters). Claims: Q4 2019—Q3 2021 (8 quarters).
January 2023	MDS: Q2 2021—Q1 2022 (4 quarters). Claims: Q4 2019—Q3 2021 (8 quarters).
Apri1 2023	MDS: Q3 2021—Q2 2022 (4 quarters). Claims: Q4 2019—Q3 2021 (8 quarters).
July 2023	MDS: Q4 2021—Q3 2022 (4 quarters). Claims: Q4 2019—Q3 2021 (8 quarters).

During 2020, we conducted testing to inform decisions about publicly reporting data for those refreshes which include partially and/or fully exempt data (discussed below). The testing helped us develop a plan for posting data that are as up-to-date as possible and that also meet acceptable standards for public reporting. We believe that the plan allows us to provide consumers with helpful information on the quality of SNF care, while also making the necessary adjustments to accommodate the exemption provided SNFs. The following sections provide the results of our testing, and explain how we used the results to develop plans for accommodating exempt and partially-exempt data in public reporting.

b. Exempted Quarters

In the March 27, 2020 Medicare Learning Network (MLN) Newsletter on Exceptions and Extensions for Quality Reporting Program (QRP) Requirements, we stated that we would not report any PAC quality data that might be greatly impacted by the exemptions granted for Quarter 1 and Quarter 2 of 2020. Given the timing of the PHE onset, we determined that we would not use SNF MDS assessments or SNF claims from Quarter 1 and Quarter 2 of 2020 for public reporting, but that we would assess the COVID-19 PHE impact on data from Quarter 4 2019. Before proceeding with the October 2020 refresh, we conducted testing to ensure that, despite the voluntary nature of reporting for that quarter, public reporting would still meet our public reporting standards. We found the level of reporting, measured in the number of eligible stays and providers, and the reported outcomes, to be in line with levels and trends observed in FY 2018 and FY 2019. We note that Quarter 4 2019 ended before the onset of the COVID-19 pandemic in the United States. Thus, we proceeded with including these data in SNF QRP measure calculations for the October 2020 refresh.

c. Update on Data Freeze and Proposal for January 2022 Public Reporting Methodology for SNF Claims-Based and MDS Assessment-Based Measures

In addition to the January 2021 refresh, there are several other forthcoming refreshes for which the original public reporting schedules included exempted quarters of SNF QRP data. The impacted refreshes for MDS assessment and claims based measures are outlined in (Table 28). We determined that freezing the data displayed on the website with the October 2020 refresh values—that is, hold data constant after the October 2020 refresh data on the website without subsequent update—would be the most straightforward, efficient, and equitable approach for SNFs. Thus, we decided that, for as many refreshes as necessary, we would hold data constant on the website with the October 2020 data, and communicate this decision to the public.

Because October 2020 refresh data will become increasingly out-of-date and thus less useful for consumers, we analyzed whether it would be possible to use fewer quarters of data for one or more refreshes and thus reduce the number of refreshes that continue to display October 2020 data. Using fewer quarters of more up-to-date data requires that (1) a sufficient percentage of SNFs would still likely have enough assessment data to report quality measures (reportability); and (2) fewer quarters would likely produce similar measure scores for providers, with similar reliability, and thus not unfairly represent the quality of care SNFs provide during the period reported in a given refresh (reliability).

To assess these criteria, we conducted reportability and reliability analysis using 3 quarters of data in a refresh, instead of the standard 4 quarters of data for reporting assessment-based measures and using 6 quarters instead of 8 for claims-based measures. Specifically, we used historical data to calculate MDS assessment based and SNF claims based quality measures under two scenarios:

1. Standard Public Reporting (SPR) Base Scenario: We used four quarters of CY 2019 data as a proxy alternative for the exempted quarters in CY 2020 in order to compare results. For assessment-based measures, the quarters used in this scenario are Q1 through Q4 2019. For claims-based measures, the quarters used in this scenario are Q1 2018 through Q4 2019.

2. COVID-19 Affected Reporting (CAR) Scenario: We calculated SNF QRP measures using 3 quarters (Q2 2019 through Q4 2019) of SNF QRP data for assessment-based measures, and 6 quarters (Q1 2018 through Q4 2018 and Q3 2019 through Q4 2019) for claims-based measures. The CAR scenario uses the most recently available data to simulate the public health emergency reality where quarters 1 and 2 of a calendar year must be excluded from calculation. Quarterly trends in MDS assessment-based and claims based measures indicate that these measures do not exhibit substantial seasonal variation.

To assess performance in these scenarios, we calculated the reportability as the percent of SNFs meeting the case minimum for public reporting (the public reporting threshold). To test the reliability of restricting the SNFs included in the SPR Base Scenario to those included in the CAR Scenario, we performed three tests on the set of SNFs included in both scenarios. First, we evaluated measure correlation using the Pearson and Spearman correlation coefficients, which assess the alignment of SNFs' provider scores. Second, for each scenario, we conducted a split-half reliability analysis and estimated intraclass correlation (ICC) scores, where higher scores imply better internal reliability. Modest differences in ICC scores between both scenarios would suggest that using fewer quarters of data does not impact the internal reliability of the results. Third, we estimated reliability scores where a higher value indicates that measure scores are relatively consistent for patients admitted to the same SNF and variation in the measure reflects true differences across providers. To calculate the reliability results, we restricted the SNFs included in the SPR scenario to those included in the CAR scenario.

Our testing indicated that the expected impact of using fewer quarters of data on reportability and reliability of MDS assessment-based and claims based measures is acceptable.

We are proposing to use the CAR scenario as the approach for the following affected refreshes for MDS assessment-based measures, the affected refresh is the January 2022 refresh; for claims-based measures, the affected refreshes occur from January 2022 through July 2023. For the earlier four affected refreshes (January, April, July, and October 2021), we decided to hold constant the Care Compare website with October 2020 data. We communicated this decision in a Public Reporting Tip Sheet, which is located at https://www.cms.gov/​files/​document/​snfqrp-covid19prtipsheet-october2020.pdf.

Our proposal of the CAR approach for the affected refreshes would allow us to begin displaying more recent data in January 2022, rather than continue displaying October 2020 data (Q1 2019 through Q4 2019 for assessment-based measures, Q4 2017 through Q3 2019 for claims-based measures). We believe that resuming public reporting starting in January 2022 with fewer quarters of data can assist consumers by providing more recent quality data as well as more actionable data for SNF providers. Our testing results indicate we can achieve these positive impacts with acceptable changes in reportability and reliability. Table 29 summarizes the revised schedule (that is, frozen data) and the proposed schedule (that is, using fewer quarters in the affected refreshes) for assessment-based measures. Tables 30 and 31 summarize the revised schedule (that is, frozen data) and the proposed schedule (that is, using fewer quarters in the affected refreshes) for claims-based measures.

We invite public comment on the proposal to use the CAR scenario to publicly report SNF measures for the January 2022-July 2023 refreshes.

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

A. Background

Section 215(b) of the Protecting Access to Medicare Act of 2014 (PAMA) (Pub. L. 113-93) authorized the SNF VBP Program (the “Program”) by adding section 1888(h) to the Act. 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 § 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), (ii), and (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 Catalogue website (https://data.cms.gov/​provider-data/​).

The SNF VBP Program applies to freestanding SNFs, SNFs affiliated with acute care facilities, and all non-CAH swing-bed rural hospitals. Section 1888(h)(1)(B) of the Act requires that the SNF VBP Program apply to payments for services furnished on or after October 1, 2018. We believe the implementation of the SNF VBP Program is an important step towards transforming how payment is made for care, moving increasingly towards rewarding better value, outcomes, and innovations instead of merely rewarding volume.

B. 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 during the Fall 2021 cycle, and to assess transition timing of the SNFPPR measure to the SNF VBP Program after NQF endorsement review is complete.

1. Proposed Flexibilities for the SNF VBP Program in Response to the Public Health Emergency Due to COVID-19

In previous rules, we have identified the need for flexibility in our quality programs to account for the impact of changing conditions that are beyond participating facilities' or practitioners' control. We identified this need because we would like to ensure that participants in our programs are not affected negatively when their quality performance suffers not due to the care provided, but due to external factors.

A significant example of the type of external factor that may affect quality measurement is the COVID-19 public health emergency (PHE), which has had, and continues to have, significant and ongoing effects on the provision of medical care in the country and around the world. The COVID-19 pandemic and associated PHE has impeded effective quality measurement in many ways. Changes to clinical practices to incorporate safety protocols for medical personnel and patients, as well as unpredicted changes in the number of stays and facility-level case mixes, have affected the data that SNFs report under the SNF VBP Program and the resulting measure calculations. CMS is currently considering whether the SNF readmission measure specifications should be updated to account for changes in SNF admission and/or hospital readmission patterns that we have observed during the PHE. Additionally, because COVID-19 prevalence is not identical across the country, facilities located in different areas have been affected differently at different times throughout the pandemic. Under those circumstances, we remain concerned that the SNF readmission measure scores are distorted, which would result in skewed payment incentives and inequitable payments, particularly for SNFs that have treated more COVID-19 patients than others.

It is not our intention to penalize SNFs based on measure scores that we believe are distorted by the COVID-19 pandemic, and are thus not reflective of the quality of care that the measure in the SNF VBP Program was designed to assess. As discussed above, the COVID-19 pandemic has had, and continues to have, significant and enduring effects on health care systems around the world, and affects care decisions, including readmissions to the hospital as measured by the SNF VBP Program. As a result of the PHE, SNFs could provide care to their patients that meets the underlying clinical standard but results in worse measured performance, and by extension, lower incentive payments in the SNF VBP Program. Additionally, because COVID-19 prevalence has not been identical across the country, SNFs located in different regions have been affected differently during the PHE. As a result, we are concerned that regional differences in COVID-19 prevalence during the revised performance period for the FY 2022 SNF VBP Program, which includes one quarter of data during the pandemic (July 1, 2020 through September 30, 2020), have directly affected SNF readmission measure scores for the FY 2022 SNF VBP program year. Although these regional differences in COVID-19 prevalence rates do not reflect differences in the quality of care furnished by SNFs, they directly affect the value-based incentive payments that these SNFs are eligible to receive and could result in an unfair and inequitable distribution of those incentives. These inequities could be especially pronounced for SNFs that have treated a large number of COVID-19 patients.

Therefore, we are proposing to adopt a policy for the duration of the PHE for COVID-19 that would enable us to suppress the use of SNF readmission measure data 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. Under this proposed policy, if we determine that the suppression of the SNF readmission measure is warranted for a SNF VBP program year, we would propose to calculate the SNF readmission measure rates for that program year but then suppress the use of those rates to generate performance scores, rank SNFs, and generate value-based incentive payment percentages based on those performance scores. We would instead assign each eligible SNF's performance score of zero for the program year to mitigate the effect that the distorted measure results would otherwise have on SNF's performance scores and incentive payment multipliers. We would also reduce each eligible SNF's adjusted Federal per diem rate by the applicable percent (2 percent) and then further adjust the resulting amounts by a value-based incentive payment amount equal to 60 percent of the total reduction. Those SNFs subject to the Low-Volume Adjustment policy would receive 100 percent of their 2 percent withhold per the policy previously finalized in the FY 2020 SNF PPS final rule (84 FR 38823 through 38824). We would also provide each SNF with its SNF readmission measure rate in confidential feedback reports so that the SNF is aware of the observed changes to its measure rates. We would also publicly report the FY 2022 SNF readmission measure rates with appropriate caveats noting the limitations of the data due to the PHE for COVID-19.

In developing this proposed policy, we considered what circumstances caused by the PHE for COVID-19 would affect a quality measure significantly enough to warrant its suppression in a value-based purchasing program. We believe that a significant deviation in measured performance that can be reasonably attributed to the PHE for COVID-19 is a significant indicator of changes in clinical conditions that affect quality measurement. Similarly, we believe that a measure may be focused on a clinical topic or subject that is proximal to the disease, pathogen, or other health impacts of the PHE. As has been the case during the COVID-19 PHE, we believe that rapid or unprecedented changes in clinical guidelines and care delivery, potentially including appropriate treatments, drugs, or other protocols, may affect quality measurement significantly and should not be attributed to the participating facility positively or negatively. We also note that scientific understanding of a particular disease or pathogen may evolve quickly during an emergency, especially in cases of new disease or conditions. Finally, we believe that, as evidenced during the COVID-19 PHE, national or regional shortages or changes in health care personnel, medical supplies, equipment, diagnostic tools, and patient case volumes or facility-level case mix may result in significant distortions to quality measurement.

Based on these considerations, we developed a number of Measure Suppression Factors that we believe should 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. We are proposing to adopt these Measure Suppression Factors for use in the SNF VBP and, for consistency, the following other value-based purchasing programs: Hospital Value-Based Purchasing Program, Hospital Readmissions Reduction Program, HAC Reduction Program, and End-Stage Renal Disease Quality Incentive Program. We believe that these Measure Suppression Factors will help us evaluate the SNF readmission measure in the SNF VBP program and that their adoption in the other value-based purchasing programs noted above will help ensure consistency in our measure evaluations across programs. The proposed Measure Suppression Factors are:

(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.

