2022-07019. Medicare Program; Inpatient Rehabilitation Facility Prospective Payment System for Federal Fiscal Year 2023 and Updates to the IRF Quality Reporting Program

AGENCY:

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

ACTION:

Proposed rule.

SUMMARY:

This rulemaking proposes updating the prospective payment rates for inpatient rehabilitation facilities (IRFs) for Federal fiscal year (FY) 2023. As required by statute, this proposed rule includes the classification and weighting factors for the IRF prospective payment system's case-mix groups and a description of the methodologies and data used in computing the prospective payment rates for FY 2023. In addition, we are proposing to codify CMS' existing teaching status adjustment policy through proposed amendments to the regulation text and proposing to update and clarify the IRF teaching policy with respect to IRF hospital closures and displaced residents. In this proposed rule, we are also soliciting comments on the methodology for updating the facility level adjustment factors. Additionally, we are soliciting comments regarding the IRF transfer payment policy. This rule proposes to establish a permanent cap policy to smooth the impact of year-to-year changes in IRF payments related to changes in the IRF wage index. This proposed rule also includes updates for the IRF Quality Reporting Program (QRP).

DATES:

To be assured consideration, comments must be received at one of the addresses provided below, no later than 5 p.m. on May 31, 2022.

ADDRESSES:

In commenting, please refer to file code CMS-1767-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-1767-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-1767-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:

Gwendolyn Johnson, [phone], for general information.

Catie Cooksey, [phone], for information about the IRF payment policies and payment rates.

Kim Schwartz, [phone] and Gwendolyn Johnson, [phone], for information about the IRF coverage policies.

Ariel Cress, [phone], for information about the IRF quality reporting 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 Information Through the Internet on the CMS Website

The IRF prospective payment system (IRF PPS) Addenda along with other supporting documents and tables referenced in this proposed rule are available through the internet on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientRehabFacPPS.

We note that prior to 2020, each rule or notice issued under the IRF PPS has included a detailed reiteration of the various regulatory provisions that have affected the IRF PPS over the years. That discussion, along with detailed background information for various other aspects of the IRF PPS, is now available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientRehabFacPPS.

I. Executive Summary

A. Purpose

This rulemaking proposes updating the prospective payment rates for IRFs for FY 2023 (that is, for discharges occurring on or after October 1, 2022, and on or before September 30, 2023) as required under section 1886(j)(3)(C) of the Social Security Act (the Act). As required by section 1886(j)(5) of the Act, this proposed rule includes the classification and weighting factors for the IRF PPS's case-mix groups (CMGs) and a description of the methodologies and data used in computing the prospective payment rates for FY 2023. This proposed rule proposes to codify CMS' existing teaching status adjustment policy through proposed amendments to the regulation text and proposes to update and clarify the IRF teaching policy with respect to IRF hospital closures and displaced residents. We are also soliciting comments on the methodology for updating the facility level adjustment factors. Additionally, we are soliciting comments regarding the IRF transfer payment policy. We are also proposing to establish a permanent cap policy to smooth the impact of year-to-year changes in IRF payments related to changes in the IRF wage index. This rule also proposes to require quality data reporting on all IRF patients beginning with the FY 2025 IRF QRP and amend the regulations consistent with the proposed requirements. This rule also proposes to correct an error in the regulations text at § 412.614(d)(2). Finally, we are seeking comment on three issues: (1) Future measure concepts under consideration for the IRF QRP; (2) a future dQM for the IRF QRP; and (3) overarching principles for measuring equity and health quality disparities across CMS Quality Programs, including the IRF QRP.

B. Summary of Major Provisions

In this proposed rule, we use the methods described in the FY 2022 IRF PPS final rule (86 FR 42362) to update the prospective payment rates for FY 2023 using updated FY 2021 IRF claims and the most recent available IRF cost report data, which is FY 2020 IRF cost report data. This proposed rule proposes to codify CMS' existing teaching status adjustment policy through proposed amendments to the regulation text and proposes to update and clarify the IRF teaching status adjustment policy with respect to IRF hospital closures and displaced residents. We are also soliciting comments on the methodology for updating the facility level adjustment factors. Additionally, we are soliciting comments regarding the IRF transfer payment policy.

We are also proposing to establish a permanent cap policy to smooth the impact of year-to-year changes in IRF payments related to changes in the IRF wage index. This rule also proposes to collect quality reporting data for all IRF patients beginning with the FY 2025 IRF QRP and revise the regulations. Finally, we are seeking comment on three issues: (1) Future measure concepts for the IRF QRP; (2) a future digital quality measure (dQM) for the IRF QRP; and (3) overarching principles for measuring equity and health quality disparities across CMS Quality Programs, including the IRF QRP.

C. Summary of Impact

II. Background

A. Statutory Basis and Scope for IRF PPS Provisions

Section 1886(j) of the Act provides for the implementation of a per-discharge PPS for inpatient rehabilitation hospitals and inpatient rehabilitation units of a hospital (collectively, hereinafter referred to as IRFs). Payments under the IRF PPS encompass inpatient operating and capital costs of furnishing covered rehabilitation services (that is, routine, ancillary, and capital costs), but not direct graduate medical education costs, costs of approved nursing and allied health education activities, bad debts, and other services or items outside the scope of the IRF PPS. A complete discussion of the IRF PPS provisions appears in the original FY 2002 IRF PPS final rule (66 FR 41316) and the FY 2006 IRF PPS final rule (70 FR 47880) and we provided a general description of the IRF PPS for FYs 2007 through 2019 in the FY 2020 IRF PPS final rule (84 FR 39055 through 39057). A general description of the IRF PPS for FYs 2020 through 2022, along with detailed background information for various other aspects of the IRF PPS, is now available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientRehabFacPPS.

Under the IRF PPS from FY 2002 through FY 2005, the prospective payment rates were computed across 100 distinct CMGs, as described in the FY 2002 IRF PPS final rule (66 FR 41316). We constructed 95 CMGs using rehabilitation impairment categories (RICs), functional status (both motor and cognitive), and age (in some cases, cognitive status and age may not be a factor in defining a CMG). In addition, we constructed five special CMGs to account for very short stays and for patients who expire in the IRF.

For each of the CMGs, we developed relative weighting factors to account for a patient's clinical characteristics and expected resource needs. Thus, the weighting factors accounted for the relative difference in resource use across all CMGs. Within each CMG, we created tiers based on the estimated effects that certain comorbidities would have on resource use.

We established the Federal PPS rates using a standardized payment conversion factor (formerly referred to as the budget-neutral conversion factor). For a detailed discussion of the budget-neutral conversion factor, please refer to our FY 2004 IRF PPS final rule (68 FR 45684 through 45685). In the FY 2006 IRF PPS final rule (70 FR 47880), we discussed in detail the methodology for determining the standard payment conversion factor.

We applied the relative weighting factors to the standard payment conversion factor to compute the unadjusted prospective payment rates under the IRF PPS from FYs 2002 through 2005. Within the structure of the payment system, we then made adjustments to account for interrupted stays, transfers, short stays, and deaths. Finally, we applied the applicable adjustments to account for geographic variations in wages (wage index), the percentage of low-income patients, location in a rural area (if applicable), and outlier payments (if applicable) to the IRFs' unadjusted prospective payment rates.

For cost reporting periods that began on or after January 1, 2002, and before October 1, 2002, we determined the final prospective payment amounts using the transition methodology prescribed in section 1886(j)(1) of the Act. Under this provision, IRFs transitioning into the PPS were paid a blend of the Federal IRF PPS rate and the payment that the IRFs would have received had the IRF PPS not been implemented. This provision also allowed IRFs to elect to bypass this blended payment and immediately be paid 100 percent of the Federal IRF PPS rate. The transition methodology expired as of cost reporting periods beginning on or after October 1, 2002 (FY 2003), and payments for all IRFs now consist of 100 percent of the Federal IRF PPS rate.

Section 1886(j) of the Act confers broad statutory authority upon the Secretary to propose refinements to the IRF PPS. In the FY 2006 IRF PPS final rule (70 FR 47880) and in correcting amendments to the FY 2006 IRF PPS final rule (70 FR 57166), we finalized a number of refinements to the IRF PPS case-mix classification system (the CMGs and the corresponding relative weights) and the case-level and facility-level adjustments. These refinements included the adoption of the Office of Management and Budget's (OMB's) Core-Based Statistical Area (CBSA) market definitions; modifications to the CMGs, tier comorbidities; and CMG relative weights, implementation of a new teaching status adjustment for IRFs; rebasing and revising the market basket index used to update IRF payments, and updates to the rural, low-income percentage (LIP), and high-cost outlier adjustments. Beginning with the FY 2006 IRF PPS final rule (70 FR 47908 through 47917), the market basket index used to update IRF payments was a market basket reflecting the operating and capital cost structures for freestanding IRFs, freestanding inpatient psychiatric facilities (IPFs), and long-term care hospitals (LTCHs) (hereinafter referred to as the rehabilitation, psychiatric, and long-term care (RPL) market basket). Any reference to the FY 2006 IRF PPS final rule in this proposed rule also includes the provisions effective in the correcting amendments. For a detailed discussion of the final key policy changes for FY 2006, please refer to the FY 2006 IRF PPS final rule.

The regulatory history previously included in each rule or notice issued under the IRF PPS, including a general description of the IRF PPS for FYs 2007 through 2020, is available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientRehabFacPPS.

In late 2019,^[1] the United States began responding to an outbreak of a virus named “SARS-CoV-2” and the disease it causes, which is named “coronavirus disease 2019” (abbreviated “COVID-19”). Due to our prioritizing efforts in support of containing and combatting the PHE for COVID-19, and devoting significant resources to that end, we published two interim final rules with comment period affecting IRF payment and conditions for participation. The interim final rule with comment period (IFC) entitled, “Medicare and Medicaid Programs; Policy and Regulatory Revisions in Response to the COVID-19 Public Health Emergency”, published on April 6, 2020 (85 FR 19230) (hereinafter referred to as the April 6, 2020 IFC), included certain changes to the IRF PPS medical supervision requirements at 42 CFR 412.622(a)(3)(iv) and 412.29(e) during the PHE for COVID-19. In addition, in the April 6, 2020 IFC, we removed the post-admission physician evaluation requirement at § 412.622(a)(4)(ii) for all IRFs during the PHE for COVID-19. In the FY 2021 IRF PPS final rule, to ease documentation and administrative burden, we also removed the post-admission physician evaluation documentation requirement at 42 CFR 412.622(a)(4)(ii) permanently beginning in FY 2021.

A second IFC entitled, “Medicare and Medicaid Programs, Basic Health Program, and Exchanges; Additional Policy and Regulatory Revisions in Response to the COVID-19 Public Health Emergency and Delay of Certain Reporting Requirements for the Skilled Nursing Facility Quality Reporting Program” was published on May 8, 2020 (85 FR 27550) (hereinafter referred to as the May 8, 2020 IFC). Among other changes, the May 8, 2020 IFC included a waiver of the “3-hour rule” at § 412.622(a)(3)(ii) to reflect the waiver required by section 3711(a) of the Coronavirus Aid, Relief, and Economic Security Act (CARES Act) (Pub. L. 116-136, enacted on March 27, 2020). In the May 8, 2020 IFC, we also modified certain IRF coverage and classification requirements for freestanding IRF hospitals to relieve acute care hospital capacity concerns in States (or regions, as applicable) experiencing a surge during the PHE for COVID-19. In addition to the policies adopted in our IFCs, we responded to the PHE with numerous blanket waivers ^[2] and other flexibilities,^[3] some of which are applicable to the IRF PPS.

B. Provisions of the PPACA and the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) Affecting the IRF PPS in FY 2012 and Beyond

The Patient Protection and Affordable Care Act (PPACA) (Pub. L. 111-148) was enacted on March 23, 2010. The Health Care and Education Reconciliation Act of 2010 (Pub. L. 111-152), which amended and revised several provisions of the PPACA, was enacted on March 30, 2010. In this proposed rule, we refer to the two statutes collectively as the “Patient Protection and Affordable Care Act” or “PPACA”.

The PPACA included several provisions that affect the IRF PPS in FYs 2012 and beyond. In addition to what was previously discussed, section 3401(d) of the PPACA also added section 1886(j)(3)(C)(ii)(I) of the Act (providing for a “productivity adjustment” for FY 2012 and each subsequent FY). The productivity adjustment for FY 2023 is discussed in section V.B. of this proposed rule. Section 1886(j)(3)(C)(ii)(II) of the Act provides that the application of the productivity adjustment to the market basket update may result in an update that is less than 0.0 for a FY and in payment rates for a FY being less than such payment rates for the preceding FY.

Sections 3004(b) of the PPACA and section 411(b) of the MACRA (Pub. L. 114-10, enacted on April 16, 2015) also addressed the IRF PPS. Section 3004(b) of PPACA reassigned the previously designated section 1886(j)(7) of the Act to section 1886(j)(8) of the Act and inserted a new section 1886(j)(7) of the Act, which contains requirements for the Secretary to establish a QRP for IRFs. Under that program, data must be submitted in a form and manner and at a time specified by the Secretary. Beginning in FY 2014, section 1886(j)(7)(A)(i) of the Act requires the application of a 2-percentage point reduction to the market basket increase factor otherwise applicable to an IRF (after application of paragraphs (C)(iii) and (D) of section 1886(j)(3) of the Act) for a FY if the IRF does not comply with the requirements of the IRF QRP for that FY. Application of the 2-percentage point reduction may result in an update that is less than 0.0 for a FY and in payment rates for a FY being less than such payment rates for the preceding FY. Reporting-based reductions to the market basket increase factor are not cumulative; they only apply for the FY involved. Section 411(b) of the MACRA amended section 1886(j)(3)(C) of the Act by adding paragraph (iii), which required us to apply for FY 2018, after the application of section 1886(j)(3)(C)(ii) of the Act, an increase factor of 1.0 percent to update the IRF prospective payment rates.

C. Operational Overview of the Current IRF PPS

As described in the FY 2002 IRF PPS final rule (66 FR 41316), upon the admission and discharge of a Medicare Part A fee-for-service (FFS) patient, the IRF is required to complete the appropriate sections of a Patient Assessment Instrument (PAI), designated as the IRF-PAI. In addition, beginning with IRF discharges occurring on or after October 1, 2009, the IRF is also required to complete the appropriate sections of the IRF-PAI upon the admission and discharge of each Medicare Advantage (MA) patient, as described in the FY 2010 IRF PPS final rule (74 FR 39762 and 74 FR 50712). All required data must be electronically encoded into the IRF-PAI software product. Generally, the software product includes patient classification programming called the Grouper software. The Grouper software uses specific IRF-PAI data elements to classify (or group) patients into distinct CMGs and account for the existence of any relevant comorbidities.

The Grouper software produces a five-character CMG number. The first character is an alphabetic character that indicates the comorbidity tier. The last four characters are numeric characters that represent the distinct CMG number. A free download of the Grouper software is available on the CMS website at http://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientRehabFacPPS/​Software.html. The Grouper software is also embedded in the internet Quality Improvement and Evaluation System (iQIES) User tool available in iQIES at https://www.cms.gov/​medicare/​quality-safety-oversight-general-information/​iqies.

Once a Medicare Part A FFS patient is discharged, the IRF submits a Medicare claim as a Health Insurance Portability and Accountability Act of 1996 (HIPAA) (Pub. L. 104-191, enacted on August 21, 1996) -compliant electronic claim or, if the Administrative Simplification Compliance Act of 2002 (ASCA) (Pub. L. 107-105, enacted on December 27, 2002) permits, a paper claim (a UB-04 or a CMS-1450 as appropriate) using the five-character CMG number and sends it to the appropriate Medicare Administrative Contractor (MAC). In addition, once a MA patient is discharged, in accordance with the Medicare Claims Processing Manual, chapter 3, section 20.3 (Pub. 100-04), hospitals (including IRFs) must submit an informational-only bill (type of bill (TOB) 111), which includes Condition Code 04 to their MAC. This will ensure that the MA days are included in the hospital's Supplemental Security Income (SSI) ratio (used in calculating the IRF LIP adjustment) for FY 2007 and beyond. Claims submitted to Medicare must comply with both ASCA and HIPAA.

Section 3 of the ASCA amended section 1862(a) of the Act by adding paragraph (22), which requires the Medicare program, subject to section 1862(h) of the Act, to deny payment under Part A or Part B for any expenses for items or services for which a claim is submitted other than in an electronic form specified by the Secretary. Section 1862(h) of the Act, in turn, provides that the Secretary shall waive such denial in situations in which there is no method available for the submission of claims in an electronic form or the entity submitting the claim is a small provider. In addition, the Secretary also has the authority to waive such denial in such unusual cases as the Secretary finds appropriate. For more information, see the “Medicare Program; Electronic Submission of Medicare Claims” final rule (70 FR 71008). Our instructions for the limited number of Medicare claims submitted on paper are available at http://www.cms.gov/​manuals/​downloads/​clm104c25.pdf.

Section 3 of the ASCA operates in the context of the administrative simplification provisions of HIPAA, which include, among others, the requirements for transaction standards and code sets codified in 45 CFR part 160 and part 162, subparts A and I through R (generally known as the Transactions Rule). The Transactions Rule requires covered entities, including covered healthcare providers, to conduct covered electronic transactions according to the applicable transaction standards. (See the CMS program claim memoranda at http://www.cms.gov/​ElectronicBillingEDITrans/​ and listed in the addenda to the Medicare Intermediary Manual, Part 3, section 3600).

The MAC processes the claim through its software system. This software system includes pricing programming called the “Pricer” software. The Pricer software uses the CMG number, along with other specific claim data elements and provider-specific data, to adjust the IRF's prospective payment for interrupted stays, transfers, short stays, and deaths, and then applies the applicable adjustments to account for the IRF's wage index, percentage of low-income patients, rural location, and outlier payments. For discharges occurring on or after October 1, 2005, the IRF PPS payment also reflects the teaching status adjustment that became effective as of FY 2006, as discussed in the FY 2006 IRF PPS final rule (70 FR 47880).

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 electronic health information.

To further interoperability in post-acute care settings, CMS and the Office of the National Coordinator for Health Information Technology (ONC) participate in the Post-Acute Care Interoperability Workgroup (PACIO) to facilitate collaboration with industry stakeholders to develop Fast Healthcare Interoperability Resources® (FHIR) standards. These standards could support the exchange and reuse of patient assessment data derived from the post-acute care (PAC) setting assessment tools, such as the Minimum Data Set (MDS), Inpatient Rehabilitation Facility-Patient Assessment Instrument (IRF-PAI), Long Term Care Hospital (LTCH) Continuity Assessment Record and Evaluation (CARE) Data Set (LCDS), Outcome and Assessment Information Set (OASIS), and other sources.^[4 5] The PACIO Project has focused on HL7 FHIR implementation guides for functional status, cognitive status and new use cases on advance directives, re-assessment timepoints, and Speech, Language, Swallowing, Cognitive communication and Hearing (SPLASCH) pathology.^[6] We encourage PAC provider and health information technology (IT) vendor participation as the efforts advance.

The CMS Data Element Library (DEL) continues to be updated and serves as a resource for PAC assessment data elements and their associated mappings to health IT standards, such as Logical Observation Identifiers Names and Codes (LOINC) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED).^[7] The DEL furthers CMS' goal of data standardization and interoperability. These interoperable data elements can reduce provider burden by allowing the use and exchange of healthcare data; supporting provider exchange of electronic health information for care coordination, person-centered care; and supporting real-time, data driven, clinical decision-making.^[8 9] Standards in the DEL can be referenced on the CMS website ( https://del.cms.gov/​DELWeb/​pubHome ) and in the ONC Interoperability Standards Advisory (ISA). The 2022 ISA is available at https://www.healthit.gov/​isa/​sites/​isa/​files/​inline-files/​2022-ISA-Reference-Edition.pdf.

The 21st Century Cures Act (Cures Act), (Pub L. 114-255, enacted December 13, 2016) requires HHS to take new steps to enable the electronic sharing of health information and to further interoperability for providers and settings across the care continuum. Section 4003 of the Cures Act required HHS to take steps to advance interoperability through the development of a trusted exchange framework and common agreement aimed at establishing a universal floor of interoperability across the country. On January 18, 2022, ONC announced a significant milestone by releasing the Trusted Exchange Framework and Common Agreement Version 1. The Trusted Exchange Framework is a set of non-binding principles for health information exchange, and the Common Agreement is a contract that advances those principles. The Common Agreement and the incorporated by reference Qualified Health Information Network Technical Framework Version 1 establish the technical infrastructure model and governing approach for different health information networks and their users to securely share clinical information with each other, all under commonly agreed to terms. The Common Agreement follows a network-of-networks structure, which allows for connection at different levels and is inclusive of many different types of entities, such as health information networks, healthcare practices, hospitals, public health agencies, and Individual Access Services (IAS) Providers.^[10] For more information, we refer readers to https://www.healthit.gov/​topic/​interoperability/​trusted-exchange-framework-and-common-agreement.

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

III. Summary of Provisions of the Proposed Rule

In this proposed rule, we are proposing to update the IRF PPS for FY 2023 and the IRF QRP for FY 2025.

The proposed policy changes and updates to the IRF prospective payment rates for FY 2023 are as follows:

• Update the CMG relative weights and average length of stay values for FY 2023, in a budget neutral manner, as discussed in section IV. of this proposed rule.
• Update the IRF PPS payment rates for FY 2023 by the market basket increase factor, based upon the most current data available, with a productivity adjustment required by section 1886(j)(3)(C)(ii)(I) of the Act, as described in section V. of this proposed rule.
• Describe the establishment of a permanent cap policy in order to smooth the impact of year-to-year changes in IRF payments related to certain changes to the IRF wage index, as discussed in section V. of this proposed rule.
• Update the FY 2023 IRF PPS payment rates by the FY 2023 wage index and the labor-related share in a budget-neutral manner, as discussed in section V. of this proposed rule.
• Describe the calculation of the IRF standard payment conversion factor for FY 2023, as discussed in section V. of this proposed rule.
• Update the outlier threshold amount for FY 2023, as discussed in section VI. of this proposed rule.
• Update the cost-to-charge ratio (CCR) ceiling and urban/rural average CCRs for FY 2023, as discussed in section VI. of this proposed rule.
• Describe the proposed codification of CMS' existing teaching status adjustment policy and proposed clarifications and updates of the IRF teaching status adjustment policy with respect to IRF hospital closures and displaced residents, as discussed in section VII. of this proposed rule.
• Solicit comments on the methodology used to update the facility-level adjustment factors, as discussed in section VIII. of this proposed rule.
• Solicit comments on the IRF transfer payment policy, as discussed in section IX. of this proposed rule.

