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###### Department of Health and Human Services

###### Centers for Medicare & Medicaid Services

          - 42 CFR Part 413
          - [CMS-1765-P]
          - RIN 0938-AU76

# AGENCY:

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

# ACTION:

Proposed rule; request for comments.

# SUMMARY:

This proposed rule would update: Payment rates; forecast error adjustment; diagnosis code mappings; the Patient Driven Payment Model (PDPM) parity adjustment, the SNF Quality Reporting Program (QRP), SNF Value-Based Purchasing (VBP) Program. It also proposes to establish a permanent cap policy. This proposed rule also includes a request for information related to long-term care (LTC) facilities. CMS requests comments on these proposals as well as on related subjects and announces the application of a risk adjustment for the SNF Readmission Measure for COVID-19 beginning in FY 2023.

# DATES:

To be assured consideration, comments must be received at one of the addresses provided below, by June 10, 2022.

# ADDRESSES:

In commenting, please refer to file code CMS-1765-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                             *https://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-1765-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-1765-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:

*PDPM@cms.hhs.gov*                             for issues related to the SNF PPS.                        

Heidi Magladry, (410) 786-6034, for information related to the skilled nursing facility quality reporting program.

Alexandre Laberge, (410) 786-8625, for information related to the skilled nursing facility value-based purchasing program.

# SUPPLEMENTARY INFORMATION:

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

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

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

Readers who experience any problems accessing any of these online SNF PPS wage index tables should contact Kia Burwell at (410) 786-7816.

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

## Table of Contents

I. Executive Summary

A. Purpose

B. Summary of Major Provisions

C. Summary of Cost and Benefits

D. Advancing Health Information Exchange

II. Background on SNF PPS

A. Statutory Basis and Scope

B. Initial Transition for the SNF PPS

C. Required Annual Rate Updates

III. Proposed SNF PPS Rate Setting Methodology and FY 2023 Update

A. Federal Base Rates

B. SNF Market Basket Update

C. Case-Mix Adjustment

D. Wage Index Adjustment

E. SNF Value-Based Purchasing Program

F. Adjusted Rate Computation Example

IV. Additional Aspects of the SNF PPS

A. SNF Level of Care—Administrative Presumption

B. Consolidated Billing

C. Payment for SNF-Level Swing-Bed Services

D. Revisions to the Regulation Text

V. Other SNF PPS Issues

A. Proposed Permanent Cap on Wage Index Decreases

B. Technical Updates to PDPM ICD-10 Mappings

C. Recalibrating the PDPM Parity Adjustment

D. Request for Information: Infection Isolation

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

A. Background and Statutory Authority

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

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

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

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

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

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

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

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

A. Statutory Background

B. SNF VBP Program Measures

C. SNF VBP Performance Period and Baseline Period Proposals

D. Performance Standards

E. SNF VBP Performance Scoring

F. Proposal To Adopt a Validation Process for the SNF VBP Program Beginning With the FY 2023 Program Year

                            G. Proposed SNF Value-Based Incentive Payments for FY 2023                                  ( print page 22721)    

H. Public Reporting on the Provider Data Catalog Website

I. Requests for Comment on Additional SNF VBP Program Measure Considerations for Future Years

VIII. Request for Information: Revising the Requirements for Long-Term Care (LTC) Facilities To Establish Mandatory Minimum Staffing Levels

IX. Collection of Information Requirements

X. Response to Comments

XI. Economic Analyses

A. Regulatory Impact Analysis

B. Regulatory Flexibility Act Analysis

C. Unfunded Mandates Reform Act Analysis

D. Federalism Analysis

E. Regulatory Review Costs

## I. Executive Summary

### A. Purpose

                        This proposed rule would update the SNF prospective payment rates for fiscal year (FY) 2023, as required under section 1888(e)(4)(E) of the Social Security Act (the Act). It also responds to section 1888(e)(4)(H) of the Act, which requires the Secretary to provide for publication of certain specified information relating to the payment update (see section II.C. of this proposed rule) in the                         **Federal Register**                        , before the August 1 that precedes the start of each FY. In addition, this proposed rule proposes requirements for the Skilled Nursing Facility Quality Reporting Program (SNF QRP) and the Skilled Nursing Facility Value-Based Purchasing Program (SNF VBP), including proposals to adopt new quality measures for the SNF VBP Program. The SNF QRP includes proposals to adopt one new measure to promote patient safety, begin collection of information which is expected to improve quality of care for all SNF patients, and revise associated regulation text. The proposal also seeks comment on several subjects related to the SNF QRP including principles for measuring healthcare quality disparities and developing measures of healthcare equity in the SNF QRP. This proposed rule also seeks comment on numerous issues related to the SNF VBP Program, including additional measures on staffing turnover and COVID-19 vaccination for healthcare personnel, the Program's exchange function, validation, and the SNF VBP Program's approach to health equity. This proposed rule also includes a request for information on revising the requirements for long-term care (LTC) facilities to establish mandatory minimum staffing levels.                    

### B. Summary of Major Provisions

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

This proposed rule proposes requirements for the SNF QRP, including the adoption of one new measure beginning with the FY 2025 SNF QRP: The Influenza Vaccination Coverage among Healthcare Personnel (HCP) (NQF #0431) measure. We are also proposing to revise the compliance date for the Transfer of Health Information measures and certain standardized patient assessment data elements. In addition, we are proposing to revise regulation text that pertains to data submission requirements for the SNF QRP. Finally, we are seeking comment on three subjects: Future measure concepts for the SNF QRP, overarching principles for measuring equity and healthcare disparities across CMS programs, including the SNF QRP, and the inclusion of the CoreQ: Short Stay Discharge Measure in the SNF QRP.

Additionally, we are proposing several updates for the SNF VBP Program, including a policy to suppress the Skilled Nursing Facility 30-Day All-Cause Readmission Measure (SNFRM) for the FY 2023 SNF VBP Program Year for scoring and payment adjustment purposes. We are also proposing to add two new measures to the SNF VBP Program beginning with the FY 2026 SNF VBP program year and one new measure beginning with the FY 2027 program year. We are also proposing several updates to the scoring methodology beginning with the FY 2026 program year and requesting public comments on several other measures we are considering for future rulemaking including a measure of staff turnover, whether we should update the exchange function, issues related to validation of SNF VBP data, and issues related to health equity. We are also proposing to revise our regulation text in accordance with our proposals.

### C. Summary of Cost and Benefits

### D. Advancing Health Information Exchange

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

                        To further interoperability in post-acute care settings, CMS and the Office of the National Coordinator for Health                               ( print page 22722)                            Information Technology (ONC) participate in the Post-Acute Care Interoperability Workgroup (PACIO) to facilitate collaboration with industry stakeholders to develop Health Level Seven International® (HL7) Fast Healthcare Interoperability Resource® (FHIR) standards. These standards could support the exchange and reuse of patient assessment data derived from the post-acute care (PAC) setting assessment tools, such as the minimum data set (MDS), inpatient rehabilitation facility-patient assessment instrument (IRF-PAI), long-Term Care Hospital (LTCH) continuity assessment record and evaluation (CARE) Data Set (LCDS), outcome and assessment information set (OASIS), and other sources.[1 2]                          The PACIO Project has focused on HL7 FHIR implementation guides for: Functional status, cognitive status and new use cases on advance directives, re-assessment timepoints, and Speech, language, swallowing, cognitive communication and hearing (SPLASCH) pathology.[3]                                                  We encourage PAC provider and health IT vendor participation as the efforts advance.                    

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

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

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

## II. Background on SNF PPS

### A. Statutory Basis and Scope

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

                        Section 215(a) of the Protecting Access to Medicare Act of 2014 (PAMA) (Pub. L. 113-93, enacted April 1, 2014) added section 1888(g) to the Act requiring the Secretary to specify an all-cause all-condition hospital readmission measure and an all-condition risk-adjusted potentially preventable hospital readmission measure for the SNF setting. Additionally, section 215(b) of PAMA added section 1888(h) to the Act requiring the Secretary to implement a VBP program for SNFs. Finally, section 2(c)(4) of the IMPACT Act amended section 1888(e)(6) of the Act, which requires the Secretary to implement a QRP for SNFs under which SNFs report data on measures and resident assessment data. Finally, section 111 of the Consolidated Appropriations Act, 2021 (CAA) updated section 1888(h) of the Act, authorizing the Secretary to apply up to nine additional measures to the VBP program for SNFs.                              ( print page 22723)    

### B. Initial Transition for the SNF PPS

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

### C. Required Annual Rate Updates

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

                        Section 1888(e)(4)(H) of the Act specifies that we provide for publication annually in the                         **Federal Register**                         the following:                    
    - The unadjusted Federal per diem rates to be applied to days of covered SNF services furnished during the upcoming FY.
    - The case-mix classification system to be applied for these services during the upcoming FY.
    - The factors to be applied in making the area wage adjustment for these services.

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

## III. Proposed SNF PPS Rate Setting Methodology and FY 2023 Update

### A. Federal Base Rates

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

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

### B. SNF Market Basket Update

#### 1. SNF Market Basket Index

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

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

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

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

#### 2. Use of the SNF Market Basket Percentage

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

#### 3. Forecast Error Adjustment

                        As discussed in the June 10, 2003 supplemental proposed rule (68 FR 34768) and finalized in the August 4, 2003 final rule (68 FR 46057 through 46059), § 413.337(d)(2) provides for an adjustment to account for market basket forecast error. The initial adjustment for market basket forecast error applied to the update of the FY 2003 rate for FY 2004 and took into account the cumulative forecast error for the period from FY 2000 through FY 2002, resulting in an increase of 3.26 percent to the FY 2004 update. Subsequent adjustments in succeeding FYs take into account the forecast error from the most recently available FY for which there is final data, and apply the difference between the forecasted and actual                               ( print page 22724)                            change in the market basket when the difference exceeds a specified threshold. We originally used a 0.25 percentage point threshold for this purpose; however, for the reasons specified in the FY 2008 SNF PPS final rule (72 FR 43425), we adopted a 0.5 percentage point threshold effective for FY 2008 and subsequent FYs. As we stated in the final rule for FY 2004 that first issued the market basket forecast error adjustment (68 FR 46058), the adjustment will reflect both upward and downward adjustments, as appropriate.                    

For FY 2021 (the most recently available FY for which there is final data), the forecasted or estimated increase in the SNF market basket index was 2.2 percent, and the actual increase for FY 2021 is 3.7 percent, resulting in the actual increase being 1.5 percentage point higher than the estimated increase. Accordingly, as the difference between the estimated and actual amount of change in the market basket index exceeds the 0.5 percentage point threshold, under the policy previously described (comparing the forecasted and actual increase in the market basket), the FY 2023 market basket percentage change of 2.8 percent, would be adjusted upward to account for the forecast error correction of 1.5 percentage point, resulting in a SNF market basket percentage change of 3.9 percent after reducing the market basket update by the productivity adjustment of 0.4 percentage point, discussed later in this section of the preamble.

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

#### 4. Productivity Adjustment

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

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

#### a. Incorporating the Productivity Adjustment Into the Market Basket Update

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

Based on the data available for this FY 2023 SNF PPS proposed rule, the current proposed productivity adjustment (the 10-year moving average of TFP for the period ending September 30, 2023) is projected to be 0.4 percentage point.

                        Consistent with section 1888(e)(5)(B)(i) of the Act and § 413.337(d)(2), as discussed previously in this section of the preamble, the market basket percentage for FY 2023 for the SNF PPS is based on IGI's fourth quarter 2021 forecast of the SNF market basket percentage, which is estimated to be 2.8 percent. This market basket percentage is then increased by 1.5 percentage point, due to application of the forecast error adjustment discussed earlier in this section of the preamble. Finally, as discussed earlier in this section of the preamble, we are applying a 0.4 percentage point productivity adjustment to the FY 2023 SNF market basket percentage. The resulting                               ( print page 22725)                            productivity-adjusted FY 2023 SNF market basket update is, therefore, equal to 3.9 percent, or 2.8 percent plus 1.5 percentage point to account for forecast error and less 0.4 percentage point to account for the productivity adjustment.                    

#### 5. Market Basket Update Factor for FY 2023

Sections 1888(e)(4)(E)(ii)(IV) and (e)(5)(i) of the Act require that the update factor used to establish the FY 2023 unadjusted Federal rates be at a level equal to the market basket index percentage change. Accordingly, we determined the total growth from the average market basket level for the period of October 1, 2021 through September 30, 2022 to the average market basket level for the period of October 1, 2022 through September 30, 2023. This process yields a percentage change in the 2018-based SNF market basket of 2.8 percent.

As further explained in section III.B.3. of this proposed rule, as applicable, we adjust the market basket percentage change by the forecast error from the most recently available FY for which there is final data and apply this adjustment whenever the difference between the forecasted and actual percentage change in the market basket exceeds a 0.5 percentage point threshold in absolute terms. Since the actual FY 2021 SNF market basket percentage change exceeded the forecasted FY 2021 SNF market basket percentage change (FY 2021 is the most recently available FY for which there is historical data) by more than the 0.5 percentage point threshold, we propose to adjust the FY 2023 market basket percentage change upward by the forecast error correction. Applying the 1.5 percentage point forecast error correction results in an adjusted FY 2023 SNF market basket percentage change of 4.3 percent (2.8 percent market basket update plus 1.5 percentage point forecast error adjustment).

Section 1888(e)(5)(B)(ii) of the Act requires us to reduce the market basket percentage change by the productivity adjustment (10-year moving average of changes in TFP for the period ending September 30, 2023) which is estimated to be 0.4 percentage point, as described in section III.B.4. of this proposed rule. Thus, we apply a net SNF market basket update factor of 3.9 percent in our determination of the FY 2022 SNF PPS unadjusted Federal per diem rates, which reflects a market basket increase factor of 2.8 percent, plus the 1.5 percentage point forecast error correction and less the 0.4 percentage point productivity adjustment.

We note that if more recent data become available (for example, a more recent estimate of the SNF market basket and/or productivity adjustment), we would use such data, if appropriate, to determine the FY 2023 SNF market basket percentage change, labor-related share relative importance, forecast error adjustment, or productivity adjustment in the FY 2023 SNF PPS final rule.

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

#### 6. Unadjusted Federal Per Diem Rates for FY 2023

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

Tables 3 and 4 reflect the updated unadjusted Federal rates for FY 2023, prior to adjustment for case-mix.
      ( print page 22726)    

### C. Case-Mix Adjustment

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

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

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

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

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

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

                        As we noted in the FY 2022 SNF PPS final rule (86 FR 42434), we continue to monitor the impact of PDPM implementation on patient outcomes and program outlays. Because of this analysis, in section V.C. of this                               ( print page 22727)                            proposed rule, we propose to recalibrate the PDPM parity adjustment discussed in the FY 2020 SNF PPS final rule (84 FR 38734). Following the methodology of this proposed change, Tables 5 and 6 incorporate the proposed recalibration of the PDPM parity adjustment.                    
      ( print page 22728)    

### D. Wage Index Adjustment

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

                        We note that section 315 of the Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000 (BIPA) (Pub. L. 106-554, enacted December 21, 2000) authorized us to establish a geographic reclassification procedure that is specific to SNFs, but only after collecting the data necessary to establish a SNF PPS wage index that is based on wage data from nursing homes. However, to date, this has proven to be unfeasible due to the volatility of existing SNF wage data and the significant amount of resources that would be required to improve the quality of the data. More specifically, auditing all SNF cost reports, similar to the process used to audit inpatient hospital cost reports for purposes of the IPPS wage index, would place a burden on providers in terms of recordkeeping and completion of the cost report worksheet. In addition, adopting such an approach would require a significant commitment of resources by CMS and the Medicare Administrative Contractors, potentially far in excess of those required under the IPPS, given that there are nearly five times as many SNFs as there are inpatient hospitals. While we continue to believe that the development of such an audit process could improve SNF cost reports in such a manner as to permit us to establish a SNF-specific wage index, we do not believe this undertaking is feasible at this time. Therefore, as discussed above in this section, in the absence of a SNF-specific wage index, we believe the use of the pre-reclassified and pre-floor hospital wage data (without the occupational mix adjustment) continue to be an appropriate and reasonable proxy for the SNF PPS.                              ( print page 22729)    

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

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

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

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

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

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

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

                        Once calculated, we would apply the wage index adjustment to the labor-related portion of the Federal rate. Each year, we calculate a revised labor-related share, based on the relative importance of labor-related cost categories (that is, those cost categories that are labor-intensive and vary with the local labor market) in the input price index. In the SNF PPS final rule for FY 2018 (82 FR 36548 through 36566), we finalized a proposal to revise the labor-related share to reflect the relative importance of the 2014-based SNF market basket cost weights for the following cost categories: Wages and Salaries; Employee Benefits; Professional Fees: Labor-Related; Administrative and Facilities Support                               ( print page 22730)                            Services; Installation, Maintenance, and Repair Services; All Other: Labor-Related Services; and a proportion of Capital-Related expenses. Effective beginning FY 2022 (86 FR 42437), we rebased and revised the labor-related share to reflect the relative importance of the 2018-based SNF market basket cost weights for the following cost categories: Wages and Salaries; Employee Benefits; Professional Fees: Labor-Related; Administrative and Facilities Support services; Installation, Maintenance, and Repair Services; All Other: Labor-Related Services; and a proportion of Capital-Related expenses. The methodology for calculating the labor-related portion beginning in FY 2022 is discussed in detail in the FY 2022 SNF PPS final rule (86 FR 42424).                    

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

Table 7 summarizes the proposed labor-related share for FY 2023, based on IGI's fourth quarter 2021 forecast of the 2018-based SNF market basket, compared to the labor-related share that was used for the FY 2022 SNF PPS final rule.

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

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

                        Section 1888(e)(4)(G)(ii) of the Act also requires that we apply this wage index in a manner that does not result in aggregate payments under the SNF PPS that are greater or less than would otherwise be made if the wage adjustment had not been made. For FY 2023 (Federal rates effective October 1, 2022), we apply an adjustment to fulfill the budget neutrality requirement. We meet this requirement by multiplying each of the components of the unadjusted Federal rates by a budget neutrality factor, equal to the ratio of the weighted average wage adjustment factor for FY 2022 to the weighted average wage adjustment factor for FY 2023. For this calculation, we would use the same FY 2021 claims utilization data for both the numerator and denominator of this ratio. We define the wage adjustment factor used in this calculation as the labor portion of the rate component multiplied by the wage index plus the non-labor portion of the rate component. The proposed budget                               ( print page 22731)                            neutrality factor for FY 2023 as set forth in this proposed rule is 1.0011.                    

We note that if more recent data become available (for example, revised wage data), we would use such data, as appropriate, to determine the wage index budget neutrality factor in the SNF PPS final rule.

### E. SNF Value-Based Purchasing Program

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

Please see section VII. of this proposed rule for further discussion of our policies and proposals for the SNF VBP Program.