(2) Clinical proximity of the measure's focus to the relevant disease, pathogen, or health impacts of the PHE for COVID-19.

(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.

(4) Significant national shortages or rapid or unprecedented changes in:

• Healthcare personnel;
• Medical supplies, equipment, or diagnostic tools or materials; or
• Patient case volumes or facility-level case mix.

We also considered alternatives to this proposed policy that could also fulfill our objective to not hold facilities accountable for measure results that are distorted due to the PHE for COVID-19. As noted above, the country continues to grapple with the effects of the COVID-19 PHE, and in March 2020, we issued a nationwide, blanket Extraordinary Circumstances Exception (ECE) for all hospitals and other facilities participating in our quality reporting and value-based purchasing programs in response to the PHE for COVID-19. This blanket ECE excepted all data reporting requirements for Q1 and Q2 2020 data. For claims-based measures, we also stated that we would exclude all qualifying Q1 and Q2 2020 claims from our measure calculations. We considered extending the blanket ECE that we issued for Q1 and Q2 2020 to also include Q3 2020 data. However, this option would result in less than 12 months of data being used to calculate the single readmissions measure in the Program for multiple Program years, which we do not believe would provide an accurate assessment of the quality of care provided in SNFs. This option would also leave no comprehensive data available for us to provide confidential performance feedback to providers nor for monitoring and to inform decision-making for potential future programmatic changes, particularly as the PHE is extended.

We view this measure suppression proposal as a necessity to ensure that the SNF VBP program does not reward or penalize facilities based on factors that the SNF readmission measure was not designed to accommodate. We intend for this proposed policy to provide short-term relief to SNFs when we have determined that one or more of the Measure Suppression Factors warrants the suppression of the SNF readmission measure.

We invite public comments on this proposal for the adoption of a measure suppression policy for the SNF VBP Program for the duration of the PHE for COVID-19, and also on the proposed Measure Suppression Factors that we developed for purposes of this proposed policy.

We are also inviting comment on whether we should consider adopting a measure suppression policy that would apply in a future national PHE, and if so, whether under such a policy, we should have the flexibility to suppress quality measures without specifically proposing to do so in rulemaking. We also request comment on whether we should in future years consider adopting any form of regional adjustment for the proposed measure suppression policy that could take into account any disparate effects of circumstances affecting hospitals around the country that would prompt us to suppress a measure. For example, COVID-19 affected different regions of the country at different rates depending on factors like time of year, geographic density, state and local policies, and health care system capacity. In future years and for future PHEs, should they arise, we also request commenters' feedback on whether we should, rather than suppress a measure completely, consider a suppression policy with more granular effects based on our assessment of the geographic effects of the circumstances, and if so, how region-based measure suppression could be accounted for within the program's scoring methodology.

2. Proposal To Suppress the SNFRM for the FY 2022 SNF VBP Program Year

In this proposed rule, we are proposing to suppress the SNFRM for the FY 2022 SNF VBP Program Year under proposed Measure Suppression Factor: (4) Significant national shortages or rapid or unprecedented changes in: (iii) Patient case volumes or facility-level case mix.

In response to the PHE for COVID-19, we granted an extraordinary circumstance exemption (ECE) for SNFs participating in the SNF VBP Program. Under the ECE, SNF qualifying claims for the period January 1, 2020-June 30, 2020 are excepted from the calculation of the SNFRM. Because this ECE excepted data for 6 months of the performance period that we had previously finalized for the FY 2022 SNF VBP program year (84 FR 38822), we updated the performance period for that program year in the “Medicare and Medicaid Programs, Clinical Laboratory Improvement Amendments, and Patient Protection and Affordable Care Act: Additional Policy and Regulatory Revisions in Response to the COVID-19 Public Health Emergency” interim final rule with comment (“the September 2nd IFC”) (85 FR 54820). Specifically, we finalized that the new performance period for the FY 2022 SNF VBP Program year would be April 1, 2019-December 31, 2019 and July 1, 2020-September 30, 2020 because we believed that this period, which combined 9 months of data prior to the start of the PHE for COVID-19 and 3 months of data after the end of the ECE, would provide sufficiently reliable data for evaluating SNFs for the FY 2022 SNF VBP Program. However, analyses conducted by our contractor since the publication of the September 2nd IFC have found that when July-September 2020 SNF data are compared with July-September 2019 SNF data, the July-September 2020 SNF data showed 25 percent fewer SNF admissions and 26 percent fewer readmissions from a SNF to a hospital. These impacts have affected the reliability of the SNFRM. Generally speaking, the SNFRM's reliability decreases as the sample size and measured outcome (that is, readmissions) decrease. A drop of 25 percent in SNF admissions and 26 percent in readmissions to the hospital from July-September 2020 has significantly reduced the sample size needed to calculate both the measure cohort and outcome for the FY 2022 SNF VBP, thus jeopardizing the measure 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). In addition, the current risk-adjustment model does not factor in COVID-19 or the fact that SNFs are treating different types of patients as a result of the COVID-19 PHE. Nearly 10 percent of SNF residents in July-September 2020 had a current or prior diagnosis of COVID-19, with uneven regional impacts. The SNFRM does not adjust for COVID-19 in the risk adjustment methodology, as the measure was developed before the pandemic. As a result, risk-adjusted rates, which compare SNFs to each other nationally, are likely to reflect variation in COVID-19 prevalence rather than variation in quality of care. We do not believe that assessing SNFs on a quality measure affected significantly by the varied regional response to the COVID-19 PHE presents a clear picture of the quality of care provided by an individual SNF. The data also demonstrated other important changes in SNF patient case-mix during the PHE for COVID-19, including an 18 percent increase in dual-eligible residents and a 9 percent increase in African-American SNF residents at the facility level. They have been disproportionately impacted by COVID, both in terms of morbidity and mortality. We are currently conducting analyses to determine whether and how the SNFRM specifications may need to be updated to account for SNF residents with a primary or secondary diagnosis of COVID-19 for future program years. We also plan to conduct such analysis for the SNFPPR measure.

We considered whether we could propose to remove the July 1, 2020-September 30, 2020 data from the updated performance period for the FY 2022 SNF VBP program year and calculate the SNFRM using a 9-month performance period (April 1, 2019-December 31, 2019). To determine whether the measure would be reliable using data during this period, which would be closer to 8 months once we remove all SNF stays whose 30-day readmission risk-window extended to or after January 1, 2020, we performed reliability analyses using a formula that relates the reliability of a measure to its intraclass correlation (ICC), and found that an estimate of reliability using all 12 combinations of potential 8-month data periods from FY 2019 (that is, October through May, November through June, and so on) ^[110] produces an average reliability estimate of 0.367, which is lower than our generally accepted minimum reliability threshold of 0.40.

We also considered substituting the July 1, 2020-September 30, 2020 period with an alternate data period; however, we are limited operationally in terms of which data may be used. Using data from further in the future would cause a delay in the calculation and dissemination of results for the FY 2022 Program. Such a delay could require us to make adjustments to the otherwise applicable Federal per diem rate paid to SNFs in FY 2022 on a delayed basis, which would be an extremely burdensome process for the MACs and a potentially confusing process for SNFs. While using older data is feasible, and we recognize that we adopted a performance period in the September 2nd IFC that duplicated the use of data from a previous performance period, our preference is to use as much new data as possible to assess SNF performance each year and to avoid, where feasible, using the same data as a performance period in multiple program years. Further revising the FY 2022 Program performance period to include older data would create a substantial overlap with the FY 2021 Program's performance period. Such a significant overlap would result in SNFs receiving payments in FY 2022 based largely on the same performance used to assess SNFs for the FY 2021 program year. Using over 80 percent of the same data twice as a performance period could result in some SNFs being penalized (or receiving a bonus) twice for nearly the same performance.

Therefore, due to concerns about the validity of the measure when calculated as currently specified on data during the PHE given the significant changes in SNF patient case volume and facility-level case mix described above, and lacking any viable alternatives, we are proposing to suppress the use of SNF readmission measure data for purposes of scoring and payment adjustments in the FY 2022 program year, under the proposed Measure Suppression Factor (4) Significant national or regional shortages or rapid or unprecedented changes in: (iii) Patient case volumes or facility-level case mix.

Under this proposed suppression policy, for all SNFs participating in the FY 2022 SNF VBP program, we will use the previously finalized performance period and baseline period to calculate each SNF's RSRR for the SNFRM. Then, we would suppress the use of SNF readmission measure data for purposes of scoring and payment adjustments. Specifically, we are proposing to change the scoring methodology to assign all SNFs a performance score of zero in the FY 2022 Program year. This would result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. We would then apply the Low-Volume Adjustment policy as previously finalized in the FY 2020 SNF PPS final rule (84 FR 38823 through 38824). That is, if a SNF has fewer than 25 eligible stays during the performance period for a program year we will assign that SNF a performance score resulting in a net-neutral payment incentive multiplier. SNFs will not be ranked for the FY 2022 SNF VBP program.

Under this proposal we would reduce each participating SNF's adjusted Federal per diem rate for FY 2022 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 2022 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 qualifying 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. Those SNFs subject to the Low-Volume Adjustment policy would receive 100 percent of their 2 percent withhold per the previously finalized policy increasing the overall payback percentage to an estimated 62.9 percent.

Further, we propose to provide quarterly confidential feedback reports to SNFs and publicly report the SNFRM rates for the FY 2022 SNF VBP Program year. However, 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. We propose to codify this policy at § 413.338(g).

We invite public comment on this proposal.

3. Proposed Revision to the SNFRM Risk Adjustment Look-Back Period for the FY 2023 SNF VBP Program

In the FY 2021 SNF PPS final rule (85 FR 47624), we finalized the FY 2023 Program performance period as FY 2021 (October 1, 2020-September 30, 2021). In the FY 2016 SNF PPS final rule (80 FR 46418), we finalized that the risk adjustment model would account for certain risk-factors within 365 days prior to the discharge from the hospital to the SNF (a 365-day lookback period). Under the COVID-19 ECE, SNF qualifying claims for the period January 1, 2020-June 30, 2020 are excepted from the calculation of the SNFRM; using FY 2021 data this results in at least 3 months of lookback being available for all SNF stays included in the measure without extending into or beyond June 30, 2020. Here, we propose instead a 90-day lookback period for risk adjustment in the FY 2023 performance period (FY 2021) only. Using a 90-day risk-adjustment period will allow us to use the most recent claims available for risk-adjustment, and an identical risk-adjustment lookback period for all stays included in the measure. It also allows us to avoid combining data from both prior to and during the COVID-19 PHE in the risk-adjustment lookback period, which would be necessary if we attempted to maintain a 12-month look-back period due to the COVID-19 ECE. Using a 90-day lookback period for risk adjustment will allow us to look back 90 days prior to the discharge from the hospital to the SNF for each SNF stay. Analyses conducted on FY 2019 performance data found that when compared to the 365-day lookback period traditionally used, a 90-day lookback period resulted in similar model performance (that is, the C-statistic was nearly identical). We are also considering similarly reducing the risk-adjustment lookback period for the applicable FY 2023 program baseline year which would align the risk-adjustment lookback period for the baseline and performance years in the FY 2023 program; we invite comments on this consideration.

We invite public comment on the proposed updates to the risk adjustment look-back period for the FY 2023 Performance Period.

4. Request for Comments on Potential Future Measures for the SNF VBP Program

On December 27, 2020, Congress enacted the Consolidated Appropriations Act, 2021 (CAA) (Pub. L. 116-260). Section 111(a)(1) of Division CC of the CAA amends section 1888(h)(1) of the Act to, with respect to payments for services furnished on or after October 1, 2022, preclude the SNF VBP from applying to a SNF for which there are not a minimum number (as determined by the Secretary) of cases for the measures that apply to the facility for the performance period for the applicable fiscal year, or measures that apply to the facility for the performance period for the applicable fiscal year. Section 111(a)(2) of the CAA amended section 1888(h)(2)(A) of the Act to, with respect to payments for services furnished on or after October 1, 2023, require the Secretary to apply the readmission measure specified under section 1888(g)(1) of the Act, and allow the Secretary to apply up to 9 additional measures determined appropriate, which may include measures of functional status, patient safety, care coordination, or patient experience. To the extent that the Secretary decides to apply additional measures, section 1888(h)(2)(A)(ii) of the Act, as amended by section 111(a)(2)(C) of the CAA, requires the Secretary to consider and apply, as appropriate, quality measures specified under section 1899B(c)(1) of the Act. Finally, section 111(a)(3) of the CAA amended section 1888(h) of the Act by adding a new paragraph (12), which requires that the Secretary apply a process to validate the measures and data submitted under the SNF VBP and the SNF QRP, as appropriate, which may be similar to the process specified under the Hospital Inpatient Quality Reporting (IQR) Program for validating inpatient hospital measures. In this proposed rule, we are seeking input from stakeholders regarding which measures we should consider adding to the SNF VBP Program. We intend to use future rulemaking to address these new statutory requirements.