We also propose updates to the IRF QRP and request information in section VII. of this proposed rule as follows:

• Update data reporting requirements under the IRF QRP beginning with FY 2025.
• Request information on (1) future measure concepts under consideration for the IRF QRP; (2) inclusion of a future dQM for the IRF QRP; and (3) CMS' overarching principles for measuring healthcare disparities across CMS Quality Programs, including the IRF QRP.

IV. Proposed Update to the Case-Mix Group (CMG) Relative Weights and Average Length of Stay (ALOS) Values for FY 2023

As specified in § 412.620(b)(1), we calculate a relative weight for each CMG that is proportional to the resources needed by an average inpatient rehabilitation case in that CMG. For example, cases in a CMG with a relative weight of 2, on average, will cost twice as much as cases in a CMG with a relative weight of 1. Relative weights account for the variance in cost per discharge due to the variance in resource utilization among the payment groups, and their use helps to ensure that IRF PPS payments support beneficiary access to care, as well as provider efficiency.

In this proposed rule, we propose to update the CMG relative weights and ALOS values for FY 2023. Typically, we use the most recent available data to update the CMG relative weights and average lengths of stay. For FY 2023, we are proposing to use the FY 2021 IRF claims and FY 2020 IRF cost report data. These data are the most current and complete data available at this time. Currently, only a small portion of the FY 2021 IRF cost report data are available for analysis, but the majority of the FY 2021 IRF claims data are available for analysis. We are proposing that if more recent data become available after the publication of this proposed rule and before the publication of the final rule, we would use such data to determine the FY 2023 CMG relative weights and ALOS values in the final rule.

We are proposing to apply these data using the same methodologies that we have used to update the CMG relative weights and ALOS values each FY since we implemented an update to the methodology. The detailed CCR data from the cost reports of IRF provider units of primary acute care hospitals is used for this methodology, instead of CCR data from the associated primary care hospitals, to calculate IRFs' average costs per case, as discussed in the FY 2009 IRF PPS final rule (73 FR 46372). In calculating the CMG relative weights, we use a hospital-specific relative value method to estimate operating (routine and ancillary services) and capital costs of IRFs. The process to calculate the CMG relative weights for this proposed rule is as follows:

Step 1. We estimate the effects that comorbidities have on costs.

Step 2. We adjust the cost of each Medicare discharge (case) to reflect the effects found in the first step.

Step 3. We use the adjusted costs from the second step to calculate CMG relative weights, using the hospital-specific relative value method.

Step 4. We normalize the FY 2023 CMG relative weights to the same average CMG relative weight from the CMG relative weights implemented in the FY 2022 IRF PPS final rule (86 FR 42362).

Consistent with the methodology that we have used to update the IRF classification system in each instance in the past, we propose to update the CMG relative weights for FY 2023 in such a way that total estimated aggregate payments to IRFs for FY 2023 are the same with or without the changes (that is, in a budget-neutral manner) by applying a budget neutrality factor to the standard payment amount. To calculate the appropriate budget neutrality factor for use in updating the FY 2023 CMG relative weights, we use the following steps:

Step 1. Calculate the estimated total amount of IRF PPS payments for FY 2023 (with no changes to the CMG relative weights).

Step 2. Calculate the estimated total amount of IRF PPS payments for FY 2023 by applying the proposed changes to the CMG relative weights (as discussed in this proposed rule).

Step 3. Divide the amount calculated in step 1 by the amount calculated in step 2 to determine the budget neutrality factor of 0.9979 that would maintain the same total estimated aggregate payments in FY 2023 with and without the proposed changes to the CMG relative weights.

Step 4. Apply the budget neutrality factor from step 3 to the FY 2023 IRF PPS standard payment amount after the application of the budget-neutral wage adjustment factor.

In section V.E. of this proposed rule, we discuss the proposed use of the existing methodology to calculate the proposed standard payment conversion factor for FY 2023.

In Table 2, “Proposed Relative Weights and Average Length of Stay Values for Case-Mix Groups,” we present the proposed CMGs, the comorbidity tiers, the corresponding relative weights, and the ALOS values for each CMG and tier for FY 2023. The ALOS for each CMG is used to determine when an IRF discharge meets the definition of a short-stay transfer, which results in a per diem case level adjustment.

Generally, updates to the CMG relative weights result in some increases and some decreases to the CMG relative weight values. Table 2 shows how we estimate that the application of the proposed revisions for FY 2023 would affect particular CMG relative weight values, which would affect the overall distribution of payments within CMGs and tiers. We note that, because we propose to implement the CMG relative weight revisions in a budget-neutral manner (as previously described), total estimated aggregate payments to IRFs for FY 2023 would not be affected as a result of the proposed CMG relative weight revisions. However, the proposed revisions would affect the distribution of payments within CMGs and tiers.

As shown in Table 3, 99.3 percent of all IRF cases are in CMGs and tiers that would experience less than a 5 percent change (either increase or decrease) in the CMG relative weight value as a result of the proposed revisions for FY 2023. The proposed changes in the ALOS values for FY 2023, compared with the FY 2022 ALOS values, are small and do not show any particular trends in IRF length of stay patterns.

We invite public comment on our proposed updates to the CMG relative weights and ALOS values for FY 2023.

V. Proposed FY 2023 IRF PPS Payment Update

A. Background

Section 1886(j)(3)(C) of the Act requires the Secretary to establish an increase factor that reflects changes over time in the prices of an appropriate mix of goods and services for which payment is made under the IRF PPS. According to section 1886(j)(3)(A)(i) of the Act, the increase factor shall be used to update the IRF prospective payment rates for each FY. Section 1886(j)(3)(C)(ii)(I) of the Act requires the application of the productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of the Act. Thus, in this proposed rule, we are proposing to update the IRF PPS payments for FY 2023 by a market basket increase factor as required by section 1886(j)(3)(C) of the Act based upon the most current data available, with a productivity adjustment as required by section 1886(j)(3)(C)(ii)(I) of the Act.

We have utilized various market baskets through the years in the IRF PPS. For a discussion of these market baskets, we refer readers to the FY 2016 IRF PPS final rule (80 FR 47046).

In FY 2016, we finalized the use of a 2012-based IRF market basket, using Medicare cost report data for both freestanding and hospital-based IRFs (80 FR 47049 through 47068). Beginning with FY 2020, we finalized a rebased and revised IRF market basket to reflect a 2016 base year. The FY 2020 IRF PPS final rule (84 FR 39071 through 39086) contains a complete discussion of the development of the 2016-based IRF market basket.

B. Proposed FY 2023 Market Basket Update and Productivity Adjustment

For FY 2023 (that is, beginning October 1, 2022 and ending September 30, 2023), we are proposing to update the IRF PPS payments by a market basket increase factor as required by section 1886(j)(3)(C) of the Act, with a productivity adjustment as required by section 1886(j)(3)(C)(ii)(I) of the Act. For FY 2023, we are proposing to use the same methodology described in the FY 2022 IRF PPS final rule (86 FR 42373 through 42376).

Consistent with historical practice, we are proposing to estimate the market basket update for the IRF PPS for FY 2023 based on IHS Global Inc.'s (IGI's) forecast using the most recent available data. Based on IGI's fourth quarter 2021 forecast with historical data through the third quarter of 2021, the proposed 2016-based IRF market basket increase factor for FY 2023 is projected to be 3.2 percent. We are also proposing that if more recent data become available after the publication of the proposed rule and before the publication of the final rule (for example, a more recent estimate of the market basket update or productivity adjustment), we would use such data, if appropriate, to determine the FY 2023 market basket update in the final rule.

According to section 1886(j)(3)(C)(i) of the Act, the Secretary shall establish an increase factor based on an appropriate percentage increase in a market basket of goods and services. Section 1886(j)(3)(C)(ii) of the Act then requires that, after establishing the increase factor for a FY, the Secretary shall reduce such increase factor for FY 2012 and each subsequent FY, by the productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of the Act. Section 1886(b)(3)(B)(xi)(II) of the Act sets forth the definition of this productivity adjustment. The statute defines the productivity adjustment to be equal to the 10-year moving average of changes in annual economy-wide, private nonfarm business multifactor 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 “productivity adjustment”). The U.S. Department of Labor's Bureau of Labor Statistics (BLS) publishes the official measures of productivity for the U.S. economy. We note that previously the productivity measure referenced in section 1886(b)(3)(B)(xi)(II) of the Act, was published by BLS as private nonfarm business multifactor productivity. Beginning with the November 18, 2021 release of productivity data, BLS replaced the term multifactor productivity (MFP) with total factor productivity (TFP). BLS noted that this is a change in terminology only and will not affect the data or methodology. As a result of the BLS name change, the productivity measure referenced in section 1886(b)(3)(B)(xi)(II) is now published by BLS as private nonfarm business total factor productivity. However, as mentioned above, the data and methods are unchanged. Please see www.bls.gov for the BLS historical published TFP data. A complete description of IGI's TFP projection methodology is available on the CMS website at https://www.cms.gov/​Research-Statistics-Data-and-Systems/​Statistics-Trends-and-Reports/​MedicareProgramRatesStats/​MarketBasketResearch. In addition, in the FY 2022 IRF final rule (86 FR 42374), we noted that effective with FY 2022 and forward, CMS changed the name of this adjustment to refer to it as the productivity adjustment rather than the MFP adjustment.

Using IGI's fourth quarter 2021 forecast, the 10-year moving average growth of TFP for FY 2023 is projected to be 0.4 percent. Thus, in accordance with section 1886(j)(3)(C) of the Act, we are proposing to base the FY 2023 market basket update, which is used to determine the applicable percentage increase for the IRF payments, on IGI's fourth quarter 2021 forecast of the 2016-based IRF market basket. We are proposing to then reduce this percentage increase by the estimated productivity adjustment for FY 2023 of 0.4 percentage point (the 10-year moving average growth of TFP for the period ending FY 2023 based on IGI's fourth quarter 2021 forecast). Therefore, the proposed FY 2023 IRF update is equal to 2.8 percent (3.2 percent market basket update reduced by the 0.4 percentage point productivity adjustment). Furthermore, we are proposing that if more recent data become available after the publication of the proposed rule and before the publication of the final rule (for example, a more recent estimate of the market basket and/or productivity adjustment), we would use such data, if appropriate, to determine the FY 2023 market basket update and productivity adjustment in the final rule.

For FY 2023, the Medicare Payment Advisory Commission (MedPAC) recommends that we reduce IRF PPS payment rates by 5 percent.^[11] As discussed, and in accordance with sections 1886(j)(3)(C) and 1886(j)(3)(D) of the Act, the Secretary is proposing to update the IRF PPS payment rates for FY 2023 by a productivity-adjusted IRF market basket increase factor of 2.8 percent. Section 1886(j)(3)(C) of the Act does not provide the Secretary with the authority to apply a different update factor to IRF PPS payment rates for FY 2023.

We invite public comment on our proposals for the FY 2023 market basket update and productivity adjustment.

C. Proposed Labor-Related Share for FY 2023

Section 1886(j)(6) of the Act specifies that the Secretary is to adjust the proportion (as estimated by the Secretary from time to time) of IRFs' costs that are attributable to wages and wage-related costs, of the prospective payment rates computed under section 1886(j)(3) of the Act, for area differences in wage levels by a factor (established by the Secretary) reflecting the relative hospital wage level in the geographic area of the rehabilitation facility compared to the national average wage level for such facilities. The labor-related share is determined by identifying the national average proportion of total costs that are related to, influenced by, or vary with the local labor market. We are proposing to continue to classify a cost category as labor-related if the costs are labor-intensive and vary with the local labor market.

Based on our definition of the labor-related share and the cost categories in the 2016-based IRF market basket, we are proposing to calculate the labor-related share for FY 2023 as the sum of the FY 2023 relative importance of 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 the Capital-Related relative importance from the 2016-based IRF market basket. For more details regarding the methodology for determining specific cost categories for inclusion in the 2016-based IRF labor-related share, see the FY 2020 IRF PPS final rule (84 FR 39087 through 39089).

The relative importance reflects the different rates of price change for these cost categories between the base year (2016) and FY 2023. Based on IGI's fourth quarter 2021 forecast of the 2016-based IRF market basket, the sum of the FY 2023 relative importance for Wages and Salaries, Employee Benefits, Professional Fees: Labor-related, Administrative and Facilities Support Services, Installation Maintenance & Repair Services, and All Other: Labor-related Services is 69.4 percent. We are proposing that the portion of Capital-Related costs that are influenced by the local labor market is 46 percent. Since the relative importance for Capital-Related costs is 8.2 percent of the 2016-based IRF market basket for FY 2023, we are proposing to take 46 percent of 8.2 percent to determine the labor-related share of Capital-Related costs for FY 2022 of 3.8 percent. Therefore, we are proposing a total labor-related share for FY 2023 of 73.2 percent (the sum of 69.4 percent for the proposed labor-related share of operating costs and 3.8 percent for the proposed labor-related share of Capital-Related costs). We are proposing that if more recent data become available after publication of the proposed rule and before the publication of the final rule (for example, a more recent estimate of the labor-related share), we would use such data, if appropriate, to determine the FY 2023 IRF labor-related share in the final rule.

Table 4 shows the current estimate of the proposed FY 2023 labor-related share and the FY 2022 final labor-related share using the 2016-based IRF market basket relative importance.

We invite public comments on the proposed labor-related share for FY 2023.

D. Proposed Wage Adjustment for FY 2023

1. Background

Section 1886(j)(6) of the Act requires the Secretary to adjust the proportion of rehabilitation facilities' costs attributable to wages and wage-related costs (as estimated by the Secretary from time to time) by a factor (established by the Secretary) reflecting the relative hospital wage level in the geographic area of the rehabilitation facility compared to the national average wage level for those facilities. The Secretary is required to update the IRF PPS wage index on the basis of information available to the Secretary on the wages and wage-related costs to furnish rehabilitation services. Any adjustment or updates made under section 1886(j)(6) of the Act for a FY are made in a budget-neutral manner.

For FY 2023, we propose to maintain the policies and methodologies described in the FY 2022 IRF PPS final rule (86 FR 42377) related to the labor market area definitions and the wage index methodology for areas with wage data. Thus, we propose to use the core based statistical areas (CBSAs) labor market area definitions and the FY 2023 pre-reclassification and pre-floor hospital wage index data. In accordance with section 1886(d)(3)(E) of the Act, the FY 2023 pre-reclassification and pre-floor hospital wage index is based on data submitted for hospital cost reporting periods beginning on or after October 1, 2018, and before October 1, 2019 (that is, FY 2019 cost report data).

The labor market designations made by the OMB include some geographic areas where there are no hospitals and, thus, no hospital wage index data on which to base the calculation of the IRF PPS wage index. We propose to continue to use the same methodology discussed in the FY 2008 IRF PPS final rule (72 FR 44299) to address those geographic areas where there are no hospitals and, thus, no hospital wage index data on which to base the calculation for the FY 2023 IRF PPS wage index.

We invite public comment on our proposals regarding the Wage Adjustment for FY 2023.

2. Core-Based Statistical Areas (CBSAs) for the FY 2023 IRF Wage Index

The wage index used for the IRF PPS is calculated using the pre-reclassification and pre-floor inpatient PPS (IPPS) wage index data and is assigned to the IRF on the basis of the labor market area in which the IRF is geographically located. IRF labor market areas are delineated based on the CBSAs established by the OMB. The CBSA delineations (which were implemented for the IRF PPS beginning with FY 2016) are based on revised OMB delineations issued on February 28, 2013, in OMB Bulletin No. 13-01. 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). We refer readers to the FY 2016 IRF PPS final rule (80 FR 47068 through 47076) for a full discussion of our implementation of the OMB labor market area delineations beginning with the FY 2016 wage index.

Generally, OMB issues major revisions to statistical areas every 10 years, based on the results of the decennial census. Additionally, OMB occasionally issues updates and revisions to the statistical areas in between decennial censuses to reflect the recognition of new areas or the addition of counties to existing areas. In some instances, these updates merge formerly separate areas, transfer components of an area from one area to another, or drop components from an area. On July 15, 2015, OMB issued OMB Bulletin No. 15-01, which provides minor updates to and supersedes OMB Bulletin No. 13-01 that was issued on February 28, 2013. The attachment to OMB Bulletin No. 15-01 provides detailed information on the update to statistical areas since February 28, 2013. The updates provided in OMB Bulletin No. 15-01 are 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.

In the FY 2018 IRF PPS final rule (82 FR 36250 through 36251), we adopted the updates set forth in OMB Bulletin No. 15-01 effective October 1, 2017, beginning with the FY 2018 IRF wage index. For a complete discussion of the adoption of the updates set forth in OMB Bulletin No. 15-01, we refer readers to the FY 2018 IRF PPS final rule. In the FY 2019 IRF PPS final rule (83 FR 38527), we continued to use the OMB delineations that were adopted beginning with FY 2016 to calculate the area wage indexes, with updates set forth in OMB Bulletin No. 15-01 that we adopted beginning with the FY 2018 wage index.

On August 15, 2017, OMB issued OMB Bulletin No. 17-01, which provided updates to and superseded OMB Bulletin No. 15-01 that was issued on July 15, 2015. The attachments to OMB Bulletin No. 17-01 provide detailed information on the update to statistical areas since July 15, 2015, and are based on the application of the 2010 Standards for Delineating Metropolitan and Micropolitan Statistical Areas to Census Bureau population estimates for July 1, 2014 and July 1, 2015. In the FY 2020 IRF PPS final rule (84 FR 39090 through 39091), we adopted the updates set forth in OMB Bulletin No. 17-01 effective October 1, 2019, beginning with the FY 2020 IRF wage index.

On April 10, 2018, OMB issued OMB Bulletin No. 18-03, which superseded the August 15, 2017 OMB Bulletin No. 17-01, and on September 14, 2018, OMB issued OMB Bulletin No. 18-04, which superseded the April 10, 2018 OMB Bulletin No. 18-03. These bulletins established revised delineations for Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Combined Statistical Areas, and provided guidance on the use of the delineations of these statistical areas. A copy of this bulletin may be obtained at https://www.whitehouse.gov/​wp-content/​uploads/​2018/​09/​Bulletin-18-04.pdf.

To this end, as discussed in the FY 2021 IRF PPS proposed (85 FR 22075 through 22079) and final (85 FR 48434 through 48440) 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 ) beginning October 1, 2020, including a 1-year transition for FY 2021 under which we applied a 5 percent cap on any decrease in an IRF'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 IRF PPS. OMB issued further revised CBSA delineations in OMB Bulletin No. 20-01, on March 6, 2020 (available on the web at https://www.whitehouse.gov/​wp-content/​uploads/​2020/​03/​Bulletin-20-01.pdf ). However, we determined that the changes in OMB Bulletin No. 20-01 do not impact the CBSA-based labor market area delineations adopted in FY 2021. Therefore, CMS did not propose to adopt the revised OMB delineations identified in OMB Bulletin No. 20-01 for FY 2022, and for these reasons CMS is likewise not making such a proposal for FY 2023.

3. Proposed Permanent Cap on Wage Index Decreases

As discussed above in this section of the rule, we have proposed and finalized temporary transition policies in the past to mitigate significant changes to payments due to changes to the IRF PPS wage index. Specifically, for FY 2016 (80 FR 47068), we implemented a 50/50 blend for all geographic areas consisting of the wage index values computed using the then-current OMB area delineations and the wage index values computed using new area delineations based on OMB Bulletin No. 13-01. In FY 2021 (85 FR 48434), we implemented a 1-year transition to mitigate any negative effects of wage index changes by applying a 5 percent cap on any decrease in an IRF's wage index from the final wage index from FY 2020. We explained that we believed the 5-percent cap would provide greater transparency and would be administratively less complex than the prior methodology of applying a 50/50 blended wage index. We indicated that no cap would be applied to the reduction in the wage index for FY 2022, and that this transition approach struck an appropriate balance by providing a transition period to mitigate the resulting short-term instability and negative impacts on providers and time for them to adjust to their new labor market area delineations and wage index values.

In the FY 2022 final rule (86 FR 42378), commenters recommended CMS extend the transition period adopted in the FY 2021 IRF PPS final rule so that wage index values do not change by more than 5 percent from year-to-year to protect IRFs from large payment volatility. Because we did not propose to modify the transition policy that was finalized in the FY 2021 IRF PPS final rule, we did not extend the transition period for FY 2022. However, we acknowledged that certain changes to wage index policy may significantly affect Medicare payments. In addition, we reiterated that our policy principles with regard to the wage index include generally using the most current data and information available and providing that data and information, as well as any approaches to addressing any significant effects on Medicare payments resulting from these potential scenarios, in notice and comment rulemaking. With these policy principles in mind, for this FY 2023 proposed rule we considered how best to address the potential scenarios about which commenters raised concerns in the FY 2022 final rule around IRF payment volatility; that is, scenarios in which changes to wage index policy may significantly affect Medicare payments.

In the past, we have established transition policies of limited duration to phase in significant changes to labor market areas. In taking this approach in the past, we sought to mitigate short-term instability and fluctuations that can negatively impact providers due to wage index changes. In accordance with the requirements of the IRF PPS wage index regulations at § 412.624(a)(2), we use an appropriate wage index based on the best available data, including the best available labor market area delineations, to adjust IRF PPS payments for wage differences. We have previously stated that, because the wage index is a relative measure of the value of labor in prescribed labor market areas, we believe it is important to implement new labor market area delineations with as minimal a transition as is reasonably possible. However, we recognize that changes to the wage index have the potential to create instability and significant negative impacts on certain providers even when labor market areas do not change. In addition, year-to-year fluctuations in an area's wage index can occur due to external factors beyond a provider's control, such as the COVID-19 PHE. For an individual provider, these fluctuations can be difficult to predict. So, we also recognize that predictability in Medicare payments is important to enable providers to budget and plan their operations.

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

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

Typical year-to-year variation in the IRF PPS wage index has historically been within 5 percent, and we expect this will continue to be the case in future years. Because providers are usually experienced with this level of wage index fluctuation, we believe applying a 5-percent cap on all wage index decreases each year, regardless of the reason for the decrease, would effectively mitigate instability in IRF PPS payments due to any significant wage index decreases that may affect providers in a year. We believe this approach would address concerns about instability that commenters raised in the FY 2022 IRF PPS rule. Additionally, we believe that applying a 5-percent cap on all wage index decreases would support increased predictability about IRF PPS payments for providers, enabling them to more effectively budget and plan their operations. Lastly, because applying a 5-percent cap on all wage index decreases would represent a small overall impact on the labor market area wage index system we believe it would ensure the wage index is a relative measure of the value of labor in prescribed labor market areas. As discussed in further detail in section XIII.C.2. of this proposed rule, we estimate that applying a 5-percent cap on all wage index decreases will have a very small effect on the wage index budget neutrality factor for FY 2023. Because the wage index is a measure of the value of labor (wage and wage-related costs) in a prescribed labor market area relative to the national average, we anticipate that in the absence of proposed policy changes most providers will not experience year-to-year wage index declines greater than 5 percent in any given year. We also believe that when the 5-percent cap would be applied under this proposal, it is likely that it would be applied similarly to all IRFs in the same labor market area, as the hospital average hourly wage data in the CBSA (and any relative decreases compared to the national average hourly wage) would be similar. While this policy may result in IRFs in a CBSA receiving a higher wage index than others in the same area (such as situations when delineations change), we believe the impact would be temporary. Therefore, we anticipate that the impact to the wage index budget neutrality factor in future years would continue to be minimal.