### F. Adjusted Rate Computation Example

                        Tables 8 through 10 provide examples generally illustrating payment calculations during FY 2023 under PDPM for a hypothetical 30-day SNF stay, involving the hypothetical SNF XYZ, located in Frederick, MD (Urban CBSA 23224), for a hypothetical patient who is classified into such groups that the patient's HIPPS code is NHNC1. Table 8 shows the adjustments made to the Federal per diem rates (prior to application of any adjustments under the SNF VBP Program as discussed previously and taking into account the proposed parity adjustment discussed in section V.C. of this proposed rule) to compute the provider's case-mix adjusted per diem rate for FY 2023, based on the patient's PDPM classification, as well as how the variable per diem (VPD) adjustment factor affects calculation of the per diem rate for a given day of the stay. Table 9 shows the adjustments made to the case-mix adjusted per diem rate from Table 8 to account for the provider's wage index. The wage index used in this example is based on the FY 2023 SNF PPS wage index that appears in Table A available on the CMS website at                         *http://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​WageIndex.html.*                         Finally, Table 10 provides the case-mix and wage index adjusted per-diem rate for this patient for each day of the 30-day stay, as well as the total payment for this stay. Table 10 also includes the VPD adjustment factors for each day of the patient's stay, to clarify why the patient's per diem rate changes for certain days of the stay. As illustrated in Table 8, SNF XYZ's total PPS payment for this particular patient's stay would equal $20,112.27.                    
      ( print page 22732)    

## IV. Additional Aspects of the SNF PPS

### A. SNF Level of Care—Administrative Presumption

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

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

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

                        In the July 30, 1999 final rule (64 FR 41670), we indicated that we would announce any changes to the guidelines for Medicare level of care determinations related to modifications in the case-mix classification structure. The FY 2018 final rule (82 FR 36544) further specified that we would henceforth disseminate the standard description of the administrative presumption's designated groups via the SNF PPS website at                         *https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​      ( print page 22733)    index.html*                         (where such designations appear in the paragraph entitled “Case Mix Adjustment”), and would publish such designations in rulemaking only to the extent that we actually intend to propose changes in them. Under that approach, the set of case-mix classifiers designated for this purpose under PDPM was finalized in the FY 2019 SNF PPS final rule (83 FR 39253) and is posted on the SNF PPS website (                        *https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​index.html*), in the paragraph entitled “Case Mix Adjustment.”                    

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

### B. Consolidated Billing

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

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

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

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

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

                        In this proposed rule, we specifically invite public comments identifying HCPCS codes in any of these five service categories (chemotherapy items, chemotherapy administration services, radioisotope services, customized prosthetic devices, and blood clotting factors) representing recent medical advances that might meet our criteria for exclusion from SNF consolidated billing. We may consider excluding a particular service if it meets our criteria for exclusion as specified previously. We request that commenters identify in their comments the specific HCPCS code that is associated with the service in question, as well as their rationale for                               ( print page 22734)                            requesting that the identified HCPCS code(s) be excluded.                    

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

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

### C. Payment for SNF-Level Swing-Bed Services

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

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

### D. Revisions to the Regulation Text

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

## V. Other SNF PPS Issues

### A. Proposed Permanent Cap on Wage Index Decreases

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

In the FY 2022 final rule (86 FR 42424, 42439), commenters recommended CMS extend the transition period adopted in the FY 2021 SNF PPS final rule so that SNFs could offset the enormous cuts scheduled for FY 2022. Because we did not propose to modify the transition policy that was finalized in the FY 2021 SNF 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 payment. In addition, we reiterated that our policy principles with regard to the wage index include generally using the most current data and information available and providing that data and information, as well as any approaches to addressing any significant effects on Medicare payments resulting from these potential scenarios, 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 SNF payment volatility; that is, scenarios in which changes to wage index policy may significantly affect Medicare payments.

                        In the past, we have established transition policies of limited duration to phase in significant changes to labor market. In taking this approach in the past, we have sought to strike an appropriate balance between maintaining the accuracy of the overall labor market area wage index system and mitigating short-term instability and negative impacts on providers due to                               ( print page 22735)                            wage index changes. In accordance with the requirements of the SNF PPS wage index regulations at § 413.337(a)(1), we use an appropriate wage index based on the best available data, including the best available labor market area delineations, to adjust SNF PPS payments for wage differences. We have previously stated that, because the wage index is a relative measure of the value of labor in prescribed labor market areas, we believe it is important to implement new labor market area delineations with as minimal a transition as is reasonably possible. However, we recognize that changes to the wage index have the potential to create instability and significant negative impacts on certain providers even when labor market areas do not change. In addition, year-to-year fluctuations in an area's wage index can occur due to external factors beyond a provider's control, such as the COVID-19 public health emergency (PHE). For an individual provider, these fluctuations can be difficult to predict. So, we also recognize that predictability in Medicare payments is important to enable providers to budget and plan their operations.                    

In light of these considerations, we 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 SNF PPS payments for providers, and mitigates instability and significant negative impacts to providers resulting from changes to the wage index.

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

Typical year-to-year variation in the SNF PPS wage index has historically been within 5 percent, and we expect this will continue to be the case in future years. For FY 2023, the provider level impact analysis indicates that approximately 97 percent of SNFs will experience a wage index change within 5 percent. Because providers are usually experienced with this level of wage index fluctuation, we believe applying a 5-percent cap on all wage index decreases each year, regardless of the reason for the decrease, would effectively mitigate instability in SNF PPS payments due to any significant wage index decreases that may affect providers in any year. We believe this approach would address concerns about instability that commenters raised in the FY 2022 SNF PPS rule. Additionally, we believe that applying a 5-percent cap on all wage index decreases would support increased predictability about SNF PPS payments for providers, enabling them to more effectively budget and plan their operations. Lastly, because applying a 5-percent cap on all wage index decreases would represent a small overall impact on the labor market area wage index system we believe it would ensure the wage index is a relative measure of the value of labor in prescribed labor market wage areas. As discussed in further detail in section XI.A.4. 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 SNFs in the same labor market area, as the hospital average hourly wage data in the CBSA (and any relative decreases compared to the national average hourly wage) would be similar. While this policy may result in SNFs in a CBSA receiving a higher wage index than others in the same area (such as situations when delineations change), we believe the impact would be temporary. Therefore, we anticipate that the impact to the wage index budget neutrality factor in future years would continue to be minimal.

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

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

We invite public comments on this proposal.

### B. Technical Updates to PDPM ICD-10 Mappings

                        In the FY 2019 SNF PPS final rule (83 FR 39162), we finalized the                               ( print page 22736)                            implementation of the Patient Driven Payment Model (PDPM), effective October 1, 2019. The PDPM utilizes International Classification of Diseases, Version 10 (ICD-10) codes in several ways, including to assign patients to clinical categories under several PDPM components, specifically the PT, OT, SLP and NTA components. The ICD-10 code mappings and lists used under PDPM are available on the PDPM website at                         *https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​SNFPPS/​PDPM.*

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

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

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

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

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

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

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

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

                        In the FY 2022 proposed rule (86 FR 19984 through 19985), we proposed to reclassify K20.81, “Other esophagitis with bleeding,” K20.91, “Esophagitis, unspecified with bleeding,” and K21.01, “Gastro-esophageal reflux disease with esophagitis, with bleeding” from “Return to Provider” to “Medical Management.” Our rationale for the change was a recognition that these codes represent these esophageal conditions with more specificity than originally considered because of the bleeding that is part of the conditions and that they would more likely be found in SNF patients. We received one comment suggesting additional changes to similar ICD-10 code mappings and comorbidity lists that at the time were outside the scope of rulemaking. This commenter suggested that we consider remapping the following similar diagnosis codes that frequently require SNF skilled care, from “Return to Provider” to “Medical Management”: K22.11, “Ulcer of esophagus with bleeding;” K25.0, “Acute gastric ulcer with hemorrhage;” K25.1, “Acute gastric ulcer with perforation;” K25.2, “Acute gastric ulcer with both hemorrhage and perforation;” K26.0, “Acute duodenal ulcer with hemorrhage;” K26.1, “Acute duodenal ulcer with perforation;” K26.2, “Acute duodenal ulcer with both hemorrhage and perforation;” K27.0 “Acute peptic ulcer, site unspecified with hemorrhage;” K27.1, “Acute peptic ulcer, site unspecified with perforation;” K27.2, “Acute peptic ulcer, site unspecified with both hemorrhage and perforation;” K28.0, “Acute gastrojejunal ulcer with hemorrhage;” K28.1, “Acute gastrojejunal ulcer with perforation;” K28.2, “Acute gastrojejunal ulcer with both hemorrhage and perforation;” and K29.01, “Acute gastritis with bleeding.” Upon review of these codes, we recognize that they represent conditions with more specificity than originally considered because of the bleeding (or perforation) that is part of the conditions and that they would more likely be found in SNF patients.” Therefore, we propose to remap these                               ( print page 22737)                            ICD-10 codes to “Medical Management.”                    

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

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

### C. Recalibrating the PDPM Parity Adjustment

#### 1. Background

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

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

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

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

                        In the FY 2022 SNF PPS proposed rule (86 FR 19987 through 19989), we solicited comments from stakeholders on a potential methodology for recalibrating the PDPM parity adjustment to account for these potential effects without compromising the accuracy of the adjustment. After considering the feedback and recommendations received, summarized in the FY 2022 SNF PPS final rule (86 FR 42469 through 42471), we are proposing an updated recalibration methodology. We also present results                               ( print page 22738)                            from our data monitoring efforts to provide transparency on our efforts to parse out the effects of PDPM implementation from the effects of the COVID-19 PHE. We invite comments on this proposal for recalibrating the PDPM parity adjustment, that is discussed throughout the subsequent sections of this proposed rule, to ensure that PDPM is implemented in a budget neutral manner, as originally intended.                    

#### 2. Methodology for Recalibrating the PDPM Parity Adjustment

#### a. Effect of COVID-19 Public Health Emergency

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

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

However, we note that the relevant issue for a recalibration of the PDPM parity adjustment is whether or not the COVID-19 PHE caused changes in the SNF case-mix distribution. In other words, the issue is whether patient classification, or the relative percentages of beneficiaries in each PDPM group, was different than what it would have been if not for the COVID-19 PHE. We remind commenters that the parity adjustment refers only to the transition between case-mix classification models (in this case, from RUG-IV to PDPM) and is not intended to include other unrelated SNF policies such as the market basket increase, which is intended to address such issues as the additional costs described previously. A key aspect of our recalibration methodology, described in further detail later in this section, involves parsing out the impacts of the COVID-19 PHE and the PHE-related modifications from those which occurred solely, or at least principally, due to the implementation of PDPM.

#### b. Effect of PDPM Implementation

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

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

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

                        In the FY 2022 SNF PPS proposed rule (86 FR 19986 through 19987), we presented a potential methodology that attempted to account for the effects of the COVID-19 PHE by removing those stays with a COVID-19 diagnosis and those stays using a PHE-related modification from our data set, and we solicited comment on how stakeholders believed the COVID-19 PHE affected the distribution of patient case-mix in ways that were not sufficiently captured by our subset population methodology. According to the latest data available, 10 percent of SNF stays in FY 2020 and 17 percent of SNF stays in FY 2021 included a COVID-19 ICD-10 diagnosis code either as a primary or secondary diagnosis, while 17 percent of SNF stays in FY 2020 and 27 percent of SNF stays in FY 2021 utilized a PHE-related                               ( print page 22739)                            modification (with the majority of these cases using the prior hospitalization modification), as identified by the presence of a “Disaster Relief (DR)” condition code on the SNF claim. As compared to prior years, when approximately 98 percent of SNF beneficiaries had a qualifying prior hospital stay, approximately 86 percent and 81 percent of SNF beneficiaries had a qualifying prior hospitalization in FY 2020 and FY 2021, respectively. These general statistics are important, as they highlight that while the PHE for COVID-19 certainly impacted many aspects of nursing home operations, the large majority of SNF beneficiaries entered into Part A SNF stays in FY 2020 and FY 2021 as they would have in any other year; that is, without using a PHE-related modification, with a prior hospitalization, and without a COVID-19 diagnosis.                    

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

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

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

#### d. FY 2023 SNF PPS Proposed Parity Adjustment Methodology

                        In this section, we propose a revised methodology for the calculating the parity adjustment that takes into account the comments received in response to the potential methodology described in the FY 2022 SNF PPS proposed rule (86 FR 19986 through 19987). In response to the comments received about the subset population methodology, we modified our definition of COVID-19, which we derived from the Centers for Disease Control and Prevention (CDC) coding guidelines, to align with the definition used by publicly available datasets from CMS's Office of Enterprise Data and Analytics (OEDA) and found no significant impact on our calculations. For the FY 2022 SNF proposed rule, we defined the COVID-19 population to include stays that have either the interim COVID-19 code B97.29 recorded as a primary or secondary diagnosis in addition to one of the symptom codes J12.89, J20.8, J22, or J80, or the new COVID-19 code U07.1 recorded as a primary or secondary diagnosis on their SNF claims or MDS 5-day admission assessments. For the FY 2023 SNF proposed rule, we define the COVID-19 population to include stays that have the interim COVID-19 code B97.29 from January 1, 2020 to March 31, 2020 or the new COVID-19 code U07.1 from April 1, 2020 onward recorded as a primary or secondary diagnosis on their SNF claims, MDS 5-day admission assessments, or MDS interim payment assessments. Both FY                               ( print page 22740)                            2022 and FY 2023 definitions of the COVID-19 population exclude transitional stays. We note that we found no significant impact on our calculations, as the COVID-19 population definition change only increased the stay count of our subset population by less than 1 percent.                    

In response to the comments described previously and based on additional data collection through FY 2021, we have identified a recalibration methodology that we believe better accounts for COVID-19 related effects. We propose to use the same type of subset population discussed earlier in section V.C.2.c.of this proposed rule, which excludes stays that either used a section 1812(f) of the Act modification or that included a COVID-19 diagnosis, with a 1-year “control period” derived from both FY 2020 and FY 2021 data. Specifically, we used 6 months of FY 2020 data from October 2019 through March 2020 and 6 months of FY 2021 data from April 2021 through September 2021 (which our data suggests were periods with relatively low COVID-19 prevalence) to create a full 1-year period with no repeated months to account for seasonality effects. As shown in Table 11, we believe this combined approach provides the most accurate representation of what the SNF case-mix distribution would look like under PDPM outside of a COVID-19 PHE environment. While using the subset population method alone for FY 2020 and FY 2021 data results in differences of 0.31 percent and 0.40 percent between the full and subset populations, respectively, introducing the control period closes the gap between the full and subset population adjustment factors to 0.02 percent, suggesting that the control period captures additional COVID-19 related effects on patient acuity that the subset population method alone does not. Accordingly, the combined methodology of using the subset population with data from the control period results in the lowest parity adjustment factor. Table 12 shows that while using the subset population method would lead to a 4.9 percent adjustment factor ($1.8 billion) using FY 2020 data and a 5.3 percent adjustment factor ($1.9 billion) using FY 2021 data, introducing the control period reduces the adjustment factor to 4.6 percent ($1.7 billion). The robustness of the control period approach is further demonstrated by the fact that using data from the control period, with either the full or subset population, would lead to approximately the same parity adjustment factor of 4.58 percent as compared to 4.6 percent. We invite comments on our proposed combined methodology of using the subset population and data from the control period for the purposes of calculating the recalibrated parity adjustment factor.

                        Our data analysis and monitoring efforts provides further support for the accuracy and appropriateness of a 4.6 percent parity adjustment factor, as we have identified numerous changes that demonstrate the different impacts of PDPM implementation and the COVID-19 PHE on reported patient clinical acuity. As described earlier, commenters stated that limitations regarding visitation and other infection control protocols due to the PHE led to higher levels of mood distress, cognitive decline, functional decline, compromised skin integrity, change in appetite, and weight loss requiring diet modifications among the non-COVID population. However, our data shows that most of these metrics, with the exception of functional decline and compromised skin integrity, had already exhibited clear changes concurrent with PDPM implementation and well before the start of the COVID-19 PHE. For example, in regard to higher levels of mood distress and cognitive decline, we observed an average of 4 percent of stays with depression and 40 percent of stays with cognitive impairment, with an average mood score of 1.9, in the fiscal year prior to PDPM implementation (FY 2019). In the 3 months directly following PDPM implementation and before the start of the COVID-19 PHE (October 2019 to December 2019), these averages increased to 11 percent of stays with depression and 44 percent of stays with cognitive impairment, with an average mood scale of 2.9. As for change in appetite and weight loss requiring diet modifications, we observed an average of 15 percent of stays with any SLP comorbidity, 5 percent of stays with a swallowing disorder, and 22 percent of stays with a mechanically altered diet in FY 2019. In the 3 months directly following PDPM implementation, these averages increased to 19 percent of stays with any SLP comorbidity, 17 percent of stays with a swallowing disorder, and 25 percent of stays with a mechanically altered diet. Notably, we also observed that the percentage of stays with a swallowing disorder that did not also receive a mechanically altered diet increased from 1 percent in FY 2019 to 5 percent in the 3 months directly                               ( print page 22741)                            following PDPM implementation. While many of these metrics increased further after the start of the COVID-19 PHE, they remained elevated at around their post-PDPM implementation levels even during periods of low COVID-19 prevalence. As a result, our parity adjustment calculations remained much the same even during months when rates of COVID-19 cases were quite low, suggesting that patient case mix classification has stabilized independent of the ongoing COVID-19 PHE.                    

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

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

                        Given the results of our data analyses, we propose adopting the methodology based upon the subset population during the control period, and lowering the PDPM parity adjustment factor from 46 percent to 38 percent for each of the PDPM case-mix adjusted components. If we applied this methodology for FY 2023, we estimated a reduction in aggregate SNF spending of 4.6 percent, or approximately $1.7 billion. We note that the parity adjustment is calculated and applied at a systemic level to all facilities paid under the SNF PPS, and there may be variation between facilities based on their unique patient population, share of non-case-mix component payment, and urban or rural status. We invite comments on the methodology described in this section of the proposed rule for recalibrating the PDPM parity adjustment, as well as the findings of our analysis described throughout this section. To assist commenters in providing comments on this issue, we have also posted a file on the CMS website, at                         *https://www.cms.gov/​medicare/​medicare-fee-for-service-payment/​snfpps,*                         which provides the FY 2019 RUG IV case-mix distribution and calculation of total payments under RUG-IV, as well as PDPM case-mix utilization data at the case mix group and component level to demonstrate the calculation of total payments under PDPM.                    

#### 3. Methodology for Applying the Recalibrated PDPM Parity Adjustment

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

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

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

However, to fully explore the alternative targeted approach that commenters suggested, we have updated our analysis of the average CMI by PDPM component from Table 23 of the FY 2022 SNF PPS proposed rule (86 FR 19987) and found that a similar pattern still holds when comparing the expected average CMIs for FY 2019 and the expected actual CMIs for the subset population during the control period. Table 13 shows significant increases in average case-mix of 18.6 percent for the SLP component and the 10.8 percent for the Nursing component, a moderate increase of 3.0 percent for the NTA component, and a slight increase of 0.4 percent for the PT and OT components, respectively. We also provide Table 14 to show the potential impact of applying the recalibrated PDPM parity adjustment to the PDPM CMIs in a targeted manner, instead of an equal approach as presented in Tables 5 and 6 in section III.C. of this proposed rule. We invite comments on whether stakeholders believe a targeted approach is preferable to our proposed equal approach.
      ( print page 22742)    

#### 4. Delayed and Phased Implementation

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

                        A delayed implementation strategy would mean that we would implement the reduction in payment in a later year than the year the reduction is finalized. For example, considering the 4.6 percent reduction discussed previously in this preamble, if this reduction is finalized in FY 2023 with a 1-year delayed implementation, this would mean that the full 4.6 percent reduction                               ( print page 22743)                            will be applied prospectively applied to the PDPM CMIs in FY 2024. By comparison, a phased implementation strategy would mean that the amount of the reduction would be spread out over some number of years. For example, if we were to use a 2-year phased implementation approach to the 4.6 percent reduction discussed previously in this proposed rule with no delayed implementation, this would mean that the PDPM CMIs would be reduced by 2.3 percent in the first year of implementation in FY 2023 and then reduced by the remaining 2.3 percent in the second and final year of implementation in FY 2024. We could also use a combination of both mitigation strategies, such as a 1-year delayed implementation with a 2-year phased approach, would mean that the PDPM CMIs would be reduced by 2.3 percent in the first year of implementation in FY 2024 and then reduced by the remaining 2.3 percent in the second and final year of implementation in FY 2025.                    