Currently, the SNF VBP Program includes only a single quality measure, the SNFRM, which we intend to transition to the SNFPPR measure as soon as practicable. Both the SNFRM and SNFPPR assess the risk-adjusted rate of readmissions to hospitals, for SNF residents within 30 days of discharge from a prior hospital stay. Consistent with amended section 1888(h)(2)(A)(ii) of the Act, in considering which measures might be appropriate to add to the SNF VBP Program, we are considering additional clinical topics such as measures of functional status, patient safety, care coordination, and patient experience, as well as measures on those topics that are utilized in the SNF Quality Reporting Program (QRP). For more information about the SNF QRP measures, 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 are also considering measures on clinical topics that are not included in the SNF QRP's measure set because we believe that other clinical topics would be helpful to our efforts to robustly assess the quality of care furnished by SNFs.

In expanding the SNF VBP measure set, we are also considering measures that we already require for Long-Term Care Facilities (LTCFs), which include both SNFs and nursing facilities (NFs), to collect and report under other initiatives. Approximately 94 percent of LTCFs are dually certified as both a SNF and NF (Provider Data Catalog Nursing Homes and Rehab Services Provider Information File January 2021) (https://data.cms.gov/​provider-data/​dataset/​4pq5-n9py). The vast majority of LTCF residents are also Medicare beneficiaries, regardless of whether they are in a Medicare Part A SNF stay, because they are enrolled in Medicare Part B and receive Medicare coverage of certain services provided by the LTCF even if they are a long-term care resident. Therefore, we believe that expanding the SNF VBP measure set to assess the quality of care that SNFs provide to all residents of the facility, regardless of payer, would best represent the quality of care provided to all Medicare beneficiaries in the facility. We are requesting public comment on whether the measures in an expanded SNF VBP measure set should require SNFs to collect data on all residents in the facility, regardless of payer.

We have identified the measures listed in Table 31 as measures we could add to the SNF VBP Program measure set, and we seek comment on those measures, including which of those measures would be best suited for the program. We also seek public comment on any measures or measure concepts that are not listed in Table 31 that stakeholders believe we should consider for the SNF VBP Program. In considering an initial set of measures with which SNFs should largely be familiar (through the SNF QRP, 5-Star Rating Program and/or the Nursing Home Quality Initiative (NHQI)), we believe we can implement a measure set that would impose minimal additional burden on SNFs.

Table 31—Quality Measures Under Consideration for an Expanded Skilled Nursing Facility Value-Based Purchasing Program

Meaningful measure area	NQF	Quality measure
Minimum Data Set
Functional Outcomes	A2635	Application of IRF Functional Outcome Measure: Discharge Self-Care Score for Medical Rehabilitation Patients.*
Functional Outcomes	A2636	Application of IRF Functional Outcome Measure: Discharge Mobility Score for Medical Rehabilitation Patients.*
Preventable Healthcare Harm	0674	Percent of Residents Experiencing One or More Falls with Major Injury (Long Stay).**
Preventable Healthcare Harm	0679	Percent of High Risk Residents with Pressure Ulcers (Long Stay).**
Functional Outcomes	N/A	Percent of Residents Whose Ability to Move Independently Worsened (Long Stay).**
Functional Outcomes	N/A	Percent of Residents Whose Need for Help with Activities of Daily Living Has Increased (Long Stay).**
Transfer of Health Information and Interoperability	N/A	Transfer of Health Information to the Provider-Post Acute Care.*
Medication Management	N/A	Percentage of Long-Stay Residents who got an Antipsychotic Medication.**
Medicare Fee-For-Service Claims Based Measures
Community Engagement	3481	Discharge to Community Measure-Post Acute Care Skilled Nursing Facility Quality Reporting Program.*
Patient-focused Episode of Care	N/A	Medicare Spending per Beneficiary (MSPB)-Post Acute Care Skilled Nursing Facility Quality Reporting Program.*
Healthcare-Associated Infections	N/A	Skilled Nursing Facility Healthcare-Associated Infections Requiring Hospitalization Measure.~
Admissions and Readmissions to Hospitals	N/A	Number of hospitalizations per 1,000 long-stay resident days (Long Stay).**
Patient-Reported Outcome-Based Performance Measure
Functional Outcomes	N/A	Patient-Reported Outcomes Measurement Information System [PROMIS]- PROMIS Global Health, Physical.
Survey Questionnaire (similar to Consumer Assessment of Healthcare Providers and Systems (CAHPS))
Patient's Experience of Care	2614	CoreQ: Short Stay Discharge Measure.
Payroll Based Journal
N/A	N/A	Nurse staffing hours per resident day: Registered Nurse (RN) hours per resident per day; Total nurse staffing (including RN, licensed practical nurse (LPN), and nurse aide) hours per resident per day.**
* Measures adopted in the SNF Quality Reporting Program (QRP).
** ** These measures are reported on the Nursing Home Care Compare website (https://www.medicare.gov/​care-compare/​).
~ Measure proposed in section VII.C.1 of this proposed rule for adoption in the SNF QRP.

In addition to the staffing measures listed in Table 31 that focus on nurse staffing hours per resident day and that are currently reported on the Nursing Home Care Compare website, we are also interested in measures that focus on staff turnover. We have been developing measures of staff turnover, as required by section 1128I(g) of the Act, with the goal of making the information publicly available. Through our implementation of the Payroll-Based Journal (PBJ) staffing data collection program, we have indicated that we will be reporting rates of turnover in the future (for more information on this program, see CMS memorandum QSO-18-17-NH ^[111] ). As we plan to report staff turnover information in the near future, we are also seeking comment on inclusion of these measures in the SNF VBP Program.

We are also considering two patient-reported measures, as listed in Table 31, to assess residents' views of their healthcare.

The CoreQ: Short Stay Discharge Measure calculates the percentage of individuals discharged in a 6-month time period from a SNF, within 100 days of admission, who are satisfied with their SNF stay. This patient reported outcome measure is based on the CoreQ: Short Stay Discharge questionnaire that utilizes four items: (1) In recommending this facility to your friends and family, how would you rate it overall; (2) Overall, how would you rate the staff; (3) How would you rate the care you receive; (4) How would you rate how well your discharge needs were met. For additional information about the CoreQ: Short Stay Discharge Measure, please visit https://cmit.cms.gov/​CMIT_​public/​ViewMeasure?​MeasureId=​3436.

We welcome public comment on future measures for the SNF VBP Program, and on whether the measures in an expanded SNF VBP measure set should require SNFs to collect data on all residents in the facility, regardless of payer.

C. SNF VBP Performance Period and Baseline Period

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.

2. Revised Performance Period for the FY 2022 SNF VBP Program

In the September 2nd IFC, we updated the performance period for the FY 2022 SNF VBP Program to April 1, 2019 through December 31, 2019 and July 1, 2020 through September 30, 2020. We also noted that the baseline period of the FY 2022 Program had not been impacted by the PHE for COVID-19 and will remain as FY 2018 (October 1, 2017 through September 30, 2018), and the FY 2022 Program performance standards included in the FY 2020 final rule (84 FR 38822 through 38823) will remain as finalized.

However, as noted in section VII.B.3. of this proposed rule, there are concerns about the validity of the measure when calculated as currently specified on data during the PHE (specifically, July 1, 2020 through September 30, 2020) given the significant changes in SNF patient case volume and facility-level case mix described above. Therefore, we are proposing to suppress the SNFRM for the FY 2022 program year. We will calculate each SNF's RSRR for the SNFRM. Then, we would change the scoring methodology to assign all SNFs a performance score of zero. This would result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. We would then apply the Low-Volume Adjustment policy as previously finalized in the FY 2020 SNF PPS final rule (84 FR 38823 through 38824). That is, if a SNF has fewer than 25 eligible stays during the performance period for a program year we will assign that SNF a performance score resulting in a net-neutral payment incentive multiplier. We will continue to provide quarterly confidential feedback reports to facilities and publicly report based on the usable data from the previously finalized performance period (April 1, 2019 through December 31, 2019) and the previously finalized baseline period (FY 2018).

3. Performance Period for the FY 2023 SNF VBP Program

In the FY 2021 SNF PPS final rule (85 FR 47624), we finalized that the Performance Period for the FY 2023 program year would be October 1, 2020-September 30, 2021 (FY 2021) and the baseline would be FY 2019 (October 1, 2018-September 30, 2019). We are not proposing any updates to the performance period and baseline period previously finalized for FY 2023.

We also considered alternatives to the previously finalized performance period for FY 2023. We considered modifying the performance period for the FY 2023 program year to Calendar Year 2021 (January 1, 2021-December 31, 2021). However, CY 2021 data are available later than FY 2021 data, and would likely result in a delay calculating SNFRM scores for SNFs and a subsequent delay in the application of payment incentives for the FY 2023 program year.

We acknowledge that the COVID-19 PHE extends into both performance period options. We believe that following the completion of testing, SNF readmission measure specifications may account for changes in SNF admission and/or hospital readmission patterns that we have observed during the PHE as noted above.

We invite public comment on this alternative to the previously finalized Performance Period for the FY 2023 SNF VBP program.

4. Performance Period and Baseline Period for the FY 2024 SNF VBP Program

Under the policy finalized in the FY 2019 SNF PPS final rule (83 FR 39277 through 39278), for the FY 2024 program year, the performance period would be FY 2022 and the baseline period would be FY 2020. However, under the ECE, SNF qualifying claims for a 6-month period in FY 2020 (January 1, 2020-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 the FY 2020 baseline period. Moreover, as described in more detail in section VII.B.3 above, we are proposing to suppress the SNFRM for the FY 2022 program year, in part because there are concerns about the validity of the measure when calculated as currently specified on data during the PHE (specifically, July 1, 2020 through September 30, 2020) given the significant changes in SNF patient case volume and facility-level case mix described above. As the SNF VBP Program uses only a single measure calculated on 1 year of data and uses each year of data first as a performance period and then later on as a baseline period in the Program, the removal of 9 months of data in light of the COVID-19 PHE as described above will necessarily result in data being used more than once in the Program. Therefore, to ensure enough data are available to reliably calculate the SNFRM, we are proposing FY 2019 data be used for the baseline period for the FY 2024 program year. We also considered using FY 2021, which will be the baseline period for the FY 2025 program year under our current policy. However, it is operationally infeasible for us to calculate the baseline for the FY 2024 program year using FY 2021 data in time to establish the performance standards for that program year at least 60 days prior to the start of the performance period, as required under section 1888(h)(3)(C) of the Act.

We invite public comment on this proposal.

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 2022 performance standards in the FY 2020 SNF PPS final rule (84 FR 38822), and adopted the final numerical values for the FY 2023 performance standards in the FY 2021 SNF PPS final rule (85 FR 47625). 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 are not proposing any changes to these performance standard policies in this 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. We are not proposing any changes to the performance standards correction policy in this proposed rule.

3. Performance Standards for the FY 2024 Program Year

In section VII.C.1, we propose to use FY 2019 data for the baseline period for the FY 2024 program year. Based on this baseline period, we estimate that the performance standards would have the numerical values noted in Table 32. We note that these values represent estimates based on the most recently available data, and that we will update the numerical values in the FY 2022 SNF PPS final rule.

Table 32—Estimated FY 2024 SNF VBP Program Performance Standards

Measure ID	Measure description	Achievement threshold	Benchmark
SNFRM	SNF 30-Day All-Cause Readmission Measure (NQF #2510)	0.79270	0.83028

E. SNF VBP Performance Scoring

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 extraordinary circumstances exception policy.

In section VII.B.3. of this proposed rule, we are proposing to suppress the SNFRM for the FY 2022 program year. If finalized, for all SNFs participating in the FY 2022 SNF VBP program, we will use the previously finalized performance period and baseline period to calculate each SNF's RSRR for the SNFRM. Then, we would assign all SNFs a performance score of zero. This would result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. We would then apply the Low-Volume Adjustment policy as previously finalized. That is, if a SNF has fewer than 25 eligible stays during the performance period for a program year we will assign that SNF a performance score resulting in a net-neutral payment incentive multiplier. SNFs will not be ranked for the FY 2022 SNF VBP program.

F. SNF Value-Based Incentive Payments

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).

In section VII.B.3. of this proposed rule, we are proposing to suppress the SNFRM for the FY 2022 program year. If finalized, for all SNFs participating in the FY 2022 SNF VBP program, we will use the previously finalized performance period and baseline period to calculate each SNF's RSRR for the SNFRM. Then, we would assign all SNFs a performance score of zero. This would result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. SNFs will not be ranked for the FY 2022 SNF VBP program. We would then apply the Low-Volume Adjustment policy as previously finalized. That is, if a SNF has fewer than 25 eligible stays during the performance period for a program year we will assign that SNF a performance score resulting in a net-neutral payment incentive multiplier.