The Secretary has broad authority to establish appropriate payment adjustments under the IRF PPS, including the wage index adjustment. As discussed earlier in this section, the IRF PPS regulations require us to use an appropriate wage index based on the best available data. For the reasons discussed in this section, we believe a 5-percent cap on wage index decreases would be appropriate for the IRF PPS. Therefore, for FY 2023 and subsequent years, we are proposing to apply a 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, regardless of the circumstances causing the decline. That is, we are proposing that an IRF's wage index for FY 2023 would not be less than 95 percent of its final wage index for FY 2022, regardless of whether the IRF is part of an updated CBSA, and that for subsequent years, a provider's wage index would not be less than 95 percent of its wage index calculated in the prior FY. This also means that if an IRF's prior FY wage index is calculated with the application of the 5-percent cap, the following year's wage index would not be less than 95 percent of the IRF's capped wage index in the prior FY. For example, if an IRF's wage index for FY 2023 is calculated with the application of the 5-percent cap, then its wage index for FY 2024 would not be less than 95 percent of its capped wage index in FY 2023. Lastly, we propose that a new IRF would be paid the wage index for the area in which it is geographically located for its first full or partial FY with no cap applied, because a new IRF would not have a wage index in the prior FY. As we have discussed in this proposed rule, we believe this proposed methodology would maintain the IRF PPS wage index as a relative measure of the value of labor in prescribed labor market areas, increase the predictability of IRF PPS payments for providers, and mitigate instability and significant negative impacts to providers resulting from significant changes to the wage index. In section XIII.C.2. of this proposed rule, we estimate the impact to payments for providers in FY 2023 based on this proposed policy. We also note that we would examine the effects of this policy on an ongoing basis in the future in order to assess its appropriateness.

Subject to the aforementioned proposal becoming final, we are also proposing to revise the regulation text at § 412.624(e)(1) to provide that starting October 1, 2022, CMS would apply a cap on decreases to the wage index such that the wage index applied is not less than 95 percent of the wage index applied to that IRF in the prior year.

We invite public comments on this proposal.

4. Proposed Wage Adjustment

To calculate the wage-adjusted facility payment for the proposed payment rates set forth in this proposed rule, we multiply the proposed unadjusted Federal payment rate for IRFs by the FY 2023 labor-related share based on the 2016-based IRF market basket relative importance (73.2 percent) to determine the labor-related portion of the standard payment amount. A full discussion of the calculation of the labor-related share is located in section V.C. of this proposed rule. We would then multiply the labor-related portion by the applicable IRF wage index. The wage index tables are available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientRehabFacPPS/​IRF-Rules-and-Related-Files.html.

Adjustments or updates to the IRF wage index made under section 1886(j)(6) of the Act must be made in a budget-neutral manner. We propose to calculate a budget-neutral wage adjustment factor as established in the FY 2004 IRF PPS final rule (68 FR 45689) and codified at § 412.624(e)(1), as described in the steps below. We propose to use the listed steps to ensure that the FY 2023 IRF standard payment conversion factor reflects the proposed update to the wage indexes (based on the FY 2019 hospital cost report data) and the proposed update to the labor-related share, in a budget-neutral manner:

Step 1. Calculate the total amount of estimated IRF PPS payments using the labor-related share and the wage indexes from FY 2022 (as published in the FY 2022 IRF PPS final rule (86 FR 42362)).

Step 2. Calculate the total amount of estimated IRF PPS payments using the proposed FY 2023 wage index values (based on updated hospital wage data and taking into account the proposed permanent cap on wage index decreases policy) and the FY 2023 labor-related share of 73.2 percent.

Step 3. Divide the amount calculated in step 1 by the amount calculated in step 2. The resulting quotient is the proposed FY 2023 budget-neutral wage adjustment factor of 1.0007.

Step 4. Apply the budget neutrality factor from step 3 to the FY 2023 IRF PPS standard payment amount after the application of the increase factor to determine the proposed FY 2023 standard payment conversion factor.

We discuss the calculation of the proposed standard payment conversion factor for FY 2023 in section V.E. of this proposed rule.

We invite public comments on the proposed IRF wage adjustment for FY 2023 (and the proposed permanent cap on wage index decreases policy).

E. Description of the Proposed IRF Standard Payment Conversion Factor and Payment Rates for FY 2023

To calculate the proposed standard payment conversion factor for FY 2023, as illustrated in Table 5, we begin by applying the proposed increase factor for FY 2023, as adjusted in accordance with sections 1886(j)(3)(C) of the Act, to the standard payment conversion factor for FY 2022 ($17,240). Applying the proposed 2.8 percent increase factor for FY 2023 to the standard payment conversion factor for FY 2022 of $17,240 yields a standard payment amount of $17,723. Then, we apply the proposed budget neutrality factor for the FY 2023 wage index (taking into account the proposed permanent cap on wage index decreases policy), and labor-related share of 1.0007, which results in a standard payment amount of $17,735. We next apply the proposed budget neutrality factor for the CMG relative weights of 0.9979, which results in the standard payment conversion factor of $17,698 for FY 2023.

We invite public comments on the proposed FY 2023 standard payment conversion factor.

After the application of the proposed CMG relative weights described in section IV. of this proposed rule to the proposed FY 2023 standard payment conversion factor ($17,698), the resulting unadjusted IRF prospective payment rates for FY 2023 are shown in Table 6.

F. Example of the Methodology for Adjusting the Proposed Prospective Payment Rates

Table 7 illustrates the methodology for adjusting the proposed prospective payments (as described in section V. of this proposed rule). The following examples are based on two hypothetical Medicare beneficiaries, both classified into CMG 0104 (without comorbidities). The proposed unadjusted prospective payment rate for CMG 0104 (without comorbidities) appears in Table 7.

Example: One beneficiary is in Facility A, an IRF located in rural Spencer County, Indiana, and another beneficiary is in Facility B, an IRF located in urban Harrison County, Indiana. Facility A, a rural non-teaching hospital has a Disproportionate Share Hospital (DSH) percentage of 5 percent (which would result in a LIP adjustment of 1.0156), a wage index of 0.8384, and a rural adjustment of 14.9 percent. Facility B, an urban teaching hospital, has a DSH percentage of 15 percent (which would result in a LIP adjustment of 1.0454 percent), a wage index of 0.8763, and a teaching status adjustment of 0.0784.

To calculate each IRF's labor and non-labor portion of the proposed prospective payment, we begin by taking the unadjusted prospective payment rate for CMG 0104 (without comorbidities) from Table 7. Then, we multiply the proposed labor-related share for FY 2023 (73.2 percent) described in section V.C. of this proposed rule by the proposed unadjusted prospective payment rate. To determine the non-labor portion of the proposed prospective payment rate, we subtract the labor portion of the Federal payment from the proposed unadjusted prospective payment.

To compute the proposed wage-adjusted prospective payment, we multiply the labor portion of the proposed Federal payment by the appropriate wage index located in the applicable wage index table. This table is available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientRehabFacPPS/​IRF-Rules-and-Related-Files.html.

The resulting figure is the wage-adjusted labor amount. Next, we compute the proposed wage-adjusted Federal payment by adding the wage-adjusted labor amount to the non-labor portion of the proposed Federal payment.

Adjusting the proposed wage-adjusted Federal payment by the facility-level adjustments involves several steps. First, we take the wage-adjusted prospective payment and multiply it by the appropriate rural and LIP adjustments (if applicable). Second, to determine the appropriate amount of additional payment for the teaching status adjustment (if applicable), we multiply the teaching status adjustment (0.0784, in this example) by the wage-adjusted and rural-adjusted amount (if applicable). Finally, we add the additional teaching status payments (if applicable) to the wage, rural, and LIP-adjusted prospective payment rates. Table 7 illustrates the components of the adjusted payment calculation.

Thus, the proposed adjusted payment for Facility A would be $28,522.97, and the adjusted payment for Facility B would be $28,333.19.

VI. Proposed Update to Payments for High-Cost Outliers Under the IRF PPS for FY 2023

A. Proposed Update to the Outlier Threshold Amount for FY 2023

Section 1886(j)(4) of the Act provides the Secretary with the authority to make payments in addition to the basic IRF prospective payments for cases incurring extraordinarily high costs. A case qualifies for an outlier payment if the estimated cost of the case exceeds the adjusted outlier threshold. We calculate the adjusted outlier threshold by adding the IRF PPS payment for the case (that is, the CMG payment adjusted by all of the relevant facility-level adjustments) and the adjusted threshold amount (also adjusted by all of the relevant facility-level adjustments). Then, we calculate the estimated cost of a case by multiplying the IRF's overall CCR by the Medicare allowable covered charge. If the estimated cost of the case is higher than the adjusted outlier threshold, we make an outlier payment for the case equal to 80 percent of the difference between the estimated cost of the case and the outlier threshold.

In the FY 2002 IRF PPS final rule (66 FR 41362 through 41363), we discussed our rationale for setting the outlier threshold amount for the IRF PPS so that estimated outlier payments would equal 3 percent of total estimated payments. For the FY 2002 IRF PPS final rule, we analyzed various outlier policies using 3, 4, and 5 percent of the total estimated payments, and we concluded that an outlier policy set at 3 percent of total estimated payments would optimize the extent to which we could reduce the financial risk to IRFs of caring for high-cost patients, while still providing for adequate payments for all other (non-high cost outlier) cases.

Subsequently, we updated the IRF outlier threshold amount in the FYs 2006 through 2022 IRF PPS final rules and the FY 2011 and FY 2013 notices (70 FR 47880, 71 FR 48354, 72 FR 44284, 73 FR 46370, 74 FR 39762, 75 FR 42836, 76 FR 47836, 76 FR 59256, 77 FR 44618, 78 FR 47860, 79 FR 45872, 80 FR 47036, 81 FR 52056, 82 FR 36238, 83 FR 38514, 84 FR 39054, 85 FR 48444, and 86 FR 42362, respectively) to maintain estimated outlier payments at 3 percent of total estimated payments. We also stated in the FY 2009 final rule (73 FR 46370 at 46385) that we would continue to analyze the estimated outlier payments for subsequent years and adjust the outlier threshold amount as appropriate to maintain the 3 percent target.

To update the IRF outlier threshold amount for FY 2023, we propose to use FY 2021 claims data and the same methodology that we used to set the initial outlier threshold amount in the FY 2002 IRF PPS final rule (66 FR 41362 through 41363), which is also the same methodology that we used to update the outlier threshold amounts for FYs 2006 through 2022. The outlier threshold is calculated by simulating aggregate payments and using an iterative process to determine a threshold that results in outlier payments being equal to 3 percent of total payments under the simulation. To determine the outlier threshold for FY 2023, we estimated the amount of FY 2023 IRF PPS aggregate and outlier payments using the most recent claims available (FY 2021) and the proposed FY 2023 standard payment conversion factor, labor-related share, and wage indexes, incorporating any applicable budget-neutrality adjustment factors. The outlier threshold is adjusted either up or down in this simulation until the estimated outlier payments equal 3 percent of the estimated aggregate payments. Based on an analysis of the preliminary data used for the proposed rule, we estimate that IRF outlier payments as a percentage of total estimated payments would be approximately 3.8 percent in FY 2022. Therefore, we propose to update the outlier threshold amount from $9,491 for FY 2022 to $13,038 for FY 2023 to maintain estimated outlier payments at approximately 3 percent of total estimated aggregate IRF payments for FY 2023.

Although we believe that updating the outlier threshold for FY 2023 would be appropriate to maintain IRF PPS outlier payments at 3 percent of total estimated payments, we recognize that the proposed outlier threshold amount for FY 2023 would result in a significant increase from the current outlier threshold amount for FY 2022. As we continue to explore the underlying reasons for the large change in the proposed outlier threshold amount, we welcome comments from stakeholders on any observations or information related to the increase in the proposed update to outlier threshold amount for FY 2023.

B. Proposed Update to the IRF Cost-to-Charge Ratio Ceiling and Urban/Rural Averages for FY 2023

CCRs are used to adjust charges from Medicare claims to costs and are computed annually from facility-specific data obtained from MCRs. IRF specific CCRs are used in the development of the CMG relative weights and the calculation of outlier payments under the IRF PPS. In accordance with the methodology stated in the FY 2004 IRF PPS final rule (68 FR45692 through 45694), we propose to apply a ceiling to IRFs' CCRs. Using the methodology described in that final rule, we propose to update the national urban and rural CCRs for IRFs, as well as the national CCR ceiling for FY 2023, based on analysis of the most recent data available. We apply the national urban and rural CCRs in the following situations:

• New IRFs that have not yet submitted their first MCR.
• IRFs whose overall CCR is in excess of the national CCR ceiling for FY 2023, as discussed below in this section.
• Other IRFs for which accurate data to calculate an overall CCR are not available.

Specifically, for FY 2023, we propose to estimate a national average CCR of 0.463 for rural IRFs, which we calculated by taking an average of the CCRs for all rural IRFs using their most recently submitted cost report data. Similarly, we propose to estimate a national average CCR of 0.393 for urban IRFs, which we calculated by taking an average of the CCRs for all urban IRFs using their most recently submitted cost report data. We apply weights to both of these averages using the IRFs' estimated costs, meaning that the CCRs of IRFs with higher total costs factor more heavily into the averages than the CCRs of IRFs with lower total costs. For this proposed rule, we have used the most recent available cost report data (FY 2020). This includes all IRFs whose cost reporting periods begin on or after October 1, 2019, and before October 1, 2020. If, for any IRF, the FY 2020 cost report was missing or had an “as submitted” status, we used data from a previous FY's (that is, FY 2004 through FY 2019) settled cost report for that IRF. We do not use cost report data from before FY 2004 for any IRF because changes in IRF utilization since FY 2004 resulting from the 60 percent rule and IRF medical review activities suggest that these older data do not adequately reflect the current cost of care. Using updated FY 2020 cost report data for this proposed rule, we estimate a national average CCR of 0.463 for rural IRFs, and a national average CCR of 0.393 for urban IRFs.

In accordance with past practice, we propose to set the national CCR ceiling at 3 standard deviations above the mean CCR. Using this method, we propose a national CCR ceiling of 1.40 for FY 2023. This means that, if an individual IRF's CCR were to exceed this ceiling of 1.40 for FY 2023, we will replace the IRF's CCR with the appropriate proposed national average CCR (either rural or urban, depending on the geographic location of the IRF). We calculated the proposed national CCR ceiling by:

Step 1. Taking the national average CCR (weighted by each IRF's total costs, as previously discussed) of all IRFs for which we have sufficient cost report data (both rural and urban IRFs combined).

Step 2. Estimating the standard deviation of the national average CCR computed in step 1.

Step 3. Multiplying the standard deviation of the national average CCR computed in step 2 by a factor of 3 to compute a statistically significant reliable ceiling.

Step 4. Adding the result from step 3 to the national average CCR of all IRFs for which we have sufficient cost report data, from step 1.

We are also proposing that if more recent data become available after the publication of this proposed rule and before the publication of the final rule, we would use such data to determine the FY 2023 national average rural and urban CCRs and the national CCR ceiling in the final rule. We invite public comment on the proposed update to the IRF CCR ceiling and the urban/rural averages for FY 2023.

VII. Proposed Codification and Clarifications of IRF Teaching Status Adjustment Policy

In the FY 2006 IRF PPS final rule (70 FR 47928 through 47932), we implemented § 412.624(e)(4) to establish a facility level adjustment for IRFs that are, teaching hospitals or units of teaching hospitals. The teaching status adjustment accounts for the higher indirect operating costs experienced by IRFs that participate in training residents in graduate medical education (GME) programs. The teaching status payment adjustment is based on the ratio of the number of full-time equivalent (FTE) interns and residents training in the IRF divided by the IRF's average daily census. Section 1886(j)(3)(A)(v) of the Act requires the Secretary to adjust the prospective payment rates for the IRF PPS by such factors as the Secretary determines are necessary to properly reflect the variations in necessary costs of treatment among rehabilitation facilities.

We established the IRF teaching status adjustment in a manner that limited the incentives for IRFs to add FTE interns and residents for the purpose of increasing their teaching status adjustment, as has been done in the payment systems for Inpatient Psychiatric Facilities (IPF) and acute care hospitals. That is, we imposed a cap on the number of FTE interns and residents that the IRF can count for the purpose of calculating the teaching status adjustment. This cap is similar to the cap established by the Balanced Budget Act of 1997 (Pub. L. 105-33, enacted August 5, 1997) section 4621, that added section 1886(d)(5)(B)(v) of the Act (indirect medical education (IME) FTE cap for IPPS hospitals. The cap limits the number of FTE interns and residents that teaching IRFs may count for the purpose of calculating the IRF PPS teaching status adjustment, not the number of interns and residents that teaching institutions care hire or train. The cap is equal to the number of FTE interns and residents that trained in the IRF during a “base year,” that is based on the most recent final settled cost report for a cost reporting period ending on or before November 15, 2004. A complete discussion of how the IRF teaching status adjustment was calculated appears in the FY 2006 IRF PPS final rule (70 FR 47928 through 47932).

In the FY 2012 IRF PPS final rule (76 FR 47846 through 47848) published on August 5, 2011, we updated the IRF PPS teaching status adjustment policy in order to maintain consistency, to the extent feasible, with the indirect medical education (IME) teaching policies that were finalized in the IPPS FY 1999 final rule (64 FR 41522), the IPPS FY 2001 final rule (66 FR 39900), and the IPF PPS teaching adjustment policies finalized in the 2012 IPF PPS final rule (76 FR 26454 through 26456). In that final rule, we adopted a policy which permits a temporary increase in the FTE intern and resident cap when an IRF increases the number of FTE residents it trains, in order to accept displaced residents because another IRF closes or closes a medical residency training program. We refer to a “displaced” resident or intern as one that is training in an IRF and is unable to complete training in that IRF, either because the IRF closes or closes a medical residency training program.

The cap adjustment for IRFs, adopted in the FY 2012 IRF PPS final rule, is considered temporary because it is resident-specific and will only apply to the residents until they have completed their training in the program in which they were training at the time of the IRF closure or the closure of the program. Similar to the IPPS and IPF policy for displaced residents, the IRF PPS temporary cap adjustment only applies to residents that were still training at the IRF at the time the IRF closed or at the time the IRF ceased training residents in the residency training program(s). Residents who leave the IRF, for whatever reason, before the closure of the IRF or the closure of the medical residency training program are not considered displaced residents for purposes of the IRF temporary cap adjustment policy.

In the FY 2012 IRF PPS final rule, we also adopted the IPPS definition of “closure of a hospital” at § 413.79(h)(1)(i) to mean the IRF terminates its Medicare provider agreement as specified in § 489.52. In this instance, we allow a temporary adjustment to an IRF's FTE cap to reflect residents added to their medical residency training program because of an IRF's closure. We allow an adjustment to an IRF's FTE cap if the IRF meets the criteria outlined in the FY 2012 IRF PPS final rule (76 FR 47847). After the displaced residents leave the accepting IRF's training program or complete their medical residency training program, the accepting IRF's cap will revert to its original level. As such, the temporary adjustment to the FTE cap will be available to the IRF only for the period of time necessary for the displaced residents to complete their training.

Additionally, in the FY 2012 IRF PPS final rule, we adopted the IPPS definition of “closure of a hospital residency training program,” as specified in § 413.79(h)(1)(ii), which means that the hospital ceases to offer training for interns and residents in a particular approved medical residency training program. In this instance, if an IRF ceases training residents in a medical residency training program(s) and agrees to temporarily reduce its FTE cap, another IRF may receive a temporary adjustment to its FTE cap to reflect the addition of the displaced residents. For more discussion regarding the methodology for adjusting the caps for the “receiving IRF” and the “IRF that closed its program,” refer to the FY 2012 IRF PPS final rule (76 FR 47847).

A. Proposed Codification of Existing Teaching Status Adjustment Policies

In an effort to streamline the IRF PPS teaching status adjustment policies that were finalized in the FY 2006 IRF PPS final rule (70 FR 47928 through 47932) and the FY 2012 IRF PPS final rule (76 FR 47846 through 47848), we are proposing to codify the longstanding policy so that these policies can be easily located by IRF providers and can also align, to the extent feasible, with the IPPS IME and IPF teaching adjustment policy regulations.

First, we are proposing to codify policy that was finalized in the FY 2006 IRF PPS final rule with respect to how CMS adjusts the Federal prospective payment on a facility basis by a factor to account for indirect teaching costs. When the teaching status adjustment policy was finalized in the FY 2006 IRF PPS final rule (70 FR 47928 through 47932), the definition of this “factor” and explanations of how it is computed were not included in the regulations. Rather, the more detailed definition and the explanation of the teaching status payment adjustment provided in the FY 2006 IRF PPS final rule, were published in the Medicare Claims Processing Manual (100-04, chapter 3, 140.2.5.4). Currently, § 412.624(e)(4) states, for discharges on or after October 1, 2005, CMS adjusts the Federal prospective payment on a facility basis by a factor as specified by CMS for facilities that are teaching institutions or units of teaching institutions. This adjustment is made on a claim basis as an interim payment and the final payment in full for the claim is made during the final settlement of the cost report.

Second, we are also proposing to codify the IRF policy that was adopted in the FY 2012 IRF PPS final rule (76 FR 47846 through 47848) allowing an IRF to receive a temporary adjustment to its FTE cap to reflect residents added to its teaching program because of another IRFs closure or an IRFs medical residency training program closure. We believe that codifying these longstanding policies would improve clarity and reduce administrative burden on IRF providers and others trying to locate all relevant information pertaining to the teaching hospital adjustment.

Thus, we are proposing to codify CMS' existing IRF PPS' teaching hospital adjustment policies through proposed amendments to §§ 412.602 and 412.624(e)(4) presented in this proposed rule; except as specifically noted in this proposed rule, our intent is to codify the existing IRF PPS teaching status adjustment policy.

We invite public comment on our proposal to amend §§ 412.602 and 412.624(e)(4) to codify our longstanding policies regarding the teaching status adjustment.