In the FY 2022 SNF PPS proposed rule (86 FR 19988 through 19990), we solicited comments on the possibility of combining the delayed and phased implementation approaches and what stakeholders believe would be appropriate to appropriately mitigate the impact of the reduction in SNF PPS payments. As described in the FY 2022 SNF PPS final rule (86 FR 42470 through 42471), the majority of commenters supported combining both mitigation strategies of delayed implementation of 2 years and a gradual phase-in of no more than 1 percent per year. In its comments to the FY 2022 SNF PPS proposed rule, MedPAC supported delayed implementation, but did not believe a phased-in approach was warranted given the high level of aggregate payment to SNFs.

As stated in the FY 2022 SNF PPS proposed rule (86 FR 19989) and FY 2022 SNF PPS final rule (86 FR 42471), we believe it is imperative that we act in a well-considered but expedient manner once excess payments are identified. Additionally, we stated that we would consider whether the delayed and phased implementation approaches were warranted to mitigate potential negative impacts on providers resulting from implementation of such a reduction in the SNF PPS rates entirely within a single year. After careful consideration, we are proposing to recalibrate the parity adjustment in FY 2023 with no delayed implementation or phase-in period, particularly after considering that we have already granted a 1-year delayed implementation by not proposing or finalizing the parity adjustment in the FY 2022 SNF PPS proposed and final rules. This proposal would lead to a prospective reduction in Medicare Part A SNF payments of approximately 4.6 percent (−$1.7 billion) in FY 2023. We would note that this reduction would be substantially mitigated by the proposed FY 2023 net SNF market basket update factor of 3.9 percent, which reflects a market basket increase factor of 2.8 percent, adjusted upward to account for the 1.5 percentage point forecast error correction and adjusted downward to account for the 0.4 percentage point productivity adjustment, as discussed in section III.B. of this proposed rule. Taken together, the preliminary net budget impact in FY 2023 would be an estimated decrease of $320 million in aggregate payment to SNFs if the parity adjustment is implemented in one year.

While we note many commenters supported both mitigation strategies of delayed implementation and phased implementation, we emphasize that we have already granted a 1-year delayed implementation by not proposing or finalizing the parity adjustment in the FY 2022 SNF PPS proposed and final rules, and instead taking a year to solicit and consider comments on our parity adjustment methodology. As stated in the FY 2022 final rule, we estimated a reduction in SNF spending of 5 percent, or approximately $1.7 billion, if we had implemented the parity adjustment in FY 2022 (86 FR 42471). Moreover, in light of the potential reduction in payments associated with each possible option outlined in Table 2, the SNF PPS has been paying in excess of budget neutrality at a rate of approximately $1.7 billion per year since PDPM was implemented in FY 2020. We therefore believe that delaying the implementation of the proposed recalibration or phasing the recalibration in over some amount of time would only serve to prolong these payments in excess of the intended policy.

                        Further, MedPAC's March 2022 Report to Congress (available at                         *https://www.medpac.gov/​wp-content/​uploads/​2022/​03/​Mar22_​MedPAC_​ReportToCongress_​Ch7_​SEC.pdf*) has found that since 2000, the aggregate Medicare margin for freestanding SNFs has consistently been above 10 percent each year. In 2020, the aggregate Medicare margin was 16.5 percent, a sizable increase from 11.9 percent in 2019. Additionally, the aggregate Medicare margin in 2020 increased to an estimated 19.2 percent when including Federal relief funds for the COVID-19 PHE (March 2022 MedPAC Report to Congress, 251-252). Given these high Medicare margins, we do not believe that a delayed implementation or a phase-in approach is needed. Rather, these mitigation strategies would continue to pay facilities at levels that significantly exceed intended SNF payments, had PDPM been implemented in a budget neutral manner as finalized by CMS in the FY 2019 SNF PPS final rule (83 FR 39256). It is also important to note that the parity adjustment recalibration would serve to remove an unintended increase in payments from moving to a new case mix classification system, rather than decreasing an otherwise appropriate payment amount. Thus, we do not believe that the recalibration should negatively affect facilities, beneficiaries, and quality of care, or create an undue hardship on providers.                    

We continue to believe that in implementing PDPM, it is essential that we stabilize the baseline as quickly as possible without creating a significant adverse effect on the industry or to beneficiaries. We invite comments on our proposal to recalibrate the parity adjustment by 4.6 percent in FY 2023, and whether stakeholders believe delayed implementation or phase-in period is warranted or not, in light of the data analysis and policy considerations presented previously.

### D. Request for Information: Infection Isolation

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

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

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

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

                        4. The patient must remain in his or her room. This requires that all services be brought to the resident (for example, rehabilitation, activities, dining, etc.).                              ( print page 22744)    

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

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

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

We would like to take this opportunity to invite the public to submit their comments about isolation due to active infection and how the PHE has affected the relative staff time resources necessary for treating these patients. Specifically, we invite comments on whether or not the relative increase in resource utilization for each of the patients within a cohorted room, all with an active infection, is the same or comparable to that of the relative increase in resource utilization associated with a patient that is isolated due to an active infection.

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

### A. Background and Statutory Authority

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

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

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

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

The SNF QRP currently has 15 measures for the FY 2023 SNF QRP, which are outlined in Table 15. For a discussion of the factors used to evaluate whether a measure should be removed from the SNF QRP, we refer readers to § 413.360(b)(3).
      ( print page 22745)    

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

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

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

                        In addition, we propose to revise the compliance date for the collection of the Transfer of Health (TOH) Information to the Provider-PAC measure, the TOH Information to the Patient-PAC measure, and certain standardized patient assessment data elements from October 1st of the year that is at least 2 full fiscal years after the end of the COVID-19                               ( print page 22746)                            PHE to October 1, 2023. We believe the COVID-19 PHE revealed why the TOH Information measures and standardized patient assessment data elements are important to the SNF QRP. The new data elements will facilitate communication and coordination across care settings as well as provide information to support our mission of analyzing the impact of the COVID-19 PHE on patients to improve the quality of care in SNFs. We describe this proposal in more detail in section VI.C.2. of this proposed rule.                    

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

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

#### a. Background

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

                        Despite the fact that influenza commonly spreads between HCP and SNF residents, vaccine hesitancy and organizational barriers often prevent influenza vaccination. For example, although the CDC emphasizes the importance for HCP to receive the influenza vaccine, the 2017 to 2018 influenza season shows higher influenza vaccination coverage among HCP working in hospitals (approximately 92 percent) and lower coverage among those working in long-term care facilities (approximately 68 percent).[22 23]                          HCP working in long-term care facilities, including SNFs, have expressed concerns about the influenza vaccine's effectiveness and safety, fearing potential side effects and adverse reactions.[24]                                                  Other HCP believe healthy individuals are not susceptible to infection and therefore find vaccination unnecessary.[25]                                                  In addition, many HCP do not prioritize influenza vaccination, expressing a lack of time to get vaccinated.[26]                                                  Lower HCP influenza vaccination in long-term care facilities also stems from organizational barriers, such as inadequate vaccine record keeping, frequent staff turnover, an                               ( print page 22747)                            absence of influenza vaccine mandates, a lack of communication about vaccination rates, and a lack of incentives encouraging HCP flu vaccination.[27]                                                  Given the fact that influenza vaccination coverage among HCP is typically lower in long-term care settings, such as SNFs, when compared to other care settings, we believe the proposed measure has the potential to increase influenza vaccination coverage in SNFs, promote patient safety, and increase the transparency of quality of care in the SNF setting.                    

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

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

                        The COVID-19 pandemic has exposed the importance of implementing infection prevention strategies, including the promotion of HCP influenza vaccination. Activity of the influenza virus has been lower during the COVID-19 pandemic as several strategies to reduce the spread of COVID-19 have also reduced the spread of influenza, including mask mandates, social distancing, and increased hand hygiene.[46]                                                  However, even though more                               ( print page 22748)                            people are receiving COVID-19 vaccines, it is still important to encourage annual HCP influenza vaccination to prevent health care systems from getting overwhelmed by the co-circulation of COVID-19 and influenza viruses. A 2020 literature search revealed several studies in which those with severe cases of COVID-19, requiring hospitalization, were less likely to be vaccinated against influenza.[47]                                                  HCP vaccinations against influenza may prevent the spread of illness between HCP and residents, thus reducing resident morbidities associated with influenza and pressure on already stressed health care systems. In fact, several thousand nursing homes voluntarily reported weekly influenza vaccination coverage through an NHSN module based on the NQF #0431 measure during the overlapping 2020 to 2021 influenza season and COVID-19 pandemic. Even after the COVID-19 pandemic ends, promoting HCP influenza vaccination is important in preventing morbidity and mortality associated with influenza.                    

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

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

#### b. Stakeholder Input and Pilot Testing

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

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

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

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

                        Face validity was assessed through a Delphi panel, which convened in June 2011 and provided stakeholder input on the proposed measure. The Delphi panel, comprised of nine experts in influenza vaccination measurement and quality improvement from several public and private organizations, rated elements of the proposed measure using a Likert scale. The Delphi panel                               ( print page 22749)                            discussed pilot testing results from the first round of ratings during a one-hour moderated telephone conference. After the conference concluded, panelists individually rated a revised set of elements. Ultimately, the Delphi panel reached a consensus that the majority of the proposed measure's numerator definitions had strong face validity. However, the panel raised concerns regarding the accuracy of self-reported data and deemed validity lowest for denominator categories of credentialed and other nonemployees of the facility.                    

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

#### c. Measure Applications Partnership (MAP) Review

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

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

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

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

#### d. Competing and Related Measures

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

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

                        After review of the NQF's consensus-endorsed measures, we were unable to identify any NQF-endorsed measures for SNFs focused on capturing influenza vaccinations among HCP. For example, although the Percent of Residents or Patients Who Were Assessed and Appropriately Given the Seasonal Influenza Vaccine (Short Stay) (NQF #0680) and the Percent of Residents                               ( print page 22750)                            Assessed and Appropriately Given the Seasonal Influenza Vaccine (Long Stay) (NQF #0681) measures are both NQF-endorsed and assess rates of influenza vaccination, they assess vaccination rates among residents in the nursing home rather than HCP in the SNF. Additionally, the Percent of Programs of All-Inclusive Care for the Elderly (PACE) Healthcare Personnel with Influenza Immunization measure resembles the proposed measure since it assesses influenza vaccination among HCP; however, it is not NQF endorsed and is not specific to the SNF setting.                    

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

#### e. Quality Measure Calculation

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

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

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

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

We invite public comment on our proposal to add a new measure, Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431), to the SNF QRP beginning with the FY 2025 SNF QRP.

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

#### a. Background

                        Section 1888(d)(6)(B)(i)(III) of the Act requires that, for FY 2019 and each subsequent year, SNFs must report standardized patient assessment data required under section 1899B(b)(1) of the Act. Section 1899B(a)(1)(C) of the Act requires, in part, the Secretary to modify the PAC assessment instruments in order for PAC providers, including SNFs, to submit standardized patient assessment data under the Medicare program. In the FY 2020 SNF PPS final rule (84 FR 38755 through 38817), we                               ( print page 22751)                            adopted two TOH Information quality measures as well as standardized patient assessment data that would satisfy five categories defined by section 1899B(c)(1). The TOH Information to the Provider—Post-Acute Care (PAC) measure and the TOH Information to the Patient—PAC measure are process-based measures that assess whether or not a current reconciled medication list is given to the subsequent provider when a patient is discharged or transferred from his or her current PAC setting or is given to the patient, family, or caregiver when the patient is discharged from a PAC setting to a private home/apartment, a board and care home, assisted living, a group home, or transitional living. Section 1899B(b)(1)(B) of the Act defines standardized patient assessment data as data required for at least the quality measures described in section 1899B(c)(1) of the Act and that is with respect to the following categories: (1) Functional status; (2) cognitive function; (3) special services, treatments, and interventions; (4) medical conditions and comorbidities; (5) impairments, and (6) other categories deemed necessary and appropriate by the Secretary.                    

                        The interim final rule with comment period that appeared in the May 8, 2020                         **Federal Register**                         (85 FR 27550) (hereafter referred to as the “May 8th COVID-19 IFC”), delayed the compliance date for certain reporting requirements under the SNF QRP (85 FR 27596 through 27597). Specifically, we delayed the requirement for SNFs to begin reporting the TOH Information to Provider—PAC and the TOH Information to Patient—PAC measures and the requirement for SNFs to begin reporting certain standardized patient assessment data elements from October 1, 2020, to October 1st of the year that is at least 2 full fiscal years after the end of the COVID-19 PHE. We also delayed the adoption of the updated version of the Minimum Data Set (MDS) 3.0 v1.18.1 [56]                                                  which SNFs would have used to report the TOH Information measures and certain standardized patient assessment data elements.                    

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

                        At the time the May 8th COVID-19 IFC was published, we believed this delay would not have a significant impact on the SNF QRP. However, we were in the initial months of the COVID-19 PHE, and very little was known about the SARS-CoV-2 virus. Additionally, we believed the delay in the collection of the TOH Information measures and standardized patient assessment data elements were necessary to allow SNFs to focus on patient care and staff safety. However, the COVID-19 PHE has illustrated the important need for these TOH Information measures and standardized patient assessment data elements under the SNF QRP. The PHE's disproportionate impact among non-Hispanic Black, or Hispanic or Latino persons [57 58 59 60 61 62 63]                          demonstrates the importance of analyzing this impact in order to improve quality of care within SNFs especially during a crisis. One important strategy for addressing these important inequities is by improving data collection to allow for better measurement and reporting on equity across post-acute care programs and policies. The information will inform our Meaningful Measures framework.                    

#### b. Current Assessment of SNFs' Capabilities

                        To accommodate the COVID-19 PHE, we provided additional guidance and flexibilities, and as a result SNFs have had the opportunity to adopt new processes and modify existing processes to accommodate the significant health crisis presented by the COVID-19 PHE. For example, we held regular “Office Hours” conference calls to provide SNFs regular updates on the availability of supplies, as well as answer questions about delivery of care, reporting, and billing. We also supported PAC providers, including SNFs, by providing flexibilities in the delivery of care in response to the PHE,[64]                                                  such as waiving the requirements at § 483.30 for physician and non-physician practitioners to perform in-person visits, allowing them to use telehealth methods where deemed appropriate. We also waived the nurse aide training and certification requirements § 483.35(d) (with the exception of § 483.35(d)(1)(i)), allowing SNFs to employ nurse aides for longer than 4 months even when they have yet not met the standard training and certification requirements, and we waived the requirement at § 483.95(g)(1) for nursing aides to receive at least 12 hours of in-service training annually. To reduce provider burden, we waived the Pre-Admission Screening and Annual Resident Review (PASARR) at § 483.20(k), allowing SNFs more flexibility in scheduling Level 1 assessments. We narrowed the scope of requirements for a SNF's Quality Assurance and Performance                               ( print page 22752)                            Improvement (QAPI) program to the aspects of care most associated with COVID-19 (§ 483.75), that is infection control and adverse events. Additionally, we waived timeframe requirements on MDS assessments and transmission at § 483.20, along with waiving requirements for submitting staffing data through the Payroll-Based Journal (PBJ) system at § 483.70(q), to grant SNFs the greater flexibility needed to adapt to the rapidly evolving burdens of the PHE. While the MDS and PBJ requirements have since been terminated, many of these waivers for SNFs are still in effect today.                    

                        In addition, as of March 1, 2022, 86.2 percent of the population aged 12 and older (81.3 percent of those 5 and older) has received at least one vaccination.[65]                                                  Further, although there is a recent increase in COVID-19 cases, vaccinated individuals aged 18 years and older through March 4, 2022 were 3.2 times less likely to test positive, over 9 times less likely to be hospitalized, and experience 41 times lower risk of death, compared to unvaccinated individuals.[66]                                                  We also believe that SNFs have more information and interventions to deploy to effectively prevent and treat COVID-19 than they had at the time the May 8th COVID-19 IFC was finalized,[67 68 69 70]                          including three vaccines that are either approved or authorized in the United States to prevent COVID-19, and antiviral drugs that are approved or authorized to treat COVID-19.[71 72 73 74 75]                          Also, recent reports suggest that the rollout of COVID-19 vaccines have alleviated some of the burden on SNFs imposed by the PHE.[76 77] 

                        Despite the COVID-19 PHE, we must maintain our commitment to the quality of care for all patients, and we continue to believe that the collection of the standardized patient assessment data elements and TOH Information measures will contribute to this effort. That includes an ongoing commitment to achieving health equity by improving data collection to better measure and analyze disparities across programs and policies.[78 79 80 81 82 83 84 85]                          We also note that in response to the “Request for Information to Close the Health Equity Gap” in the FY 2022 SNF PPS proposed rule (86 FR 20000), we heard from stakeholders that it is important to gather additional information about race, ethnicity, gender, language and other social determinants of health (SDOH). Some SNFs noted they had already begun to collect some of this information for use in their operations. Our commitment to the quality of care for all patients also includes improving the quality of care in SNFs through a reduction in preventable adverse events. Health information, such as medication information, that is incomplete or missing increases the likelihood of a patient or resident safety risk, and is often life-threatening.[86 87 88 89 90 91]                          Poor communication and coordination across health care settings contributes to patient complications, hospital readmissions, emergency department visits and medication errors.[92 93 94 95 96 97 98 99 100 101]                          Further                               ( print page 22753)                            delaying the data collection has the potential to further exacerbate these issues. We believe the benefit of having this information available in a standardized format outweighs the potential burden of collecting this data, as data availability is a necessary step in addressing health disparities in SNFs.                    

                        Given the flexibilities described earlier in this section, SNFs' increased knowledge and interventions to deploy to effectively prevent and treat COVID-19, and the trending data on COVID-19, we believe that SNFs are in a better position to accommodate the reporting of the TOH Information measures and certain standardized patient assessment data elements. Specifically, we believe SNFs have learned how to adapt and now have the administrative capacity to attend training, train their staff, and work with their vendors to incorporate the updated assessment instruments into their operations. Moreover, these standardized patient assessment data elements are reflective of patient characteristic that providers may already be recording for their own purposes, such as preferred language, race, ethnicity, hearing, vision, health literacy, and cognitive function. It is also important to align the collection of this data with the IRFs and LTCHs who will begin collecting this information on October 1, 2022, and home health agencies (HHAs) who will begin collecting this information on January 1, 2023.[102] 

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

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

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

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

#### 3. Proposed Revisions to the Regulation Text (§ 413.360)

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

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

                        At § 413.360(b),                         *Data submission requirement,*                         we propose to remove                               ( print page 22754)                            paragraph (b)(2) and redesignate paragraph (b)(3) as paragraph (b)(2). At § 413.360, we propose to add a new paragraph (f),                         *Data completion thresholds.*

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

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

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

We invite public comment on this proposal.

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

We are seeking input on the importance, relevance, and applicability of the concepts under consideration listed in Table 16 in the SNF 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 are seeking input on the value of a COVID-19 Vaccination Coverage measure that would assess whether SNF patients were up to date on their COVID-19 vaccine.