We are also proposing to reduce each participating SNF's adjusted Federal per diem rate for FY 2022 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 2022 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 in light of our proposal to award a performance score of zero to all SNFs. Those SNFs subject to the Low-Volume Adjustment policy which would receive 100 percent of their 2 percent withhold per the previously finalized policy, increasing the overall payback percentage to an estimated 62.9 percent. We propose to codify this policy at § 413.338(g).

We invite public comment on this proposed change to the SNF VBP payment policy for the FY 2022 program year.

G. Public Reporting on the Nursing Home Compare Website or a Successor 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 Catalogue 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 stakeholders with the necessary information to evaluate SNFs' performance under the Program (82 FR 36623).

In section VII.B.3. of this proposed rule, we are proposing to suppress the SNFRM for the FY 2022 program year. Under this proposal, for all SNFs participating in the FY 2022 SNF VBP program, we will use the previously finalized performance period and baseline period to calculate each SNF's RSRR for the SNFRM. Then, we would assign all SNFs a performance score of zero. This would result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. We would then apply the Low-Volume Adjustment policy as previously finalized. That is, if a SNF has fewer than 25 eligible stays during the performance period for a program year we will assign that SNF a performance score resulting in a net-neutral payment incentive multiplier.

While we will publicly report the SNFRM rates for the FY 2022 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 2022 SNF VBP given the significant changes in SNF patient case volume and facility-level case mix described above. SNFs will not be ranked for the FY 2022 SNF VBP program.

2. Data Suppression Policy for Low-Volume SNFs

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), (ii), and (iii).

In section VII.B.3. of this proposed rule, we are proposing to suppress the SNFRM for the FY 2022 program year. Under this proposal, for all SNFs participating in the FY 2022 SNF VBP program, we will use the previously finalized performance period and baseline period to calculate each SNF's RSRR for the SNFRM. Then, we would assign all SNFs a performance score of zero. This would result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. We would then apply the Low-Volume Adjustment policy as previously finalized. That is, if a SNF has fewer than 25 eligible stays during the performance period for a program year we will assign that SNF a performance score resulting in a net-neutral payment incentive multiplier. SNFs will not be ranked for the FY 2022 SNF VBP program.

3. Public Reporting of SNF VBP Performance Information on Nursing Home Compare or a Successor Website

Section 1888(h)(9)(A) of the Act requires that the Secretary make available to the public on the Nursing Home Compare website or a successor website information regarding the performance of individual SNFs for a fiscal year, including the performance score for each SNF for the fiscal year and each SNF's ranking, as determined under section 1888(h)(4)(B) of the Act. Additionally, section 1888(h)(9)(B) of the Act requires that the Secretary periodically post aggregate information on the SNF VBP Program on the Nursing Home Compare website or a successor website, 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 2018 SNF PPS final rule (82 FR 36622 through 36623), we finalized our policy to publish SNF measure performance information under the SNF VBP Program on Nursing Home Compare.

In the FY 2021 SNF PPS final rule (85 FR 47626), we finalized an amendment to § 413.338(e)(3) to reflect that we will publicly report SNF performance information on the Nursing Home Compare website or a successor website located at https://www.medicare.gov/​care-compare/​. We are not proposing any changes to the public reporting policies in this proposed rule.

H. Proposal To Update and Codify the Phase One Review and Correction Claims “Snapshot” Policy

In the FY 2017 SNF PPS final rule (81 FR 52007 through 52009), we adopted a two-phase review and corrections process for SNFs' quality measure data that will be made public under section 1888(g)(6) of the Act and SNF performance information that will be made public under section 1888(h)(9) of the Act. We detailed the process for requesting Phase One corrections and finalized a policy whereby we would accept Phase One corrections to a quarterly report provided during a calendar year until the following March 31.

In the FY 2020 SNF PPS final rule (84 FR 38824 through 38835), we updated this policy to reflect a 30-day Phase One Review and Correction deadline rather than through March 31st following receipt of the performance period quality measure quarterly report.

In the FY 2021 SNF PPS final rule (85 FR 47626 through 47627), we updated the 30-day deadline for Phase One Review and Correction and codified the policy at § 413.338(e)(1). Under the updated policy, beginning with the baseline period quality report issued on or after October 1, 2020 that contains the baseline period measure rate and underlying claim information used to calculate the measure rate for the applicable program year, SNFs have 30 days following the date that CMS provides those reports to review and submit corrections to the data contained in those reports. We also stated that if the issuance dates of these reports are significantly delayed or need to be shifted for any reason, we would notify SNFs through routine communication channels including, but not limited to memos, emails, and notices on the CMS SNF VBP website at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​SNF-VBP-Page.

We are now proposing to include a Phase One Review and Correction claims “snapshot” policy beginning with the baseline period and performance period quality measure quarterly reports issued on or after October 1, 2021. This proposed policy would limit the Phase One Review and Correction to errors made by CMS or its contractors when calculating a SNF's readmission measure rate and will not allow corrections to the underlying administrative claims data used to calculate those rates. Under this proposed policy, the administrative claims data we use to calculate a SNF's readmission measure rate for purposes of a baseline period or performance period for a given SNF VBP program year would be held constant (that is, frozen in a “snapshot”) from the time we extract it for that purpose. This proposal would align the review and correction policy for the SNF VBP Program with the review and correction policy we have adopted for other value-based purchasing programs, including the Hospital Readmissions Reduction Program (HRRP), Hospital-Acquired Condition (HAC) Reduction Program, and Hospital Value-Based Purchasing (VBP) Program.

For purposes of this program, we propose to calculate the SNF readmission measure rates using a static “snapshot” of claims updated as of 3 months following the last index SNF admission in the applicable baseline period or performance period. The source of the administrative claims data we use to calculate the SNF readmission measure is the Medicare Provider Analysis and Review (MedPAR). For example, if the last index SNF admission date for the applicable baseline period or performance period is September 30th, 2019, we would extract the administrative claims data from the MedPAR file as that data exists on December 31st, 2019. SNFs would then receive their SNF readmission measure rate and accompanying stay-level information in their confidential quality measure quarterly reports, and they would have an opportunity to review and submit corrections to our calculations as part of the Phase One corrections process. SNFs, however, would not be able to correct any of the underlying administrative claims data (for example, a SNF discharge destination code) we use to generate the measure rate.

The use of a data “snapshot” enables us to provide as timely quality data as possible, both to SNFs for the purpose of quality improvement and to the public for the purpose of transparency. After the claims “snapshot” is taken through our extraction of the data from MedPAR, it takes several months to incorporate other data needed for the SNF readmission measure calculations, generate and check the calculations, as well as program, populate, and deliver the confidential quarterly reports and accompanying data to SNFs. Because several months lead time is necessary after acquiring the input data to generate these calculations, if we were to delay our data extraction point beyond the date that is 3 months after the last SNF index admission attributable to a baseline period or performance period, we believe this would create an unacceptably long delay both for SNFs to receive timely data for quality improvement and transparency, and, incentive payments for purposes of this program. Therefore, we believe that a 3-month claims “run-out” period between the date of the last SNF index admission and the date of the data extraction is a reasonable period that allows SNFs time to correct their administrative claims or add any missing claims before those claims are used for measure calculation purposes while enabling us to timely calculate the measure. If unforeseen circumstances require the use of additional months of claims “run-out”, that is, more than 3 months, we would notify SNFs through routine communication channels including, but not limited to, memos, emails, quarterly reports and notices on the CMS SNF VBP website at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​SNF-VBP-Page.

We believe this proposed policy would address both fairness and operational concerns associated with calculating measure rates and would provide consistency across value-based purchasing programs.

We are also proposing to codify this policy in our regulations by revising § 413.338(e)(1) to remove the policies that would no longer be applicable beginning October 1, 2021 and state the newly proposed policy that would be effective, if finalized, on October 1, 2021.

We invite public comment on this proposal to update the Phase One Review and Correction policy.

I. Proposal To Update the Instructions for Requesting an ECE in § 413.338(d)(4)(ii) of the SNF VBP Regulations

We are proposing to update the instructions for a SNF to request an extraordinary circumstances exception (ECE). Specifically, we are proposing to update the email address that a SNF must use to send the request, as well as the URL for our QualityNet website from QualityNet.org to QualityNet.cms.gov. We are also proposing to remove the separate reference to newspapers because newspapers are already included in the broader term “media articles.” We are proposing to update § 413.338(d)(4)(ii) of our regulations to reflect these changes.

We invite public comment on this proposal.

VIII. Collection of Information Requirements

This proposed rule would not impose any new or revised “collection of information” requirements or burden as it pertains to CMS. For the purpose of this section of the preamble, collection of information is defined under 5 CFR 1320.3(c) of the Paperwork Reduction Act of 1995's (PRA) (44 U.S.C. 3501 et seq.) implementing regulations. Consequently, this rule is not subject to the requirements of the PRA.

We propose in section VI.C.1. of this proposed rule, the SNF HAIs Requiring Hospitalization measure beginning with the FY 2023 SNF QRP. All claims-based measures are calculated using data that are already reported to the Medicare program for payment purposes. Since the data source for this quality measure is Medicare fee-for-service claims, there is no additional burden for providers.

In section VI.C.2. of this proposed rule, we propose that SNFs submit data on the COVID-19 Vaccination Coverage among Healthcare Personnel (HCP) measure beginning with the FY 2023 SNF QRP. We note that the CDC would account for the burden associated with the COVID-19 Vaccination Coverage among HCP measure collection under OMB control number 0920-1317 (expiration January 31, 2024). However, the CDC currently has a PRA waiver for the collection and reporting of vaccination data under section 321 of the National Childhood Vaccine Injury Act of 1986 (Pub. L. 99-660, enacted on November 14, 1986) (NCVIA).^[112] We refer readers to section X.A.5. of this proposed rule, where CMS has provided an estimate of the burden and cost to SNFs, and note that the CDC will include it in a revised information collection request for 0920-1317.

In section VI.C.3. of this proposed rule, we are proposing to update the Transfer of Health (TOH) Information to the Patient—Post Acute Care (PAC) measure to exclude residents discharged home under the care of an organized home health service or hospice. This measure was adopted in the FY 2020 SNF PPS final rule (84 FR 38728) and the associated burden was accounted for in OMB 0938-1140 (expiration November 30, 2022). The proposed update would not affect the information collection burden already established.

In section VI.G.3. of this proposed rule, we are proposing that SNFs submit data on the COVID-19 Vaccination among HCP measure through the CDC/National Healthcare Safety Network (NHSN). The NHSN is a secure, internet-based surveillance system maintained by the CDC and provided free of charge to healthcare facilities including SNFs.

While the NHSN is currently not utilized by SNFs for purposes of meeting the SNF QRP requirements, nursing homes were enrolled in the NHSN in 2020 and are currently submitting mandatory COVID-19 data through the Long-term Care Facility COVID-19 module (https://www.cdc.gov/​nhsn/​ltc/​covid19/​index.html). As such, there is no additional information collection burden related to the onboarding and training of SNF providers to utilize this system. In section VII.B.3. of this proposed rule, we are proposing to suppress the Skilled Nursing Facility 30-Day All-Cause Readmission Measure (SNFRM) for the FY 2022 SNF VBP Program Year. Because the data source for this quality measure is Medicare fee-for-service claims, there is no additional burden for SNFs. All claims-based measures can be calculated based on data that are already reported to the Medicare program for payment purposes.

IX. Response to Comments

Because of the large number of public comments we normally receive on Federal Register documents, we are not able to acknowledge or respond to them individually. We will consider all comments we receive by the date and time specified in the DATES section of this preamble, and, when we proceed with a subsequent document, we will respond to the comments in the preamble to that document.

X. Economic Analyses

A. Regulatory Impact Analysis

1. Statement of Need

This proposed rule updates the FY 2022 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. As these statutory provisions prescribe a detailed methodology for calculating and disseminating payment rates under the SNF PPS, we do not have the discretion to adopt an alternative approach on these issues.

2. Introduction

We have examined the impacts of this proposed 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. This rule has been designated an economically significant rule, under section 3(f)(1) of Executive Order 12866. Accordingly, we have prepared a regulatory impact analysis (RIA) as further discussed below. Also, the rule has been reviewed by OMB.

3. Overall Impacts

This rule would update the SNF PPS rates contained in the SNF PPS final rule for FY 2021 (85 FR 47594). We estimate that the aggregate impact would be an increase of approximately $444 million in Part A payments to SNFs in FY 2022. This reflects a $445 million increase from the update to the payment rates and a $1.2 million decrease due to the proposed reduction to the SNF PPS rates to account for the recently excluded blood-clotting factors (and items and services related to the furnishing of such factors) in section 1888(e)(2)(A)(iii)(VI) of the Act. We note that these impact numbers do not incorporate the SNF VBP reductions that we estimate would total $191.64 million in FY 2022. We would note 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.