B. Proposed Update to the IRF Teaching Policy on IRF Program Closures and Displaced Residents

For FY 2023, we are also proposing to change the IRF policy pertaining to displaced residents resulting from IRF closures and closures of IRF residency teaching programs. Specifically, we are proposing to adopt conforming changes to the IRF PPS teaching status adjustment policy to align with the policy changes that the IPPS finalized in the FY 2021 IPPS final rule (85 FR 58865 through 58870) and that the IPF finalized in the FY 2022 IPF PPS final rule (86 FR 42618 through 42621). We believe that the IRF teaching status adjustment policy relating to hospital closure and displaced residents is susceptible to the same vulnerabilities as IPPS IME policy. Hence, if an IRF with residents training in its residency program announces it is closing, these residents will become displaced and will need to find alternative positions at other IRFs or risk being unable to become board-certified.

We are proposing to implement the policy discussed in this section to remain consistent with the IPPS policy for calculating the temporary IME resident cap adjustment in situations where the receiving hospital assumes the training of displaced residents due to another hospital or residency program's closure. We are also proposing that, in the future, we would deviate from the IPPS IME policy as it pertains to counting displaced residents for the purposes of the IRF teaching status adjustment only when it is necessary and appropriate for the IRF PPS.

The policy adopted in the FY 2012 IRF PPS final rule (76 FR 47846 through 47848), published August 5, 2011, permits an IRF to temporarily adjust its FTE cap to reflect displaced residents added to their residency program because of another IRF closure or IRF residency program closure. In that final rule, we adopted the IPPS definition of “closure of a hospital” at § 413.79(h)(1)(i) to also apply to IRF, and to mean that the IRF terminates its Medicare provider agreement as specified in § 489.52. We also adopted the IPPS definition of “closure of a hospital residency training program” as it is currently defined at § 413.79(h)(1)(ii) to also apply to IRF residency training program closures, and to mean that the IRF ceases to offer training for residents in a particular approved medical residency training program. In this proposed rule, we are proposing to codify both of these definitions within the IRF PPS definitions section provided at § 412.602 so that the IRF teaching policies are more centrally located and more easily accessible.

Although not explicitly stated in the regulations, our current policy is that a displaced resident is one that is physically present at the hospital training on the day prior to or the day of hospital or residency program closure. This longstanding policy derived from the fact that there are requirements that the receiving IRF identifies the residents “who have come from the closed IRF” or identifies the residents “who have come from another IRF's closed residency program,” and that the IRF that closed its program identifies “the residents who were in training at the time of the residency program's closure.” We considered the residents who were physically present at the IRF to be those residents who were “training at the time of the program's closure,” thereby granting them the status of “displaced residents.” Although we did not want to limit the “displaced residents” to only those physically present at the time of closure, it becomes much more administratively challenging for the following groups of residents at closing IRFs/residency programs to continue their training:

(1) Residents who leave the program after the closure is publicly announced to continue training at another IRF, but before the actual closure;

(2) Residents assigned to and training at planned rotations at other IRFs who will be unable to return to their rotations at the closing IPF or program; and

(3) Individuals (such as medical students or would-be fellows) who matched into resident programs at the closing IRF or residency program, but have not yet started training at the closing IRF or residency program.

Other groups of residents who, under current policy, are already considered “displaced residents” include—

(1) Residents who are physically training in the IRF on the day prior to or day of residency program or IRF closure; and

(2) Residents who would have been at the closing IRF or IRF residency program on the day prior to or day of closure, but were on approved leave at that time, and are unable to return to their training at the closing IRF or IRF residency training program.

We are proposing to amend our IRF policy with regard to closing teaching IRFs and closing IRF medical residency training programs to address the needs of interns and residents attempting to find alternative IRFs in which to complete their training. Additionally, this proposal addresses the incentives of originating and receiving IRFs with regard to ensuring we appropriately account for their indirect teaching costs by way of an appropriate IRF teaching adjustment based on each program's FTE resident count. We are proposing to make changes to the current IRF teaching status adjustment policy related to displaced residents as discussed below.

First, rather than link the status of displaced residents for the purpose of the receiving IRF's request to increase their FTE cap to the resident's presence at the closing IRF or program on the day prior to or the day of the residency program or IRF closure, we are proposing to link the status of the displaced residents to the day that the closure was publicly announced (for example, via a press release or a formal notice to the Accreditation Council on Graduate Medical Education). This would provide great flexibility for the interns and residents to transfer while the IRF operations or teaching programs are winding down, rather than waiting until the last day of IRF or IRF teaching program operation. This would address the needs of the group of residents who would leave the program after the closure was publicly announced to continue training at another hospital, but before the day of actual closure.

Second, by removing the link between the status of displaced residents and their presence at the closing IRF or residency program on the day prior to or the day of the IRF closure or program closure, we propose to also allow the residents assigned to and training at planned rotations at other IRFs who will be unable to return to their rotations at the closing IRF or program and individuals (such as medical students or would-be fellows) who matched into resident programs at the closing IRF or residency program, but have not yet started training at the closing IRF or residency program, to be considered a displaced resident.

Thus, we are proposing to revise our teaching policy with regard to which residents can be considered “displaced” for the purpose of the receiving IRF's request to increase their IRF cap in the situation where an IRF announces publicly that it is closing, and/or that it is closing an IRF residency program. Specifically, we are proposing to adopt the FY 2021 IPPS final rule definition of “displaced resident” as defined at § 413.79(h)(1)(ii), for the purpose of calculating the IRF's teaching status adjustment.

In addition, we are proposing to change another detail of the policy specific to the requirements for the receiving IRF. To apply for the temporary increase in the FTE resident cap, the receiving IRF would have to submit a letter to its Medicare Administrative Contractor (MAC) within 60 days after beginning to train the displaced interns and residents. As established in the FY 2012 IRF PPS final rule, this letter must identify the residents who have come from the closed IRF or closed residency program and caused the receiving IRF to exceed its cap, and must specify the length of time that the adjustment is needed. Furthermore, to maintain consistency with the IPPS IME policy, we are proposing that the letter must also include:

(1) The name of each displaced resident;

(2) The last four digits of each displaced resident's social security number; this will reduce the amount of personally identifiable information (PII);

(3) The name of the IRF and the name of the residency program or programs in which each resident was training at previously; and

(4) The amount of the cap increase needed for each resident (based on how much the receiving IRF is in excess of its cap and the length of time for which the adjustments are needed).

As we previously discussed in the FY 2012 IRF PPS final rule (76 FR 47846 through 47848), we are also clarifying that the maximum number of FTE resident cap slots that could be transferred to all receiving IRFs is the number of FTE resident cap slots belonging to the IRF that has closed the resident training program, or that is closing. Therefore, if the originating IRF is training residents in excess of its cap, then being a displaced resident does not guarantee that a cap slot will be transferred along with the resident. Therefore, we are proposing that if there are more IRF displaced residents than available cap slots, the slots may be apportioned according to the closing IRF's discretion. The decision to transfer a cap slot if one is available would be voluntary and made at the sole discretion of the originating IRF. However, if the originating IRF decides to do so, then it would be the originating IRF's responsibility to determine how much of an available cap slot would go with a particular resident (if any). We also note that, as we previously discussed in the FY 2012 IRF PPS final rule (76 FR 47846 through 47848), only to the extent a receiving IRF would exceed its FTE cap by training displaced residents would it be eligible for a temporary adjustment to its resident FTE cap. As such, displaced residents are factored into the receiving IRF's ratio of resident FTEs to the facility's average daily census.

We invite public comment on the proposed updates to the IRF teaching policy.

VIII. Solicitation of Comments Regarding the Facility-Level Adjustment Factor Methodology

Section 1886(j)(3)(A)(v) of the Act confers broad authority upon the Secretary to adjust the per unit payment rate “by such . . . factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among rehabilitation facilities.” Under this authority, we currently adjust the prospective payment amount associated with a CMG to account for facility-level characteristics such as a facility's percentage of low-income patients (LIP), teaching status, and location in a rural area, if applicable, as described in §  412.624(e).

The facility-level adjustment factors are intended to account for differences in costs attributable to the different types of IRF providers and to better align payments with the costs of providing IRF care. The LIP and rural facility-level adjustment factors have been utilized since the inception of the IRF PPS, while the teaching status adjustment factor was finalized in the FY 2006 IRF PPS final rule (70 FR 47880) when our regression analysis indicated that it had become statistically significant in predicting IRF costs. Each of the facility-level adjustment factors were implemented using the same statistical approach, that is, utilizing coefficients determined from regression analysis.

Historically, we have observed relatively large fluctuations in these factors from year-to-year which lead us to explore a number of options to provide greater stability and predictability between years and increase the accuracy of Medicare payments for IRFs. In addition to holding these factors constant over multiple years to mitigate fluctuations in payments, we also implemented a number of refinements to the methodology used to calculate the adjustment factors in efforts to better align payments with the costs of care. For example, in FY 2010 (74 FR 39762) we implemented a 3-year moving average approach to updating the facility-level adjustment factors to promote more consistency in the adjustment factors over time. Additionally, in FY 2014 (78 FR 47859) we added an indicator variable for a facility's freestanding or hospital-based status to the payment regression to improve the accuracy of the IRF payment adjustments. This variable was added to control for differences in cost structure between hospital-based and freestanding IRFs in the regression analysis, so that these differences would not inappropriately influence the adjustment factor estimates. We refer readers to the FY 2015 IRF PPS final rule (79 FR 45882 through 45883) for a full discussion of the refinements that have been made to the methodology used to determine the facility-level adjustment factors and other analysis that has been considered over time. Due to the revisions to the regression analysis and the substantive changes to the facility-level adjustment factors that were adopted in the FY 2014 IRF PPS final rule, we finalized a proposal in the FY 2015 IRF PPS final rule (79 FR 45871) to freeze the facility-level adjustment factors for FY 2015 and all subsequent years at the FY 2014 levels while we continued to monitor changes in the adjustment factors over time. Table 8 shows how the IRF facility-level adjustment factors have changed over time since the start of the IRF PPS:

We have continued monitoring the adjustment factors using the same methodology described in the FY 2014 IRF PPS final rule (78 FR 47869). That is, we have continued to calculate the facility-level adjustment factors using the following the steps:

(Steps 1 and 2 are performed independently for each of three years of IRF claims data)

Step 1. Calculate the average cost per case for each IRF in the available IRF claims data.

Step 2. Perform a logarithmic regression analysis on the average cost per case to compute the coefficients for the rural, LIP, and teaching status adjustments. This regression analysis incorporates an indicator variable to account for whether a facility is a freestanding IRF hospital or a unit of an acute care hospital (or a CAH).

Step 3. Calculate a mean for each of the coefficients across the 3 years of data (using logarithms for the LIP and teaching status adjustment coefficients (because they are continuous variables), but not for the rural adjustment coefficient (because the rural variable is either zero (if not rural) or 1 (if rural)). To compute the LIP and teaching status adjustment factors, we convert these factors back out of the logarithmic form.

Additional information on the regression analysis used to calculate the facility-level adjustment factors can be found on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientRehabFacPPS/​Research. We have continued to monitor changes in the facility-level adjustment factors for each FY since they were frozen in FY 2015 at the FY 2014 levels. Table 9, contains the rural, LIP, and teaching status adjustment factors for each FY since they were frozen at their 2014 levels.

Table 10. Shows the potential estimated impacts of updating the facility-level adjustments for FY 2023.

Table 10 shows how we estimate that the application of the FY 2023 facility-level adjustment factors would affect particular groups if we were to implement updates to these factors for FY 2023. Table 10 categorizes IRFs by geographic location, including urban or rural location, and location for CMS' 9 Census divisions of the country. In addition, Table 10 divides IRFs into those that are separate rehabilitation hospitals (otherwise called freestanding hospitals in this section), those that are rehabilitation units of a hospital (otherwise called hospital units in this section), rural or urban facilities, ownership (otherwise called for-profit, non-profit, and government), by teaching status, and by disproportionate share patient percentage (DSH PP).

Note that, because the facility-level adjustment factors are implemented in a budget-neutral manner, total estimated aggregate payments to IRFs would not be affected. However, these updates would affect the distribution of payments across providers.

Typically, the facility-level adjustment factors have been updated on an intermittent basis to reflect changes in the costs of caring for patients. However, given the magnitude of the increases we are consistently seeing in the teaching status adjustment we do not believe that they are true reflections of the higher costs of teaching IRFs. In addition, we are concerned with the negative effects that the inordinately high teaching status adjustments would have on rural IRFs, given that the updates would be implemented in a budget neutral manner.

Given the changes in the teaching status adjustment and the rural adjustment from their 2014 levels and the potential payment impacts associated with these adjustments, we are soliciting comments from stakeholders on the methodology used to determine the facility-level adjustment factors and suggestions for possible updates and refinements to this methodology. Additionally, we welcome ideas and suggestions as to what could be driving the changes observed in these adjustment factors from year-to-year.

IX. Solicitation of Comments Regarding the IRF Transfer Payment Policy

In the Medicare Program; Prospective Payment System for Inpatient Rehabilitation Facilities final rule that appeared in the August 7, 2001 Federal Register (66 FR 41353 through 41355), we finalized a transfer payment policy under § 412.624(f) to provide for payments that more accurately reflect facility resources used and services delivered. This reflected our belief that it is important to minimize the inherent incentives specifically associated with the early transfer of patients in a discharge-based payment system. Specifically, we were concerned that incentives might exist for IRFs to discharge patients prematurely, as well as to admit patients that may not be able to endure intense inpatient therapy services. Even if patients were transferred before receiving the typical, full course of inpatient rehabilitation, the IRF could still be paid the full CMG payment rate in the absence of a transfer payment policy. Length of stay has been shown to be a good proxy measure of costs. Thus, in general, reducing lengths of stay would be profitable under the IRF prospective payment system. To address these concerns, we therefore implemented a transfer payment policy, which took effect beginning January 1, 2002, that, under certain circumstances, reduced the full CMG payment rate when a Medicare beneficiary is transferred.

The IRF transfer payment policy applies to IRF stays that are less than the average length of stay for the applicable CMG and tier and are transferred directly to another institutional site, including another IRF, an inpatient hospital, a nursing home that accepts payment under Medicare and Medicaid, or a long-term care hospital. However, the IRF transfer payment policy currently does not apply to IRF stays that are less than the average length of stay for the applicable CMG and tier and are transferred to home health care.

In the August 7, 2001 final rule (66 FR 41353 through 41355), we stated that we did not propose to include early discharges to home health care as part of the transfer payment policy because there were analytical challenges as a result of the recent implementation of the new home health prospective payment system. However, to date, the analytical challenges would not present an issue as we feel the home health payment system is well established with an adequate supply of claims data.

A recent Office of Inspector General (OIG) report, “Early Discharges From Inpatient Rehabilitation Facilities to Home Health Services” ^[12] recommends that CMS expand the IRF transfer payment policy to apply to early discharges to home health. The OIG recommends that the IRF PPS should update its transfer payment policy, similar to the IPPS transfer payment policy, to include home health. The OIG conducted an audit of calendar year 2017 and 2018 Medicare claims data and determined that if CMS had expanded its IRF transfer payment policy to include early discharges to home health it could have realized a significant savings of approximately $993 million over the 2-year period to Medicare.

Initially, home health was not added to the IRF transfer policy due to a lack of home health claims data under the newly-established prospective payment system that we could analyze to determine the impact of this policy change. However, given the findings from the recent OIG report mentioned above, we plan to analyze home health claims data to determine the appropriateness of including home health in the IRF transfer policy:

• Beyond the existing Medicare claims data, under what circumstances, and for what types of patients (in terms of clinical, demographic, and geographic characteristics) do IRFs currently transfer patients to home health?
• Should we consider a policy similar to the IPPS transfer payment policy (see § 412.4(a), (b) and (c))—such as including as part of the IRF transfer payment policy a discharge from an IRF to home health under a written plan for the provision of home health services from a home health agency and those services to begin within 48 hours of referral, or within 48 hours of the patient's return home (see § 484.55(a)(1)), or on the provider's start of care date?
• What impact, if any, do stakeholders believe this proposed policy change could have on patient access to appropriate post-acute care services?

While we are not proposing to include home health care as part of the IRF transfer payment policy at this time, we hope to use this information from stakeholders in conjunction with our future analysis for potential rulemaking.

X. Inpatient Rehabilitation Facility (IRF) Quality Reporting Program (QRP)

A. Background and Statutory Authority

The Inpatient Rehabilitation Facility Quality Reporting Program (IRF QRP) is authorized by section 1886(j)(7) of the Act, and it applies to freestanding IRFs, as well as inpatient rehabilitation units of hospitals or Critical Access Hospitals (CAHs) paid by Medicare under the IRF PPS. Under the IRF QRP, the Secretary must reduce by 2 percentage points the annual increase factor for discharges occurring during a fiscal year for any IRF that does not submit data in accordance with the IRF QRP requirements established by the Secretary. For more information on the background and statutory authority for the IRF QRP, we refer readers to the FY 2012 IRF PPS final rule (76 FR 47873 through 47874), the CY 2013 Hospital Outpatient Prospective Payment System/Ambulatory Surgical Center (OPPS/ASC) Payment Systems and Quality Reporting Programs final rule (77 FR 68500 through 68503), the FY 2014 IRF PPS final rule (78 FR 47902), the FY 2015 IRF PPS final rule (79 FR 45908), the FY 2016 IRF PPS final rule (80 FR 47080 through 47083), the FY 2017 IRF PPS final rule (81 FR 52080 through 52081), the FY 2018 IRF PPS final rule (82 FR 36269 through 36270), the FY 2019 IRF PPS final rule (83 FR 38555 through 38556), the FY 2020 IRF PPS final rule (84 FR 39054 through 39165) and the FY 2022 IRF PPS final rule (86 FR 42384 through 42408).

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

For a detailed discussion of the considerations we use for the selection of IRF QRP quality, resource use, or other measures, we refer readers to the FY 2016 IRF PPS final rule (80 FR 47083 through 47084).

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

The IRF QRP currently has 18 measures for the FY 2023 program year, which are set out in Table 11.

There are no proposals in this proposed rule for new measures for the IRF QRP.

C. IRF QRP Quality Measure Concepts Under Consideration for Future Years: Request for Information (RFI)

We are seeking input on the importance, relevance, and applicability of each of the concepts under consideration listed in Table 12 for future years in the IRF QRP. More specifically, we are seeking input on a cross-setting functional measure that would incorporate the domains of self-care and mobility. Our measure development contractor for the cross-setting functional outcome measure convened a Technical Expert Panel (TEP) on June 15 and June 16, 2021 to obtain expert input on the development of a functional outcome measure for PAC. During this meeting, the possibility of creating one measure to capture both self-care and mobility was discussed. We are also seeking input on measures of health equity, such as structural measures that assess an organization's leadership in advancing equity goals or assess progress towards achieving equity priorities. Finally, we seek input on the value of a COVID-19 Vaccination Coverage measure that would assess whether IRF patients were up to date on their COVID-19 vaccine.

While we will not be responding to specific comments in response to this Request for Information in the FY 2023 IRF PPS final rule, we intend to use this input to inform our future measure development efforts.

D. Inclusion of the National Healthcare Safety Network (NHSN) Healthcare-Associated Clostridioides Difficile Infection Outcome Measure in the IRF QRP—Request for Information

1. Background

The IRF QRP is authorized by section 1886(j)(7) 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 IRF QRP and CMS' 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.^[13] As a result, we have identified 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.

2. Potential Future Inclusion of an Electronic Health Record Driven Digital National Healthcare Safety Network (NHSN) Measure

In the FY 2015 IRF PPS final rule (79 FR 45913 through 45914), we finalized the National Healthcare Safety Network (NHSN) Facility-Wide Inpatient Hospital-onset Clostridium difficile Infection (CDI) Outcome Measure (NQF #1717) for inclusion in the IRF QRP. Clostridioides difficile ( C. difficile ) is responsible for a spectrum of CDIs, including uncomplicated diarrhea, pseudomembranous colitis, and toxic megacolon, which can, in some instances, lead to sepsis and even death. CDIs are one of the most common healthcare-associated infections (HAIs), as healthcare-associated CDIs affected 0.54 percent of all hospitalizations in a 2015 survey.^[14] In 2017, the CDC estimated there were 223,900 CDIs requiring hospitalizations in the United States with 12,800 resulting in deaths.^[15] We have recently identified the NHSN Healthcare-Associated Clostridioides Difficile Infection (HA-CDI) Outcome measure as a potential measure which utilizes Electronic Health Record (EHR)-derived data to help address hospital-based adverse events, specifically hospital-onset infections.

CDIs are currently reported to the CDC's NHSN by various mechanisms, one of which is based on laboratory-identified events collected in the NHSN. The IRF QRP measure, the NHSN Facility-Wide Inpatient Hospital CDI Outcome Measure does not utilize EHR-derived data. Rather IRFs collect data and submit it on a monthly basis to the CDC's NHSN using the CDC's NHSN Multidrug-Resistant Organism & Clostridioides difficile Infection (MDRO/CDI) Module. The CDC has now developed the NHSN HA-CDI Outcome measure that utilizes EHR-derived data.

The newly-developed version of the measure, the NHSN HA-CDI, would improve on the original version of the measure in two ways. First, the new measure would require both microbiologic evidence of C. difficile in stool and evidence of antimicrobial treatment, whereas the original measure only requires C. difficile facility-wide Laboratory-Identified (Lab-ID) event reporting. Second, consistent with the Meaningful Measures Framework, we specifically believe it would reduce reporting and regulatory burden on providers and accelerate the move to fully digital measures.^[16] We discuss each of these improvements below.

CDI testing practices have continued to evolve, with recent guidelines from the Infectious Disease Society of America recommending a multi-step testing algorithm to better distinguish between C. difficile colonization and active infection.^[17] However, the growing number of testing algorithms in use, each with different performance characteristics, poses a challenge for CDI surveillance. This new CDI measure defines CDI using both a positive microbiological test for C. difficile and evidence of treatment, increasing the specificity and sensitivity of the measure. Adding a requirement of CDI treatment to a CDI surveillance measure would increase the clinical validity of the measure, since a record of CDI treatment serves as a proxy for C. difficile test results that were interpreted as true infections by the clinician.

We believe there are important reasons for IRFs to adopt and utilize EHRs, although we understand that for IRFs who do not yet use EHRs, there will be initial implementation and training costs. EHRs facilitate moving to fully digital measures which we believe reduces reporting and regulatory burden on providers. Additionally, both surveys ^[18 19] and studies ^[20 21] have demonstrated that when healthcare providers have access to complete and accurate information, patients receive better medical care, including timely identification and treatment of infections. We believe the utilization of EHRs can improve the ability to diagnose diseases and reduce (even prevent) medical errors, both of which improve patient outcomes. Additionally, the use of a fully digital measure using a Measure Calculation Tool (MCT) that pulls data directly from the EHR via a standardized FHIR interface would eliminate multiple steps for the provider, including creating or updating monthly reporting plans, and completing the data fields required for both numerator and denominator every month, even when no events were identified. Finally, the locally installed MCT would be responsible for extracting data, calculating the measure and submitting the data and would eliminate the need for the IRF to manually enter the data into the NHSN web-based application or via file imports. For example, if each IRF executed approximately one C. difficile event per month (12 events per IRF annually), then using 2020 Bureau of Labor Statistics (BLS) data,^[22] we estimate a potential cost savings of approximately 3 hours per IRF per year and a total of $191.38 per IRF per year if a digital version of the measure replaced the NHSN-based measure.^[23]

3. Overview of the NHSN Healthcare-Associated Clostridioides difficile Infection Outcome Measure

The EHR driven digital version of the NHSN HA-CDI Outcome measure would track the development of new CDI among patients already admitted to IRFs, using algorithmic determinations from data sources widely available in EHRs.