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

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

                        Significant and persistent disparities in healthcare outcomes exist in the United States. Belonging to an underserved community is often associated with worse health outcomes.[103]                          [104]                          [105]                          [106]                          [107]                          [108]                          [109]                          [110]                          [111]                                                  With this in mind, we aim 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. We are working to advance health equity by designing, implementing, and operationalizing policies and programs that support health for all the people served by our programs, eliminating avoidable differences in health outcomes experienced by people who are disadvantaged or underserved, and providing the care and support that our beneficiaries need to thrive.[112] 

                        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                               ( print page 22755)                            disparities.[113]                                                  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 XI.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 the approaches that could be used in the SNF QRP to assess drivers of healthcare quality disparities in the SNF QRP. Additionally, this section discusses measures of health equity that could be adapted for use in the SNF 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 SNF QRP.

#### 1. Cross-Setting Framework To Assess Healthcare Quality Disparities

We have identified five key considerations that we could apply consistently across our 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 Measure Stratification Across CMS Quality Programs

By quantifying healthcare disparities through quality 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 provider 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, two complementary approaches 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 (SRF). 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 a SNF underperformed (when compared to other facilities on average) for patients with a given SRF, which would signal the need to improve care for this population. However, if the SNF also underperformed for patients without that SRF (the “within-hospital” disparity, as described earlier in this section), the measured difference, or disparity in care, 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.[114] 

We are interested in whether similar approaches to the two discussed in the previous paragraph could be used to produce confidential stratified measure results for selected SNF QRP measures, as appropriate and feasible. However, final decisions regarding disparity reporting will be made at the program-level, as we intend 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 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 SRFs 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 SRF 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.                              ( print page 22756)    

                        (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 (SRF) and Demographic Data Selection and Use

                        SRFs 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.[115 116 117 118 119 120 121 122] 

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 SRFs 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 are 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 SRFs. [123]                                                   While data sources for many SRFs and demographic variables are still developing among several CMS settings, demographic data elements collected through assessments already exist in SNFs. Beginning October 1, 2022, other PAC settings ( 86 FR 62345  through  62347 ,  62381  through  62390 ) will begin collecting additional standardized patient data elements about race, ethnicity, preferred language, transportation, health literacy, and social isolation. Data collection for these items in SNF has been proposed for October 1, 2023 (See section VI.C.2. of this proposed rule).                    
    -  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. [124]                                                   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 neighborhood context. Several indexes, such as Agency for Healthcare Research and Quality (AHRQ) Socioeconomic Status (SES) Index, [125]                                                   the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI), [126]                                                   and the Health Resources and Services Administration (HRSA) Area Deprivation Index (ADI), [127]                                                   provide multifaceted contextual information about an area and may be considered as an efficient way to stratify measures that include many SRFs.                    
    -  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 race and ethnicity of patients at a population level. [128]  

#### d. Identifying Meaningful Performance Differences

While we aim to use standardized approaches where possible, differences in performance on stratified results will be identified 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 in this section:
    -  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 providers.                    
    - SNFs could be categorized into groups based on their performance using defined thresholds,                          such as fixed intervals of results of disparity                                ( print page 22757)                              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 is not appropriate for all programs and healthcare settings, and some programs may only report disparity results. 

#### e. Guiding Principles for Reporting Disparity Measures

Reporting of the results as discussed previously in this section 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 us. One important consideration is to assess differential impact on SNFs, 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 SNF QRP

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

#### a. Performance Disparity Decomposition

In response to the FY 2022 SNF PPS proposed rule's RFI (86 FR 20000 through 20001), “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 SNFs 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 SNF has limited information or resources to determine the extent to which a patient's 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 SNF which of these factors are the primary drivers.

                        Consequently, we 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.[129]                                                  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, we 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 health care outcomes. Additionally, provider-specific decomposition results could be shared through confidential feedback so that SNFs 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 SNFs 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 SNF. Figures 1 through 3 depict an example (using hypothetical data) of how a disparity in a measure of Medicare Spending Per Beneficiary (MSPB) between dually eligible beneficiaries (that is, those enrolled in Medicare and Medicaid) and non-dually 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 SNF, the healthcare provider could mitigate their effects. Additionally, high-volume ED use is used as a potential mediating factor that could be difficult for SNFs to determine on their own, as it would require having longitudinal data for patients across multiple facilities.
      ( print page 22758)    
                        In this example (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 SNF, 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                               ( print page 22759)                            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-dual 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, a SNF 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 SNF 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 (for example, 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 SNF QRP, we will draw from its experience with the CMS Measures Management System (MMS) Blueprint [130]                                                  and 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.
    - Measures should adhere to high scientific acceptability standards.

We have developed measures assessing health equity, or designed to promote health equity, in other settings outside of the SNF. As a result, there may be measures that could be adapted for use in the SNF 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 Office of Minority Health (OMH) [131]                                                  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 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 in development for the Hospital Inpatient Quality Reporting (HIQR) program.[132]                                                  This 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.[133] 

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 Healthcare Effectiveness Data and Information Set (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 a SRF-specific blended score, which is then combined with the blended score for another SRF to produce the overall HESS.                              ( print page 22760)    

#### (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 (MUC 2021-106), was included on the CMS List of Measures Under Consideration (MUC List) [134]                                                  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 it attests to the corresponding statement (for a total of 5 points). At a high level, the five domains cover the following: (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; [135]                                                  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 MUC List for December 2021 at                         *https://www.cms.gov/​files/​document/​measures-under-consideration-list-2021-report.pdf.*                         A SNF could receive a point for each domain where data are submitted through a CMS portal to reflect actions taken by the SNF for each corresponding domain (for a point total).                    

                        We believe this type of organizational commitment structural measure may complement the health disparities approach described in previous sections, and support SNFs 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.” [136]                                                  We acknowledge that collection of this structural measure may impose administrative and/or reporting requirements for SNFs.                    

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

#### 3. Solicitation of Public Comment

The goal of this request for information is to describe some key principles and approaches that we will consider when advancing the use of quality measure development and stratification to address health care 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 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 SNFs to present stratified measure results.

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

++ Alternative methods to identify disparities and the drivers of disparities.
    - Guiding Principles for Selecting and Prioritizing Measures for Disparity Reporting:

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

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

++ Ways that meaningful difference in disparity results should be considered.
    - Guiding Principles for Reporting Disparity Measures:

++ Guiding principles for the use and application of the results of disparity measurement.
    - Measures Related to Health Equity:

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

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

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

++ Other options for measures that address health equity.

                        While we will not be responding to specific comments submitted in response to this RFI in the FY 2023 SNF PPS final rule, we will actively consider all input as we develop future regulatory proposals or future subregulatory policy guidance. Any updates to specific program requirements related to quality measurement and reporting provisions would be addressed through separate and future notice-and-comment rulemaking, as necessary.                              ( print page 22761)    

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

#### 1. Background

                        The SNF QRP furthers our mission to improve the quality of health care for beneficiaries through measurement, transparency, and public reporting of data. The SNF QRP and CMS' 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, reducing measurement burden, and promoting patient perspectives, while also focusing on modernization and innovation.[137]                                                  Meaningful Measures 2.0 builds on the initial framework by establishing a goal of increasing Patient Reported Outcomes Measures (PROMs) by 50 percent.[138]                                                  Ensuring that patients and families are engaged as partners in their care can be an effective way to measure the quality of patient care.                    

#### 2. Potential Future Inclusion of the CoreQ: Short Stay Discharge Measure

                        Collecting satisfaction information from SNF patients is more important now than ever. There has been a philosophical change in healthcare that now includes the patient and their preferences as an integral part of the system of care. The Institute of Medicine (IOM) endorsed this change by putting the patient as central to the care system (IOM, 2001).[139]                                                  To achieve the goal of patient-centered care, there must be a way to measure patient satisfaction since it is necessary to understand patient preferences. Measuring patients' satisfaction can also help organizations identify deficiencies that other quality metrics may struggle to identify, such as communication between a patient and the healthcare provider.                    

                        We define a Patient Reported Outcome (PRO) as any report of the status of a patient's health condition or health behavior that comes directly from the patient, without interpretation of the patient's response by a clinician or anyone else.[140]                                                  Therefore, they are an important component of assessing whether healthcare providers are improving the health and well-being of patients. We have demonstrated interest in consumers' perspective on quality of care in nursing homes by supporting the development of the CAHPS survey for patients in nursing facilities,[141]                                                  and adding provisions for comprehensive person-centered care planning and quality of life to the nursing home requirements of participation at §§ 483.21 and 483.24 respectively effective November 28, 2017.                    

In the FY 2022 SNF PPS proposed rule (86 FR 19998), we sought comments on potential future PROMs for the SNF QRP. We summarized the comments received in the FY 2022 SNF PPS final rule (86 FR 42490 through 42491). In this year's proposed rule, we are requesting stakeholder feedback specifically on the inclusion of the CoreQ: Short Stay Discharge measure in the SNF QRP in future program years, including whether there are any challenges or impacts we should consider for a potential future proposal.

                        Collection of patient experience data aligns with the person-centered care domain of CMS's Meaningful Measures 2.0 Framework,[142]                                                  and addresses an aspect of patient experience that is not currently included in the SNF QRP. We believe collecting and assessing satisfaction data from SNF patients is important for understanding patient experiences and preferences, while ensuring the patient can easily and discretely share their information and provide information to help consumers choose a trusted SNF. PRO data could be incorporated into QAPI strategies to help facilities improve their quality of care.                    

#### 3. Overview of the CoreQ: Short Stay Discharge Measure in a Future SNF QRP Program Year

*The CoreQ:*                         Short Stay Discharge Measure calculates the percentage of individuals discharged in a 6-month period from a SNF, within 100 days of admission, who are satisfied with their SNF stay. This patient-reported outcome measure is based on the CoreQ: Short Stay Discharge questionnaire that utilizes four items: (1) In recommending this facility to your friends and family, how would you rate it overall; (2) Overall, how would you rate the staff; (3) How would you rate the care you receive; (4) How would you rate how well your discharge needs were met. The CoreQ questionnaire uses a 5-point Likert Scale: Poor (1); Average (2); Good (3); Very Good (4); and Excellent (5).                    

The numerator is the sum of the individuals in the facility that have an average satisfaction score of greater than or equal to 3 for the four questions on the CoreQ: Short Stay Discharge questionnaire. The denominator includes all patients, regardless of payer, that are admitted to the SNF for post-acute care and are discharged within 100 days, receive the survey and who respond to the CoreQ: Short Stay Discharge questionnaire within two months of receiving the questionnaire.

*The CoreQ:*                         Short Stay Discharge Measure excludes certain patients from the denominator, such as patients who die during their SNF stay, patients discharged to another hospital, another SNF, psychiatric facility, IRF or LTCH, patients with court appointed legal guardians for all decisions, patients who have dementia impairing their ability to answer the questionnaire,[143]                                                  patients discharged on hospice, and patients who left the SNF against medical device. For additional information about the CoreQ: Short Stay Discharge Measure, please visit                         *https://cmit.cms.gov/​CMIT_​public/​ViewMeasure?​MeasureId=​3436.*

#### 4. Measure Application Partnership (MAP) Review

*The CoreQ:*                         Short Stay Discharge Measure (NQF #2614) was endorsed by the National Quality Forum (NQF) in 2016 and achieved re-endorsement in 2020. We included the CoreQ: Short Stay Discharge Measure (NQF #2614) under the SNF QRP Program in the publicly available “List of Measures                               ( print page 22762)                            Under Consideration for December 1, 2017” (MUC List).[144]                                                  The NQF-convened Measure Applications Partnership (MAP) Post-Acute Care/Long-Term Care (PACLTC) workgroup met on December 13, 2017 and provided input on the measure. The MAP offered support of the CoreQ Short Stay Discharge Measure (NQF #2614) for rulemaking, noting that it adds value by adding addressing a gap area for the QRP. The MAP reiterated the value of resident-reported outcomes and noted that this measure could reflect quality of care from the resident's perspective, but also noted the potential burden of collecting the data and cautioned the implementation of a new data collection requirement should be done with the least possible burden to the facility. We refer readers to the final MAP report available at                         *https://www.qualityforum.org/​Publications/​2018/​02/​MAP_​2018_​Considerations_​for_​Implementing_​Measures_​in_​Federal_​Programs_​-_​PAC-LTC.aspx.*

#### 5. Data Sources

                        CoreQ is administered by customer satisfaction vendors that have added CoreQ to their questionnaires. Currently, nearly 40 customer satisfaction vendors have incorporated or will incorporate CoreQ into their surveys when asked by clients. For information on customer satisfaction vendors that have added CoreQ to their questionnaires, we refer readers to                         *http://www.CoreQ.org.*                         For more information about administering CoreQ, we encourage readers to visit                         *http://www.CoreQ.org*                         and review the CoreQ Satisfaction Questionnaire and User's Manual available at                         *http://www.coreq.org/​CoreQ%20Satisfaction%20Questionnaire%20and%20User%20Manual.pdf.*

#### 6. Solicitation of Public Comment

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

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

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

#### 1. Background

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

#### 2. Proposed Schedule for Data Submission of the Influenza Vaccination Coverage Among Healthcare Personnel (NQF #0431) Measure Beginning With the FY 2025 SNF QRP

As discussed in section VI.C.1. of this proposed rule, we are proposing to adopt the Influenza Vaccination Coverage among HCP quality measure beginning with the FY 2025 SNF QRP. The CDC has determined that the influenza vaccination season begins on October 1st (or when the vaccine becomes available) and ends on March 31st of the following year. Therefore, we propose an initial data submission period from October 1, 2022 through March 31, 2023. In subsequent years, data collection for this measure will be from October 1 through March 31 of the following year.

                        This measure requires that the provider submit a minimum of one report to the NHSN by the data submission deadline of May 15 for each influenza season following the close of the data collection period each year to meet our requirements. Although facilities may edit their data after May 15, the revised data will not be shared with us.[145]                                                  SNFs would submit data for the measure through the CDC/NHSN web-based surveillance system. SNFs would use the Influenza Vaccination Summary option under the NHSN HPS Component to report the number of HCP who receive the influenza vaccination (numerator) among the total number of HCP in the facility for at least 1 working day between October 1 and March 31 of the following year, regardless of clinical responsibility or patient contact (denominator).                    

We invite public comment on this proposal.

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

#### 1. Background

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

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

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

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

### A. Statutory Background

                        Section 215(b) of the Protecting Access to Medicare Act of 2014 (Pub. L. 113-93) authorized the SNF VBP Program (the “Program”) by adding section 1888(h) to the Act. Additionally, section 111 of the Consolidated                               ( print page 22763)                            Appropriations Act, 2021 authorized the Secretary to apply additional measures to the SNF VBP Program for payments for services furnished on or after October 1, 2023. The SNF VBP Program applies to freestanding SNFs, SNFs affiliated with acute care facilities, and all non-CAH swing bed rural hospitals. We believe the SNF VBP Program has helped to transform how payment is made for care, moving increasingly towards rewarding better value, outcomes, and innovations instead of merely rewarding volume.                    

As a prerequisite to implementing the SNF VBP Program, in the FY 2016 SNF PPS final rule (80 FR 46409 through 46426), we adopted an all-cause, all-condition hospital readmission measure, as required by section 1888(g)(1) of the Act and discussed other policies to implement the Program such as performance standards, the performance period and baseline period, and scoring. SNF VBP Program policies have been codified in our regulations at 42 CFR 413.338. For additional background information on the SNF VBP Program, including an overview of the SNF VBP Report to Congress and a summary of the Program's statutory requirements, we refer readers to the following prior final rules:
    - In the FY 2017 SNF PPS final rule ( 81 FR 51986  through  52009 ), we adopted an all-condition, risk-adjusted potentially preventable hospital readmission measure for SNFs, as required by section 1888(g)(2) of the Act, adopted policies on performance standards, performance scoring, and sought comment on an exchange function methodology to translate SNF performance scores into value-based incentive payments, among other topics.
    - In the FY 2018 SNF PPS final rule ( 82 FR 36608  through  36623 ), we adopted additional policies for the Program, including an exchange function methodology for disbursing value-based incentive payments.
    - In the FY 2019 SNF PPS final rule ( 83 FR 39272  through  39282 ), we adopted more policies for the Program, including a scoring adjustment for low-volume facilities.
    - In the FY 2020 SNF PPS final rule ( 84 FR 38820  through  38825 ), we adopted additional policies for the Program, including a change to our public reporting policy and an update to the deadline for the Phase One Review and Correction process. We also adopted a data suppression policy for low-volume SNFs.
    - In the FY 2021 SNF PPS final rule ( 85 FR 47624  through  47627 ), we amended regulatory text definitions at § 413.338(a)(9) and (11) to reflect the definition of Performance Standards and the updated Skilled Nursing Facility Potentially Preventable Readmissions after Hospital Discharge measure name, respectively. We also updated the Phase One Review and Correction deadline and codified that update at § 413.338(e)(1). Additionally, we codified the data suppression policy for low-volume SNFs at § 413.338(e)(3)(i) through (iii) and amended § 413.338(e)(3) to reflect that SNF performance information will be publicly reported on the Nursing Home Compare website and/or successor website ( 84 FR 38823  through  38824 ), which since December 2020 is the Provider Data Catalog website (                         https://data.cms.gov/​provider-data/​ ).                    
    - In the September 2nd interim final rule with comment (IFC) ( 85 FR 54837 ), we revised the performance period for the FY 2022 SNF VBP Program to be April 1, 2019 through December 31, 2019 and July 1, 2020 through September 30, 2020, in response to the COVID-19 Public Health Emergency (PHE).
    - In the FY 2022 SNF PPS final rule ( 86 FR 42502  through  42517 ), we adopted additional policies for the Program, including a measure suppression policy to offer flexibility in response to the COVID-19 PHE. We adopted policies to suppress the SNFRM for scoring and payment purposes for the FY 2022 SNF VBP program year, to revise the SNFRM risk adjustment lookback period for the FY 2023 SNF VBP program year, and to use FY 2019 data for the baseline period for the FY 2024 SNF VBP program year. We also updated the Phase One Review and Correction process and updated the instructions for requesting an Extraordinary Circumstances Exception (ECE). Finally, we finalized a special scoring policy assigning all SNFs a performance score of zero, effectively ranking all SNFs equally in the FY 2022 SNF VBP program year. This policy was codified at § 413.338(g) of our regulations.

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

### B. SNF VBP Program Measures

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

#### 1. Proposal To Suppress the SNFRM for the FY 2023 Program Year

#### a. Background

                        We remain concerned about the effects of the PHE for COVID-19 on our ability to assess performance on the SNFRM in the SNF VBP Program. As of mid-December 2021, more than 50 million COVID-19 cases and 800,000 COVID-19 deaths have been reported in the United States (U.S.) [146]                                                  COVID-19 has overtaken the 1918 influenza pandemic as the deadliest disease in American history.[147]                                                  Moreover, the individual and public health ramifications of COVID-19 extend beyond the direct effects of COVID-19 infections. Several studies have demonstrated significant mortality increases in 2020, beyond those attributable to COVID-19 deaths. One paper quantifies the net impact (direct and indirect effects) of the pandemic on the U.S. population during 2020 using three metrics: Excess deaths, life expectancy, and total years of life lost. The findings indicate there were 375,235 excess deaths, with 83 percent attributable to direct effects, and 17 percent attributable to indirect effects, of COVID-19. The decrease in life expectancy was 1.67 years, translating to a reversion of 14 years in historical life expectancy gains. Total years of life lost in 2020 was 7,362,555 across the U.S. (73 percent directly attributable, 27 percent indirectly attributable to COVID-19), with considerable heterogeneity at the individual State level.[148] 
      ( print page 22764)    

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

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

We also adopted a series of Measure Suppression Factors to guide our determination of whether to propose to suppress the SNF readmission measure for one or more program years that overlap with the PHE for COVID-19. The Measure Suppression Factors that we adopted are:
    - Measure Suppression Factor 1: Significant deviation in national performance on the measure during the PHE for COVID-19, which could be significantly better or significantly worse compared to historical performance during the immediately preceding program years.
    - Measure Suppression Factor 2: Clinical proximity of the measure's focus to the relevant disease, pathogen, or health impacts of the PHE for COVID-19.
    - Measure Suppression Factor 3: Rapid or unprecedented changes in:

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

++ The generally accepted scientific understanding of the nature or biological pathway of the disease or pathogen, particularly for a novel disease or pathogen of unknown origin.
    - Measure Suppression Factor 4: Significant national shortages or rapid or unprecedented changes in:

++ Healthcare personnel.