In accordance with sections 1888(e)(4)(E) and (e)(5) of the Act and implementing regulations at § 413.337(d), we would update the FY 2021 payment rates by a factor equal to the market basket index percentage change reduced by the forecast error adjustment and the MFP adjustment to determine the payment rates for FY 2022. The impact to Medicare is included in the total column of Table 33. In proposing the SNF PPS rates for FY 2022, we are proposing a number of standard annual revisions and clarifications mentioned elsewhere in this proposed rule (for example, the proposed update to the wage and market basket indexes used for adjusting the Federal rates).

The annual update proposed in this rule would apply to SNF PPS payments in FY 2022. 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 2022 SNF PPS payment impacts appear in Table 33. Using the most recently available data, in this case FY 2020, we apply the current FY 2021 CMIs, wage index and labor-related share value to the number of payment days to simulate FY 2021 payments. Then, using the same FY 2020 data, we apply the proposed FY 2022 CMIs, wage index and labor-related share value to simulate FY 2022 payments. We would note that, given that this same data is being used for both parts of this calculation, as compared to other analyses discussed in this proposed rule which compare data from FY 2020 to data from other fiscal years, any issues discussed throughout this proposed rule with regard to data collected in FY 2020 would not cause any difference in this economic analysis. We tabulate the resulting payments according to the classifications in Table 33 (for example, facility type, geographic region, facility ownership), and compare the simulated FY 2021 payments to the simulated FY 2022 payments to determine the overall impact. The breakdown of the various categories of data in Table 33 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 annual update to the wage index. This represents the effect of using the most recent wage data available. The total impact of this change is 0.0 percent; however, there are distributional effects of the proposed change.
• The fourth column shows the effect of all of the changes on the FY 2022 payments. The proposed update of 1.3 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 1.3 percent, assuming facilities do not change their care delivery and billing practices in response.

As illustrated in Table 33, the combined effects of all of the changes vary by specific types of providers and by location. For example, due to changes in this proposed rule, rural providers would experience a 1.8 percent increase in FY 2022 total payments. Finally, we note that we did not include in Table 33 the distributional impacts associated with the blood-clotting factor exclusion because the reduction is so minor that it does not have any visible effect on the distributional impacts included in the Table 33.

Table 33—Impact to the SNF PPS for FY 2022

Provider characteristics	Number providers	Update wage data (%)	Total change (%)
Group:
Total	15,440	0.0	1.3
Urban	10,887	−0.1	1.2
Rural	4,553	0.4	1.8
Hospital-based urban	385	−0.2	1.1
Freestanding urban	10,502	−0.1	1.2
Hospital-based rural	451	0.3	1.6
Freestanding rural	4,102	0.4	1.7
Urban by region:
New England	742	−0.7	0.6
Middle Atlantic	1,447	−0.5	0.8
South Atlantic	1,820	0.4	1.7
East North Central	2,145	−0.2	1.1
East South Central	539	−0.4	0.9
West North Central	919	0.4	1.7
West South Central	1,342	−0.3	1.0
Mountain	536	0.1	1.4
Pacific	1,391	0.2	1.5
Outlying	6	0.4	1.7
Rural by region:
New England	129	−0.9	0.4
Middle Atlantic	245	0.5	1.8
South Atlantic	597	1.2	2.5
East North Central	909	0.5	1.8
East South Central	526	−0.1	1.2
West North Central	1,058	−0.3	1.0
West South Central	756	0.4	1.7
Mountain	222	0.5	1.8
Pacific	111	0.3	1.6
Ownership:
For profit	10,809	0.0	1.3
Non-profit	3,637	0.0	1.3
Government	994	0.2	1.5
Note: The Total column includes the proposed FY 2022 1.3 percent market basket increase factor. Additionally, we found no SNFs in rural outlying areas.

5. Impacts for the SNF QRP for FY 2022

Estimated impacts for the SNF QRP are based on analysis discussed in section VIII.B. of this proposed rule. The proposed SNF QRP requirements add no additional burden to the active collection under OMB control number #0938-1140 (CMS-10387; expiration November 30, 2022).

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 this proposed rule, we discuss 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. of this proposed rule, we are proposing to add two new measures to the SNF QRP beginning with the FY 2023 SNF QRP: SNF Healthcare-Associated Infections Requiring Hospitalization Measure (SNF-HAI) and the COVID-19 Vaccination Coverage among Healthcare Personnel measure. The SNF-HAI measure is a claims-based measure, and therefore, would impose no additional burden to the SNFs.

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 COVID-19 Vaccination Coverage among HCP measure is not accounted for under the CDC PRA package currently approved under OMB control number 0920-1317 due to the NCVIA waiver the cost and burden is discussed here and will be included in a revised information collection request for 0920-1317.

Consistent with the CDC's experience of collecting data using the NHSN, we estimate that it would take each SNF an average of 1 hour per month to collect data for the COVID-19 Vaccination Coverage among HCP measure and enter it into NHSN. We have estimated the time to complete this entire activity, since it could vary based on provider systems and staff availability. We believe it would take an administrative assistant from 45 minutes up to 1 hour and 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 2019 National Occupational Employment and Wage Estimates.^[113] To account for overhead and fringe benefits, we have doubled the hourly wage. These amounts are detailed in Table 34.

Table 34—U.S. Bureau of Labor and Statistics' May 2019 National Occupational Employment and Wage Estimates

Occupation title	Occupation code	Mean hourly wage ($/hr)	Overhead and fringe benefit ($/hr)	Adjusted hourly wage ($/hr)
Administrative Assistant	43-6013	$18.31	$18.31	$36.62

Based on this time range, it would cost each SNF between $27.47 and $45.78 each month or an average cost of $36.62 each month, and between $329.64 and $549.36 each year, or an average cost of $439.44 each year. We believe the data submission for the COVID-19 Vaccination Coverage among HCP measure would cause SNFs to incur additional average burden of 12 hours per year for each SNF and a total annual burden of 180,936 hours for all SNFs. The estimated annual cost across all 15,078 SNFs in the U.S. for the submission of the COVID-19 Vaccination Coverage among HCP measure would be between $4,970,312 and $8,283,250.08, and an average of $6,625,872.

We recognize that many SNFs may also be reporting other COVID-19 data to HHS. However, we believe the benefits of reporting data on the COVID-19 Vaccination Coverage among HCP measure to assess whether SNFs are taking steps to limit the spread of COVID-19 among their HCP, reduce the risk of transmission of COVID-19 within their facilities, and to help sustain the ability of SNFs to continue serving their communities throughout the PHE and beyond outweigh the costs of reporting. We welcome comments on the estimated time to collect data and enter it into NHSN.

6. Impacts for the SNF VBP Program

The estimated impacts of the FY 2022 SNF VBP Program are based on historical data and appear in Table 35. We modeled SNF performance in the Program using SNFRM data from FY 2018 as the baseline period and an 8-month period from February 1, 2019 through September 30, 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), though we note that the 60 percent payback percentage for FY 2022 will be adjusted to account for the low-volume scoring adjustment that we adopted in the FY 2019 SNF PPS final rule (83 FR 39278 through 39280). However, in section VII.B.3. of this proposed rule, we are proposing to suppress the SNFRM for the FY 2022 program year. If finalized, we will award each participating SNF 60 percent of their 2 percent withhold, except those SNFs subject to the low-volume scoring adjustment, which would receive 100 percent of their 2 percent withhold. We estimated that the low-volume scoring adjustment would increase the 60 percent payback percentage for FY 2022 by approximately 2.9 percentage points (or $16.4 million), resulting in a payback percentage for FY 2022 that is 62.9 percent of the estimated $516.2 million in withheld funds for that fiscal year. Based on the 60 percent payback percentage (as modified by the low-volume scoring adjustment), we estimated that we will redistribute approximately $324.5 million in value-based incentive payments to SNFs in FY 2022, which means that the SNF VBP Program is estimated to result in approximately $191.6 million in savings to the Medicare Program in FY 2022.

Our detailed analysis of the estimated impacts of the FY 2022 SNF VBP Program follows in Table 35.

Table 35—SNF VBP Program Estimated Impacts for FY 2022

Characteristic	Number of facilities	Mean Risk- Standardized Readmission Rate (SNFRM) (%)	Mean performance score	Mean incentive multiplier	Percent of total payment after applying incentives
Group:
Total	15,026	19.90	1.4545	0.99426	100
Urban	10,845	19.94	1.1528	0.99379	85.29
Rural	4,181	19.81	2.2371	0.99547	14.71
Hospital-based urban *	284	19.68	1.1794	0.99383	1.79
Freestanding urban *	10,520	19.95	1.1423	0.99377	83.47
Hospital-based rural *	182	19.55	2.6050	0.99604	0.43
Freestanding rural *	3,803	19.81	2.1749	0.99538	14.12
Urban by region:
New England	744	20.10	0.8104	0.99326	5.38
Middle Atlantic	1,462	19.78	0.7155	0.99311	16.57
South Atlantic	1,874	20.00	0.6407	0.99299	17.01
East North Central	2,065	20.08	1.3950	0.99417	13.32
East South Central	555	20.08	0.9471	0.99347	3.53
West North Central	923	19.92	2.1104	0.99528	4.23
West South Central	1,312	20.11	1.6811	0.99461	7.48
Mountain	523	19.56	1.4090	0.99419	3.72
Pacific	1,381	19.67	0.9702	0.99351	14.05
Outlying	6	20.96	2.5766	0.9960	0.00
Rural by region:
New England	122	19.30	1.6896	0.99462	0.64
Middle Atlantic	210	19.53	1.1779	0.99383	0.90
South Atlantic	473	19.91	1.5144	0.99435	2.11
East North Central	895	19.69	1.8310	0.99484	3.35
East South Central	495	20.06	1.1139	0.99373	2.26
West North Central	1,006	19.77	3.5653	0.99753	1.99
West South Central	689	20.13	2.5430	0.99595	2.18
Mountain	199	19.43	2.5378	0.99594	0.66
Pacific	91	19.22	1.5856	0.99446	0.60
Outlying	1	19.37	5.1533	1.0000	0.00
Ownership:
Government	877	19.77	2.5149	0.9959	3.28
Profit	10,583	19.95	1.3693	0.9941	74.38
Non-Profit	3,566	19.81	1.4466	0.9943	22.33
* The group category which includes hospital-based/freestanding by urban/rural excludes 237 swing-bed SNFs.

7. Alternatives Considered

As described in this section, we estimated that the aggregate impact for FY 2022 under the SNF PPS would be an increase of approximately $444 million in Part A payments to SNFs. This reflects a $445 million increase from the update to the payment rates, and a $1.2 million decrease due to the proposed reduction to the SNF PPS rates to account for the recently excluded blood-clotting factors (and items and services related to the furnishing of such factors) in section 1888(e)(2)(A)(iii)(VI) of the Act.

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 other provisions contained in this proposed rule, such as the proposed methodology for calculating the proportional reduction to the rates to account for the exclusion of blood clotting factors from SNF consolidated billing, we discuss any alternatives considered within those sections.

With regard to the proposed SNF VBP measure suppression policy, we discuss any alternatives considered within those sections.

8. Accounting Statement

As required by OMB Circular A-4 (available online at https://obamawhitehouse.archives.gov/​omb/​circulars_​a004_​a-4/​), in Tables 36, 37 and 38, we have prepared an accounting statement showing the classification of the expenditures associated with the provisions of this proposed rule for FY 2022. Tables 33 and 36 provide our best estimate of the possible changes in Medicare payments under the SNF PPS as a result of the policies in this proposed rule, based on the data for 15,440 SNFs in our database. Tables 35 and 37 provide our best estimate of the possible changes in Medicare payments under the SNF VBP as a result of the policies we have proposed for this program. Tables 34 and 38 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 proposed rule.

Table 36—Accounting Statement: Classification of Estimated Expenditures, From the 2021 SNF PPS Fiscal Year to the 2022 SNF PPS Fiscal Year

Category	Transfers
Annualized Monetized Transfers	$444 million.*
From Whom To Whom?	Federal Government to SNF Medicare Providers.
* The net increase of $444 million in transfer payments is a result of the $445 million increase due to the proposed market basket increase of 1.3 percent, reduced by $1.2 million due to the proposed proportional reduction associated with excluding blood clotting factors from SNF consolidated billing.

Table 37—Accounting Statement: Classification of Estimated Expenditures for the FY 2022 SNF VBP Program

Category	Transfers
Annualized Monetized Transfers	$324.5 million.*
From Whom To Whom?	Federal Government to SNF Medicare Providers.
* This estimate does not include the two percent reduction to SNFs' Medicare payments (estimated to be $516.15 million) required by statute.

Table 38—Accounting Statement: Classification of Estimated Expenditures for the FY 2022 SNF QRP Program

Category	Transfers/Costs
Costs for SNFs to Submit Data for QRP	$6.6 million.*
* Costs associated with the submission of data for the COVID-19 Vaccination Coverage among HCP will occur in FY 2022 and is likely to continue in future years.