The numerator would include those patient records with a qualifying C. difficile -positive assay on an inpatient encounter on day 4 or later of an IRF admission and with no previously positive event in ≤14 days before the IRF encounter, and new qualifying antimicrobial therapy for C. difficile started within the appropriate window period of stool specimen collection. The denominator would be the number of patients admitted to IRFs.

The NHSN HA-CDI Outcome measure would use the Standardized Infection Ratio (SIR) of hospital-onset CDIs among patients to compare within facility types. SIR is a primary summary statistic used by the NHSN to track HAIs. The Adjusted Ranking Metric (ARM) is a new statistic currently available for acute care hospitals that accounts for differences in the volume of exposure (specifically, in the denominator) between facilities. ARM provides complementary information to SIR and was developed for use in acute-care hospitals, but is also intended for use in post-acute care facilities.^[24]

4. Measure Application Partnership (MAP) Review

The NHSN HA-CDI Outcome measure (MUC2021-098) was included in the publicly available “List of Measures Under Consideration for December 1, 2021” (MUC List),^[25] a list of measures under consideration for use in various Medicare programs, including the IRF QRP. This allows multi-stakeholder groups to provide recommendations to the Secretary on the measures included on the list.

The NHSN HA-CDI Outcome measure was included under the IRF QRP Program on the MUC List. The National Quality Forum (NQF)-convened MAP Post-Acute Care—Long Term Care (PAC-LTC) Workgroup met on January 19, 2022 and provided input on the proposed measure. The MAP offered conditional support of the NHSN HA-CDI Outcome measure for rulemaking contingent upon NQF endorsement, noting that the measure has the potential to mitigate unintended consequences from the current measure's design, which counts a case based on a positive test only, which may have led to a historical under-counting of observed HA-CDIs. The MAP recognized that the measure is consistent with the program's priority to measure HAIs and the Patient Safety Meaningful Measures 2.0 area.^[26] The final MAP report is available at https://www.qualityforum.org/​Publications/​2022/​03/​MAP_​2021-2022_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx.

5. Data Sources

The data source for the NHSN HA-CDI Outcome measure would be the IRFs' EHR. The primary sources of data for determining numerator events would include microbiology data ( C. difficile infection test), medication administration data ( C. difficile infection antimicrobial treatment), and patient encounter, demographic, and location information.

To facilitate rapid, automated, and secure data exchange, the CDC's NHSN is planning to enable and promote reporting of this measure using Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). However, as HL7 FHIR capabilities are evolving and not uniform across healthcare systems, CDC is also planning on enabling reporting using the existing HL7 Clinical Document Architecture (CDA), and potentially other formats as well in order to provide all facilities with an option for reporting. Furthermore, this measure would not immediately replace the current NHSN CDI measure. NHSN would continue to host and support the current CDI measure until sufficient experience is achieved with the new measure to phase out the current CDI measure in each applicable setting.

6. Solicitation of Public Comment

In this proposed rule, we are requesting stakeholder input on the potential electronic submission of quality data from IRFs via their EHRs under the IRF QRP. We specifically seek comment on the future inclusion of the NHSN Healthcare-Associated Clostridioides difficile Infection Outcome measure (HA-CDI) (MUC2021-098) as a digital quality measure in the IRF QRP.

Specifically, we seek comment on the following:

• Would you support utilizing IRF EHRs as the mechanism of data collection and submission for IRF QRP measures?
• Would your EHR support exposing data via HL7 FHIR to a locally installed MCT? For IRFs using certified health IT systems, how can existing certification criteria under the Office of the National Coordinator (ONC) Health Information Technology (IT) Certification Program support reporting of this data? What updates, if any, to the Certification Program would be needed to better support capture and submission of this data?
• Is a transition period between the current method of data submission and an electronic submission method necessary? If so, how long of a transition would be necessary and what specific factors are relevant in determining the length of any transition?
• Would vendors, including those that service IRFs, be interested in or willing to participate in pilots or voluntary electronic submission of quality data?
• Do IRFs anticipate challenges, other than the adoption of EHR to adopting the HA-CDI, and if so, what are potential solutions for those challenges?

While we will not be responding to specific comments submitted in response to this RFI in the FY 2023 IRF PPS final rule, we will actively consider all input as we develop future regulatory proposals. 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.

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

Significant and persistent disparities in healthcare outcomes exist in the United States. Belonging to an underserved community is often associated with worse health outcomes.^{[27 28 29 30 31 32 33 34 35]} With this in mind, CMS aims to advance health equity, by which we mean the attainment of the highest level of health for all people, where everyone has a fair and just opportunity to attain their optimal health regardless of race, ethnicity, disability, sexual orientation, gender identity, socioeconomic status, geography, preferred language, or other factors that affect access to care and health outcomes. CMS is working to advance health equity by designing, implementing, and operationalizing policies and programs that support health for all the people served by our programs, eliminating avoidable differences in health outcomes experienced by people who are disadvantaged or underserved, and providing the care and support that our beneficiaries need to thrive.^[36]

We are committed to achieving equity in healthcare outcomes for our enrollees by supporting healthcare providers' quality improvement activities to reduce health disparities, enabling them to make more informed decisions, and promoting healthcare provider accountability for healthcare disparities.^[37] Measuring healthcare disparities in quality measures is a cornerstone of our approach to advancing healthcare equity. Hospital performance results that illustrate differences in outcomes between patient populations have been reported to hospitals confidentially since 2015. We provide additional information about this program in section X.E.1.a. of this proposed rule.

This RFI consists of three sections. The first section discusses a general framework that could be utilized across CMS quality programs to assess disparities in healthcare quality. The next section outlines approaches that could be used in the IRF QRP to assess drivers of healthcare quality disparities in the IRF QRP. Additionally, this section discusses measures of health equity that could be adapted for use in the IRF QRP. Finally, the third section solicits public comment on the principles and approaches listed in the first two sections as well as seeking other thoughts about disparity measurement guidelines for the IRF QRP.

1. Cross-Setting Framework To Assess Healthcare Quality Disparities

CMS has identified five key considerations that we could apply consistently across CMS programs when advancing the use of measurement and stratification as tools to address health care disparities and advance health equity. The remainder of this section describes each of these considerations.

a. Identification of Goals and Approaches for Measuring Healthcare Disparities and Using Measures Stratification Across CMS Quality Programs

By quantifying healthcare disparities through measure stratification (that is, measuring performance differences among subgroups of beneficiaries), we aim to provide useful tools for healthcare providers to drive improvement based on data. We hope that these results support healthcare providers efforts in examining the underlying drivers of disparities in their patients' care and to develop their own innovative and targeted quality improvement interventions. Quantification of health disparities can also support communities in prioritizing and engaging with healthcare providers to execute such interventions, as well as providing additional tools for accountability and decision-making.

There are several different conceptual approaches to reporting health disparities in the acute care setting, including two complementary approaches that are already used to confidentially provide disparity information to hospitals for a subset of existing measures. The first approach, referred to as the “within-hospital disparity method,” compares measure performance results for a single measure between subgroups of patients with and without a given factor. This type of comparison directly estimates disparities in outcomes between subgroups and can be helpful to identify potential disparities in care. This type of approach can be used with most measures that include patient-level data. The second approach, referred to as the “between-hospital disparity methodology,” provides performance on measures for only the subgroup of patients with a particular social risk factor. These approaches can be used by a healthcare provider to compare their own measure performance on a particular subgroup of patients against subgroup-specific state and national benchmarks. Alone, each approach may provide an incomplete picture of disparities in care for a particular measure, but when reported together with overall quality performance, these approaches may provide detailed information about where differences in care may exist or where additional scrutiny may be appropriate. For example, the between-provider disparity method may indicate that an IRF underperformed (when compared to other facilities on average) for patients with a given social risk factor, which would signal the need to improve care for this population. However, if the IRF also underperformed for patients without that social risk factor, the measured difference, or disparity in care, (the “within-hospital” disparity, as described above) could be negligible even though performance for the group that has been historically marginalized remains poor. We refer readers to the technical report describing the CMS Disparity Methods in detail as well as the FY 2018 IPPS/LTCH PPS final rule (82 FR 38405 through 38407) and the posted Disparity methods Updates and Specifications Report posted on the QualityNet website.^[38]

CMS is interested in whether similar approaches to the two discussed in the previous paragraph could be used to produce confidential stratified measure results for selected IRF QRP measures, as appropriate and feasible. However, final decisions regarding disparity reporting will be made at the program-level, as CMS intends to tailor the approach used in each setting to achieve the greatest benefit and avoid unintentional consequences or biases in measurement that may exacerbate disparities in care.

b. Guiding Principles for Selecting and Prioritizing Measures for Disparity Reporting

We intend to expand our efforts to provide stratified reporting for additional clinical quality measures, provided they offer meaningful, actionable, and valid feedback to healthcare providers on their care for populations that may face social disadvantage or other forms of discrimination or bias. We are mindful, however, that it may not be possible to calculate stratified results for all quality measures, and that there may be situations where stratified reporting is not desired. To help inform prioritization of the next generation of candidate measures for stratified reporting, we aim to receive feedback on several systematic principles under consideration that we believe will help us prioritize measures for disparity reporting across programs:

(1) Programs may consider stratification among existing clinical quality measures for further disparity reporting, prioritizing recognized measures which have met industry standards for measure reliability and validity.

(2) Programs may consider measures for prioritization that show evidence that a treatment or outcome being measured is affected by underlying healthcare disparities for a specific social or demographic factor. Literature related to the measure or outcome should be reviewed to identify disparities related to the treatment or outcome, and should carefully consider both social risk factors and patient demographics. In addition, analysis of Medicare-specific data should be done in order to demonstrate evidence of disparity in care for some or most healthcare providers that treat Medicare patients.

(3) Programs may consider establishing statistical reliability and representation standards (for example, the percent of patients with a social risk factor included in reporting facilities) prior to reporting results. They may also consider prioritizing measures that reflect performance on greater numbers of patients to ensure that the reported results of the disparity calculation are reliable and representative.

(4) After completing stratification, programs may consider prioritizing the reporting of measures that show differences in measure performance between subgroups across healthcare providers.

c. Principles for Social Risk Factor and Demographic Data Selection and Use

Social risk factors are the wide array of non-clinical drivers of health known to negatively impact patient outcomes. These include factors such as socioeconomic status, housing availability, and nutrition (among others), often inequitably affecting historically marginalized communities on the basis of race and ethnicity, rurality, sexual orientation and gender identity, religion, and disability.^{[39 40 41 42 43 44 45 46]}

Identifying and prioritizing social risk or demographic variables to consider for disparity reporting can be challenging. This is due to the high number of variables that have been identified in the literature as risk factors for poorer health outcomes and the limited availability of many self-reported social risk factors and demographic factors across the healthcare sector. Several proxy data sources, such as area-based indicators of social risk and imputation methods, may be used if individual patient-level data is not available. Each source of data has advantages and disadvantages for disparity reporting:

• Patient-reported data are considered to be the gold standard for evaluating quality of care for patients with social risk factors.^[47] While data sources for many social risk factors and demographic variables are still developing among several CMS settings, demographic data elements collected through assessments already exist in IRFs. Beginning October 1, 2022, IRFs (86 FR 62386) will also begin collecting additional standardized patient data elements about race, ethnicity, preferred language, transportation, health literacy, and social isolation.

• CMS Administrative Claims data have long been used for quality measurement due to their availability and will continue to be evaluated for usability in measure development and or stratification. Using these existing data allows for high impact analyses with negligible healthcare provider burden. For example, dual eligibility for Medicare and Medicaid has been found to be an effective indicator of social risk in beneficiary populations.^[48] There are, however, limitations in these data's usability for stratification analysis.

• Area-based indicators of social risk create approximations of patient risk based on the neighborhood or context that a patient resides in. Several indexes, such as Agency for Healthcare Research and Quality (AHRQ) Socioeconomic Status (SES) Index,^[49] Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI),^[50] and Health Resources and Services Administration (HRSA) Area Deprivation Index (ADI),^[51] provide multifaceted contextual information about an area and may be considered as an efficient way to stratify measures that include many social risk factors.

• Imputed data sources use statistical techniques to estimate patient-reported factors, including race and ethnicity. One such tool is the Medicare Bayesian Improved Surname Geocoding (MBISG) method (currently in version 2.1), which combines information from administrative data, surname, and residential location to estimate patient race and ethnicity.^[52]

d. Identifying Meaningful Performance Differences

While we aim to use standardized approaches where possible, identifying differences in performance on stratified results will be made at the program level due to contextual variations across programs and settings. We look forward to feedback on the benefits and limitations of the possible reporting approaches described below:

• Statistical approaches could be used to reliably group results, such as using confidence intervals, creating cut points based on standard deviations, or using a clustering algorithm.

• Programs could use a ranked ordering and percentile approach, ordering healthcare providers in a ranked system based on their performance on disparity measures to quickly allow them to compare their performance to other similar healthcare providers.

• Healthcare providers could be categorized into groups based on their performance using defined thresholds, such as fixed intervals of results of disparity measures, indicating different levels of performance.

• Benchmarking, or comparing individual results to state or national average, is another potential reporting strategy.

• Finally, a ranking system may not be appropriate for all programs and care settings, and some programs may only report disparity results.

e. Guiding Principles for Reporting Disparity Measures

Reporting of the results discussed above can be employed in several ways to drive improvements in quality. Confidential reporting, or reporting results privately to healthcare providers, is generally used for new programs or new measures recently adopted for programs through notice and comment rulemaking to give healthcare providers an opportunity to become more familiar with calculation methods and to improve before other forms of reporting are used. In addition, many results are reported publicly, in accordance with the statute. This method provides all stakeholders with important information on healthcare provider quality, and in turn, relies on market forces to incentivize healthcare providers to improve and become more competitive in their markets without directly influencing payment from CMS. One important consideration is to assess differential impact on IRFs, such as those located in rural, or critical access areas, to ensure that reporting does not disadvantage already resource-limited settings. The type of reporting chosen by programs will depend on the program context.

Regardless of the methods used to report results, it is important to report stratified measure data alongside overall measure results. Review of both measures results along with stratified results can illuminate greater levels of detail about quality of care for subgroups of patients, providing important information to drive quality improvement. Unstratified quality measure results address general differences in quality of care between healthcare providers and promote improvement for all patients, but unless stratified results are available, it is unclear if there are subgroups of patients that benefit most from initiatives. Notably, even if overall quality measure scores improve, without identifying and measuring differences in outcomes between groups of patients, it is impossible to track progress in reducing disparity for patients with heightened risk of poor outcomes.

2. Approaches To Assessing Drivers of Healthcare Quality Disparities and Developing Measures of Healthcare Equity in the IRF QRP

This section presents information on two approaches for the IRF QRP. The first section presents information about a method that could be used to assist IRFs in identifying potential drivers of healthcare quality disparities. The second section describes measures of healthcare equity that might be appropriate for inclusion in the IRF QRP.

a. Performance Disparity Decomposition

In response to the FY 2022 IRF PPS proposed rule's RFI (86 FR 19110 through 19112), “Closing the Health Equity Gap in Post-Acute Care Quality Reporting Programs”, some stakeholders noted that, while stratified results provide more information about disparities compared to overall measure scores, they provide limited information towards understanding the drivers of these disparities. As a result, it is up to the IRFs to determine which factors are leading to performance gaps so that they can be addressed. Unfortunately, identifying which factors are contributing to the performance gaps may not always be straightforward, especially if the IRF has limited information or resources to determine the extent to which a patient's social determinants of health (SDOH) or other mediating factors (for example: Health histories) explain a given disparity. An additional complicating factor is the reality that there are likely multiple SDOH and other mediating factors responsible for a given disparity, and it may not be obvious to the IRF which of these factors are the primary drivers.

Consequently, CMS may consider methods to use the data already available in enrollment, claims, and assessment data to estimate the extent to which various SDOH (for example, transportation, health literacy) and other mediating factors drive disparities in an effort to provide more actionable information. Researchers have utilized decomposition techniques to examine inequality in health care and, specifically, as a way to understand and explain the underlying causes of inequality.^[53] At a high level, regression decomposition is a method that allows one to estimate the extent to which disparities (that is, differences) in measure performance between subgroups of patient populations are due to specific factors. These factors can be either non-clinical (for example, SDOH) or clinical. Similarly, CMS may utilize regression decomposition to identify and calculate the specific contribution of SDOHs and other mediating factors to observed disparities. This approach may better inform our understanding of the extent to which providers and policy-makers may be able to narrow the gap in healthcare outcomes. Additionally, provider-specific decomposition results could be shared through confidential results so that IRFs can see the disparities within their facility with more granularity, allowing them to set priority targets in some performance areas while knowing which areas of their care are already relatively equitable. Importantly, these results could help IRFs identify reasons for disparities that might not be obvious without having access to additional data sources (for example: The ability to link data across providers).

To more explicitly demonstrate the types of information that could be provided through decomposition of a measure disparity, consider the following example for a given IRF. Figures 1 through 3 depict an example (using hypothetical data) of how a disparity in a measure of Medicare Spending Per Beneficiary (MSPB) between dual eligible beneficiaries (that is, those enrolled in Medicare and Medicaid) and non-dual eligible beneficiaries (that is, those with Medicare only) could be decomposed among two mediating factors, one SDOH and one clinical factor: (1) Low health literacy and (2) high volume of emergency department (ED) use. These examples were selected because if they were shown to be drivers of disparity in their IRF, the healthcare provider could mitigate their effects. Additionally, high volume ED use is used as a potential mediating factor that could be difficult for IRFs to determine on their own, as it would require having longitudinal data for patients across multiple facilities.

In Figure 1, the overall Medicare spending disparity is $1,000: Spending, on average, is $5,000 per non-dual beneficiary and $6,000 per dual beneficiary. We can also see from Figure 2 that in this IRF, the dual population has twice the prevalence of beneficiaries with low health literacy and high ED use compared to the non-dual population. Using regression techniques, the difference in overall spending between non-dual and dual beneficiaries can be divided into three causes: (1) A difference in the prevalence of mediating factors (for example: Low health literacy and high ED use) between the two groups, (2) a difference in how much spending is observed for beneficiaries with these mediating factors between the two groups, and (3) differences in baseline spending that are not due to either (1) or (2). In Figure 3, the `Non-Dual Beneficiaries' column breaks down the overall spending per non-dual beneficiary, $5,000, into a baseline spending of $4,600 plus the effects of the higher spending for the 10 percent of non-dual beneficiaries with low health literacy ($300) and the 5 percent with high ED use ($100). The `Dual Beneficiaries' column similarly decomposes the overall spending per dual beneficiary ($6,000) into a baseline spending of $5,000, plus the amounts due to dual beneficiaries' 20 percent prevalence of low health literacy ($600, twice as large as the figure for non-dual beneficiaries because the prevalence is twice as high), and dual beneficiaries' 10 percent prevalence of high-volume ED use ($200, similarly twice as high as for non-duals beneficiaries due to higher prevalence). This column also includes an additional $100 per risk factor because dual beneficiaries experience a higher cost than non-dual beneficiaries within the low health literacy risk factor, and similarly within the high ED use risk factor. Based on this information, an IRF can determine that the overall $1,000 disparity can be divided into differences simply due to risk factor prevalence ($300 + $100 = $400 or 40 percent of the total disparity), disparities in costs for beneficiaries with risk factors ($100 + $100 = $200 or 20 percent) and disparities that remain unexplained (differences in baseline costs: $400 or 40 percent).

In particular, the IRF can see that simply having more patients with low health literacy and high ED use accounts for a disparity of $400. In addition, there is still a $200 disparity stemming from differences in costs between non-dual and dual patients for a given risk factor, and another $400 that is not explained by either low health literacy or high ED use. These differences may instead be explained by other SDOH that have not yet been included in this breakdown, or by the distinctive pattern of care decisions made by providers for dual and non-dual beneficiaries. These cost estimates would provide additional information that facilities could use when determining where to devote resources aimed at achieving equitable health outcomes (that is, facilities may choose to focus efforts on the largest drivers of a disparity).

b. Measures Related to Health Equity

Beyond identifying disparities in individual health outcomes and by individual risk factors, there is interest in developing more comprehensive measures of health equity that reflect organizational performance. When determining which equity measures could be prioritized for development for IRF QRP, CMS may consider the following:

• Measures should be actionable in terms of quality improvement;
• Measures should help beneficiaries and their caregivers make informed healthcare decisions;
• Measures should not create incentives to lower the quality of care; and
• Measures should adhere to high scientific acceptability standards.

CMS has developed measures assessing health equity, or designed to promote health equity, in other settings outside of the IRF. As a result, there may be measures that could be adapted for use in the IRF QRP. The remainder of this section discusses two such measures, beginning with the Health Equity Summary Score (HESS), and then a structural measure assessing the degree of hospital leadership engagement in health equity performance data.

(1) Health Equity Summary Score

The HESS measure was developed by the CMS OMH ^[54 55] to identify and to reward healthcare providers (that is, Medicare Advantage [MA] plans) that perform relatively well on measures of care provided to beneficiaries with social risk factors (SRFs), as well as to discourage the non-treatment of patients who are potentially high-risk, in the context of value-based purchasing. Additionally, a version of the HESS is under consideration for the Hospital Inpatient Quality Reporting (HIQR) program.^[56] The HESS composite measure provides a summary of equity of care delivery by combining performance and improvement across multiple measures and multiple at-risk groups. The HESS was developed with the following goals: Allow for “multiple grouping variables, not all of which will be measurable for all plans,” allow for “disaggregation by grouping variable for nuanced insights,” and allow for the future usage of additional and different SRFs for grouping.^[57]

The HESS computes across-provider disparity in performance, as well as within-provider and across-provider disparity improvement in performance. Calculation starts with a cross-sectional score and an overall improvement score for each SRF of race/ethnicity and dual eligibility, for each plan. The overall improvement score is based on two separate improvement metrics: Within-plan improvement and nationally benchmarked improvement. Within-plan improvement is defined as how that plan improves the care of patients with SRFs relative to higher-performing patients between the baseline period and performance period, and is targeted at eliminating within-plan disparities. Nationally benchmarked improvement is improvement of care for beneficiaries with SRFs served by that MA plan, relative to the improvement of care for similar beneficiaries across all MA plans, and is targeted at improving the overall care of populations with SRFs. Within-plan improvement and nationally benchmarked improvement are then combined into an overall improvement score. Meanwhile, the cross-sectional score measures overall measure performance among beneficiaries with SRFs during the performance period, regardless of improvement.