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

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

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

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

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

                        We also remain concerned that the pandemic's disparate effects on different regions of the country throughout the PHE have presented challenges to our assessments of performance on the SNFRM. According to CDC data,[149]                                                  for example, new COVID-19 cases at the beginning of FY 2021 (October 1, 2020) were highest in Texas (3,534 cases), California (3,062 cases), and Wisconsin (3,000 cases). By April 1, 2021, however, new cases were highest in Michigan (6,669 cases), Florida (6,377 cases), and New Jersey (5,606 cases). This variation in COVID-19 case rates throughout the PHE has also been demonstrated in several studies. For example, studies have found widespread geographic variation in county-level COVID-19 cases across the U.S.[150 151 152]                          Specifically, one study found that, across US census regions, counties in the Midwest had the greatest cumulative rate of COVID-19 cases.[153]                                                  Another study found that U.S. counties with more immigrant residents, as well as more Central American or Black residents, have more COVID-19 cases.[154]                                                  These geographic variations in COVID-19 case rates are often linked to a wide range of county-level characteristics, including sociodemographic and health-related factors.[155]                                                  In addition, these studies have found evidence of temporal variation in county-level COVID-19 cases. For example, one study found that while many county-level factors show persistent effects on COVID-19 severity over time, some factors have varying effects on COVID-19 severity over time.[156]                                                  The significant variation in COVID-19 case rates across the U.S. can affect the validity of performance data. Therefore, we do not believe it would be fair or equitable to assess SNFs' performance on the measure using FY                               ( print page 22765)                            2021 data, which has been affected by these variations in COVID-19 case rates.                    

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

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

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

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

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

However, as discussed more fully in section VII.E.3.a. of this proposed rule, beginning with the FY 2023 program year, we are proposing to remove the low-volume adjustment policy from the SNF VBP Program and instead, implement case and measure minimums that SNFs must meet in order to be eligible to participate in the SNF VBP for a program year.

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

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

                        We continue to be concerned about effects of the COVID-19 PHE, but are encouraged by the rollout of COVID-19 vaccinations and treatment for those diagnosed with COVID-19 and believe that SNFs are better prepared to adapt to this virus. Our measure suppression policy focuses on a short-term, equitable approach during this unprecedented PHE, and it was not intended for indefinite application. Additionally, we want to emphasize the importance of value-based care and incentivizing quality care tied to payment. The SNF VBP Program is an example of our effort to link payments to healthcare quality in the SNF setting. We understand that the COVID-19 PHE is ongoing and unpredictable in nature; however, we believe that 2022 presents a more promising outlook in the fight against COVID-19. Over the course of the pandemic, providers have gained experience managing the disease, surges of COVID-19 infection, and supply chain fluctuations.[157]                                                  While COVID-19 cases among nursing home staff reached a recent peak in January of 2022, those case counts dropped significantly by the week ending February 6, 2022, to 22,206.[158]                                                  COVID-19 vaccinations and boosters have also been taken up by a significant majority of nursing home residents, and according to CDC, by February 6, 2022, more than 68 percent of completely vaccinated nursing home residents had received boosters.[159]                                                  Finally, the Biden-Harris Administration has mobilized efforts to distribute home test kits,[160]                                                  N-95 masks,[161]                                                  and increase COVID-19 testing in schools.[162]                                                  In light of this                               ( print page 22766)                            more promising outlook, we intend to resume the use of the SNFRM for scoring and payment adjustment purposes beginning with the FY 2024 program year. That is, for FY 2024, for each SNF, we would calculate measure scores in the SNF VBP Program. We would then calculate a SNF performance score for each SNF and convert the SNF performance scores to value-based incentive payments.                    

We invite public comment on this proposal to suppress the SNFRM for the FY 2023 program year and to codify our scoring and payment proposals for FY 2023 in our regulation text.

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

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

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

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

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

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

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

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

                        The percentage of SNFs that would receive a measure score (75 percent), measure reliability (0.45), and C-statistic (0.66) was identical for the first 3 risk-adjustment options. The percentage of SNFs with a measure score, measure reliability score, and C-statistic values was 71 percent, 0.41, and 0.67 for Option 4 (excluding COVID-19 patients), respectively. The percentage                               ( print page 22767)                            of SNFs with a measure score was lower for the first 3 options than the baseline period (75 percent versus 82 percent), but the measure reliability was nearly identical (0.45 versus 0.46), as was the C-statistic (0.66 versus 0.68).                    

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

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

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

#### 3. Quality Measure Proposals for the SNF VBP Expansion Beginning With the FY 2026 Program Year

#### a. Background

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

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

Based on the input we received, and for reasons discussed in sections VII.B.3.b. and VII.B.3.c. of this proposed rule, we are proposing to adopt two new quality measures for the SNF VBP Program beginning with the FY 2026 program year: (1) Skilled Nursing Facility (SNF) Healthcare Associated Infections (HAI) Requiring Hospitalization (SNF HAI) measure; and (2) Total Nursing Hours per Resident Day Staffing (Total Nurse Staffing) measure. We are also proposing to adopt an additional quality measure for the SNF VBP Program beginning with the FY 2027 program year: Discharge to Community (DTC)—Post-Acute Care (PAC) Measure for Skilled Nursing Facilities (NQF #3481), which we discuss in section VII.B.3.d. of this proposed rule.

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

                        The proposed SNF HAI measure is a patient safety measure, and the proposed DTC PAC SNF measure is a care coordination measure. With regard to the proposed Total Nurse Staffing measure, many studies have found that the level of nurse staffing is associated with patient safety,[163]                                                  patient functional status,[164 165]                          and patient experience.[166 167]                          Nursing home staffing, including SNF staffing, is also a high priority for the Department of Health and Human Services (HHS) and the Biden-Harris Administration because of its central role in the quality of care for Medicare beneficiaries.[168] 

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

                        As we discuss in section VII.H.1 of this proposed rule, we are also considering and requesting public comment on additional quality measures for potential adoption in the SNF VBP through future rulemaking.                              ( print page 22768)    

We propose to update our regulations at § 413.338(d)(5) to note that, for a given fiscal year, CMS will specify the measures for the SNF VBP Program.

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

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

#### (1) Background

                        Healthcare-associated infections (HAIs) are defined as infections acquired while receiving care at a health care facility that were not present or incubating at the time of admission.[169]                                                  HAIs are a particular concern in the SNF setting, and thus, monitoring the occurrence of HAIs among SNF residents can provide valuable information about a SNF's quality of care. A 2014 report from the Office of the Inspector General (OIG) estimated that one in four adverse events among SNF residents is due to HAIs, and approximately half of all HAIs are potentially preventable.[170]                                                  In addition, analyses from FY 2019 found a wide variation in facility-level HAI rates among SNF providers with 25 or more stays, which indicates a performance gap. Specifically, among the 14,102 SNFs included in the sample, the FY 2019 facility-level, risk-adjusted rate of SNF HAIs requiring hospitalization ranged from 2.36 percent to 17.62 percent.[171] 

                        While HAIs are not considered “never events,” or serious adverse errors in the provision of health care services that should never occur, most are preventable.[172]                                                  HAIs are most often the result of poor processes and structures of care. Specifically, evidence suggests that inadequate patient management following a medical intervention, such as surgery or device implantation, and poor adherence to infection control protocols and antibiotic stewardship guidelines contribute to the occurrence of HAIs.[173 174 175]                          In addition, several provider characteristics relate to the occurrence of HAIs, including staffing levels (for example, low staff-to-resident ratios), facility structure characteristics (for example, high occupancy rates), and adoption, or lack thereof, of infection surveillance and prevention policies.[176 177 178 179 180 181] 

                        Inadequate prevention and treatment of HAIs is likely to result in poor health care outcomes for SNF residents, as well as wasteful resource use. Specifically, studies find that HAIs are associated with longer lengths of stay, use of higher-intensity care (for example, critical care services and hospital readmissions), increased mortality, and higher health care costs.[182 183 184 185]                          Addressing HAIs in SNFs is particularly important as several factors place SNF residents at increased risk for infections, including increased age, cognitive and functional decline, use of indwelling devices, frequent care transitions, and close contact with other residents and healthcare workers.[186 187]                          Further, infection prevention and control                               ( print page 22769)                            deficiencies are consistently among the most frequently cited deficiencies in surveys conducted to assess SNF compliance with Federal quality standards.[188]                                                  Infection prevention and control deficiencies can include practices directly related to the occurrence and risks of HAIs, such as inconsistent use of hand hygiene practices or improper use of protective equipment or procedures during an infectious disease outbreak, which further underscores the importance of efforts to improve practices to reduce the prevalence of HAIs.                    

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

                        Evidence suggests there are several interventions that SNFs may utilize to effectively reduce HAI rates among their residents and thus, improve quality of care. These interventions include adoption of infection surveillance and prevention policies, safety procedures, antibiotic stewardship, and staff education and training programs.[189 190 191 192 193 194 195]                          In addition, infection prevention and control programs with core components in education, monitoring, and feedback have been found to be successful in reducing HAI rates.[196]                                                  The effectiveness of these interventions suggest improvement of HAI rates among SNF residents is possible through modification of provider-led processes and interventions, which supports the overall goal of the SNF VBP Program.                    

#### (2) Overview of Measure

The proposed SNF HAI measure, which was finalized for adoption in the SNF QRP in the FY 2022 SNF PPS final rule (86 FR 42473 through 42480), is an outcome measure that estimates the risk-standardized rate of HAIs that are acquired during SNF care and result in hospitalization using 1 year of Medicare FFS claims data. A HAI is defined, for the purposes of this measure, as an infection that is likely to be acquired during SNF care and severe enough to require hospitalization, or an infection related to invasive (not implanted) medical devices (for example, catheters, insulin pumps, and central lines). Several types of infections are excluded from the measure. We discuss those exclusions in detail in section VII.B.2.b.(5) of this proposed rule. In addition, all SNF stays with an admission date during the 1-year period are included in the measure cohort, except those meeting the exclusion criteria, which we also discuss in section VII.B.2.b.(5) of this proposed rule.

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

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

#### (a) Measure Applications Partnership (MAP) Review

                        The SNF HAI measure was included as a SNF VBP measure under consideration in the publicly available “List of Measures Under Consideration for December 1, 2021.” [197] 

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

#### (3) Data Sources

                        The proposed SNF HAI measure uses Medicare FFS claims data to estimate the risk-adjusted rate of HAIs that are acquired during SNF care and result in hospitalization. Specifically, this measure uses data from the Medicare Enrollment Database (EDB), as well as Medicare SNF and inpatient hospital claims from the CMS Common Working File (CWF). HAIs are identified using the principal diagnosis code and the Present on Admission (POA) indicators on the Medicare inpatient rehospitalization claim within a specified incubation window. We refer readers to the SNF HAI Measure Technical Report for further details on how these data components are utilized in calculating the SNF HAI measure                               ( print page 22770)                            available at                         *https://www.cms.gov/​files/​document/​snfhaitechnicalreport.pdf.*                         We note that the proposed SNF HAI measure is calculated entirely using administrative data and therefore, it would not impose any additional data collection or submission burden for SNF providers.                    

#### (4) Inclusion and Exclusion Criteria

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

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

                        The measure numerator includes several HAI conditions. We refer readers to Appendix A of the SNF HAI Measure Technical Report, available at                         *https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf,*                         for a complete list of the ICD-10 codes that correspond to the HAI conditions included in the measure numerator. There are also several types of HAIs that are excluded from the proposed measure numerator. For example, HAIs reported during emergency department visits and observations stays are excluded from the numerator. In addition, the HAI definition excludes infections that meet any of the following criteria:                    
    - Chronic infections (for example, chronic viral hepatitis B).
    - Infections that typically require a long period of time to present (for example, typhoid arthritis).
    - Infections that are likely related to the prior hospital stay (for example, postprocedural retroperitoneal abscess).
    - Sequela (a condition which is the consequence of a previous disease or injury) and subsequent encounter codes.
    - Codes that include “cause disease classified elsewhere.”
    - Codes likely to represent secondary infection, where the primary infection would likely already be coded (for example, pericarditis, myocarditis, or cardiomyopathy).
    - Infections likely to be community acquired.
    - Infections common in other countries and/or acquired through animal contact.
    - Preexisting infections that fall within the CDC's National Healthcare Safety Network (NHSN) Repeat Infection Timeframe (RIT) of 14 days. We refer readers to the SNF HAI Measure Technical Report for additional information on the repeat infection timeframe (RIT) and conditions that are considered preexisting ( https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf ).                    

#### (5) Risk Adjustment

Risk adjustment is a statistical process used to account for risk factor differences across SNF residents. By controlling for these differences in resident case-mix, we can better isolate the proposed measure's outcome and its relationship to the quality of care delivered by SNFs. For the proposed SNF HAI measure, the measure's numerator and denominator are both risk-adjusted. Specifically, the denominator is risk-adjusted for resident characteristics excluding the SNF effect. The numerator is risk-adjusted for resident characteristics, as well as a statistical estimate of the SNF effect beyond resident case -mix. The SNF effect, or the provider-specific behaviors that influence a SNF's HAI rates, accounts for clustering of patients within the same SNF and captures variation in the measure outcome across SNFs, which helps isolate differences in measure performance. The risk adjustment model for this proposed measure includes the following resident characteristic variables:
    - Age and sex category.
    - Original reason for Medicare entitlement.
    - Surgery or procedure category from the prior proximal inpatient (IP) stay.
    - Dialysis treatment, but not end-stage renal disease (ESRD) on the prior proximal IP claim.
    - Principal diagnosis on the prior proximal IP hospital claim.
    - Hierarchical Condition Categories (HCC) comorbidities.
    - Length of stay of the prior proximal IP stay.
    - Prior intensive care or coronary care utilization during the prior proximal IP stay.
    - The number of prior IP stays within a 1-year lookback period from SNF admission.

#### (6) Measure Calculation

#### (a) Numerator

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

#### (b) Denominator

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

                        The SNF HAI measure rate, which is reported at the facility-level, is the risk-standardized rate of HAIs that are acquired during SNF care and result in hospitalization. This risk-adjusted HAI rate is calculated by multiplying the                               ( print page 22771)                            standardized risk ratio (SRR) for a given SNF by the national average observed rate of HAIs for all SNFs. The SRR is a ratio that measures excess HAIs and is the predicted number of HAIs (adjusted numerator) divided by the expected number of HAIs (adjusted denominator). A lower measure score for the SNF HAI measure indicates better performance in prevention and management of HAIs. For technical information on the proposed measure's calculation, we refer readers to the SNF HAI Measure Technical Report available at                         *https://www.cms.gov/​files/​document/​snf-hai-technical-report.pdf.*

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

*SNF HAI Inverted Rate*                         = 1−                        *Facility's SNF HAI rate.*

This calculation would invert SNFs' HAI measure rates such that higher SNF HAI measure rates would reflect better performance. We believe this inversion is important to incentivize improvement in a clear and understandable manner, so that “higher is better” for all measure rates included in the Program.

#### (7) Proposed Confidential Feedback Reports and Public Reporting

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

We invite public comment on our proposal to adopt the SNF HAI measure beginning with the FY 2026 SNF VBP program year.

#### c. Proposal To Adopt the Total Nursing Hours per Resident Day Staffing Measure Beginning With the FY 2026 SNF VBP Program Year

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

#### (1) Background

                        Staffing is a crucial component of quality care for nursing home residents. Numerous studies have explored the relationship between nursing home staffing levels and quality of care. The findings and methods of these studies have varied, but most have found a strong, positive relationship between staffing and quality outcomes.[198 199 200 201 202]                          Specifically, studies have shown an association between nurse staffing levels and hospitalizations,[203 204]                          pressure ulcers,[205 206 207]                          weight loss,[208 209]                          functional status,[210 211]                          and survey deficiencies,[212 213]                          among other quality and clinical outcomes. The strongest relationships have been identified for registered nurse (RN) staffing; several studies have found that higher RN staffing is associated with better care quality.[214 215]                          We recognize that the relationship between nurse staffing and quality of care is multi-faceted, with elements such as staff turnover playing a critical role.[216]                                                  We refer readers to additional discussion of staffing turnover in section VII.B.3. of this proposed rule.                    

                        The PHE due to COVID-19 has further underscored the critical importance of sufficient staffing to quality and clinical outcomes. Several recent studies have found that higher staffing is associated with lower COVID-19 incidence and fewer deaths.[217 218 219] 
      ( print page 22772)    
                        Multiple Institute of Medicine (IOM) reports have examined the complex array of factors that influence care quality in nursing homes, including staffing variables such as staffing levels and turnover.[220 221]                          In the 2004 report, “Keeping Patients Safe: Transforming the Work Environment of Nurses,” the IOM's Committee on the Work Environment for Nurses and Patient Safety highlighted the positive relationships between higher nursing staffing levels, particularly RN levels, and better patient outcomes, and recognized the need for minimum staffing standards to support appropriate levels of nursing staff in nursing homes.[222] 

                        Previously published Phase I and Phase II “Reports to Congress on the Appropriateness of Minimum Staffing Ratios in Nursing Homes” further studied the relationship between quality and nurse staffing levels and provided compelling evidence of the relationship between staffing ratios and quality of care.[223 224]                          The Phase II report, completed in 2001, identified staffing thresholds that maximized quality outcomes, demonstrating a pattern of incremental benefits of increased nurse staffing until a threshold was reached. Specifically, the Phase II study used Medicaid Cost Report data from a representative sample of 10 states, including over 5,000 facilities, to identify staffing thresholds below which quality of care was compromised and above which there was no further benefit of additional staffing with respect to quality. The study found evidence of a relationship between higher staffing and better outcomes for total nurse staffing levels up to 4.08 hours per resident day and RN staffing levels up to 0.75 RN hours per resident day. In the 2001 study, minimum staffing levels at any level up to these thresholds were associated with incremental quality improvements, and no significant quality improvements were observed for staffing levels above these thresholds. The findings were also supported by case studies of individual facilities, units, and residents.                    

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

                        Current Federal requirements for nurse staffing are outlined in the long-term care facility requirements for participation (requirements).[225]                                                  The regulations at 42 CFR 483.35 specify, in part, that every facility must have sufficient nursing staff with the appropriate competencies and skill sets to provide nursing and related services to assure resident safety and attain or maintain the highest practicable physical, mental, and psychosocial well-being of each resident, as determined by resident assessments and individual plans of care and considering the number, acuity and diagnoses of the facility's resident population in accordance with the facility assessment required at § 483.70(e). We adopted this competency-based approach to sufficient staffing to ensure every nursing home provides the staffing levels needed to meet the specific needs of their resident population, including their person-centered care goals. We also note that current regulations require (unless these requirements are waived) facilities to have an RN onsite at least 8 consecutive hours a day, 7 days a week and around-the-clock services from licensed nursing staff under sections 1819(b)(4)(C) and 1919(b)(4)(C) of the Act, and § 483.35(a) and (b).                    