9. Conclusion

This rule updates the SNF PPS rates contained in the SNF PPS final rule for FY 2021 (85 FR 47594). Based on the above, we estimate that the overall payments for SNFs under the SNF PPS in FY 2022 are projected to increase by approximately $444 million, or 1.3 percent, compared with those in FY 2021. We estimate that in FY 2022, SNFs in urban and rural areas would experience, on average, a 1.2 percent increase and 1.8 percent increase, respectively, in estimated payments compared with FY 2021. Providers in the rural South Atlantic region would experience the largest estimated increase in payments of approximately 2.5 percent. Providers in the rural New England region would experience the smallest estimated increase in payments of 0.4 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 http://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 would update the SNF PPS rates contained in the SNF PPS final rule for FY 2021 (85 FR 47594). Based on the above, we estimate that the aggregate impact for FY 2022 would be an increase of $444 million in payments to SNFs, resulting from the SNF market basket update to the payment rates, reduced by the impact of excluding blood clotting factors (and items and services related to the furnishing of such factors) from SNF consolidated billing under section 1888(e)(2)(A)(iii)(VI) and (e)(4)(G)(iii) of the Act. While it is projected in Table 33 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 2022 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 2021 Report to Congress (available at http://www.medpac.gov/​docs/​default-source/​reports/​mar21_​medpac_​ch7_​sec.pdf), MedPAC states that Medicare covers approximately 9 percent of total patient days in freestanding facilities and 16 percent of facility revenue (March 2020 MedPAC Report to Congress, 224). As indicated in Table 33, the effect on facilities is projected to be an aggregate positive impact of 1.3 percent for FY 2022. 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 proposed rule would not have a significant impact on a substantial number of small entities for FY 2022.

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 603 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 proposed rule would 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 a positive impact. Moreover, as noted in previous SNF PPS final rules (most recently, the one for FY 2021 (85 FR 47594)), the category of small rural hospitals is included within the analysis of the impact of this proposed rule on small entities in general. As indicated in Table 33, the effect on facilities for FY 2022 is projected to be an aggregate positive impact of 1.3 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 proposed rule would not have a significant impact on a substantial number of small rural hospitals for FY 2022.

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 2021, that threshold is approximately $158 million. This proposed rule would 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 proposed rule would have no substantial direct effect on state and local governments, preempt state law, or otherwise have federalism implications.

E. Congressional Review Act

This proposed regulation is subject to the Congressional Review Act provisions of the Small Business Regulatory Enforcement Fairness Act of 1996 (5 U.S.C. 801 et seq.) and has been transmitted to the Congress and the Comptroller General for review.

F. Regulatory Review Costs

If regulations impose administrative costs on private entities, such as the time needed to read and interpret this proposed 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 last year's proposed rule would be the number of reviewers of this year's proposed rule. We acknowledge that this assumption may understate or overstate the costs of reviewing this rule. It is possible that not all commenters reviewed last 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 last year's proposed rule is a fair estimate of the number of reviewers of this proposed rule.

We also recognize that different types of entities are in many cases affected by mutually exclusive sections of the proposed 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 2019 BLS Occupational Employment Statistics (OES) for medical and health service managers (SOC 11-9111), we estimate that the cost of reviewing this rule is $110.74 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 proposed rule. For each SNF that reviews the rule, the estimated cost is $442.96 (4 hours × $110.74). Therefore, we estimate that the total cost of reviewing this regulation is $20,819.12 ($442.96 × 47 reviewers).

In accordance with the provisions of Executive Order 12866, this proposed rule was reviewed by the Office of Management and Budget.

List of Subjects

42 CFR Part 411

• Diseases
• Medicare
• Reporting and recordkeeping requirements

42 CFR Part 413

• Principles of reasonable cost reimbursement; payment for end-stage renal disease services; optional prospectively determined payment rates for skilled nursing facilities; payment for acute kidney injury dialysis

42 CFR Part 489

• Health facilities
• Medicare
• Reporting and recordkeeping requirements

For the reasons set forth in the preamble, the Centers for Medicare & Medicaid Services proposes to amend 42 CFR chapter IV as set forth below:

PART 411—EXCLUSIONS FROM MEDICARE AND LIMITATIONS ON MEDICARE PAYMENT

1. The authority citation for part 411 continues to read as follows:

Authority: 42 U.S.C. 1302, 1395w-101 through 1395w-152, 1395hh, and 1395nn.

2. Amend § 411.15 by—

a. Revising paragraphs (p)(2)(xiii) through (xvi);

b. Redesignating paragraph (p)(2)(xvii) as (p)(2)(xviii); and

c. Adding new paragraph (p)(2)(xvii).

The revisions and addition read as follows:

§ 411.15

Particular services excluded from coverage.

* * * * *

(p) * * *

(2) * * *

(xiii) Those chemotherapy items identified, as of July 1, 1999, by HCPCS codes J9000-J9020, J9040-J9151, J9170-J9185, J9200-J9201, J9206-J9208, J9211, J9230-J9245, and J9265-J9600, and as of January 1, 2004, by HCPCS codes A9522, A9523, A9533, and A9534 (as subsequently modified by CMS), and any additional chemotherapy items identified by CMS.

(xiv) Those chemotherapy administration services identified, as of July 1, 1999, by HCPCS codes 36260-36262, 36489, 36530-36535, 36640, 36823, and 96405-96542 (as subsequently modified by CMS), and any additional chemotherapy administration services identified by CMS.

(xv) Those radioisotope services identified, as of July 1, 1999, by HCPCS codes 79030-79440 (as subsequently modified by CMS), and any additional radioisotope services identified by CMS.

(xvi) Those customized prosthetic devices (including artificial limbs and their components) identified, as of July 1, 1999, by HCPCS codes L5050-L5340, L5500-L5611, L5613-L5986, L5988, L6050-L6370, L6400-6880, L6920-L7274, and L7362-L7366 (as subsequently modified by CMS) and any additional customized prosthetic devices identified by CMS, which are delivered for a resident's use during a stay in the SNF and intended to be used by the resident after discharge from the SNF.

(xvii) Those blood clotting factors indicated for the treatment of patients with hemophilia and other bleeding disorders identified, as of July 1, 2020, by HCPCS codes J7170, J7175, J7177-J7183, J7185-J7190, J7192-J7195, J7198-J7203, J7205, and J7207-J7211 (as subsequently modified by CMS) and items and services related to the furnishing of such factors, and any additional blood clotting factors identified by CMS and items and services related to the furnishing of such factors.

* * * * *

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

3. The authority citation for part 413 continues to read as follows:

Authority: 42 U.S.C. 1302, 1395d(d), 1395f(b), 1395g, 1395l(a), (i), and (n), 1395x(v), 1395hh, 1395rr, 1395tt, and 1395ww.

4. Amend § 413.338 by revising paragraphs (d)(4)(ii) and (e)(1) and adding paragraph (g) to read as follows:

§ 413.338

Skilled nursing facility value-based purchasing program.

* * * * *

(d) * * *

(4) * * *

(ii) A SNF may request an exception within 90 days of the date that the extraordinary circumstances occurred by sending an email to the designated email address for SNF VBP ECE requests, which is [e-mail]. The email must include a completed Extraordinary Circumstances Request form (available on https://qualitynet.cms.gov/​) and any available evidence of the impact of the extraordinary circumstances on the care that the SNF furnished to patients including, but not limited to, photographs and media articles.

* * * * *

(e) * * *

(1) CMS will provide quarterly confidential feedback reports to SNFs on their performance on the SNF readmission measure. 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 SNF readmission measure rates contained in that report. The administrative claims data used to calculate a SNF's readmission measure rates are not subject to review and correction under this paragraph (e)(1). All correction requests must be accompanied by appropriate evidence showing the basis for the correction to the SNF readmission measure rates.

* * * * *

(g) Special rules for the FY 2022 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 2022, 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, with the exception of those SNFs qualifying for the low-volume scoring adjustment described in paragraph (d)(3) of this section.

(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. CMS will then apply low-volume scoring adjustment described in paragraph (d)(3) of this section.

(3) CMS will provide confidential feedback reports to SNFs on their performance on the SNF readmission measure in accordance with paragraphs (e)(1) and (2) of this section.

(4) CMS will publicly report SNF performance on the SNF readmission measure in accordance with paragraph (e)(3) of this section.

PART 489—PROVIDER AGREEMENTS AND SUPPLIER APPROVAL

5. The authority citation for part 489 continues to read as follows:

Authority: 42 U.S.C. 1302, 1395i-3, 1395x, 1395aa(m), 1395cc, 1395ff, and 1395(hh).

6. Amend § 489.20 by—

a. Revising paragraphs (s)(13) through (16);

b. Redesignating paragraph (s)(17) as paragraph (s)(18); and

c. Adding new paragraph (s)(17) to read as follows:

§ 489.20

Basis commitments.

* * * * *

(s) * * *

(13) Those chemotherapy items identified, as of July 1, 1999, by HCPCS codes J9000-J9020, J9040-J9151, J9170-J9185, J9200-J9201, J9206-J9208, J9211, J9230-J9245, and J9265-J9600, and as of January 1, 2004, by HCPCS codes A9522, A9523, A9533, and A9534 (as subsequently modified by CMS), and any additional chemotherapy items identified by CMS.

(14) Those chemotherapy administration services identified, as of July 1, 1999, by HCPCS codes 36260-36262, 36489, 36530-36535, 36640, 36823, and 96405-96542 (as subsequently modified by CMS), and any additional chemotherapy administration services identified by CMS.

(15) Those radioisotope services identified, as of July 1, 1999, by HCPCS codes 79030-79440 (as subsequently modified by CMS), and any additional radioisotope services identified by CMS.

(16) Those customized prosthetic devices (including artificial limbs and their components) identified, as of July 1, 1999, by HCPCS codes L5050-L5340, L5500-L5611, L5613-L5986, L5988, L6050-L6370, L6400-6880, L6920-L7274, and L7362-L7366 (as subsequently modified by CMS) and any additional customized prosthetic devices identified by CMS, which are delivered for a resident's use during a stay in the SNF and intended to be used by the resident after discharge from the SNF.

(17) Those blood clotting factors indicated for the treatment of patients with hemophilia and other bleeding disorders identified, as of July 1, 2020, by HCPCS codes J7170, J7175, J7177-J7183, J7185-J7190, J7192-J7195, J7198-J7203, J7205, and J7207-J7211 (as subsequently modified by CMS) and items and services related to the furnishing of such factors, and any additional blood clotting factors identified by CMS and items and services related to the furnishing of such factors.

* * * * *

Dated: March 29, 2021.

Elizabeth Richter,

Acting Administrator, Centers for Medicare & Medicaid Services.

Dated: April 8, 2021.

Xavier Becerra,

Secretary, Department of Health and Human Services.

Footnotes

1.  ONC, Draft 2 Trusted Exchange Framework and Common Agreement, https://www.healthit.gov/​sites/​default/​files/​page/​2019-04/​FINALTEFCAQTF41719508version.pdf.

2.  https://www.aon.com/​risk-services/​thought-leadership/​report-2018-long-term-care.jsp.

3.  http://www.bea.gov/​papers/​pdf/​IOmanual_​092906.pdf.

4.  The measure steward changed the name of the measure from SARS-CoV-2 Vaccination Coverage among Healthcare Personnel to COVID-19 Vaccination Coverage among Healthcare Personnel. There were no changes to the measure itself, other than the name change.

5.  CMS. (2006). Eliminating Serious Preventable, and Costly Medical Errors—Never Events. Retrieved from https://www.cms.gov/​newsroom/​fact-sheets/​eliminating-serious-preventable-and-costly-medical-errors-never-events.

6.  Office of Inspector General. (2014). Adverse events in skilled nursing facilities: National incidence among Medicare beneficiaries. Retrieved from https://oig.hhs.gov/​oei/​reports/​oei-06-11-00370.pdf.

7.  Beganovic, M., & Laplante, K. (2018). Communicating with Facility Leadership; Metrics for Successful Antimicrobial Stewardship Programs (Asp) in Acute Care and Long-Term Care Facilities. Rhode Island medical journal (2013), 101(5) (2018), 45-49.

8.  Cooper, D., McFarland, M., Petrilli, F., & Shells, C. (2019). Reducing inappropriate antibiotics for urinary tract infections in long-term care: A replication study. Journal of Nursing Care Quality, 34(1), 16-21. http://dx.doi.org/​10.1097/​NCQ.0000000000000343.

9.  Feldstein, D., Sloane, P.D., & Feltner, C. (2018). Antibiotic stewardship programs in nursing homes: A systematic review. Journal of the American Medical Directors Association, 19(2), 110-116. http://dx.doi.org/​10.1016/​j.jamda.2017.06.019.