To calculate a provider's overall score, the HESS uses a composite of five clinical quality measures based on HEDIS data and seven MA Consumer Assessment of Healthcare Providers and Systems (CAHPS) patient experience measures. A provider's overall HESS score is calculated once using only CAHPS-based measures and once using only HEDIS-based measures, due to incompatibility between the two data sources. The HESS uses a composite of these measures to form a cross-sectional score, a nationally benchmarked improvement score, and a within-plan improvement score, one for each SRF. These scores are combined to produce an SRF-specific blended score, which is then combined with the blended score for another SRF to produce the overall HESS.

(2) Degree of Hospital Leadership Engagement in Health Equity Performance Data

We have developed a structural measure for use in acute care hospitals assessing the degree to which hospital leadership is engaged in the collection of health equity performance data, with the motivation that that organizational leadership and culture can play an essential role in advancing equity goals. This structural measure, entitled the Hospital Commitment to Health Equity measure (MUC2021-106) was included on the 2021 CMS List of Measures Under Consideration (MUC List) ^[58] and assesses hospital commitment to health equity using a suite of equity-focused organizational competencies aimed at achieving health equity for racial and ethnic minorities, people with disabilities, sexual and gender minorities, individuals with limited English proficiency, rural populations, religious minorities, and people facing socioeconomic challenges. The measure will include five attestation-based questions, each representing a separate domain of commitment. A hospital will receive a point for each domain where they attest to the corresponding statement (for a total of 5 points). At a high level, the five domains cover the following areas: (1) Strategic plan to reduce health disparities; (2) approach to collecting valid and reliable demographic and SDOH data; (3) analyses performed to assess disparities; (4) engagement in quality improvement activities; ^[59] and (5) leadership involvement in activities designed to reduce disparities. The specific questions asked within each domain, as well as the detailed measure specification are found in the CMS List of MUC for December 2021 at https://www.cms.gov/​files/​document/​measures-under-consideration-list-2021-report.pdf. An IRF could receive a point for each domain where data are submitted through a CMS portal to reflect actions taken by the IRF for each corresponding domain (for a point total).

CMS believes this type of organizational commitment structural measure may complement the health disparities approach described in previous sections, and support IRFs in quality improvement, efficient, effective use of resources, and leveraging available data. As defined by AHRQ, structural measures aim to “give consumers a sense of a healthcare provider's capacity, systems, and processes to provide high-quality care.” ^[60] We acknowledge that collection of this structural measure may impose administrative and/or reporting requirements for IRFs.

We are interested in obtaining feedback from stakeholders on conceptual and measurement priorities for the IRF QRP to better illuminate organizational commitment to health equity.

3. Solicitation of Public Comment

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

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

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

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

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

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

• Guiding Principles for Selecting and Prioritizing Measures for Disparity Reporting

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

• Principles for Social Risk Factor and Demographic Data Selection and Use

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

• Identification of Meaningful Performance Differences

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

• Guiding Principles for Reporting Disparity Measures

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

• Measures Related to Health Equity

++ The usefulness of a HESS score for IRFs, both in terms of provider actionability to improve health equity, and in terms of whether this information would support Care Compare website users in making informed healthcare decisions.

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

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

++ Other options for measures that address health equity.

While we will not be responding to specific comments submitted in response to this RFI in the FY 2023 IRF 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. Proposals Relating to the Form, Manner, and Timing of Data Submission Under the IRF QRP

1. Background

We refer readers to the regulatory text at § 412.634(b) for information regarding the current policies for reporting IRF QRP data.

2. Proposal To Require Quality Data Reporting on All IRF Patients Beginning With the FY 2025 IRF QRP

a. Background

We have received public input for the past 10 years on the need to standardize measurement data collection across all payers in the PAC settings. For example, as part of their recommendations on Coordination Strategy for Post-Acute Care and Long-term Care Performance Measurement,^[61] the National Quality Forum (NQF)-convened Measures Application Partnership (MAP) defined priorities and core measure concepts for PAC, including IRFs, in order to improve care coordination for patients. The MAP concluded that standardized measurement data collection is needed to support the flow of information and data among PAC providers and recommended CMS collect data across all payers. Since the implementation of the Improving Medicare Post-Acute Care Transformation Act of 2014 (IMPACT Act) and the development of the statutorily required quality measures, we have also received public input suggesting that the quality measures used in the IRF QRP should be calculated using data collected from all IRF patients, regardless of the patients' payer. This input has been provided to us through different mechanisms, including comments requested about quality measure development. Specifically, in response to the call for public comment on quality measures to satisfy the IMPACT Act domain of Transfer of Health Information and Care Preferences When an Individual Transitions,^[62] the majority of comments expressed concern over the non-standardized populations across the PAC setting and urged CMS to standardize the patient populations. One commenter stated having an all payer policy in place in some, but not all PAC settings, limits the ability of providers and consumers to interpret the information. In the FY 2018 IRF PPS proposed rule, (82 FR 20740), we sought input on expanding the quality measures to include all patients regardless of payer status. In response to the Request for Information (RFI), several commenters supported expanding the IRF QRP to include all patients regardless of payer. The Medicare Payment Advisory Committee (MedPAC) was supportive of the effort to ensure quality care for all patients, but sensitive to the issue of additional burden, while another commenter questioned whether the use of additional data would outweigh the burden of additional reporting. Other commenters were also supportive, noting that it would not be overly burdensome since most of their organizations' members already complete the IRF-PAI on all patients, regardless of payer status. One commenter supported the idea since collecting information on only a subset of patients could be interpreted as having provided different levels of care based on the payer.

In the FY 2020 IRF PPS Proposed Rule (84 FR 17326 to 17327), CMS proposed to expand IRF quality data reporting on all patients regardless of payer for purposes of the IRF QRP. In the FY 2020 IRF PPS final rule (84 FR 39161 through 39163), we decided not to finalize the proposal at the time, but rather use the comments to help inform a future all payer proposal.

b. Support for Expanding Quality Reporting Data on All IRF Patients

Currently, IRF-PAI assessment data are collected on patients admitted under the Medicare Part A fee-for-service (FFS) and Medicare Part C benefits.^[63]

The concept of requiring quality data reporting on all patients regardless of payer is not new; as part of the Long-Term Care Hospital (LTCH) quality reporting program, CMS currently collects quality data on all patients regardless of payer. CMS also collects quality data on all Hospice patients for the Hospice Quality Reporting Program (HQRP) regardless of payer. Eligible clinicians participating in the Merit-based Incentive Payment System (MIPS) who submit quality measure data on Qualified Clinical Qualified Data Registry (QCDR) measures, MIPS clinical quality measures (CQMs) or electronic clinical quality measures (eCQMs) must submit such data on a specified percentage of patients regardless of payer. Collecting such quality data on all patients in the IRF setting would provide the most robust and accurate representation of quality in the IRFs since CMS does not have access to other payer claims. Additionally, the data would promote higher quality and more efficient health care for Medicare beneficiaries and all patients through the exchange of information and longitudinal analysis of that data.

We believe that data reporting on standardized patient assessment data elements using the IRF-PAI should include all IRF patients for the same reasons we believe that collecting data on Medicare beneficiaries for the IRF QRP's quality measures is important: To achieve equity in healthcare outcomes for our beneficiaries by supporting providers in quality improvement activities, enabling them to make more informed decisions, and promoting provider accountability for healthcare disparities.^[64 65] We believe that we have authority to collect all payer data for the IRF QRP under section 1886(j)(7) of the Act. We believe it is necessary to obtain admission and discharge assessment information on all patients admitted to IRFs in order to obtain full and complete data regarding the quality of care provided by the IRF to the Medicare patients receiving care in that facility. We note, however that this data would not be used by CMS for purposes of updating the IRF PPS payment rates annually. In addition, we note that section 1886(j)(7) of the Act does not limit the Secretary to collecting data only on individuals with Medicare, and therefore this proposal is not inconsistent with CMS' statutory obligations.

We take the appropriate access to care in IRFs very seriously, and routinely monitor the QRP measures' performance, including performance gaps across IRFs. We intend to monitor closely whether any proposed change to the IRF QRP has unintended consequences on access to care for high risk patients. Should we find any unintended consequences, we will take appropriate steps to address these issues in future rulemaking. Expanding the reporting of quality measures to include all patients, regardless of payer, would ensure that the IRF QRP makes publicly available information regarding the quality of services furnished to the IRF population as a whole, rather than limiting it to only those patients with Medicare fee-for service or Medicare Advantage benefits.

We also take the privacy and security of protected health information (PHI) very seriously. Our systems conform to all applicable Federal laws and regulations as well as Federal government, HHS, and CMS policies and standards as they relate to information security and data privacy. The system limits data access to authorized users and monitors such users to ensure against unauthorized data access or disclosures.

While we appreciate that collecting quality data on all patients regardless of payer may create additional burden, we also note that this burden may be partially offset by eliminating the effort to separate out Medicare beneficiaries from other patients, which is also burdensome. We also acknowledge the concerns raised by some stakeholders in the past with respect to the administrative challenges of implementing all payer data collection and the need to account for the burden related to this proposal. In section XI.B. of this proposed rule, we have provided an estimate of additional burden related to this proposal.

c. Proposal To Require Quality Data Reporting on All IRF Patients

In order to facilitate and ensure that high quality care is delivered to all patients, including Medicare beneficiaries, in the IRF setting, we are proposing to require that the IRF-PAI assessment be collected on each patient receiving care in an IRF, regardless of payer, beginning with the FY 2025 IRF QRP. If finalized as proposed, IRFs would be required to report these data with respect to admission and discharge for all patients, regardless of payer, discharged between October 1, 2023 and December 31, 2023. This data would be used (in addition to the data collected January 1, 2023 through September 30, 2023) to calculate an IRF's data completion threshold for the FY 2025 IRF QRP.

If finalized as proposed, we would revise the IRF-PAI in order for IRFs to submit data pursuant to the finalized policy. A new item would replace the current item identifying payment source on the IRF-PAI admission assessment to collect additional payer(s) information. The collection of this item would align with the LTCH setting. A draft IRF PAI containing this new item will be available at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​IRF-Quality-Reporting. We will notify stakeholders when the draft IRF PAI is available.

We invite public comments on this proposal.

3. Proposed Revisions to the Regulation Text To Require IRFs To Submit Patient Assessments on All Patients Beginning With the FY 2025 IRF QRP

As discussed in section X.F.2 of this proposed rule, we are proposing to require that the IRF-PAI assessment be collected on each patient receiving care in an IRF, regardless of payer. Therefore, we also propose, subject to the aforementioned proposal becoming final, to revise the regulation text in §§ 412.604, 412.606, 412.610, 412.614, and 412.618 so that the requirements that IRFs must currently satisfy with respect to collection and submission of IRF-PAI data for Medicare Part A and Medicare Part C patients would also apply to data on all other IRF patients, regardless of payer.

In addition, we note that CMS' regulations at § 412.610(f) currently require IRFs to maintain all PAIs completed on Medicare Part A fee-for-service patients within the previous 5 years and Medicare Part C (Medicare Advantage) patients within the previous 10 years either in a paper format in the patient's clinical record or in an electronic computer file format that the IRF can easily obtain and produce upon request to CMS or its contractors. Subject to the aforementioned all-payer proposal becoming final, we are therefore also proposing to revise the regulation text at § 410.610(f) to require that IRFs maintain PAIs completed on patients receiving care under all other payer sources (that is, other than Medicare Part A and Medicare Part C) for 5 years. We are proposing a 5-year period for the same reasons we proposed a 5-year requirement for Medicare Part A patients in the original Medicare Program; Prospective Payment System for Inpatient Rehabilitation Facilities final rule that appeared in the August 7, 2001 Federal Register (66 FR 41329). Specifically, the assessments may be needed as part of a retrospective review conduced at the IRF for various purposes, including the fact that the completed patient assessments could be beneficial to other entities that appropriately have access to these records (for example, a State or Federal agency conducting an investigation due to a complaint of patient abuse).

The proposed revisions are outlined in §§ 412.604, 412.606, 412.610, 412.614, and 412.618 in the regulation text of this proposed rule. We invite public comments on this proposal.

4. Proposed Revisions to § 412.614(d)(2) To Correct an Error to the Regulatory Text

In accordance with the Administrative Procedure Act, 5 U.S.C. 553, it is the Secretary's practice to offer interested parties the opportunity to comment on proposed regulations. However, the regulatory changes in this proposal are necessary to correct an error and do not establish any new substantive rules.

We are proposing to revise the regulatory text at § 412.614(d)(2) to correct a reference to another part of the regulations. Specifically, we are replacing a reference to § 412.23(b)(2) with the correct reference to § 412.29(b)(1). The proposed revisions are outlined in the regulation text of this proposed rule.

We invite public comments on this proposal.

G. Policies Regarding Public Display of Measure Data for the IRF QRP

We are not proposing any new policies regarding the public display of measure data at this time.

XI. Collection of Information Requirements

A. Statutory Requirement for Solicitation of Comments

Under the Paperwork Reduction Act of 1995, we are required to provide 60-day notice in the Federal Register and solicit public comment before a collection of information requirement is submitted to the Office of Management and Budget (OMB) for review and approval. In order to fairly evaluate whether an information collection should be approved by OMB, section 3506(c)(2)(A) of the Paperwork Reduction Act of 1995 requires that we solicit comment on the following issues:

• The need for the information collection and its usefulness in carrying out the proper functions of our agency.
• The accuracy of our estimate of the information collection burden.
• The quality, utility, and clarity of the information to be collected.
• Recommendations to minimize the information collection burden on the affected public, including automated collection techniques.

This proposed rule makes reference to associated information collections that are not discussed in the regulation text contained in this document.

B. Collection of Information Requirements for Updates Related to the IRF QRP Beginning With the FY 2025 IRF QRP

An IRF that does not meet the requirements of the IRF QRP for a fiscal year will receive a 2-percentage point reduction to its otherwise applicable annual increase factor for that fiscal year.

We believe that the burden associated with the IRF QRP is the time and effort associated with complying with the requirements of the IRF QRP. In section X.F.2. of this proposed rule, we are proposing to update the data reporting requirements for the IRF QRP beginning with the FY 2025 IRF QRP. We are proposing to require IRFs to collect IRF-PAI assessment information on each patient receiving care in an IRF, regardless of payer. We believe the IRF-PAI items are completed by Registered Nurses (RN), Licensed Practical and Licensed Vocational Nurses (LVN), Respiratory Therapists (RT), Speech-Language Pathologists (SLP), Occupational Therapists (OT), Physical Therapists (PT), and/or Psychologists (Psy), depending on the item. We identified the staff type per item based on past IRF burden calculations in conjunction with expert opinion. Our assumptions for staff type were based on the categories generally necessary to perform an assessment. Individual providers determine the staffing resources necessary; therefore, we averaged the national average for these labor types and established a composite cost estimate. This composite estimate was calculated by weighting each salary based on the following breakdown regarding provider types most likely to collect this data: RN 50 percent; LVN 31.7 percent; RT 7 percent; SLP 6 percent; PT 2.5 percent; OT 2.5 percent; Psy 2 percent. For the purposes of calculating the costs associated with the collection of information requirements, we obtained mean hourly wages for these staff from the U.S. Bureau of Labor Statistics' May 2020 National Occupational Employment and Wage Estimates.^[66] To account for overhead and fringe benefits, we have doubled the hourly wage. These amounts are detailed in Table 13.

As a result of this proposal, the estimated burden and cost for IRFs for complying with requirements of the FY 2025 IRF QRP will increase. Specifically, we believe that there will be a 1.8 hours addition in clinical staff time to report data for each additional IRF-PAI completed. We estimate the collection of an additional 263,988 IRF-PAIs from 1,115 IRFs annually. This equates to an increase of 475,178 hours in burden for all IRFs (1.8 hours × 263,988 discharges). Given the clinician times estimated in the previous paragraph and the wages in Table 13, we calculated a blended hourly rate of $66.82. We estimate that each IRF will complete an average of 237 additional IRF-PAIs per year, the total cost related to the additional reporting requirements is estimated at $28,505.41 per IRF annually [(237 assessment × 1.8 hours) x $66.82], or $31,783,532.15 for all IRFs annually ($28.505.41 × 1,115). The increase in burden will be accounted for in a revised information collection request under OMB control number (0938-0842). The required 60-day and 30-day notices will publish in the Federal Register and the comment periods will be separate from those associated with this rulemaking. A 60-day Federal Register notice was published on February 3, 2022 (87 FR 6175) to extend the information collection request. The 60-day comment period for the extension ends April 4, 2022. The revision will be submitted at the conclusion of the extension process.

As described in section X.F.2.c. of this proposed rule, a new item would replace Item 20 on the IRF-PAI V4.0. However, since this item is replacing another item already accounted for in the PRA, we do not believe this would add any additional burden to the estimate described above.

C. Submission of PRA-Related Comments

We have submitted a copy of this rule's information collection and recordkeeping requirements to OMB for review and approval. These requirements are not effective until they have been approved by the OMB.

To obtain copies of the supporting statement and any related forms for the proposed collections discussed above, please visit CMS' website at www.cms.hhs.gov/​PaperworkReductionActof1995, or call the Reports Clearance Office at 410-786-1326.

We invite public comments on these potential information collection requirements. If you wish to comment, please refer to the DATES and addresses sections of this rulemaking for instructions. We will consider all ICR-related comments received by the date and time specified in the DATES section, and when we proceed with a subsequent document, we will respond to the comments in the preamble to that document.

XII. 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.

XIII. Regulatory Impact Analysis

A. Statement of Need

This proposed rule would update the IRF prospective payment rates for FY 2023 as required under section 1886(j)(3)(C) of the Act and in accordance with section 1886(j)(5) of the Act, which requires the Secretary to publish in the Federal Register on or before August 1 before each FY, the classification and weighting factors for CMGs used under the IRF PPS for such FY and a description of the methodology and data used in computing the prospective payment rates under the IRF PPS for that FY. This proposed rule would also implement section 1886(j)(3)(C) of the Act, which requires the Secretary to apply a productivity adjustment to the market basket increase factor for FY 2012 and subsequent years.

Furthermore, this proposed rule would adopt policy changes under the statutory discretion afforded to the Secretary under section 1886(j) of the Act. We are proposing to update the data reporting requirements for the IRF QRP and to amend the regulations consistent with the proposed requirements. We are also proposing to correct an error in the regulations text at § 412.614(d)(2). Finally, we are seeking comment on three issues: (1) Future measure concepts under consideration for the IRF QRP; (2) the potential future inclusion of the National Healthcare Safety Network (NHSN) Healthcare associated Clostridioides difficile Infection Outcome measure in the IRF QRP; and (3) overarching principles for measuring equity and health disparities across CMS Quality Programs, including the IRF QRP.

B. Overall Impact

We have examined the impacts of this 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 Social Security Act, section 202 of the Unfunded Mandates Reform Act of 1995 (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). Section 3(f) of Executive Order 12866 defines a “significant regulatory action” as an action that is likely to result in a rule: (1) Having an annual effect on the economy of $100 million or more in any 1 year, or adversely and materially affecting a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local or tribal governments or communities (also referred to as “economically significant”); (2) creating a serious inconsistency or otherwise interfering with an action taken or planned by another agency; (3) materially altering the budgetary impacts of entitlement grants, user fees, or loan programs or the rights and obligations of recipients thereof; or (4) raising novel legal or policy issues arising out of legal mandates, the President's priorities, or the principles set forth in Executive Order 12866.

Section (6)(a) of Executive Order 12866 provides that a regulatory impact analysis (RIA) must be prepared for major rules with economically significant effects ($100 million or more in any 1 year). We estimate the total impact of the policy updates described in this proposed rule by comparing the estimated payments in FY 2023 with those in FY 2022. This analysis results in an estimated $170 million increase for FY 2023 IRF PPS payments. Additionally, we estimate that costs associated with the proposal to update the reporting requirements under the IRF QRP result in an estimated $31,783,532.15 additional cost in FY 2025 for IRFs. Based on our estimates OMB's Office of Information and Regulatory Affairs has determined that this rulemaking is “economically significant” as measured by the $100 million threshold. Also, the rule has been reviewed by OMB. Accordingly, we have prepared an RIA that, to the best of our ability, presents the costs and benefits of the rulemaking.

C. Anticipated Effects

1. Effects on IRFs

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, nonprofit organizations, and small governmental jurisdictions. Most IRFs and most other providers and suppliers are small entities, either by having revenues of $8.0 million to $41.5 million or less in any 1 year depending on industry classification, or by being nonprofit organizations that are not dominant in their markets. (For details, see the Small Business Administration's final rule that set forth size standards for health care industries, at 65 FR 69432 at https://www.sba.gov/​sites/​default/​files/​2019-08/​SBA%20Table%20of%20Size%20Standards_​Effective%20Aug%2019%2C%202019_​Rev.pdf, effective January 1, 2017 and updated on August 19, 2019.) Because we lack data on individual hospital receipts, we cannot determine the number of small proprietary IRFs or the proportion of IRFs' revenue that is derived from Medicare payments. Therefore, we assume that all IRFs (an approximate total of 1,115 IRFs, of which approximately 52 percent are nonprofit facilities) are considered small entities and that Medicare payment constitutes the majority of their revenues. HHS generally uses a revenue impact of 3 to 5 percent as a significance threshold under the RFA. As shown in Table 14, we estimate that the net revenue impact of this proposed rule on all IRFs is to increase estimated payments by approximately 2.0 percent. The rates and policies set forth in this proposed rule would not have a significant impact (not greater than 3 percent) on a substantial number of small entities. The estimated impact on small entities is shown in Table 14. MACs are not considered to be small entities. Individuals and States are not included in the definition of a small entity.

In addition, section 1102(b) of the Act requires us to prepare an RIA 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 a Metropolitan Statistical Area and has fewer than 100 beds. As shown in Table 14, we estimate that the net revenue impact of this proposed rule on rural IRFs is to increase estimated payments by approximately 1.8 percent based on the data of the 133 rural units and 12 rural hospitals in our database of 1,115 IRFs for which data were available. We estimate an overall impact for rural IRFs in all areas between -1.8 percent and 2.9 percent. As a result, we anticipate that this proposed rule would not have a significant impact on a substantial number of small entities.

Section 202 of the Unfunded Mandates Reform Act of 1995 (Pub. L. 104-04, enacted on March 22, 1995) (UMRA) also requires that agencies assess anticipated costs and benefits before issuing any rule whose mandates require spending in any 1 year of $100 million in 1995 dollars, updated annually for inflation. In 2022, that threshold is approximately $165 million. This proposed rule does not mandate any requirements for State, local, or tribal governments, or for the private sector.