                        Section 1128I(g) of the Act requires facilities to electronically submit direct care staffing information (including agency and contract staff) based on payroll and other auditable data. In August 2015, we amended the requirements for long term care facilities at § 483.70(q) to require the electronic submission of payroll-based staffing data, which includes RNs, licensed practical nurses (LPNs) or vocational nurses, certified nursing assistants, and other types of medical personnel as specified by us, along with census data, data on agency and contract staff, and information on turnover, tenure and hours of care provided by each category of staff per resident day.[226]                                                  We developed the PBJ system to enable facilities to submit the required staffing information in a format that is auditable to ensure accuracy. Development of the PBJ system built on several earlier studies that included extensive testing of payroll-based staffing measures. The first mandatory PBJ reporting period began July 1, 2016.                    

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

                        Given the strong evidence regarding the relationship between sufficient staffing levels and improved care for patients, inclusion of this measure in the SNF VBP Program adds an important new dimension to provide a more comprehensive assessment of and accountability for the quality of care provided to residents and serves to drive improvements in staffing that are likely to translate into better resident care. PBJ data show that there is variability across SNFs in performance on the proposed measure, and that there is an opportunity and potential for many SNFs to improve their staffing levels. For Q4 CY 2020, average total                               ( print page 22773)                            nurse staffing was 4.09 hours per resident day for the case-mix adjusted Total Nurse Staffing measure, with considerable variability across facilities ranging from 2.81 hours per resident day to 5.93 hours per resident day. Staffing levels increased after April 2018, when we first reported PBJ-based staffing measures on Nursing Home Compare and using them in the Five-Star Quality Rating System. Average nursing staffing hours per resident day increased from 3.85 in Q4 CY 2017 (publicly reported in April 2018) to 4.08 for Q4 CY 2020 (publicly reported in April 2021).                    

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

#### (2) Overview of Measure

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

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

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

                        We note that payroll data are considered the gold standard for nurse staffing measures and a significant improvement over the manual data previously used, wherein staffing information was calculated based on a form (CMS-671) filled out manually by the facility.[227]                                                  In contrast, PBJ staffing data are electronically submitted and are auditable back to payroll and other verifiable sources. Analyses of PBJ-based staffing measures show a relationship between higher nurse staffing levels and higher ratings for other dimensions of quality such as health inspection survey results and quality measures.[228] 

#### (a) Stakeholder and TEP Input

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

#### (b) MAP Review

                        The Total Nurse Staffing measure was included in the publicly available “List of Measures Under Consideration for December 1, 2021.” [229]                                                  The MAP conditionally supported the Total Nurse Staffing measure for rulemaking, pending NQF endorsement. We refer readers to the final 2021-2022 MAP report available at                         *https://www.qualityforum.org/​Publications/​2022/​03/​MAP_​2021-2022_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx.*

#### (3) Data Sources

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

#### (4) Inclusion and Exclusion Criteria

The target population for the measure is all SNFs to whom the SNF VBP applies and that are not excluded for the reasons listed below. A set of exclusion criteria are used to identify facilities with highly improbable staffing data and these facilities are excluded. The exclusion criteria are as follows:
    - Total nurse staffing, aggregated over all days in the quarter that the facility reported both residents and staff is excessively low (<1.5 hours per resident day).
    - Total nurse staffing, aggregated over all days in the quarter that the facility reported both residents and staff is excessively high (>12 hours per resident day).
    - Nurse aide staffing, aggregated over all days in the quarter that the facility reported both residents and staff is excessively high (>5.25 hours per resident day).       ( print page 22774)      

#### (5) Measure Calculation and Case-Mix Adjustment

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

                        HoursAdjusted                         = (HoursReported                        /HoursCaseMix) * HoursNational Average

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

                        The measure is case-mix adjusted based on the distribution of MDS assessments by RUG-IV groups. The CMS Staff Time Resource Intensity Verification (STRIVE) Study measured the average number of RN, LPN, and NA minutes associated with each RUG-IV group (using the 66-group version of RUG-IV).[230]                                                  We refer to these as “case-mix hours.” The case-mix values for each facility are based on the daily distribution of residents by RUG-IV group in the quarter covered by the PBJ reported staffing and estimates of daily RN, LPN, and NA hours from the CMS STRIVE Study. This adjustment is based on the distribution of MDS assessments by RUG-IV groups to account for differences in acuity, functional status, and care needs of residents, and therefore is appropriate for the SNF VBP program. For more information, see the Proposed Specifications for the SNF VBP Program Total Nursing Hours per Resident Day Measure, at                         *https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​Measure.*

#### (a) Numerator

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

#### (b) Denominator

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

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

#### (6) Proposed Confidential Feedback Reports and Public Reporting

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

We invite public comment on our proposal to adopt the Total Nurse Staffing measure beginning with the FY 2026 SNF VBP program year.

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

                        As part of the SNF VBP Program expansion authorized under the CAA, we are proposing to adopt the DTC PAC SNF measure for the FY 2027 SNF VBP Program and subsequent years. The DTC PAC SNF measure (NQF #3481) is an outcome measure that assesses the rate of successful discharges to community from a SNF setting, using 2 years of Medicare FFS claims data. This proposed measure addresses an important health care outcome for many SNF residents (returning to a previous living situation and avoiding further institutionalization) and would align the Program with the Seamless Care Coordination domain of CMS's Meaningful Measures 2.0 Framework. In addition, the DTC PAC SNF measure is currently part of the SNF QRP measure set.[231]                                                  For more information on this measure in the SNF QRP, see                         *https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​NursingHomeQualityInits/​Skilled-Nursing-Facility-Quality-Reporting-Program/​SNF-Quality-Reporting-Program-Measures-and-Technical-Information.*

#### (1) Background

                        We believe it is an important goal in post-acute care settings to return patients to their previous levels of independence and functioning with discharge to community being one of the primary goals for post-acute patients. We also believe it is important to improve access to community discharge options for SNF residents. Discharge to community is considered a valuable outcome to measure because it provides important information about patient outcomes after being discharged from a SNF and is a multifaceted measure that captures the patient's functional status, cognitive capacity,                               ( print page 22775)                            physical ability, and availability of social support at home.                    

                        In 2019, 1.5 million of Medicare's FFS beneficiaries (4 percent of all Medicare FFS beneficiaries) utilized Medicare coverage for a SNF stay.[232]                                                  However, almost half of the older adults that are admitted to SNFs are not discharged to the community, and for a significant proportion of those that are discharged back to the community, it may take up to 365 days.[233 234]                          In 2017, the SNF QRP and other PAC QRP programs adopted this measure; however, there remains considerable variation in performance on this measure. In 2019, the lowest performing SNFs had risk-adjusted rates of successful discharge to the community at or below 39.5 percent, while the best performing SNFs had rates of 53.5 percent or higher, indicating considerable room for improvement.[235] 

                        In addition to being an important outcome from a resident and family perspective, residents discharged to community settings, on average, incur lower costs over the recovery episode, compared with those discharged to institutional settings.[236 237]                          We believe including this measure in the SNF VBP Program will further encourage SNFs to prepare residents for discharge to community, when clinically appropriate, which may have significant cost-saving implications for the Medicare program given the high costs of care in institutional settings. Also, providers have discovered that successful discharge to community is a key factor in their ability to achieve savings, where capitated payments for post-acute care were in place.[238]                                                  For residents who require long-term care due to persistent disability, discharge to community could result in lower long-term care costs for Medicaid and for residents' out-of-pocket expenditures.[239] 

                        Discharge to community is also an actionable health care outcome, as targeted interventions have been shown to successfully increase discharge to community rates in a variety of post-acute settings. Many of these interventions involve discharge planning or specific rehabilitation strategies, such as addressing discharge barriers and improving medical and functional status.[240 241 242 243]                          Other factors that have shown positive associations with successful discharge to community include patient safety culture within the SNF and availability of home and community-based services.[244 245]                          The effectiveness of these interventions suggests that improvement in discharge to community rates among post-acute care residents is possible through modifying provider-led processes and interventions. Therefore, including the DTC PAC SNF measure in the SNF VBP Program may provide further incentive for providers to continue improving on current interventions or implement new interventions.                    

#### (2) Overview of Measure

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

#### (a) Stakeholder and TEP Input

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

#### (b) MAP Review

                        The DTC PAC SNF measure was included in the publicly available “List of Measures Under Consideration for                               ( print page 22776)                            December 1, 2021,” [246]                                                  and the MAP supported the DTC PAC SNF measure for rulemaking for the SNF VBP Program. We refer readers to the final MAP report available at                         *https://www.qualityforum.org/​Publications/​2022/​03/​MAP_​2021-2022_​Considerations_​for_​Implementing_​Measures_​Final_​Report_​-_​Clinicians,_​Hospitals,_​and_​PAC-LTC.aspx.*

#### (3) Data Sources

                        We are proposing to use data from the Medicare FFS claims and Medicare eligibility files to calculate this measure. We would use data from the “Patient Discharge Status Code” on Medicare FFS claims to determine whether a resident was discharged to a community setting for calculation of this measure. The eligibility files provide information such as date of birth, date of death, sex, reasons for Medicare eligibility, periods of Part A coverage, and periods in the Medicare FFS program. The data elements from the Medicare FFS claims are those basic to the operation of the Medicare payment systems and include data such as date of admission, date of discharge, diagnoses, procedures, indicators for use of dialysis services, and indicators of whether the Part A benefit was exhausted. The inpatient claims data files contain patient-level PAC and other hospital records. SNFs would not need to report additional data in order for us to calculate this measure.[247] 

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

#### (4) Inclusion and Exclusion Criteria

                        We are proposing that the DTC PAC SNF measure would use the same specifications under the SNF VBP Program as the Discharge to Community—PAC SNF QRP measure used in the SNF QRP, which are available at                         *https://www.cms.gov/​files/​zip/​snf-qrp-measure-calculations-and-reporting-users-manual-v301-addendum-effective-10-01-2020.zip.*                         The target population for the measure is the group of Medicare FFS residents who are admitted to a SNF and are not excluded for the reasons listed in this paragraph. The measure exclusion criteria are determined by processing Medicare claims and eligibility data to determine whether the individual exclusion criteria are met. All measure exclusion criteria are based on administrative data. Only SNF stays that are preceded by a short-term acute care stay in the 30 days prior to the SNF admission date are included in the measure. Stays ending in transfers to the same level of care are excluded. The measure excludes residents for which the following conditions are true:                    
    - Age under 18 years;
    - No short-term acute care stay within the 30 days preceding SNF admission;
    - Discharges to a psychiatric hospital;
    - Discharges against medical advice;
    - Discharges to disaster alternative care sites or Federal hospitals;
    - Discharges to court/law enforcement;
    - Residents discharged to hospice and those with a hospice benefit in the post-discharge observation window;
    - Residents not continuously enrolled in Part A FFS Medicare for the 12 months prior to the post-acute admission date, and at least 31 days after post-acute discharge date;
    - Residents whose prior short-term acute care stay was for non-surgical treatment of cancer;
    - Post-acute stays that end in transfer to the same level of care;
    - Post-acute stays with claims data that are problematic ( e.g.,                          anomalous records for stays that overlap wholly or in part, or are otherwise erroneous or contradictory);                    
    - Planned discharges to an acute or LTCH setting;
    - Medicare Part A benefits exhausted;
    - Residents who received care from a facility located outside of the U.S., Puerto Rico or a U.S. territory; and
    - Swing Bed Stays in Critical Access Hospitals.

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

#### (5) Risk Adjustment

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

#### (6) Measure Calculation

                        We are proposing to adopt the DTC PAC SNF measure for the SNF VBP Program for FY 2027 and subsequent years. This measure is calculated using 2 years of data. Since Medicare FFS claims data are already reported to the                               ( print page 22777)                            Medicare program for payment purposes, and Medicare eligibility files are also available, SNFs will not be required to report any additional data to us for calculation of this measure.                    

#### (a) Numerator

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

#### (b) Denominator

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

#### (7) Proposed Confidential Feedback Reports and Public Reporting

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

We invite public comment on our proposal to adopt the DTC PAC SNF measure beginning with the FY 2027 SNF VBP program year.

### C. SNF VBP Performance Period and Baseline Period Proposals

#### 1. Background

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

#### 2. Proposal To Revise the Baseline Period for the FY 2025 SNF VBP Program

Under the policy finalized in the FY 2019 SNF PPS final rule (83 FR 39277 through 39278), the baseline period for the SNFRM for the FY 2025 program year would be FY 2021. However, as more fully described in section VII.B.1. of this proposed rule, we have determined that the significant decrease in SNF admissions and staffing shortages associated with the PHE for COVID-19 in FY 2021 has impacted SNFRM validity and reliability. Because the baseline period for this measure is used to calculate the performance standards under the SNF VBP Program, we are concerned about using COVID-19 impacted data for the FY 2025 baseline period for scoring and payment purposes.

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

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

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

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

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

                        In considering the appropriate performance period for the SNF HAI measure for the FY 2026 SNF VBP Program, we recognize that we must balance the length of the performance period with our need to calculate valid and reliable performance scores and                               ( print page 22778)                            announce the resulting payment adjustments no later than 60 days prior to the program year involved, in accordance with section 1888(h)(7) of the Act. In our testing of the measure, we found that a 1-year performance period produced moderately reliable performance scores. We refer readers to the SNF HAI Measure Technical Report for further information on measure testing results, available at                         *https://www.cms.gov/​files/​document/​snfhaitechnicalreport.pdf.*                         In addition, we refer readers to the FY 2017 SNF PPS final rule (81 FR 51998 through 51999) for a discussion of the factors we should consider when specifying performance periods for the SNF VBP Program, as well as our stated preference for 1-year performance periods. Based on these considerations, we believe that a 1-year performance period for the SNF HAI measure would be operationally feasible for the SNF VBP Program and would provide sufficiently accurate and reliable SNF HAI measure rates and resulting performance scores.                    

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

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

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

We invite public comment on our proposals related to the performance period for the SNF HAI measure for the FY 2026 program year and subsequent years.

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

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

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

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

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

We invite public comment on our proposals related to the baseline period for the SNF HAI measure for the FY 2026 program year and subsequent years.

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

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

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

                        We also recognize that we must balance our desire to specify a performance period for a fiscal year as close to the fiscal year's start date as possible to ensure clear connections between quality measurement and value-based payment with our need to announce the net results of the Program's adjustments to Medicare payments not later than 60 days prior to                               ( print page 22779)                            the fiscal year involved, in accordance with section 1888(h)(7) of the Act. In considering these constraints, and in alignment with the SNFRM, we believe that a performance period that occurs 2 fiscal years prior to the applicable fiscal program year is most appropriate for the Total Nurse Staffing measure.                    

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

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

We invite public comment on our proposals related to the performance period for the Total Nurse Staffing measure for the FY 2026 program year and subsequent years.

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

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

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

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

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

We invite public comment on our proposals related to the baseline period for the Total Nurse Staffing measure for the FY 2026 program year and subsequent years.

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

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

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

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

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

We invite public comment on our proposals related to the performance period for the DTC PAC SNF measure for FY 2027 program year and subsequent years.

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

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

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

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

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

                        We invite public comment on our proposals related to the baseline period for the DTC PAC SNF measure for FY 2027 program year and subsequent years.                              ( print page 22780)    

### D. Performance Standards

#### 1. Background

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

We are not proposing any changes to these performance standard policies in this proposed rule.

#### 2. SNF VBP Performance Standards Correction Policy

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

We are not proposing any changes to the performance standards correction policy in this proposed rule. We seek public comment on our changes to the text at § 413.338(a)(12) and (d)(6).

#### 3. Proposed Performance Standards for the FY 2025 Program Year

As discussed in section VII.C.2. of this proposed rule, we are proposing to use FY 2019 data as the baseline period for the FY 2025 program year. Based on this proposed updated baseline period and our previously finalized methodology for calculating performance standards (81 FR 51996 through 51998), the proposed estimated numerical values for the FY 2025 program year performance standards are shown in Table 18.

### E. SNF VBP Performance Scoring

#### 1. Background

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

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

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

We invite public comment on our proposal to use a special scoring policy for the FY 2023 Program year.

#### 3. Proposed Case Minimum and Measure Minimum Policies

#### a. Background

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

                        To implement this provision, we are proposing to establish case and measure minimums that SNFs must meet to be included in the Program for a given                               ( print page 22781)                            program year. These proposed case and measure minimum requirements would serve as eligibility criteria for determining whether a SNF is included in, or excluded from, the Program for a given program year. Inclusion in the Program for a program year means that a SNF would receive a SNF performance score and would be eligible to receive a value-based incentive payment. Exclusion from the Program for a program year means that, for the applicable fiscal year, a SNF would not be subject to the requirements under § 413.338 and would also not be subject to a payment reduction under § 413.337(f). Instead, the SNF would receive its full Federal per diem rate under § 413.337 for the applicable fiscal year.                    

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

We are proposing that the case and measure minimums would be based on statistical accuracy and reliability, such that only SNFs that have sufficient data would be included in the SNF VBP Program for a program year. We believe this would ensure that we apply program requirements only to SNFs for which we can calculate reliable measure rates and SNF performance scores.

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

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

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

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

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

We invite public comment on our proposal to adopt a case minimum requirement for the SNFRM beginning with the FY 2023 SNF VBP program year.

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

In this proposed rule, we are proposing to adopt the SNF HAI and Total Nurse Staffing measures beginning with the FY 2026 program year, as well as the DTC PAC SNF measure beginning with the FY 2027 program year.

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

                        For the Total Nurse Staffing measure, we are proposing that SNFs must have a minimum of 25 residents, on average, across all available quarters during the applicable 1-year performance period in order to be eligible to receive a score on the measure. We tested three potential case minimums for this measure: a 25-resident minimum, a minimum of one quarter of PBJ data, and a minimum of two quarters of PBJ data. Among all SNFs eligible for the SNF VBP Program, over 94 percent of SNFs satisfied the case minimum under all three alternatives tested. There were very minimal differences observed between the case minimums tested, and this finding held for most subgroups tested as well, including rural SNFs, large SNFs, and those SNFs serving the highest proportion of dually eligible beneficiaries. The only notable observed difference occurred within small SNFs, defined as those with fewer than 46 beds as a proxy for size. About 90 percent of small SNFs reported two quarters of PBJ data, and about 92 percent of small SNFs reported one quarter of PBJ data, but only about 63 percent of small SNFs satisfied the 25-resident minimum, indicating that even                               ( print page 22782)                            after two quarters of successful PBJ reporting there was a substantial proportion of small SNFs (about 27 percent) reporting minimal numbers of residents, calling into question the utility of their limited staffing data. After considering these alternatives, we determined that the proposed 25-resident minimum best balances quality measure reliability with our desire to score as many SNFs as possible on this measure. We also note that the 25-resident minimum for this measure would align with the case minimums we are proposing for the other proposed measures.                    

                        Further, for the DTC PAC SNF measure, we are proposing that SNFs must have a minimum of 25 eligible stays during the applicable 2-year performance period in order to be eligible to receive a score on the measure. We believe this case minimum requirement for the DTC PAC SNF measure (                        *https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​Other-VBPs/​SNFRM-Reliability-Testing-Memo.pdf*) is appropriate and consistent with the findings of measure testing analyses. Analyses conducted by CMS contractors found that a 25 eligible stay minimum produced good to excellent measure score reliability. In addition, analyses using 2015 through 2016 Medicare FFS claims data found that 94 percent of SNFs met the 25 eligible stay minimum during the 2-year performance period. We believe these testing results for the 25 eligible stay minimum support our objective, which is to establish case minimums that appropriately balance quality measure reliability with our continuing desire to score as many SNFs as possible on this measure. The complete measure testing results conducted by our contractors that we included as part of the documentation supporting our request for NQF to endorse the measure are available at                         *https://www.qualityforum.org/​QPS/​3481.*

We invite public comment on our proposal to adopt case minimums for the SNF HAI, Total Nurse Staffing, and DTC PAC SNF measures.