10.  Castle, N., Engberg, J.B., Wagner, L.M., & Handler, S. (2017). Resident and facility factors associated with the incidence of urinary tract infections identified in the Nursing Home Minimum Data Set. Journal of Applied Gerontology, 36(2), 173-194. http://dx.doi.org/​10.1177/​0733464815584666.

11.  Crnich, C.J., Jump, R., Trautner, B., Sloane, P.D., & Mody, L. (2015). Optimizing antibiotic stewardship in nursing homes: A narrative review and recommendations for improvement. Drugs & Aging, 32(9), 699-716. http://dx.doi.org/​10.1007/​s40266-015-0292-7.

12.  Dick, A.W., Bell, J.M., Stone, N.D., Chastain, A.M., Sorbero, M., & Stone, P.W. (2019). Nursing home adoption of the National Healthcare Safety Network Long-term Care Facility Component. American Journal of Infection Control, 47(1), 59-64. http://dx.doi.org/​10.1016/​j.ajic.2018.06.018.

13.  Cooper, D., McFarland, M., Petrilli, F., & Shells, C. (2019). Reducing inappropriate antibiotics for urinary tract infections in long-term care: A replication study. Journal of Nursing Care Quality, 34(1), 16-21. http://dx.doi.org/​10.1097/​NCQ.0000000000000343.

14.  Gucwa, A.L., Dolar, V., Ye, C., & Epstein, S. (2016). Correlations between quality ratings of skilled nursing facilities and multidrug-resistant urinary tract infections. American Journal of Infection Control, 44(11), 1256-1260. http://dx.doi.org/​10.1016/​j.ajic.2016.03.015.

15.  Travers, J.L., Stone, P.W., Bjarnadottir, R.I., Pogorzelska-Maziarz, M., Castle, N.G., & Herzig, C.T. (2016). Factors associated with resident influenza vaccination in a national sample of nursing homes. American Journal of Infection Control, 44(9), 1055-1057. http://dx.doi.org/​10.1016/​j.ajic.2016.01.019.

16.  CMS. (2006). Eliminating Serious Preventable, and Costly Medical Errors—Never Events. Retrieved from https://www.cms.gov/​newsroom/​fact-sheets/​eliminating-serious-preventable-and-costly-medical-errors-never-events.

17.  Centers for Disease Control and Prevention (2009). The Direct Medical Costs of Healthcare-Associated Infections in U.S. Hospitals and the Benefits of Prevention. Retrieved from https://www.cdc.gov/​hai/​pdfs/​hai/​scott_​costpaper.pdf.

18.  Ouslander, J.G., Diaz, S., Hain, D., & Tappen, R. (2011). Frequency and diagnoses associated with 7- and 30-day readmission of skilled nursing facility patients to a nonteaching community hospital. Journal of the American Medical Directors Association, 12(3), 195-203. http://dx.doi.org/​10.1016/​j.jamda.2010.02.015.

19.  Zimlichman, E., Henderson, D., Tamir, O., Franz, C., Song, P., Yamin, C.K., . . . Bates, D.W. (2013). Health care-associated infections: A meta-analysis of costs and financial impact on the US health care system. JAMA Internal Medicine, 173(22), 2039-2046. Retrieved from https://jamanetwork.com/​journals/​jamainternalmedicine/​fullarticle/​1733452.

20.  Montoya, A., & Mody, L. (2011). Common infections in nursing homes: A review of current issues and challenges. Aging Health, 7(6), 889-899. http://dx.doi.org/​10.2217/​ahe.11.80.

21.  Office of Disease Prevention and Health Promotion. (2013). Long-term care facilities. In U.S. Department of Health and Human Services, National action plan to prevent health care-associated infections: Road map to elimination (pp. 194-239). Retrieved from https://health.gov/​our-work/​health-care-quality/​health-care-associated-infections/​national-hai-action-plan.

22.  Chidambaram, P., Neuman T., Garfield R. (2020). Racial and Ethnic Disparities in COVID-19 Cases and Deaths in Nursing Homes. Retrieved from https://www.kff.org/​coronavirus-covid-19/​issue-brief/​racial-and-ethnic-disparities-in-covid-19-cases-and-deaths-in-nursing-homes/​.

23.  Li Y., Cen X., Temkin-Greener R. (2020). Racial and Ethnic Disparities in COVID-19 Infections and Deaths Across U.S. Nursing Homes. Journal of the American Geriatrics Society, 68(11), 2454-2461. https://pubmed.ncbi.nlm.nih.gov/​32955105/​.

24.  Kimball, A., Hatfield, K.M., Arons, M., James, A., Taylor, J., Spicer, K., Bardossy, A.C., Oakley, L.P., Tanwar, S., Chisty, Z., Bell, J.M., Methner, M., Harney, J., Jacobs, J.R., Carlson, C.M., McLaughlin, H.P., Stone, N., Clark, S., Brostrom-Smith, C., Page, L.C., . . . CDC COVID-19 Investigation Team (2020). Asymptomatic and Presymptomatic SARS-CoV-2 Infections in Residents of a Long-Term Care Skilled Nursing Facility—King County, Washington, March 2020. MMWR. Morbidity and mortality weekly report, 69 (13), 377-381. https://doi.org/​10.15585/​mmwr.mm6913e1.

25.  McMichael, T.M., Clark, S., Pogosjans, S., Kay, M., Lewis, J., Baer, A., Kawakami, V., Lukoff, M.D., Ferro, J., Brostrom-Smith, C., Riedo, F.X., Russell, D., Hiatt, B., Montgomery, P., Rao, A.K., Currie, D.W., Chow, E.J., Tobolowsky, F., Bardossy, A.C., Oakley, L.P., . . . Public Health—Seattle & King County, EvergreenHealth, and CDC COVID-19 Investigation Team (2020). COVID-19 in a Long-Term Care Facility—King County, Washington, February 27-March 9, 2020. MMWR. Morbidity and mortality weekly report, 69 (12), 339-342. https://doi.org/​10.15585/​mmwr.mm6912e1.

26.  The CMS COVID-19 Nursing Home Dataset used in this analysis was not limited to just the SNF, but applied to the entire nursing home. The study population of the analysis includes Medicare-certified nursing homes providing SNF care.

27.  Office of Inspector General. (2014). Adverse events in skilled nursing facilities: National incidence among Medicare beneficiaries. Retrieved from https://oig.hhs.gov/​oei/​reports/​oei-06-11-00370.pdf.

28.  Beganovic, M., & Laplante, K. (2018). Communicating with Facility Leadership; Metrics for Successful Antimicrobial Stewardship Programs (Asp) in Acute Care and Long-Term Care Facilities. Rhode Island medical journal (2013), 101(5) (2018), 45-49.

29.  Crnich, C.J., Jump, R., Trautner, B., Sloane, P.D., & Mody, L. (2015). Optimizing antibiotic stewardship in nursing homes: A narrative review and recommendations for improvement. Drugs & Aging, 32(9), 699-716. http://dx.doi.org/​10.1007/​s40266-015-0292-7.

30.  Freeman-Jobson, J.H., Rogers, J.L., & Ward-Smith, P. (2016). Effect of an education presentation on the knowledge and awareness of urinary tract infection among non-licensed and licensed health care workers in long-term care facilities. Urologic Nursing, 36(2), 67-71. http://dx.doi.org/​10.7257/​1053-816X.2016.36.2.67 Crnich, C.J., Jump, R., Trautner, B., Sloane, P.D., & Mody, L. (2015). Optimizing antibiotic stewardship in nursing homes: A narrative review and recommendations for improvement. Drugs & Aging, 32(9), 699-716. http://dx.doi.org/​10.1007/​s40266-015-0292-7.

31.  Hutton, D.W., Krein, S.L., Saint, S., Graves, N., Kolli, A., Lynem, R., & Mody, L. (2018). Economic evaluation of a catheter-associated urinary tract infection prevention program in nursing homes. Journal of the American Geriatrics Society, 66(4), 742-747. http://dx.doi.org/​10.1111/​jgs.15316.

32.  Nguyen, H.Q., Tunney, M.M., & Hughes, C.M. (2019). Interventions to Improve Antimicrobial Stewardship for Older People in Care Homes: A Systematic Review. Drugs & aging, 36 (4), 355-369. https://doi.org/​10.1007/​s40266-019-00637-0.

33.  Sloane, P.D., Zimmerman, S., Ward, K., Kistler, C.E., Paone, D., Weber, D.J., Wretman, C.J., & Preisser, J.S. (2020). A 2-Year Pragmatic Trial of Antibiotic Stewardship in 27 Community Nursing Homes. Journal of the American Geriatrics Society, 68 (1), 46-54. https://doi.org/​10.1111/​jgs.16059.

34.  Lee, M.H., Lee GA, Lee SH, Park YH (2019). Effectiveness and core components of infection prevention and control programmes in long-term care facilities: A systematic review. Retrieved from https://pubmed.ncbi.nlm.nih.gov/​30794854/​.

35.  National Quality Forum. List of Measures Under Consideration for December 21, 2020. Accessed at https://www.cms.gov/​files/​document/​measures-under-consideration-list-2020-report.pdf on January 12, 2021.

36.  U.S. Dept. of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response. (2020). Determination that a Public Health Emergency Exists. Available at https://www.phe.gov/​emergency/​news/​healthactions/​phe/​Pages/​2019-nCoV.aspx.

37.  Centers for Disease Control and Prevention. (2020). Your Health: Symptoms of Coronavirus. Available at https://www.cdc.gov/​coronavirus/​2019-ncov/​symptoms-testing/​symptoms.html.

38.  Centers for Disease Control and Prevention (2021). Health Equity Considerations and Racial and Ethnic Minority Groups. Available at https://www.cdc.gov/​coronavirus/​2019-ncov/​community/​health-equity/​race-ethnicity.html.

39.  Centers for Disease Control and Prevention. (2020). Your Health: Symptoms of Coronavirus. Available at https://www.cdc.gov/​coronavirus/​2019-ncov/​symptoms-testing/​symptoms.html.

40.  Centers for Disease Control and Prevention. (2020). CDC COVID Data Tracker. Available at https://covid.cdc.gov/​covid-data-tracker/​#cases_​casesper100klast7days.

41.  Associated Press. Tired to the Bone. Hospitals Overwhelmed with Virus Cases. November 18, 2020. Accessed on December 16, 2020, at https://apnews.com/​article/​hospitals-overwhelmed-coronavirus-cases-74a1f0dc3634917a5dc13408455cd895. Also see: New York Times. Just how full are U.S. intensive care units? New data paints an alarming picture. November 18, 2020. Accessed on December 16, 2020, at https://www.nytimes.com/​2020/​12/​09/​world/​just-how-full-are-us-intensive-care-units-new-data-paints-an-alarming-picture.html.

42.  NPR. U.S. Hits 100,000 COVID-19 Hospitalizations, Breaks Daily Death Record. Dec. 2, 2020. Accessed on December 17, 2020 at https://www.npr.org/​sections/​coronavirus-live-updates/​2020/​12/​02/​941902471/​u-s-hits-100-000-covid-19-hospitalizations-breaks-daily-death-record;​; The Wall Street Journal. Coronavirus Live Updates: U.S. Hospitalizations, Newly Reported Cases, Deaths Edge Downward. Accessed on January 11 at https://www.wsj.com/​livecoverage/​covid-2021-01-11.

43.  Centers for Disease Control and Prevention. (2021). COVID-19. Your Health. Frequently Asked Questions. Accessed on January 11, 2021 at https://www.cdc.gov/​coronavirus/​2019-ncov/​faq.html.

44.  Centers for Disease Control and Prevention (2021). COVID-19. Your Health. Frequently Asked Questions. Accessed on January 11, 2021 at https://www.cdc.gov/​coronavirus/​2019-ncov/​faq.html.

45.  Centers for Disease Control and Prevention (2021). COVID-19. Your Health. Frequently Asked Questions. Accessed on January 11, 2021 at https://www.cdc.gov/​coronavirus/​2019-ncov/​faq.html.

46.  Centers for Disease Control and Prevention. (2020). Centers for Disease Control Scientific Brief: SARS-CoV-2 and Potential Airborne Transmission. Available at https://www.cdc.gov/​coronavirus/​2019-ncov/​more/​scientific-brief-sars-cov-2.html.

47.  Centers for Disease Control and Prevention. (2020). Clinical Questions about COVID-19: Questions and Answers. Accessed on December 2, 2020 at https://www.cdc.gov/​coronavirus/​2019-ncov/​hcp/​faq.html.

48.  Centers for Disease Control and Prevention. (2020). Interim U.S. Guidance for Risk Assessment and Work Restrictions for Healthcare Personnel with Potential Exposure to COVID-19. Accessed on December 2 at https://www.cdc.gov/​coronavirus/​2019-ncov/​hcp/​guidance-risk-assesment-hcp.html.

49.  Dooling, K, McClung, M, et al. “The Advisory Committee on Immunization Practices' Interim Recommendations for Allocating Initial Supplies of COVID-19 Vaccine—United States, 2020.” Morb Mortal Wkly Rep. 2020; 69(49): 1857-1859.