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. As stated, this proposed rule would not have a substantial effect on State and local governments, preempt State law, or otherwise have a federalism implication.

2. Detailed Economic Analysis

This proposed rule would update the IRF PPS rates contained in the FY 2022 IRF PPS final rule (86 FR 42362). Specifically, this proposed rule would update the CMG relative weights and ALOS values, the wage index, and the outlier threshold for high-cost cases. This proposed rule would apply a productivity adjustment to the FY 2023 IRF market basket increase factor in accordance with section 1886(j)(3)(C)(ii)(I) of the Act. Further, this proposed rule would codify CMS' existing teaching status adjustment policy through proposed amendments to the regulation text and would update and clarify the IRF teaching policy with respect to IRF hospital closures and displaced residents. Additionally, this proposed rule would establish a permanent cap policy to smooth the impact of year-to-year changes in IRF payments related to changes in the IRF wage index.

We estimate that the impact of the changes and updates described in this proposed rule would be a net estimated increase of $170 million in payments to IRF providers. The impact analysis in Table 14 of this proposed rule represents the projected effects of the updates to IRF PPS payments for FY 2023 compared with the estimated IRF PPS payments in FY 2022. We determine the effects by estimating payments while holding all other payment variables constant. We use the best data available, but we do not attempt to predict behavioral responses to these changes, and we do not make adjustments for future changes in such variables as number of discharges or case-mix.

We note that certain events may combine to limit the scope or accuracy of our impact analysis, because such an analysis is future-oriented and, thus, susceptible to forecasting errors because of other changes in the forecasted impact time period. Some examples could be legislative changes made by the Congress to the Medicare program that would impact program funding, or changes specifically related to IRFs. Although some of these changes may not necessarily be specific to the IRF PPS, the nature of the Medicare program is such that the changes may interact, and the complexity of the interaction of these changes could make it difficult to predict accurately the full scope of the impact upon IRFs.

In updating the rates for FY 2023, we are proposing standard annual revisions described in this proposed rule (for example, the update to the wage index and market basket increase factor used to adjust the Federal rates). We are also reducing the FY 2023 IRF market basket increase factor by a productivity adjustment in accordance with section 1886(j)(3)(C)(ii)(I) of the Act. We estimate the total increase in payments to IRFs in FY 2023, relative to FY 2022, would be approximately $170 million.

This estimate is derived from the application of the FY 2023 IRF market basket increase factor, as reduced by a productivity adjustment in accordance with section 1886(j)(3)(C)(ii)(I) of the Act, which yields an estimated increase in aggregate payments to IRFs of $240 million. However, there is an estimated $70 million decrease in aggregate payments to IRFs due to the proposed update to the outlier threshold amount. Therefore, we estimate that these updates would result in a net increase in estimated payments of $170 million from FY 2022 to FY 2023.

The effects of the proposed updates that impact IRF PPS payment rates are shown in Table 14. The following proposed updates that affect the IRF PPS payment rates are discussed separately below:

• The effects of the proposed update to the outlier threshold amount, from approximately 3.8 percent to 3.0 percent of total estimated payments for FY 2023, consistent with section 1886(j)(4) of the Act.
• The effects of the proposed annual market basket update (using the IRF market basket) to IRF PPS payment rates, as required by sections 1886(j)(3)(A)(i) and (j)(3)(C) of the Act, including a productivity adjustment in accordance with section 1886(j)(3)(C)(i)(I) of the Act.
• The effects of applying the proposed budget-neutral labor-related share and wage index adjustment, as required under section 1886(j)(6) of the Act.
• The effects of applying the proposed budget-neutral permanent cap on wage index decreases policy.
• The effects of the proposed budget-neutral changes to the CMG relative weights and ALOS values under the authority of section 1886(j)(2)(C)(i) of the Act.
• The total change in estimated payments based on the FY 2023 payment changes relative to the estimated FY 2022 payments.

3. Description of Table 14

Table 14 shows the overall impact on the 1,115 IRFs included in the analysis.

The next 12 rows of Table 14 contain IRFs categorized according to their geographic location, designation as either a freestanding hospital or a unit of a hospital, and by type of ownership; all urban, which is further divided into urban units of a hospital, urban freestanding hospitals, and by type of ownership; and all rural, which is further divided into rural units of a hospital, rural freestanding hospitals, and by type of ownership. There are 970 IRFs located in urban areas included in our analysis. Among these, there are 653 IRF units of hospitals located in urban areas and 317 freestanding IRF hospitals located in urban areas. There are 145 IRFs located in rural areas included in our analysis. Among these, there are 133 IRF units of hospitals located in rural areas and 12 freestanding IRF hospitals located in rural areas. There are 431 for-profit IRFs. Among these, there are 396 IRFs in urban areas and 35 IRFs in rural areas. There are 577 non-profit IRFs. Among these, there are 489 urban IRFs and 88 rural IRFs. There are 107 government-owned IRFs. Among these, there are 85 urban IRFs and 22 rural IRFs.

The remaining four parts of Table 14 show IRFs grouped by their geographic location within a region, by teaching status, and by DSH patient percentage (PP). First, IRFs located in urban areas are categorized for their location within a particular one of the nine Census geographic regions. Second, IRFs located in rural areas are categorized for their location within a particular one of the nine Census geographic regions. In some cases, especially for rural IRFs located in the New England, Mountain, and Pacific regions, the number of IRFs represented is small. IRFs are then grouped by teaching status, including non-teaching IRFs, IRFs with an intern and resident to average daily census (ADC) ratio less than 10 percent, IRFs with an intern and resident to ADC ratio greater than or equal to 10 percent and less than or equal to 19 percent, and IRFs with an intern and resident to ADC ratio greater than 19 percent. Finally, IRFs are grouped by DSH PP, including IRFs with zero DSH PP, IRFs with a DSH PP less than 5 percent, IRFs with a DSH PP between 5 and less than 10 percent, IRFs with a DSH PP between 10 and 20 percent, and IRFs with a DSH PP greater than 20 percent.

The estimated impacts of each policy described in this rule to the facility categories listed are shown in the columns of Table 14. The description of each column is as follows:

• Column (1) shows the facility classification categories.
• Column (2) shows the number of IRFs in each category in our FY 2023 analysis file.
• Column (3) shows the number of cases in each category in our FY 2023 analysis file.
• Column (4) shows the estimated effect of the proposed adjustment to the outlier threshold amount.
• Column (5) shows the estimated effect of the proposed update to the IRF labor-related share and wage index, in a budget-neutral manner.
• Column (6) shows the estimated effect of the proposed permanent cap on wage index decreases policy, in a budget-neutral manner.
• Column (7) shows the estimated effect of the proposed update to the CMG relative weights and ALOS values, in a budget-neutral manner.

• Column (8) compares our estimates of the payments per discharge, incorporating all of the policies reflected in this proposed rule for FY 2023 to our estimates of payments per discharge in FY 2022.

The average estimated increase for all IRFs is approximately 2.0 percent. This estimated net increase includes the effects of the proposed IRF market basket increase factor for FY 2023 of 2.8 percent, which is based on a proposed IRF market basket update of 3.2 percent, less a 0.4 percentage point productivity adjustment, as required by section 1886(j)(3)(C)(ii)(I) of the Act. It also includes the approximate 0.8 percent overall decrease in estimated IRF outlier payments from the proposed update to the outlier threshold amount. Since we are making the updates to the IRF wage index, labor-related share and the CMG relative weights in a budget-neutral manner, they will not be expected to affect total estimated IRF payments in the aggregate. However, as described in more detail in each section, they will be expected to affect the estimated distribution of payments among providers.

4. Impact of the Proposed Update to the Outlier Threshold Amount

The estimated effects of the proposed update to the outlier threshold adjustment are presented in column 4 of Table 14.

For this proposed rule, we are using preliminary FY 2021 IRF claims data, and, based on that preliminary analysis, we estimated that IRF outlier payments as a percentage of total estimated IRF payments would be 3.8 percent in FY 2023. Thus, we propose to adjust the outlier threshold amount in this proposed rule to maintain total estimated outlier payments equal to 3 percent of total estimated payments in FY 2023. The estimated change in total IRF payments for FY 2023, therefore, includes an approximate 0.8 percentage point decrease in payments because the estimated outlier portion of total payments is estimated to decrease from approximately 3.8 percent to 3 percent.

The impact of this proposed outlier adjustment update (as shown in column 4 of Table 14) is to decrease estimated overall payments to IRFs by 0.8 percentage point.

5. Impact of the Proposed Wage Index and Labor-Related Share

In column 5 of Table 14, we present the effects of the proposed budget-neutral update of the wage index and labor-related share. The proposed changes to the wage index and the labor-related share are discussed together because the wage index is applied to the labor-related share portion of payments, so the proposed changes in the two have a combined effect on payments to providers. As discussed in section V.C. of this proposed rule, we are proposing to update the labor-related share from 72.9 percent in FY 2022 to 73.2 percent in FY 2023. In aggregate, we do not estimate that these proposed updates will affect overall estimated payments to IRFs. However, we do expect these updates to have small distributional effects.

6. Impact of the Proposed Wage Index Policy

In column 6 of Table 14, we present the effects of the budget-neutral proposed permanent cap on wage index decreases policy. As discussed in section V.D.3 of this proposed rule, we are proposing to apply a permanent 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year to smooth the impact of year-to-year changes in IRF payments related to changes in the IRF wage index. We are required by section 1886(j)(6) of the Act, to implement changes to the wage index in a budget-neutral manner. Thus, there will not be an impact on aggregate Medicare payments to IRFs.

7. Impact of the Proposed Update to the CMG Relative Weights and ALOS Values

In column 7 of Table 14, we present the effects of the proposed budget-neutral update of the CMG relative weights and ALOS values. In the aggregate, we do not estimate that these proposed updates will affect overall estimated payments of IRFs. However, we do expect these updates to have small distributional effects.

8. Effects of Proposed Codification and Clarifications of IRF Teaching Status Adjustment Policy

As discussed in section VII. of this proposed rule, we are proposing to codify the longstanding teaching status adjustment policy through the proposed amendments to the regulation text at § 412.602 and § 412.624(e)(4) provided in this proposed rule.

We do not anticipate a financial impact associated with the proposed codification of the IRF teaching status adjustment policies. However, the clarification of certain teaching status adjustment policies and proposed codification of these policies will enable us to align the IRF policies with recent updates to the IPPS and IPF teaching status adjustment policies. Aligning the policy guidance with other post-acute care setting regulations will also assist stakeholders in providing care for Medicare beneficiaries.

9. Effects of Requirements for the IRF QRP for FY 2025

In accordance with section1886(j)(7)(A) of the Act, the Secretary must reduce by 2 percentage points the annual market basket increase factor otherwise applicable to an IRF for a fiscal year if the IRF does not comply with the requirements of the IRF QRP for that fiscal year. In section X.A. of this proposed rule, we discuss the method for applying the 2 percentage point reduction to IRFs that fail to meet the IRF QRP requirements.

As discussed in section X.F.2. of this proposed rule, we are proposing to require the reporting of quality data on all patients discharged from the IRF beginning with the FY 2025 IRF QRP. We describe the estimated burden for this proposal in section XI.B. of this proposed rule. In summary, the proposed changes to the IRF QRP will result in a burden addition of $28,505.41 per IRF annually, or $31,783,532.15 for all IRFs annually beginning with the FY 2025 IRF QRP. We note, however, that this estimate may be partially offset by eliminating the effort to separate out Medicare beneficiaries from other patients.

D. Alternatives Considered

The following is a discussion of the alternatives considered for the IRF PPS updates contained in this proposed rule.

Section 1886(j)(3)(C) of the Act requires the Secretary to update the IRF PPS payment rates by an increase factor that reflects changes over time in the prices of an appropriate mix of goods and services included in the covered IRF services.

As noted previously in this proposed rule, section 1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to apply a productivity adjustment to the market basket increase factor for FY 2023. Thus, in accordance with section 1886(j)(3)(C) of the Act, we propose to update the IRF prospective payments in this proposed rule by 2.8 percent (which equals the 3.2 percent estimated IRF market basket increase factor for FY 2023 reduced by a 0.4 percentage point productivity adjustment as determined under section 1886(b)(3)(B)(xi)(II) of the Act (as required by section 1886(j)(3)(C)(ii)(I) of the Act)).

We considered maintaining the existing CMG relative weights and average length of stay values for FY 2023. However, in light of recently available data and our desire to ensure that the CMG relative weights and average length of stay values are as reflective as possible of recent changes in IRF utilization and case mix, we believe that it is appropriate to propose to update the CMG relative weights and average length of stay values at this time to ensure that IRF PPS payments continue to reflect as accurately as possible the current costs of care in IRFs.

We considered maintaining the existing outlier threshold amount for FY 2022. However, analysis of updated FY 2021 data indicates that estimated outlier payments would be more than 3 percent of total estimated payments for FY 2023, by approximately 0.8 percent, unless we updated the outlier threshold amount. Consequently, we propose adjusting the outlier threshold amount in this proposed rule to reflect a 0.8 percent decrease thereby setting the total outlier payments equal to 3 percent, instead of 3.8 percent, of aggregate estimated payments in FY 2023.

We considered not amending § 412.602 and § 412.624(e)(4) to codify our longstanding guidance on the teaching status adjustment policies and update the IRF teaching policy on IRF program closures and displaced residents. However, we believe that codifying these longstanding policies into regulation text would improve clarity and reduce administrative burden on IRF providers trying to locate all relevant information regarding the teaching status adjustment. Additionally, we believe that we should streamline all teaching status adjustment policy information in the same place for ease of reference.

E. 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 the FY 2023 IRF PPS proposed rule will be the number of reviewers of last year's proposed rule. We acknowledge that this assumption may understate or overstate the costs of reviewing this proposed. It is possible that not all commenters reviewed the FY 2022 IRF PPS proposed rule in detail, and it is also possible that some reviewers chose not to comment on the FY 2022 proposed rule. For these reasons, we thought that the number of commenters would be 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 this proposed rule, and therefore, for the purposes of our estimate we assume that each reviewer reads approximately 50 percent of the rule. We seek comments on this assumption.

Using the national mean hourly wage data from the May 2020 BLS for Occupational Employment Statistics (OES) for medical and health service managers (SOC 11-9111), we estimate that the cost of reviewing this rule is $114.24 per hour, including overhead and fringe benefits ( https://www.bls.gov/​oes/​current/​oes_​nat.htm ). Assuming an average reading speed, we estimate that it would take approximately 3 hours for the staff to review half of this proposed rule. For each reviewer of the rule, the estimated cost is $342.72 (3 hours × $114.24). Therefore, we estimate that the total cost of reviewing this regulation is $17,478.72 ($342.72 × (50 reviewers).

F. Accounting Statement and Table

As required by OMB Circular A-4 (available at https://www.whitehouse.gov/​wp-content/​uploads/​legacy_​drupal_​files/​omb/​circulars/​A4/​a-4.pdf ), in Table 15 we have prepared an accounting statement showing the classification of the expenditures associated with the provisions of this proposed rule. Table 15 provides our best estimate of the increase in Medicare payments under the IRF PPS as a result of the proposed updates presented in this proposed rule based on the data for 1,115 IRFs in our database.

G. Conclusion

Overall, the estimated payments per discharge for IRFs in FY 2023 are projected to increase by 2.0 percent, compared with the estimated payments in FY 2022, as reflected in column 8 of Table 14.

IRF payments per discharge are estimated to increase by 2.0 percent in urban areas and 1.8 percent in rural areas, compared with estimated FY 2022 payments. Payments per discharge to rehabilitation units are estimated to increase 1.2 percent in urban areas and 1.5 percent in rural areas. Payments per discharge to freestanding rehabilitation hospitals are estimated to increase 2.6 percent in urban areas and increase 2.5 percent in rural areas.

Overall, IRFs are estimated to experience a net increase in payments as a result of the proposed policies in this proposed rule. The largest payment increase is estimated to be a 2.9 percent increase for rural IRFs located in the rural New England region. The analysis above, together with the remainder of this preamble, provides an RIA.

In accordance with the provisions of Executive Order 12866, this regulation was reviewed by OMB.

Chiquita Brooks-LaSure, Administrator of the Centers for Medicare & Medicaid Services, approved this document on March 22, 2022.

List of Subjects in 42 CFR Part 412

• Administrative practice and procedure
• Health facilities
• Medicare
• Puerto Rico
• 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 412—PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL SERVICES

1. The authority citation for part 412 continues to read as follows:

Authority: 42 U.S.C. 1302 and 1395hh.

2. Amend § 412.602 by adding the definitions of “Closure of an IRF”, “Closure of an IRF's residency training program”, and “Displaced resident” in alphabetical order to read as follows:

§ 412.602

Definitions.

* * * * *

Closure of an IRF has the same meaning as “closure of a hospital” as defined in § 413.79(h)(1)(i) as applied to an IRF meeting the requirements of § 412.604(b) for the purposes of accounting for indirect teaching costs.

Closure of an IRF's residency training program has the same meaning as “closure of a hospital residency training program” as defined in § 413.79(h)(1)(ii) as applied to an IRF meeting the requirements of § 412.604(b) for the purposes of accounting for indirect teaching costs.

* * * * *

Displaced resident has the same meaning as a “displaced resident” as defined in § 413.79(h)(1)(iii) as applied to an IRF, for purposes of accounting for indirect teaching costs.

* * * * *

3. Amend § 412.604 by revising paragraph (c) to read as follows:

§ 412.604

Conditions for payment under the prospective payment system for inpatient rehabilitation facilities.

* * * * *

(c) Completion of patient assessment instrument. For each Medicare part A fee-for-service patient admitted to or discharged from an IRF on or after January 1, 2002, the inpatient rehabilitation facility must complete a patient assessment instrument in accordance with § 412.606. IRFs must also complete a patient assessment instrument in accordance with § 412.606 for each Medicare Part C (Medicare Advantage) patient admitted to or discharged from an IRF on or after October 1, 2009. In addition, IRFs must complete a patient assessment instrument in accordance with § 412.606 for all other patients, regardless of payer, admitted to or discharged from an IRF on or after October 1, 2023.

* * * * *

4. Amend § 412.606 by—

a. Revising paragraphs (a) and (b)(1) to read as follows:

§ 412.606

Patient assessments.

(a) Patient assessment instrument. An inpatient rehabilitation facility must use the CMS inpatient rehabilitation facility patient assessment instrument to assess Medicare Part A fee-for-service and Medicare Part C (Medicare Advantage) inpatients who are admitted on or after January 1, 2002, or were admitted before January 1, 2002, and are still inpatients as of January 1, 2002.

(1) Starting on October 1, 2023, inpatient rehabilitation facilities must use the CMS inpatient rehabilitation facility patient assessment instrument to assess all inpatients, regardless of payer, who are admitted on or after October 1, 2023, or who were admitted before October 1, 2023 and are still inpatients as of October 1, 2023.

(2) [Reserved]

(b) * * * (1) A clinician of the inpatient rehabilitation facility must perform a comprehensive, accurate, standardized, and reproducible assessment of each Medicare Part A fee-for-service inpatient using the inpatient rehabilitation facility patient assessment instrument specified in paragraph (b) of this section as part of his or her patient assessment in accordance with the schedule described in § 412.610. IRFs must also complete a patient assessment instrument in accordance with § 412.606 for each Medicare Part C (Medicare Advantage) patient admitted to or discharged from an IRF on or after October 1, 2009. In addition, IRFs must complete a patient assessment instrument in accordance with § 412.606 for all other patients, regardless of payer, admitted to or discharged from an IRF on or after October 1, 2023.

* * * * *

5. Amend § 412.610 by revising paragraphs (a), (b), (c) introductory text, (c)(1)(i)(A), (c)(2)(ii)(B) and (f) to read as follows:

§ 412.610

Assessment schedule.

(a) General. For each inpatient, an inpatient rehabilitation facility must complete a patient assessment instrument as specified in § 412.606 that covers a time period that is in accordance with the assessment schedule specified in paragraph (c) of this section.

(b) Starting the assessment schedule day count. The first day that the inpatient is furnished services during his or her current inpatient rehabilitation facility hospital stay is counted as day one of the patient assessment schedule.

(c) Assessment schedules and references dates. The inpatient rehabilitation facility must complete a patient assessment instrument upon the patient's admission and discharge as specified in paragraphs (c)(1) and (2) of this section.

(1) * * *

(i) * * *

(A) General. Time period is a span of time that covers calendar days 1 through 3 of the patient's current hospitalization.

* * * * *

(2) * * *

(ii) * * *

(B) The patient stops being furnished inpatient rehabilitation services.

* * * * *

(f) Patient assessment instrument record retention. An inpatient rehabilitation facility must maintain all patient assessment data sets completed on all Medicare Part A fee-for-service patients within the previous 5 years, on Medicare Part C (Medicare Advantage) patients within the previous 10 years, and all other patients within the previous 5 years either in a paper format in the patient's clinical record or in an electronic computer file format that the inpatient rehabilitation facility can easily obtain and produce upon request to CMS or its contractors.

6. Amend § 412.614 by—

a. Revising paragraphs (a) introductory text and (b)(1);

b. Revising paragraph (d)(2)

c. Adding paragraph (d)(3).

d. Revising paragraph (e).

The revisions and additions read as follows:

§ 412.614

Transmission of patient assessment data.

(a) Data format. General Rule. The inpatient rehabilitation facility must encode and transmit data for each inpatient—

* * * * *

(b) * * *

(1) Electronically transmit complete, accurate, and encoded data from the patient assessment instrument for each inpatient to our patient data system in accordance with the data format specified in paragraph (a) of this section; and

* * * * *

(d) * * *

(2) Medicare Part C (Medicare Advantage) data. Failure of the inpatient rehabilitation facility to transmit all of the required patient assessment instrument data for its Medicare Part C (Medicare Advantage) patients to our patient data system in accordance with the transmission timeline in paragraph (c) of this section will result in a forfeiture of the facility's ability to have any of its Medicare Part C (Medicare Advantage) data used in the calculations for determining the facility's compliance with the regulations in § 412.29(b)(1).

(3) All other payer data. Failure of the inpatient rehabilitation facility to transmit all of the required patient assessment instrument data for all other patients, regardless of payer, to our patient data system in accordance with the transmission timeline in paragraph (c) of this section will result in a forfeiture of the facility's ability to have any of its other payer data used in the calculations for determining the facility's compliance with the regulations in § 412.29(b)(1).