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

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

In this proposed rule, we are proposing to adopt two new quality measures (SNF HAI and Total Nurse Staffing measures) beginning with the FY 2026 Program. If finalized, the SNF VBP Program would consist of three quality measures in FY 2026 (SNF Readmission Measure, SNF HAI, and Total Nurse Staffing measures). We are proposing that for FY 2026, SNFs must have the minimum number of cases for two of these three measures during the performance period to receive a performance score and value-based incentive payment. SNFs that do not meet these minimum requirements would be excluded from the FY 2026 program and would receive their full Federal per diem rate for that fiscal year. Under these proposed minimum requirements, we estimate that approximately 14 percent of SNFs would be excluded from the FY 2026 Program. Alternatively, if we required SNFs to have the minimum number of cases for all three measures during the performance period, approximately 21 percent of SNFs would be excluded from the FY 2026 Program. We also assessed the consistency of value-based incentive payment adjustment factors, or incentive payment multipliers (IPMs), between time periods as a proxy for performance score reliability under the different measure minimum options. The testing results indicated that the reliability of the SNF performance score would be relatively consistent across the different measure minimum requirements. Specifically, for the FY 2026 program year, we estimate that under the proposed minimum of two measures, 82 percent of SNFs receiving a net-negative IPM in the first testing period also received a net-negative IPM in the second testing period. Alternatively, under a minimum of three measures for the FY 2026 program year, we found that the consistency was 81 percent. Based on these testing results, we believe the proposed minimum of two out of three measures for FY 2026 best balances SNF performance score reliability with our desire to ensure that as many SNFs as possible can receive a performance score and value-based incentive payment.

We are also proposing to adopt an additional quality measure (DTC PAC SNF measure) beginning with the FY 2027 Program. If finalized, the SNF VBP Program would consist of four quality measures in FY 2027 (SNF Readmission Measure, SNF HAI, Total Nurse Staffing, and DTC PAC SNF measures). We are proposing that for FY 2027, SNFs must have the minimum number of cases for three of the four measures during a performance period to receive a performance score and value-based incentive payment. SNFs that do not meet these minimum requirements would be excluded from the FY 2027 program and would receive their full Federal per diem rate for that fiscal year. Under these proposed minimum requirements, we estimate that approximately 16 percent of SNFs would be excluded from the FY 2027 Program. Alternatively, if we required SNFs to have the minimum number of cases for all four measures, we estimate that approximately 24 percent of SNFs would be excluded from the FY 2027 Program. We also assessed the consistency of incentive payment multipliers (IPMs) between time periods as a proxy for performance score reliability under the different measure minimum options. The testing results indicated that the reliability of the SNF performance score for the FY 2027 program year would be relatively consistent across the different measure minimum requirements. That is, among the different measure minimums for the FY 2027 program year, a strong majority (between 85 and 87 percent) of the SNFs receiving a net-negative IPM for the first testing period also received a net-negative IPM for the second testing period. These findings indicate that increasing the measure minimum requirements does not meaningfully increase the consistency of the performance score. Based on these testing results, we believe the proposed minimum of three out of four measures for FY 2027 best balances SNF performance score reliability with our desire to ensure that as many SNFs as possible can receive a performance score and value-based incentive payment.

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

                        We invite public comment on our proposed measure minimums for the FY 2026 and FY 2027 SNF VBP program years.                              ( print page 22783)    

#### 4. Proposed Update to the Scoring Policy for SNFs Without Sufficient Baseline Period Data Beginning With the FY 2026 Program Year

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

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

For example, if a SNF has fewer than the minimum of 25 eligible stays during the applicable 1-year baseline period for the SNF HAI measure for FY 2026, that SNF would only be scored on achievement during the performance period for the SNF HAI measure for FY 2026, so long as that SNF meets the case minimum for that measure during the applicable performance period.

We are also proposing to codify this update in our regulation text at § 413.338(e)(1)(iv).

We invite public comment on this proposal to update the policy for scoring SNFs that do not have sufficient baseline period data.

#### 5. Proposal To Remove the LVA Policy From the SNF VBP Program Beginning With the FY 2023 Program Year

In the FY 2019 SNF PPS final rule (83 FR 39278 through 39280), we finalized our LVA policy, which provides an adjustment to the Program's scoring methodology to ensure low-volume SNFs receive sufficiently reliable performance scores for the SNF readmission measure. In that final rule, we also codified the LVA policy in § 413.338(d)(3) of our regulations. As we discussed in the FY 2019 SNF PPS final rule, we found that the reliability of the SNFRM measure rates and resulting performance scores were adversely affected if SNFs had fewer than 25 eligible stays during the performance period for a program year (83 FR 39279). Therefore, we believed that assigning a performance score that would result in a value-based incentive payment amount that is equal to the adjusted Federal per diem rate that the SNF would have received in the absence of the Program, to any SNF with fewer than 25 eligible stays for the SNFRM during the performance period, was the most appropriate adjustment for ensuring reliable performance scores.

However, we no longer believe the LVA policy is necessary because we are now required under the statute to have case and measure minimum policies for the SNF VBP Program, and those policies will achieve the same payment objective as the LVA policy. Therefore, we are proposing to remove the LVA Policy from the SNF VBP Program's scoring methodology beginning with the FY 2023 program year. With the removal of the LVA policy, the total amount available for a fiscal year would no longer be increased as appropriate for each fiscal year to account for the assignment of a performance score to low-volume SNFs. We are proposing to update the Total amount available for a fiscal year to 60 percent of the total amount of the reduction to the adjusted SNF PPS payments for that fiscal year, as estimated by CMS, in our regulations at§ 413.338(c)(2)(i). We are proposing to update the LVA policy at § 413.338(d)(3) to reflect its removal from the program.

We invite public comment on our proposal to remove the LVA policy from the SNF VBP Program beginning with the FY 2023 program year.

#### 6. Proposal To Update the SNF VBP Scoring Methodology Beginning in the FY 2026 Program Year

#### a. Background

In the FY 2017 SNF PPS final rule (81 FR 52000 through 52005), we adopted a scoring methodology for the SNF VBP Program where we score SNFs on their performance on the SNFRM, award between 0 and 100 points to each SNF (with up to 90 points available for improvement), and award each SNF a SNF performance score consisting of the higher of its scores for achievement and improvement. The SNF performance score is then translated into a value-based incentive payment multiplier that can be applied to each SNF's Medicare claims during the SNF VBP Program year using an exchange function. Additionally, in the FY 2018 SNF PPS final rule (82 FR 36615), we adopted a clarification of our rounding policy in SNF VBP scoring to award SNF performance scores that are rounded to the nearest ten-thousandth of a point, or with no more than five significant digits to the right of the decimal point. We have also codified numerous aspects of the SNF VBP Program's policies in our regulations at § 413.338, and our scoring policies appear in paragraph (d) of that section.

We refer readers to the FY 2017 rule cited above for a detailed discussion of the SNF VBP Program's scoring methodology, public comments on the proposed policies, and examples of our scoring calculations.

#### b. Proposed Measure-Level Scoring Update

We are proposing to update our achievement and improvement scoring methodology to allow a SNF to earn a maximum of 10 points on each measure for achievement, and a maximum of 9 points on each measure for improvement. For purposes of determining these points, we are proposing to define the benchmark as the mean of the top decile of SNF performance on the measure during the baseline period and the achievement threshold as the 25th percentile of national SNF performance on the measure during the baseline period.

We are proposing to award achievement points to SNFs based on their performance period measure rate for each measure according to the following:
    - If a SNF's performance period measure rate is equal to or greater than the benchmark, the SNF would be awarded 10 points for achievement.
    - If a SNF's performance period measure rate is less than the achievement threshold, the SNF would receive 0 points for achievement.
    - If a SNF's performance period measure rate is equal to or greater than the achievement threshold, but less than the benchmark, we will award between 0 and 10 points according to the following formula:
      ( print page 22784)    
We are also proposing to award improvement points to SNFs based on their performance period measure rate according to the following:
    - If a SNF's performance period measure rate is equal to or lower than its baseline period measure rate, the SNF would be awarded 0 points for improvement.
    - If a SNF's performance period measure rate was equal to or higher than the benchmark, the SNF would be awarded 9 points for improvement.
    - If a SNF's performance period measure rate was greater than its baseline period measure rate but less than the benchmark, we will award between 0 and 9 points according to the following formula:

Under this proposal, we will score SNFs' performance on achievement and improvement for each measure and award them the higher of the two scores for each measure to be included in the SNF performance score, except in the instance that the SNF does not meet the case minimum threshold for the measure during the applicable baseline period, in which case we propose in section VII.E.4. that the SNF would only be scored on achievement. As discussed in the following subsection of this proposed rule, we will then sum each SNFs' measure points and normalize them to arrive at a SNF performance score that ranges between 0 and 100 points. We believe that this policy appropriately recognizes the best performers on each measure and reserves the maximum points for their performance levels while also recognizing that improvement over time is important and should also be rewarded.

We further propose that this change would apply beginning with the FY 2026 SNF VBP program year. Under this proposal, all measures in the expanded SNF VBP Program would be weighted equally, as we believe that an equal weighting approach is simple for participating SNFs to understand and assigns significant scoring weight (that is, 33.33 percentage points if a SNF has sufficient data on all three measures proposed for FY 2026) to each measure topic covered by the expanded SNF VBP Program. However, as we consider whether we should propose to adopt additional measures, we also intend to consider whether we should group the measures into domains and weight them, similar to what we do under the Hospital VBP Program scoring methodology.

We view this proposed change to measure-level scoring as a necessary update to the SNF VBP Program's scoring methodology to incorporate additional quality measures and to allow us to add more measures in the future. We are also proposing to codify these updates to our scoring methodology in our regulation text by revising the heading for paragraph (d) and adding paragraph (e)(1) at § 413.338.

We invite public comment on this proposal.

#### c. Proposed Normalization Policy

We continue to believe that awarding SNF performance scores out of a total of 100 points helps stakeholders more easily understand the performance evaluation that we provide through the SNF VBP Program. We therefore believe that continuing to award SNF performance scores out of 100 points would help stakeholders understand the revised scoring methodology and would allow the scoring methodology to accommodate additional measures in the future without more methodological changes.

Therefore, we considered how we could construct the SNF performance score such that the scores continue to range between 0 and 100 points. We considered our past experience in our VBP programs, specifically including our experience with the Hospital VBP Program, where we award between 0 and 10 points to participating providers for their performance on each measure, and to arrive at a Total Performance Score that ranges between 0 and 100 points regardless of the number of measures on which the hospital has sufficient data, we normalize hospitals' scores. We believe the Hospital VBP Program's success in comprehensible measure-level scoring provides a strong model for the expanded SNF VBP Program.

We are therefore proposing to adopt a “normalization” policy for SNF performance scores under the expanded SNF VBP Program, effective in the FY 2026 program year. Under this policy, we would calculate a raw point total for each SNF by adding up the SNF's score on each of the measures. For example, a SNF that met the case minimum to receive a score on three quality measures would receive a score between 0 to 30 points, while a SNF that met the case minimum to receive a score on two quality measures would receive a score between 0 to 20 points. We would then normalize the raw point totals by converting them to a 100-point scale, with the normalized values being awarded as the SNF performance score. For example, we would normalize a SNF's raw point total of 27 points out of 30 by converting that total to a 100-point scale, with the result that the SNF would receive a SNF performance score of 90.

                        In addition to allowing us to maintain a 100-point total performance score scale, this policy would enable us to adopt additional quality measures for the program without making further changes to the scoring methodology. If, for example, we proposed to adopt a total of seven quality measures in the future, the normalization policy would enable us to continue to award SNF performance scores on a 100-point scale, even though the maximum raw point total would be 70 points.                              ( print page 22785)    

We view this proposed normalization policy as a useful update to the SNF VBP Program's scoring methodology to accommodate additional quality measures and to ensure that the public understands the SNF performance scores that we award. We are also proposing to codify these updates to our scoring methodology by adding paragraph (e)(2) to our regulation text at § 413.338.

We invite public comment on our proposal.

### F. Proposal To Adopt a Validation Process for the SNF VBP Program Beginning With the FY 2023 Program Year

Section 1888(h)(12) of the Act (as added by Division CC, section 111(a)(4) of the Consolidated Appropriations Act, 2021 (Pub. L. 116-120)), requires the Secretary to apply a process to validate SNF VBP program measures and data, as appropriate. We are proposing to adopt a validation process for the Program beginning with the FY 2023 Program year.

For the SNFRM measure, we are proposing that the process we currently use to ensure the accuracy of the SNFRM satisfies this statutory requirement. Information reported through claims for the SNFRM measure are validated for accuracy by Medicare Administrative Contractors (MACs) to ensure accurate Medicare payments. MACs use software to determine whether billed services are medically necessary and should be covered by Medicare, review claims to identify any ambiguities or irregularities, and use a quality assurance process to help ensure quality and consistency in claim review and processing. They conduct prepayment and post-payment audits of Medicare claims, using both random selection and targeted reviews based on analyses of claims data. We are proposing to codify these proposals for the FY 2023 SNF VBP in our regulation text at § 413.338(j).

We are considering additional validation methods that may be appropriate to include in the future for the proposed SNF HAI, DTC PAC SNF, and Total Nurse Staffing measures, as well as for other new measures we may consider for the program, and for other SNF quality measures and assessment data. For more information, see section VII.I.c.3. of this proposed rule, Request for Comment on the SNF VBP Program Approach to Validation.

We invite public comment on our proposal to adopt a validation process for the SNF VBP Program beginning with the FY 2023 program year.

### G. Proposed SNF Value-Based Incentive Payments for FY 2023

We refer readers to the FY 2018 SNF PPS final rule (82 FR 36616 through 36621) for discussion of the exchange function methodology that we have adopted for the Program, as well as the specific form of the exchange function (logistic, or S-shaped curve) that we finalized, and the payback percentage of 60 percent. We adopted these policies for FY 2019 and subsequent fiscal years.

We also discussed the process that we undertake for reducing SNFs' adjusted Federal per diem rates under the Medicare SNF PPS and awarding value-based incentive payments in the FY 2019 SNF PPS final rule (83 FR 39281 through 39282).

As discussed in section VII.B.1. of this proposed rule, we are proposing to suppress the SNFRM for the FY 2023 program year and assigning all SNFs a performance score of zero, which would result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier. Under this proposal, we are proposing to not rank SNFs for FY 2023. We are also proposing to reduce each participating SNF's adjusted Federal per diem rate for FY 2023 by 2 percentage points and to award each participating SNF 60 percent of that 2 percent withhold, resulting in a 1.2 percent payback for the FY 2023 program year. We believe this continued application of the 2 percent withhold is spread evenly across all SNFs is the most equitable way to reduce the impact of the withhold considering our proposal to award a performance score of zero to all SNFs. We are also proposing that those SNFs that do not meet the proposed case minimum for the SNFRM for FY 2023 would be excluded from the Program for FY 2023. We are proposing to update § 413.338(i) to reflect that this special scoring and payment policy will apply for FY 2023 in addition to FY 2022. As noted in section VII.B.1. of this proposed rule, our goal is to resume use of the scoring methodology we finalized for the program prior to the PHE beginning with the FY 2024 program year.

We invite public comment on this proposed change to the SNF VBP payment policy for the FY 2023 program year.

### H. Public Reporting on the Provider Data Catalog Website

#### 1. Background

                        Section 1888(g)(6) of the Act requires the Secretary to establish procedures to make SNFs' performance information on SNF VBP Program measures available to the public on the Nursing Home Compare website or a successor website, and to provide SNFs an opportunity to review and submit corrections to that information prior to its publication. We began publishing SNFs' performance information on the SNFRM in accordance with this directive and the statutory deadline of October 1, 2017. In December 2020, we retired the Nursing Home Compare website and are now using the Provider Data Catalog website (                        *https://data.cms.gov/​provider-data/​*) to make quality data available to the public, including SNF VBP performance information.                    

Additionally, section 1888(h)(9)(A) of the Act requires the Secretary to make available to the public certain information on SNFs' performance under the SNF VBP Program, including SNF performance scores and their ranking. Section 1888(h)(9)(B) of the Act requires the Secretary to post aggregate information on the Program, including the range of SNF performance scores and the number of SNFs receiving value-based incentive payments, and the range and total amount of those payments.

In the FY 2017 SNF PPS final rule (81 FR 52009), we discussed the statutory requirements governing public reporting of SNFs' performance information under the SNF VBP Program. In the FY 2018 SNF PPS final rule (82 FR 36622 through 36623), we finalized our policy to publish SNF VBP Program performance information on the Nursing Home Compare or successor website after SNFs have had an opportunity to review and submit corrections to that information under the two-phase Review and Correction process that we adopted in the FY 2017 SNF PPS final rule (81 FR 52007 through 52009) and for which we adopted additional requirements in the FY 2018 SNF PPS final rule. In the FY 2018 SNF PPS final rule, we also adopted requirements to rank SNFs and adopted data elements that we will include in the ranking to provide consumers and stakeholders with the necessary information to evaluate SNF's performance under the Program (82 FR 36623).

                        As discussed in section VII.B.1. of this proposed rule, we are proposing to suppress the SNFRM for the FY 2023 program year due to the impacts of the PHE for COVID-19. If that proposal is finalized, for all SNFs participating in the FY 2023 SNF VBP Program, we would use the performance period (FY 2021, October 1, 2020 through September 30, 2021) we adopted in the FY 2021 SNF PPS final rule (85 FR 47624), as well as the previously finalized baseline period (FY 2019,                               ( print page 22786)                            October 1, 2018 through September 30, 2019) to calculate each SNF's RSRR for the SNFRM. We are also proposing in section VII.E.2. of this proposed rule to assign all SNFs a performance score of zero. This will result in all participating SNFs receiving an identical performance score, as well as an identical incentive payment multiplier.                    

While we would publicly report the SNFRM rates for the FY 2023 program year, we would make clear in the public presentation of those data that we are suppressing the use of those data for purposes of scoring and payment adjustments in the FY 2023 SNF VBP Program given the significant changes in SNF patient case volume and facility-level case-mix described earlier.

#### 2. Proposed Changes to the Data Suppression Policy for Low-Volume SNFs Beginning With the FY 2023 SNF VBP Program Year

In the FY 2020 SNF PPS final rule (84 FR 38823 through 38824), we adopted a data suppression policy for low-volume SNF performance information. Specifically, we finalized that we will suppress the SNF performance information available to display as follows: (1) If a SNF has fewer than 25 eligible stays during the baseline period for a program year, we will not display the baseline risk-standardized readmission rate (RSRR) or improvement score, although we will still display the performance period RSRR, achievement score, and total performance score if the SNF had sufficient data during the performance period; (2) if a SNF has fewer than 25 eligible stays during the performance period for a program year and receives an assigned SNF performance score as a result, we will report the assigned SNF performance score and we will not display the performance period RSRR, the achievement score, or improvement score; and (3) if a SNF has zero eligible cases during the performance period for a program year, we will not display any information for that SNF. We codified this policy in the FY 2021 SNF PPS final rule (85 FR 47626) at § 413.338(e)(3)(i) through (iii).