50.  Centers for Disease Control and Prevention. (2020). COVID-19 Vaccination Program Interim Playbook for Jurisdiction Operations. Accessed on December 18 at https://www.cdc.gov/​vaccines/​imz-managers/​downloads/​COVID-19-Vaccination-Program-Interim_​Playbook.pdf.

51.  U.S. Food and Drug Administration. (2020). Pfizer-BioNTech COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/​media/​144412/​download.

52.  Ibid.

53.  U.S. Food and Drug Administration. (2021). ModernaTX, Inc. COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/​media/​144636/​download.

54.  U.S. Food and Drug Administration (2020). Janssen Biotech, Inc. COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/​media/​146303/​download.

55.  The White House. Remarks by President Biden on the COVID-19 Response and the State of Vaccinations. March 29, 2021. Accessed at https://www.whitehouse.gov/​briefing-room/​speeches-remarks/​2021/​03/​29/​remarks-by-president-biden-on-the-covid-19-response-and-the-state-of-vaccinations/​.

56.  Health and Human Services, Department of Defense. (2020) From the Factory to the Frontlines: The Operation Warp Speed Strategy for Distributing a COVID-19 Vaccine. Accessed December 18 at https://www.hhs.gov/​sites/​default/​files/​strategy-for-distributing-covid-19-vaccine.pdf;​; Centers for Disease Control (2020). COVID-19 Vaccination Program Interim Playbook for Jurisdiction Operations. Accessed December 18 at https://www.cdc.gov/​vaccines/​imz-managers/​downloads/​COVID-19-Vaccination-Program-Interim_​Playbook.pdf.

57.  Dooling, K, McClung, M, et al. “The Advisory Committee on Immunization Practices' Interim Recommendations for Allocating Initial Supplies of COVID-19 Vaccine—United States, 2020.” Morb. Mortal Wkly Rep. 2020; 69(49): 1857-1859. ACIP also recommended that long-term care residents be prioritized to receive the vaccine, given their age, high levels of underlying medical conditions, and congregate living situations make them high risk for severe illness from COVID-19.

58.  Kates, J, Michaud, J, Tolbert, J. “How Are States Prioritizing Who Will Get the COVID-19 Vaccine First?” Kaiser Family Foundation. December 14, 2020. Accessed on December 16 at https://www.kff.org/​policy-watch/​how-are-states-prioritizing-who-will-get-the-covid-19-vaccine-first/​.

59.  Associated Press. `Healing is Coming:' US Health Workers Start Getting Vaccine. December 15, 2020. Accessed on December 16 at https://apnews.com/​article/​us-health-workers-coronavirus-vaccine-56df745388a9fc12ae93c6f9a0d0e81f.

60.  Centers for Disease Control and Prevention. Weekly COVID-19 Vaccination Data Reporting. Accessed at https://www.cdc.gov/​nhsn/​ltc/​weekly-covid-vac/​index.html.

61.  Centers for Medicare & Medicaid Services. Pre-rulemaking. Accessed at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​QualityMeasures/​Pre-Rulemaking.

62.  National Quality Forum. List of Measures Under Consideration for December 21, 2020. Accessed at https://www.cms.gov/​files/​document/​measures-under-consideration-list-2020-report.pdf on January 12, 2021.

63.  Measure Applications Partnership. MAP Preliminary Recommendations 2020-2021. Accessed on February 3, 2021 at https://www.qualityforum.org/​WorkArea/​linkit.aspx?​LinkIdentifier=​id&​ItemID=​94650.

64.  Ibid.

65.  Ibid.

66.  Ibid.

67.  The Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431) measure which is NQF endorsed and was adopted in the IRF QRP in the FY 2014 IRF PPS Final Rule (78 FR 47905 through 47906), and in the LTCH QRP in the FY 2013 IPPS/LTCH PPS Final Rule (77 FR 53630 through 53631).

68.  Centers for Disease Control and Preventions. Morbidity and Mortality Weekly Report. March 29, 2021. Available at https://www.cdc.gov/​mmwr/​volumes/​70/​wr/​mm7013e3.htm?​s_​cid=​mm7013e3_​w.

69.  National Quality Form. Key Points for Evaluating Scientific Acceptability. Revised January 3, 2020. https://www.qualityforum.org/​Measuring_​Performance/​Scientific_​Methods_​Panel/​Docs/​Evaluation_​Guidance.aspx#:~:text=​NQF%20is%20not%20prescriptive%20about,reliability%20or%20validity%20testing%20results.&​text=​Reliability%20and%20validity%20must%20be,source%20and%20level%20of%20analysis).

70.  Ibid.

71.  Centers for Disease Control and Prevention. Interim Clinical Considerations for Use of COVID-19 Vaccines Currently Authorized in the United Sates. Contraindications found in Appendix B: Triage of people presenting for the vaccination. Accessed at https://www.cdc.gov/​vaccines/​covid-19/​info-by-product/​clinical-considerations.html.

72.  Centers for Disease Control and Prevention. Surveillance for Weekly HCP COVID-19 Vaccination. Accessed at https://www.cdc.gov/​nhsn/​hps/​weekly-covid-vac/​index.html on February 10, 2021.

73.  Meaningful Measures 2.0: Moving from Measure Reduction to Modernization. Available at https://www.cms.gov/​meaningful-measures-20-moving-measure-reduction-modernization.

74.  Definition taken from the CMS Quality Conference 2021.

75.  Department of Health and Human Services. National Health Quality Roadmap. May 15, 2020. Available at https://www.hhs.gov/​sites/​default/​files/​national-health-quality-roadmap.pdf.

76.  Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for Medicare Beneficiaries by Race and Site of Care. JAMA. 2011; 305(7):675-681.

77.  Lindenauer PK, Lagu T, Rothberg MB, et al. Income Inequality and 30 Day Outcomes After Acute Myocardial Infarction, Heart Failure, and Pneumonia: Retrospective Cohort Study. British Medical Journal. 2013; 346.

78.  Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and Equity of Care in U.S. Hospitals. New England Journal of Medicine. 2014; 371(24):2298-2308.

79.  Polyakova, M., et al. Racial Disparities In Excess All-Cause Mortality During The Early COVID-19 Pandemic Varied Substantially Across States. Health Affairs. 2021; 40(2): 307-316.

80.  Rural Health Research Gateway. Rural Communities: Age, Income, and Health Status. Rural Health Research Recap. November 2018.

81.  https://www.minorityhealth.hhs.gov/​assets/​PDF/​Update_​HHS_​Disparities_​Dept-FY2020.pdf.

82.  www.cdc.gov/​mmwr/​volumes/​70/​wr/​mm7005a1.htm.

83.  Poteat TC, Reisner SL, Miller M, Wirtz AL. COVID-19 Vulnerability of Transgender Women With and Without HIV Infection in the Eastern and Southern U.S. Preprint. medRxiv. 2020;2020.07.21.20159327. Published 2020 Jul 24. doi:10.1101/2020.07.21.20159327.

84.  Martino, SC, Elliott, MN, Dembosky, JW, Hambarsoomian, K, Burkhart, Q, Klein, DJ, Gildner, J, and Haviland, AM. Racial, Ethnic, and Gender Disparities in Health Care in Medicare Advantage. Baltimore, MD: CMS Office of Minority Health. 2020.

85.  Guide to Reducing Disparities in Readmissions. CMS Office of Minority Health. Revised August 2018. Available at https://www.cms.gov/​About-CMS/​Agency-Information/​OMH/​Downloads/​OMH_​Readmissions_​Guide.pdf.

86.  Singh JA, Lu X, Rosenthal GE, Ibrahim S, Cram P. Racial disparities in knee and hip total joint arthroplasty: an 18-year analysis of national Medicare data. Ann Rheum Dis. 2014 Dec;73(12):2107-15.

87.  Rivera-Hernandez M, Rahman M, Mor V, Trivedi AN. Racial Disparities in Readmission Rates among Patients Discharged to Skilled Nursing Facilities. J Am Geriatr Soc. 2019 Aug;67(8):1672-1679.

88.  Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for Medicare Beneficiaries by Race and Site of Care. JAMA. 2011;305(7):675-681.

89.  Tsai TC, Orav EJ, Joynt KE. Disparities in surgical 30-day readmission rates for Medicare beneficiaries by race and site of care. Ann Surg. Jun 2014;259(6):1086-1090.

90.  Rodriguez F, Joynt KE, Lopez L, Saldana F, Jha AK. Readmission rates for Hispanic Medicare beneficiaries with heart failure and acute myocardial infarction. Am Heart J. Aug 2011;162(2):254-261 e253.

91.  Centers for Medicare and Medicaid Services. Medicare Hospital Quality Chartbook: Performance Report on Outcome Measures; 2014.

92.  Guide to Reducing Disparities in Readmissions. CMS Office of Minority Health. Revised August 2018. Available at https://www.cms.gov/​About-CMS/​Agency-Information/​OMH/​Downloads/​OMH_​Readmissions_​Guide.pdf.

93.  Prieto-Centurion V, Gussin HA, Rolle AJ, Krishnan JA. Chronic obstructive pulmonary disease readmissions at minority-serving institutions. Ann Am Thorac Soc. Dec 2013;10(6):680-684.

94.  Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for Medicare Beneficiaries by Race and Site of Care. JAMA. 2011;305(7):675-681.

95.  HHS. Heart disease and African Americans. (March 29, 2021). https://www.minorityhealth.hhs.gov/​omh/​browse.aspx?​lvl=​4&​lvlid=​19.

96.  https://www.cms.gov/​files/​document/​medicare-covid-19-data-snapshot-fact-sheet.pdf.

97.  Ochieng N, Cubanski J, Neuman T, Artiga S, and Damico A. Racial and Ethnic Health Inequities and Medicare. Kaiser Family Foundation. February 2021. Available at https://www.kff.org/​medicare/​report/​racial-and-ethnic-health-inequities-and-medicare/​.

98.  https://www.cdc.gov/​coronavirus/​2019-ncov/​community/​health-equity/​race-ethnicity.html.

99.  https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​QualityInitiativesGenInfo/​Downloads/​CMS-Quality-Strategy.pdf.

100.  Report to Congress: Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014 Strategic Plan for Accessing Race and Ethnicity Data. January 5, 2017. Available at https://www.cms.gov/​About-CMS/​Agency-Information/​OMH/​Downloads/​Research-Reports-2017-Report-to-Congress-IMPACT-ACT-of-2014.pdf.

101.  https://www.federalregister.gov/​documents/​2021/​01/​25/​2021-01753/​advancing-racial-equity-and-support-for-underserved-communities-through-the-Federal-government.

102.  Centers for Medicare & Medicaid Services Office of Minority Health. The CMS Equity Plan for Improving Quality in Medicare. https://www.cms.gov/​About-CMS/​Agency-Information/​OMH/​OMH_​Dwnld-CMS_​EquityPlanforMedicare_​090615.pdf.

103.  https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​QualityInitiativesGenInfo/​MMF/​General-info-Sub-Page.

104.  Centers for Medicare and Medicaid Services. Building an Organizational Response to Health Disparities Inventory of Resources for Standardized Demographic and Language Data Collection. 2020. https://www.cms.gov/​About-CMS/​Agency-Information/​OMH/​Downloads/​Data-Collection-Resources.pdf.

105.  In response to the COVID-19 PHE, CMS released an Interim Final Rule (85 FR 27595 through 27597) which delayed the compliance date for the collection and reporting of the SDOH for at least two full fiscal years after the end of the PHE.

106.  https://qualitynet.cms.gov/​inpatient/​measures/​disparity-methods/​methodology.

107.  https://www.phe.gov/​emergency/​news/​healthactions/​section1135/​Pages/​covid19-13March20.aspx.

108.  https://www.cms.gov/​files/​document/​guidance-memo-exceptions-and-extensions-quality-reporting-and-value-based-purchasing-programs.pdf.

109.  More information about the SNF QRP Public Reporting schedule can be found on the SNF QRP Public Reporting website at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Skilled-Nursing-Facility-Quality-Reporting-Program/​SNF-Quality-Reporting-Program-Public-Reporting.

110.  We assessed multiple 8-month data periods and averaged the reliability results to obtain a complete understanding of reliability across FY 2019, the most recent full year of production data available for analysis, and avoid potential issues caused by seasonality.

111.  https://www.cms.gov/​Medicare/​Provider-Enrollment-and-Certification/​SurveyCertificationGenInfo/​Downloads/​QSO18-17-NH.pdf.

112.  Section 321 of the NCVIA provides the PRA waiver for activities that come under the NCVIA, including those in the NCVIA at section 2102 of the Public Health Service Act (42 U.S.C. 300aa-2). Section 321 is not codified in the U.S. Code, but can be found in a note at 42 U.S.C. 300aa-1.

113.  https://www.bls.gov/​oes/​current/​oes_​nat.htm. Accessed on March 30, 2021.