(e) Exemption to the consequences for transmitting the IRF-PAI data late for Medicare Part C (Medicare Advantage) patients and all other patients, regardless of payer. CMS may waive the consequences of failure to submit complete and timely IRF-PAI data specified in paragraph (d) of this section when, due to an extraordinary situation that is beyond the control of an inpatient rehabilitation facility, the inpatient rehabilitation facility is unable to transmit the patient assessment data in accordance with paragraph (c) of this section. Only CMS can determine if a situation encountered by an inpatient rehabilitation facility is extraordinary and qualifies as a situation for waiver of the forfeiture specified in paragraphs (d)(2) or (3) of this section. An extraordinary situation may be due to, but is not limited to, fires, floods, earthquakes, or similar unusual events that inflect extensive damage to an inpatient facility. An extraordinary situation may be one that produces a data transmission problem that is beyond the control of the inpatient rehabilitation facility, as well as other situations determined by CMS to be beyond the control of the inpatient rehabilitation facility. An extraordinary situation must be fully documented by the inpatient rehabilitation facility.

7. Amend § 412.618 by amending the introductory text to read as follows:

§ 412.618

Assessment process for interrupted stays.

For purposes of the patient assessment process, if any patient has an interrupted stay, as defined under § 412.602, the following applies:

* * * * *

8. Amend § 412.624 by revising paragraphs (e)(1) and (4) to read as follows:

§ 412.624

Methodology for calculating the Federal prospective payment rates.

* * * * *

(e) * * *

(1) Adjustment for area wage levels. The labor portion of a facility's Federal prospective payment is adjusted to account for geographical differences in the area wage levels using an appropriate wage index.

(i) The application of the wage index is made on the basis of the location of the facility in an urban or rural area as defined in § 412.602.

(ii) Starting on October 1, 2022, CMS applies a cap on decreases to the wage index such that the wage index applied to an IRF is not less than 95 percent of the wage index applied to that IRF in the prior FY.

(iii) Adjustments or updates to the wage data used to adjust a facility's Federal prospective payment rate under paragraph (e)(1) of this section will be made in a budget neutral manner. CMS determines a budget neutral wage adjustment factor, based on any adjustment or update to the wage data, to apply to the standard payment conversion factor.

* * * * *

(4) Adjustments for teaching hospitals. (i) General. For discharges on or after October 1, 2005, CMS adjusts the Federal prospective payment on a facility basis by a factor as specified by CMS for facilities that are teaching institutions or units of teaching institutions.

(A) An IRF's teaching adjustment is based on the ratio of the number of full-time equivalent residents training in the IRF divided by the facility's average daily census.

(B) As described in § 412.105(f)(1)(iii)(A), residents with less than full-time status are counted as partial full time equivalent based on the proportion of time assigned to the inpatient rehabilitation facility compared to the total time necessary to fill a residency slot. Residents rotating to more than one hospital or non-hospital setting will be counted in proportion to the time they are assigned to inpatient rehabilitation facility compared to the total time worked in all locations. An inpatient rehabilitation facility cannot claim time spent by the resident at another inpatient rehabilitation facility or hospital.

(C) Except as described in paragraph (e)(4)(i)(D) of this section, the actual number of current year full-time equivalent residents used in calculating the teaching adjustment is limited to the number of full-time equivalent residents in the IRF's final settled cost report for the most recent cost reporting period ending on or before November 15, 2004 (base year).

(D) If the inpatient rehabilitation facility first begins training residents in a new approved graduate medical education program after November 15, 2004, the number of full-time equivalent residents determined under paragraph (e)(4)(i)(C) of this section may be adjusted using the method described in § 413.79(e)(1)(i).

(E) The teaching adjustment is made on a claim basis as an interim payment, and the final payment in full for the claim is made during the final settlement of the cost report.

(ii) Closure of an IRF or IRF residency training program. (A) Closure of an IRF. For cost reporting periods beginning on or after October 1, 2011, an IRF may receive a temporary adjustment to its FTE cap to reflect displaced residents added because of another IRFs closure if the IRF meets the following criteria:

( 1 ) The IRF is training additional displaced residents from an IRF that closed on or after October 1, 2011.

( 2 ) No later than 60 days after the IRF begins to train the displaced residents, the IRF submits a request to its Medicare contractor for a temporary adjustment by identifying the displaced residents who have come from the closed IRF and have caused the IRF to exceed its cap, and specifies the length of time the adjustment is needed.

(B) Closure of an IRF's residency training program. If an IRF that closes its residency training program on or after October 1, 2011, agrees to temporarily reduce its FTE cap according to the criteria specified in paragraph (e)(4)(ii)(A)( 2 ) of this section, another IRF(s) may receive a temporary adjustment to its FTE cap to reflect displaced residents added because of the closure of the residency training program if the criteria specified in paragraph (e)(4)(ii)(A)( 1 ) of this section are met.

( 1 ) Receiving IRF(s). For cost reporting periods beginning on or after October 1, 2011, an IRF may receive a temporary adjustment to its FTE cap to reflect displaced residents added because of the closure of another IRF's residency training program if the IRF is training additional displaced residents from the residency training program of an IRF that closed a program; and if no later than 60 days after the IRF begins to train the displaced residents the IRF submits to its Medicare Contractor a request for a temporary adjustment to its FTE cap, documents that it is eligible for this temporary adjustment by identifying the displaced residents who have come from another IRF's closed program and have caused the IRF to exceed its cap, specifies the length of time the adjustment is needed, and submits to its Medicare Contractor a copy of the FTE reduction statement by the hospital that closed its program, as specified in paragraph (ii)(A)( 2 ) of this section.

( 2 ) IRF that closed its program. An IRF that agrees to train displaced residents who have been displaced by the closure of another IRF's program may receive a temporary FTE cap adjustment only if the hospital with the closed program temporarily reduces its FTE cap based on the FTE of displaced residents in each program year training in the program at the time of the programs closure. This yearly reduction in the FTE cap will be determined based on the number of those displaced residents who would have been training in the program during that year had the program not closed. No later than 60 days after the displaced residents who were in the hospital that closed its program(s) begin training at another hospital must submit to its Medicare Contractor a statement signed and dated by its representative that specifies that it agrees to the temporary reduction in its FTE cap to allow the IRF training the displaced residents to obtain a temporary adjustment to its cap; identifies the displaced residents who were in the training at the time of the program's closure; identifies the IRFs to which the displaced residents are transferring once the program closes; and specifies the reduction for the applicable program years.

* * * * *

Dated: March 30, 2022.

Xavier Becerra,

Secretary, Department of Health and Human Services.

Footnotes

1.  Patel A, Jernigan DB. Initial Public Health Response and Interim Clinical Guidance for the 2019 Novel Coronavirus Outbreak—United States, December 31, 2019-February 4, 2020. MMWR Morb Mortal Wkly Rep 2020;69:140-146. DOI http://dx.doi.org/​10.15585/​mmwr.mm6905e1.

2.  CMS, “COVID-19 Emergency Declaration Blanket Waivers for Health Care Providers,” (updated Feb. 19 2021) (available at https://www.cms.gov/​files/​document/​summary-covid-19-emergency-declaration-waivers.pdf ).

3.  CMS, “COVID-19 Frequently Asked Questions (FAQs) on Medicare Fee-for-Service (FFS) Billing,” (updated March 5, 2021) (available at https://www.cms.gov/​files/​document/​03092020-covid-19-faqs-508.pdf ).

4.  HL7 FHIR Release 4. Available at https://www.hl7.org/​fhir/​.

5.  HL7 FHIR. PACIO Functional Status Implementation Guide. Available at https://paciowg.github.io/​functional-status-ig/​.

6.  The IMPACT Act (Pub. L. 113-185) requires the reporting of standardized patient assessment data with regard to quality measures and standardized patient assessment data elements. The Act also requires the submission of data pertaining to measure domains of resource use, and other domains. In addition, the IMPACT Act requires assessment data to be standardized and interoperable to allow for exchange of the data among post-acute providers and other providers. The Act intends for standardized post-acute care data to improve Medicare beneficiary outcomes through shared-decision making, care coordination, and enhanced discharge planning.

7.  Centers for Medicare & Medicaid Services. Newsroom. Fact sheet: CMS Data Element Library Fact Sheet. June 21, 2018. Available at https://www.cms.gov/​newsroom/​fact-sheets/​cms-data-element-library-fact-sheet.

8.  Centers for Medicare & Medicaid Services. Health Informatics and Interoperability Group. Policies and Technology for Interoperability and Burden Reduction. Available at https://www.cms.gov/​Regulations-and-Guidance/​Guidance/​Interoperability/​index.

9.  Bates, David W, and Lipika Samal. “Interoperability: What Is It, How Can We Make It Work for Clinicians, and How Should We Measure It in the Future?.” Health services research vol. 53,5 (2018): 3270-3277. doi:10.1111/1475-6773.12852.

10.  The Common Agreement defines Individual Access Services (IAS) as “with respect to the Exchange Purposes definition, the services provided utilizing the Connectivity Services, to the extent consistent with Applicable Law, to an Individual with whom the QHIN, Participant, or Subparticipant has a Direct Relationship to satisfy that Individual's ability to access, inspect, or obtain a copy of that Individual's Required Information that is then maintained by or for any QHIN, Participant, or Subparticipant.” The Common Agreement defines “IAS Provider” as: “Each QHIN, Participant, and Subparticipant that offers Individual Access Services.” See Common Agreement for Nationwide Health Information Interoperability Version 1, at 7 (Jan. 2022), https://www.healthit.gov/​sites/​default/​files/​page/​2022-01/​Common_​Agreement_​for_​Nationwide_​Health_​Information_​Interoperability_​Version_​1.pdf.

11.   https://www.medpac.gov/​wp-content/​uploads/​2022/​03/​Mar22_​MedPAC_​ReportToCongress_​SEC.pdf.

12.  Office of the Inspector General. December 7, 2021 Early Discharges From Inpatient Rehabilitation Facilities to Home Health Services [Report No. A-01-20-00501] https://oig.hhs.gov.

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

14.  Magil S.M., O'Leary, E., Janelle, S. J. et al. Changes in Prevalence of Health Care-Associated Infections in U.S. Hospitals. N Engl J Med 2018; 379:1732-1744. Available at https://www.nejm.org/​doi/​full/​10.1056/​NEJMoa1801550. Accessed February 3, 2022.

15.  U.S. Department of Health and Human Services. Centers for Disease Control and Prevention. Antibiotic Resistance Threats in the United States, 2019. Available at https://www.cdc.gov/​drugresistance/​pdf/​threats-report/​2019-ar-threats-report-508.pdf. Accessed February 3, 2022.

16.  Centers for Medicare and Medicaid Services. (2021) Quality Measurement Action Plan. Available at https://www.cms.gov/​files/​document/​2021-cms-quality-conference-cms-quality-measurement-action-plan-march-2021.pdf.

17.  Clinical Practice Guidelines for Clostridium difficile Infection in Adults and Children: 2017 Update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA) ( idsociety.org ).

18.  King J., Patel, V., Jamoom, E., & Furukawa, M. (2012, August). Clinical Benefits of Electronic Health Record Use: National Findings. Health Serv Res. 2014 Feb; 49(1 pt 2): 392-404. Available at https://www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC3925409/​.

19.  Hoover, R. Benefits of using an electronic health record. Nursing Critical Care: January 2017—Volume 12—Issue 1—p 9-10. Available at https://journals.lww.com/​nursingcriticalcare/​fulltext/​2017/​01000/​benefits_​of_​using_​an_​electronic_​health_​record.3.aspx.

20.  Escobar, G., Turk B., Ragins A., Ha J., et al. Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals. J Hosp Med. 2016 Nov; 11 Suppl 1(Suppl 1):S18-S24. Available at https://www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC5510649/​.

21.  Uslu A., Stausberg J. Value of the Electronic Medical Record for Hospital Care: Update from the literature. JMed internet Res 2021;23(12):e26323. Available at https://www.jmir.org/​2021/​12/​e26323.

22.  U.S. bureau of Labor Statistics. Occupational Employment and Wage Statistics. May 2020 National Occupational Employment and Wage Estimates. United States. Available at https://www.bls.gov/​oes/​current/​oes_​nat.htm#43-0000. Accessed February 3, 2022.

23.  Estimated using 10 minutes of clinical nursing time (Occupation Code 29-1141) and 15 minutes of clerical time (Occupation Code 43-6013) necessary to enter the data into the NHSN.

24.  More information on how ARM and SIR compare can be found at https://www.cdc.gov/​nhsn/​ps-analysis-resources/​arm/​index.html.

25.  Centers for Medicare & Medicaid Services. List of Measures Under Consideration for December 1, 2021. Available at https://www.cms.gov/​files/​document/​measures-under-consideration-list-2021-report.pdf. Accessed February 7, 2022.

26.  2021-2022 MAP Final Recommendations. Available at https://www.qualityforum.org/​map/​. Accessed February 3, 2021.

27.  Joynt KE, Orav E, Jha AK. (2011). Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA, 305(7):675-681.

28.  Lindenauer PK, Lagu T, Rothberg MB, et al. (2013). Income inequality and 30-day outcomes after acute myocardial infarction, heart failure, and pneumonia: Retrospective cohort study. British Medical Journal, 346.

29.  Trivedi AN, Nsa W, Hausmann LRM, et al. (2014). Quality and equity of care in U.S. hospitals. New England Journal of Medicine, 371(24):2298- 2308.

30.  Polyakova, M., et al. (2021). Racial disparities in excess all-cause mortality during the early COVID-19 pandemic varied substantially across states. Health Affairs, 40(2): 307-316.

31.  Rural Health Research Gateway. (2018). Rural communities: Age, Income, and Health status. Rural Health Research Recap. Available at https://www.ruralhealthresearch.org/​assets/​2200-8536/​rural-communities-age-income-health-status-recap.pdf. Accessed February 3, 2022.

32.  U.S. Department of Health and Human Services. Office of the Secretary. Progress Report to Congress. HHS Office of Minority Health. 2020 Update on the Action Plan to Reduce Racial and Ethnic Health Disparities. FY 2020. Available at https://www.minorityhealth.hhs.gov/​assets/​PDF/​Update_​HHS_​Disparities_​Dept-FY2020.pdf. Accessed February 3, 2022.

33.  Centers for Disease Control and Prevention. Morbidity and Mortality Weekly Report (MMWR). Heslin, KC, Hall JE. Sexual Orientation Disparities in Risk Factors for Adverse COVID-19-Related Outcomes, by Race/Ethnicity—Behavioral Risk Factor Surveillance System, United States, 2017-2019. February 5, 2021/70(5); 149-154. Available at https://www.cdc.gov/​mmwr/​volumes/​70/​wr/​mm7005a1.htm?​s_​cid=​mm7005a1_​w. Accessed February 3, 2022.

34.  Poteat TC, Reisner SL, Miller M, Wirtz AL. (2020). 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. doi:10.1101/2020.07.21.20159327.

35.  Milkie Vu et al. Predictors of Delayed Healthcare Seeking Among American Muslim Women, Journal of Women's Health 26(6) (2016) at 58; S.B. Nadimpalli, et al., The Association between Discrimination and the Health of Sikh Asian Indians.

36.  Centers for Medicare and Medicaid Services. Available at https://www.cms.gov/​pillar/​health-equity. Accessed February 9, 2022.

37.  CMS Quality Strategy. 2016. Available at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Qualityinitiativesgeninfo/​downloads/​cms-quality-strategy.pdf. Accessed February 3, 2022.

38.  Centers for Medicare & Medicaid Services (CMS), HHS. Disparity Methods Confidential Reporting. Available at https://qualitynet.cms.gov/​inpatient/​measures/​disparity-methods. Accessed February 3, 2022.

39.  Joynt KE, Orav E, Jha AK. (2011). Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA, 305(7):675-681.

40.  Lindenauer PK, Lagu T, Rothberg MB, et al. (2013). Income inequality and 30-day outcomes after acute myocardial infarction, heart failure, and pneumonia: Retrospective cohort study. British Medical Journal, 346.

41.  Trivedi AN, Nsa W, Hausmann LRM, et al. (2014). Quality and equity of care in U.S. hospitals. New England Journal of Medicine, 371(24):2298- 2308.

42.  Polyakova, M., et al. (2021). Racial disparities in excess all-cause mortality during the early COVID-19 pandemic varied substantially across States. Health Affairs, 40(2): 307-316.

43.  Rural Health Research Gateway. (2018). Rural communities: Age, Income, and Health status. Rural Health Research Recap. Available at https://www.ruralhealthresearch.org/​assets/​2200-8536/​rural-communities-age-income-health-status-recap.pdf. Accessed February 3, 2022.

44.  HHS Office of Minority Health (2020). 2020 Update on the Action Plan to Reduce Racial and Ethnic Health Disparities. Available at https://www.minorityhealth.hhs.gov/​assets/​PDF/​Update_​HHS_​Disparities_​Dept-FY2020.pdf. Accessed February 3, 2022.

45.  Poteat TC, Reisner SL, Miller M, Wirtz AL. 2020. COVID-19 vulnerability of transgender women with and without HIV infection in the Eastern and Southern U.S. medRxiv [Preprint]. 2020.07.21.20159327. doi: 10.1101/2020.07.21.20159327. PMID: 32743608; PMCID: PMC7386532.

46.  Milkie Vu et al. Predictors of Delayed Healthcare Seeking Among American Muslim Women, Journal of Women's Health 26(6) (2016) at 58; S.B. Nadimpalli, et al., The Association between Discrimination and the Health of Sikh Asian Indians.

47.  Jarrín OF, Nyandege AN, Grafova IB, Dong X, Lin H. (2020). Validity of race and ethnicity codes in Medicare administrative data compared with gold-standard self-reported race collected during routine home health care visits. Med Care, 58(1):e1-e8. doi: 10.1097/MLR.0000000000001216. PMID: 31688554; PMCID: PMC6904433.

48.  Office of the Assistant Secretary for Planning and Evaluation. Report to Congress: Social Risk factors and Performance Under Medicare's Value-Based Purchasing Program. December 20, 2016. Available at https://www.aspe.hhs.gov/​reports/​report-congress-social-risk-factors-performance-under-medicares-value-based-purchasing-programs. Accessed February 3, 2022.

49.  Bonito A., Bann C., Eicheldinger C., Carpenter L. Creation of New Race-Ethnicity Codes and Socioeconomic Status (SES) Indicators for Medicare Beneficiaries. Final Report, Sub-Task 2. (Prepared by RTI International for the Centers for Medicare and Medicaid Services through an interagency agreement with the Agency for Healthcare Research and Policy, under Contract No. 500-00-0024, Task No. 21) AHRQ Publication No. 08-0029-EF. Rockville, MD, Agency for Healthcare Research and Quality. January 2008. Available at https://archive.ahrq.gov/​research/​findings/​final-reports/​medicareindicators/​medicareindicators1.html. Accessed February 7, 2022.

50.  Flanagan, B.E., Gregory, E.W., Hallisey, E.J., Heitgerd, J.L., Lewis, B. (2011). A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management, 8 (1). Available at https://www.atsdr.cdc.gov/​placeandhealth/​svi/​img/​pdf/​Flanagan_​2011_​SVIforDisasterManagement-508.pdf. Accessed February 3, 2022.

51.  Center for Health Disparities Research. University of Wisconsin School of Medicine and Public health. Neighborhood Atlas. Available at https://www.neighborhoodatlas.medicine.wisc.edu/​. Accessed February 3, 2022.

52.  Haas A., Elliott M.N., Dembosky J.W., Adams J.L., Wilson-Frederick S.M., Mallett J.S., Gaillot S, Haffer S.C., Haviland A.M. (2019). Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity. Health Serv Res, 54(1):13-23. doi: 10.1111/1475-6773.13099. Epub 2018 Dec 3. PMID: 30506674; PMCID: PMC6338295. Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity. Available at https://www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC6338295/​pdf/​HESR-54-13.pdf. Accessed February 3, 2022.

53.  Rahimi E, Hashemi Nazari S. A detailed explanation and graphical representation of the Blinder-Oaxaca decomposition method with its application in health inequalities. Emerg Themes Epidemiol. (2021)18:12. https://doi.org/​10.1186/​s12982-021-00100-9. Retrieved 2/24/2022.

54.  Agniel D., Martino S.C., Burkhart Q., Hambarsoomian K., Orr N., Beckett M.K., James C., Scholle S.H., WilsonFrederick S., Ng J., Elliott M.N. (2021). Incentivizing excellent care to at-risk groups with a health equity summary score. J Gen Intern Med, 36(7):1847-1857. doi: 10.1007/s11606-019-05473-x. Epub 2019 Nov 11. PMID: 31713030; PMCID: PMC8298664. Available at https://link.springer.com/​content/​pdf/​10.1007/​s11606-019-05473-x.pdf. Accessed February 3, 2022.

55.  2021 Quality Conference. Health Equity as a “New Normal”: CMS Efforts to Address the Causes of Health Disparities. Available at https://s3.amazonaws.com/​bizzabo.file.upload/​83kO1DYXTs6mKHjVtuk8_​1%20-%20Session%2023%20Health%20Equity%20New%20Normal%20FINAL_​508.pdf. Accessed March 2, 2022.

56.  Centers for Medicare & Medicaid Services, FY 2022 IPPS/LTCH PPS Proposed Rule. 88 FR 25560. May 10, 2021.

57.  Centers for Medicare & Medicaid Services Office of Minority Health (CMS OMH). 2021b. “Health Equity as a `New Normal': CMS Efforts to Address the Causes of Health Disparities.” Presented at CMS Quality Conference, March 2-3, 2021.

58.  Centers for Medicare & Medicaid Services. List of Measures Under Consideration for December 1, 2021. Available at https://www.cms.gov/​files/​document/​measures-under-consideration-list-2021-report.pdf. Accessed 3/1/2022.

59.  Quality is defined by the National Academy of Medicine as the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge. Quality improvement is the framework used to systematically improve care. Quality improvement seeks to standardize processes and structure to reduce variation, achieve predictable results, and improve outcomes for patients, healthcare systems, and organizations. Structure includes things like technology, culture, leadership, and physical capital; process includes knowledge capital ( e.g., standard operating procedures) or human capital ( e.g., education and training). Available at https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​MMS/​Quality-Measure-and-Quality-Improvement-. Accessed 3/1/2022.

60.  Agency for Healthcare Research and Quality. Types of Health Care Quality Measures. 2015. Available at https://www.ahrq.gov/​talkingquality/​measures/​types.html. Accessed February 3, 2022.

61.  National Quality Forum. MAP Coordination Strategy for Post-Acute Care and Long-Term Care Performance Measurement. February 2012. Available at https://www.qualityforum.org/​Publications/​2012/​02/​MAP_​Coordination_​Strategy_​for_​Post-Acute_​Care_​and_​Long-Term_​Care_​Performance_​Measurement.aspx. Accessed January 31, 2022.

62.   https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​MMS/​MMS-Blueprint. Accessed January 31, 2022.

63.  In the FY 2010 IRF PPS final rule (74 FR 39798 through 39800), CMS revised the regulation text in §§ 412.604, 412.606, 412.610, 412.614, and 412.618 to require that all IRFs submit IRF-PAI data on all of their Medicare Part C patients.

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

65.  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.

66.   https://www.bls.gov/​oes/​current/​oes_​nat.htm .