As discussed in section VII.B.1. of this proposed rule, we are proposing to suppress the SNFRM for the FY 2023 program year, and we are proposing special scoring and payment policies for FY 2023. In section VII.E.3.b of this proposed rule, we are proposing to adopt a new case minimum that would apply to the SNFRM beginning with FY 2023, new case minimums that would apply to the SNF HAI and Total Nurse Staffing measures and a measure minimum that would apply beginning with FY 2026, a new case minimum that would apply to the DTC PAC SNF measure and a new measure minimum that would apply beginning with FY 2027. As a result of these proposed policies, and in order to implement them for purposes of clarity and transparency in our public reporting, we propose revising the data suppression policy as follows:

(1) If a SNF does not have the minimum number of cases during the baseline period that applies to a measure for a program year, we would publicly report the SNF's measure rate and achievement score if the SNF had minimum number of cases for the measure during the performance period for the program year;

(2) If a SNF does not have the minimum number of cases during the performance period that applies to a measure for a program year, we would not publicly report any information on the SNF's performance on that measure for the program year;

(3) If a SNF does not have the minimum number of measures during the performance period for a program year, we would not publicly report any data for that SNF for the program year.

We are proposing to codify this policy at § 413.338(f)(4).

We invite public comment on these proposals.

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

#### 1. Requests for Comment on Additional SNF VBP Program Measure Considerations for Future Years

#### (a) Request for Comment on Including a Staffing Turnover Measures in a Future SNF VBP Program Year

                        In the FY 2022 SNF PPS final rule (86 FR 42507 through 42511), we summarized stakeholder feedback on our request for comments related to potential future measures for the SNF VBP Program, including a specific request for comment on measures that focus on staffing turnover. Specifically, we noted that we have been developing measures of staff turnover with data that are required to be submitted under section 1128I(g)(4) of the Act, with the goal of making the information publicly available. We stated that, through our implementation of the PBJ staffing data collection program, we indicated that we will be reporting rates of employee turnover in the future (for more information on this program, see CMS memorandum QSO-18-17-NH).[248]                                                  We refer readers to the FY 2022 SNF PPS final rule for additional details on this request for public comments and a summary of the public comments we received (86 FR 42507 through 42511).                    

                        Nursing staff turnover has long been identified as a meaningful factor in nursing home quality of care.[249]                                                  Studies have shown a relationship between staff turnover and quality outcomes; for example, higher staff turnover is associated with an increased likelihood of receiving an infection control citation.[250]                                                  The collection of auditable payroll-based daily staffing data through the PBJ system has provided an opportunity to calculate, compare, and publicly report turnover rates; examine facility characteristics associated with higher or lower turnover rates; and further measure the relationship between turnover and quality outcomes. For example, a recent study using PBJ data found that nursing staff turnover is higher than previously understood, variable across facilities, and correlated with organizational characteristics such as for-profit status, chain ownership, and higher Medicaid census.[251]                                                  In addition, we have found that higher overall star ratings are associated with lower average staff turnover rates, suggesting that lower staff turnover rates are associated with higher overall nursing home quality.[252] 

                        In January of 2022, we began publicly reporting a staffing turnover measure on the Compare tool currently hosted by HHS, available at                         *https://www.medicare.gov/​care-compare,*                         and this information will be included in the Nursing Home Five Star Quality Rating System in July 2022. We refer readers to the Nursing Home Staff Turnover and Weekend Staffing Levels Memo for additional information related to this measure at                         *https://www.cms.gov/​files/​document/​qso-22-08-nh.pdf.*                         We believe staffing turnover is an important indicator of quality of care provided in                               ( print page 22787)                            nursing homes and SNFs. Additionally, in response to our request for comment on a staffing turnover measure, stakeholders strongly recommended that we consider measures of staffing turnover to assess patterns and consistency in staffing levels. As a part of our goals to build a robust and comprehensive measure set for the SNF VBP Program and in alignment with stakeholder recommendations, we intend to propose to adopt a staffing turnover measure in the SNF VBP Program in the FY 2024 SNF PPS proposed rule. Specifically, the measure we intend to include in the SNF VBP program is the percent of total nurse staff that have left the facility over the last year. Total nurse staff include RNs, LPNs, and nurse aides. More information on this measure, can be found in the Five Star Rating Technical Users' Guide at                         *https://www.cms.gov/​medicare/​provider-enrollment-and-certification/​certificationandcomplianc/​downloads/​usersguide.pdf.*

The Biden-Harris Administration is committed to improving the quality of care in nursing homes. As stated in a fact sheet entitled “Protecting Seniors by Improving Safety and Quality of Care in the Nation's Nursing Homes,” we are committed to strengthening the SNF VBP Program and have begun to measure and publish staff turnover and weekend staffing levels, metrics which closely align with the quality of care provided in a nursing home. We intend to propose new measures based on staffing adequacy, the resident experience, as well as how well facilities retain staff. Accordingly, we seek commenters' feedback on including the staff turnover measure that captures the percent of total nurse staff that have left the facility over the last year for the SNF VBP Program as currently specified or whether the measure should be revised before being proposed for inclusion in the SNF VBP program.

                        In addition, we are interested in whether we should explore the development of a composite measure that would capture multiple aspects of staffing, including both total nurse hours and the staff turnover measure rather than having separate but related measures related to nursing home staffing, such a measure could potentially replace the initial measure we intend to propose to include in SNF VBP for FY 2024. Preliminary analyses using the staff turnover data on the                         *Medicare.gov*                         Care Compare website have indicated that as the lower average staff turnover decreases, the overall star ratings for facilities increases, suggesting that lower turnover is associated with higher overall quality,[253]                                                  and research has indicated that staff turnover has been linked with increased infection control issues.[254]                                                  We believe it is important to capture and tie aspects of both staffing levels and staffing turnover to quality payment and welcome commenter's feedback for how to balance those goals under the SNF VBP program. We are also interested to hear about actions SNFs may take or have taken to reduce staff turnover in their facilities, and for SNFs that did reduce staff turnover, the reduction's observed impact on quality of care. In particular, we are interested in best practices for maintaining continuity of staffing among both nursing and nurse aide staff. Finally, we are interested in commenters feedback on any considerations we should take into account related to the impact that including a Nursing Home Staff Turnover measure may have on health equity. Before proposing to include this measure in the SNF VBP Program in the FY 2024 SNF PPS proposed rule, we would include the measure on a list of measures under consideration, as described in section 1890A of the Act.                    

#### (b) Request for Comment on Including the National Healthcare Safety Network (NHSN) COVID-19 Vaccination Coverage Among Healthcare Personnel Measure in a Future SNF VBP Program Year

In addition to the staffing turnover measure and the other potential future measures listed in the FY 2022 SNF PPS final rule, we are also considering the inclusion of the National Healthcare Safety Network (NHSN) COVID-19 Vaccination Coverage among Healthcare Personnel measure, which measures the percentage of healthcare personnel who receive a complete COVID-19 vaccination course. This measure data is collected by the CDC NHSN and the measure was finalized for use in the SNF QRP in the FY 2022 SNF PPS final rule (86 FR 42480 through 42489). We seek commenters' feedback on whether to propose to include this measure in a future SNF VBP program year. Before proposing to include any such measure, we would include the measure on a list of measures under consideration, as required by section 1890A of the Act.

#### (c) Request for Comment on Updating the SNF VBP Program Exchange Function

In the FY 2018 SNF PPS final rule (82 FR 36616 through 36619), we adopted an exchange function methodology for translating SNFs' performance scores into value-based incentive payments. We illustrated four possibilities for the functional forms that we considered—linear, cube, cube root, and logistic—and discussed how we assessed how each of the four possible exchange function forms would affect SNFs' incentive payments under the Program. We also discussed several important factors that we considered when adopting an exchange function, including the numbers of SNFs that receive more in value-based incentive payments in each scenario compared to the number of SNFs for which a reduction is applied to their Medicare payments, as well as the resulting incentives for SNFs to reduce hospital readmissions. We also evaluated the distributions of value-based incentive payment adjustments and the functions' results for compliance with the Program's statutory requirements. We found that the logistic function maximized the number of SNFs with positive payment adjustments among SNFs measured using the SNFRM. We also found that the logistic function best fulfilled the requirement that SNFs in the lowest 40 percent of the Program's ranking receive a lower payment rate than would otherwise apply, resulted in an appropriate distribution of value-based incentive payment percentages, and otherwise fulfilled the Program's requirements specified in statute.

                        Additionally, we published a technical paper describing the analyses of the SNF VBP Program exchange function forms and payback percentages that informed the policies that we adopted in the FY 2018 SNF PPS final rule. The paper is available on our website at                         *https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​Other-VBPs/​SNF-VBP-exchange-function-analysis.pdf.*

                        As discussed earlier, we are proposing numerous policy changes to expand the SNF VBP Program's measure set based on authority provided by the Consolidated Appropriations Act, 2021, including additional quality measures and adjustments to the Program's scoring methodology to accommodate the presence of more than one quality measure. We are also considering whether we should propose a new form for the exchange function or modify the                               ( print page 22788)                            logistic exchange function in future years.                    

When we adopted the logistic function for the SNF VBP Program, we focused on that function's ability, coupled with the 60 percent payback percentage, to provide net-positive value-based incentive payments to as many top-performing SNFs as possible. We believed that structuring the Program's incentive payments in this manner enabled us to reward the Program's top-performing participants and provide significant incentives for SNFs that were not performing as well to improve over time.

We continue to believe that these considerations are important and that net-positive incentive payments help drive quality improvement in the SNF VBP Program. However, in the context of a value-based purchasing program employing multiple measures, we are considering whether a new functional form or modifications to the existing logistic exchange function may provide the best incentives to SNFs to improve on the Program's measures.

                        If finalized, the additional measures that we are proposing for the SNF VBP Program would align the Program more closely with the Hospital VBP Program, on which some of SNF VBP's policies, like the exchange function methodology, are based. The Hospital VBP Program employs a linear exchange function to translate its Total Performance Scores into value-based incentive payment percentages that can be applied to hospitals' Medicare claims. A linear exchange function is somewhat simpler for stakeholders to understand but presents less of an opportunity to reward top performers than the logistic form that we currently employ in the SNF VBP Program at                         *https://data.cms.gov/​provider-data/​*                         or                         *https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​SNF-VBP/​SNF-VBP-Page.*

We request stakeholders' feedback on whether we should consider proposing either a new functional form or modified logistic exchange function for the SNF VBP Program. Specifically, we request comments on whether the proposed addition of new quality measures in the Program should weigh in favor of a new exchange function form, a modified logistic exchange function, or no change to the existing exchange function, whether stakeholders believe that the increased incentive payment percentages for top performers offered by the logistic function should outweigh the simplicity of the linear function, and whether we should further consider either the cube, cube root, or other functional forms.

#### 3. Request for Comment on the Validation of SNF Measures and Assessment Data

                        We have proposed to adopt measures for the SNF VBP Program that are calculated using data from a variety of sources, including Medicare FFS claims, the minimum data set (MDS), and the PBJ system, and we are seeking feedback on the adoption of additional validation procedures. In addition, section 1888(h)(12) of the Act requires the Secretary to apply a process to validate SNF VBP program measures, quality measure data, and assessment data as appropriate. MDS information is transmitted electronically by nursing homes to the national MDS database at CMS. The data set was updated in 2010 from MDS 2.0 to MDS 3.0 to address concerns about the quality and validity of the MDS 2.0 data. Final testing of MDS 3.0 showed strong results, with the updated database outperforming MDS 2.0 in terms of accuracy, validity for cognitive and mood items, and clinical relevance.[255]                                                  Research has also shown that MDS 3.0 discharge data match Medicare enrollment and hospitalization claims data with a high degree of accuracy.[256] 

Although The MDS data sets are assessed for accuracy, as described above, we are interested in ensuring the validity of the data reported by skilled nursing facilities because use of this data would have payment implications under the SNF VBP Program. Accordingly, we are requesting stakeholder feedback on the feasibility and need to select SNFs for validation via a chart review to determine the accuracy of elements entered into MDS 3.0 and PBJ. Additionally, we request feedback on data validation methods and procedures that could be utilized to ensure data element validity and accuracy.

We note that other programs, including the Hospital IQR (85 FR 58946) and Hospital OQR programs (76 FR 74485), have developed validation processes for chart-abstracted measures and electronic clinical quality measures (eCQMs), data sources not utilized for the SNF VBP Program. However, there are other elements of existing programs' validation procedures that may be considered for a future SNF VBP Program validation effort. For example, we request feedback on the volume of facilities to select for validation under the SNF VBP Program. We estimate that 3,300 hospitals report data under the Hospital OQR (86 FR 63961) and Hospital IQR (86 FR 45508) Programs. We estimate that over 15,000 SNFs are eligible for the SNF VBP Program. The Hospital OQR Program randomly selects the majority of hospitals (450 hospitals) for validation and additionally select a subset of targeted hospitals (50 hospitals) (86 FR 63872). Under the Hospital IQR Program, 400 hospitals are selected randomly and up to 200 hospitals are targeted for chart-abstracted data validation and up to 200 hospitals are randomly selected for eCQM data validation (86 FR 45424). We sample approximately 10 records from 300 randomly selected facilities under the ESRD QIP Program (82 FR 50766).

We also request stakeholder's feedback on the use of both random and targeted selection of facilities for validation. The Hospital OQR program identifies hospitals for targeted validation based on whether they have previously failed validation or have reported an outlier value deviating markedly from the measure values for other hospitals (more than 3 standard deviations of the mean) (76 FR 74485). Validation targeting criteria utilized by the Hospital IQR Program include factors such as: (1) Abnormal, conflicting or rapidly changing data patterns; (2) facilities which have joined the program within the previous 3 years, and which have not been previously validated or facilities which have not been randomly selected for validation in any of the previous 3 years; and (3) any hospital that passed validation in the previous year, but had a two-tailed confidence interval that included 75 percent (85 FR 58946).

                        Finally, we request stakeholder feedback on the implementation timeline for additional SNF VBP Program validation processes, as well as validation processes for other quality measures and assessment data. We believe it may be feasible to implement additional validation procedures beginning with data from the FY 2026 program year, at the earliest. Additionally, we may consider the adoption of a pilot of additional data validation processes; such an approach would be consistent with the implementation of the ESRD QIP data validation procedures, which began with a pilot in CY 2014 (82 FR 50766).                              ( print page 22789)    

We request stakeholder's feedback on the data validation considerations for the SNF VBP Program discussed previously in this section.

#### 4. Request for Comment on a SNF VBP Program Approach To Measuring and Improving Health Equity

                        Significant and persistent inequities in healthcare outcomes exist in the U.S. Belonging to a racial or ethnic minority group; living with a disability; being a member of the lesbian, gay, bisexual, transgender, and queer (LGBTQ+) community; living in a rural area; being a member of a religious minority; or being near or below the poverty level, is often associated with worse health outcomes.[257 258 259 260 261 262 263 264 265]                          In accordance with Executive Order 13985 of January 20, 2021 on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government, equity is defined as consistent and systematic fair, just, and impartial treatment of all individuals, including individuals who belong to underserved communities that have been denied such treatment, such as Black, Latino, and Indigenous and Native American persons, Asian Americans and Pacific Islanders and other persons of color; members of religious minorities; lesbian, gay, bisexual, transgender, and queer (LGBTQ+) persons; persons with disabilities; persons who live in rural areas; and persons otherwise adversely affected by persistent poverty or inequality (86 FR 7009). In February 2022, we further expanded on this definition by defining health equity as the attainment of the highest level of health for all people, where everyone has a fair and just opportunity to attain their optimal health regardless of race, ethnicity, disability, sexual orientation, gender identity, socioeconomic status, geography, preferred language, or other factors that affect access to care and health outcomes. We are working to advance health equity by designing, implementing, and operationalizing policies and programs that support health for all the people served by our programs, eliminating avoidable differences in health outcomes experienced by people who are disadvantaged or underserved, and providing the care and support that our enrollees need to thrive. Over the past decade we have enacted a suite of programs and policies aimed at reducing health care disparities including the CMS Mapping Medicare Disparities Tool,[266]                                                  the CMS Innovation Center's Accountable Health Communities Model,[267]                                                  the CMS Disparity Methods stratified reporting program,[268]                                                  and efforts to expand social risk factor data collection, such as the collection of Standardized Patient Assessment Data Elements in the post-acute care setting.[269] 

                        As we continue to leverage our value-based purchasing programs to improve quality of care across settings, we are interested in exploring the role of health equity in creating better health outcomes for all populations in these programs. As the March 2020 ASPE Report to Congress on Social Risk Factors and Performance in Medicare's VBP Program notes, it is important to implement strategies that cut across all programs and health care settings to create aligned incentives that drive providers to improve health outcomes for all beneficiaries.[270]                                                  Therefore, in this proposed rule, we are requesting stakeholder feedback on guiding principles for a general framework that could be utilized across our quality programs to assess disparities in healthcare quality in a broader Request for Information (RFI) in section VI.E. of this proposed rule. We refer readers to this RFI titled, “Overarching Principles for Measuring Healthcare Quality Disparities Across CMS Quality Programs—A Request for Information,” which includes a complete discussion on the key considerations that we intend to take into account when determining how to address healthcare disparities and advance health equity across all of our quality programs. Additionally, we are interested in stakeholder feedback on specific actions the SNF VBP Program can take to align with other value-based purchasing and quality programs to address healthcare disparities and advance health equity.                    

As we continue assessing the SNF VBP Program's policies in light of its operation and its expansion as directed by the CAA, we request public comments on policy changes that we should consider on the topic of health equity. We specifically request comments on whether we should consider incorporating adjustments into the SNF VBP Program to reflect the varied patient populations that SNFs serve around the country and tie health equity outcomes to SNF payments under the Program. These adjustments could occur at the measure level in forms such as stratification (for example, based on dual status or other metrics) or including measures of social determinants of health (SDOH). These adjustments could also be incorporated at the scoring or incentive payment level in forms such as modified benchmarks, points adjustments, or modified incentive payment multipliers (for example, peer comparison groups based on whether the facility includes a high proportion of dual eligible beneficiaries or other metrics). We request commenters' views on which of these adjustments, if any, would be most effective for the SNF VBP Program at accounting for any health equity issues that we may observe in the SNF population.

## VIII. Request for Information: Revising the Requirements for Long-Term Care (LTC) Facilities To Establish Mandatory Minimum Staffing Levels

                        The COVID-19 Public Health Emergency has highlighted and exacerbated long-standing concerns                               ( print page 22790)                            with inadequate staffing in long-term care (LTC) facilities. The Biden-Harris Administration is committed to improving the quality of U.S. nursing homes so that seniors and others living in nursing homes get the reliable, high-quality care they deserve.[271]                                                  As a result, we intend to propose minimum standards for staffing adequacy that nursing homes would be required to meet. We will conduct a new research study to help inform policy decisions related to determining the level and type of staffing needed to ensure safe and quality care and expect to issue proposed rules within 1 year. We are seeking opportunities to improve our health and safety standards to promote thoughtful, informed staffing plans and decisions within LTC facilities that aim to meet resident needs, including maintaining or improving resident function and quality of life. Such an approach is essential to effective person-centered care. Therefore, we are considering policy options for future rulemaking to establish specific minimum direct care staffing standards and are seeking stakeholder input to inform our policy decisions.                    

### A. Background

The requirements for participation for LTC facilities are the baseline health and safety standards that Medicare-certified providers and suppliers must meet to receive Medicare and Medicaid payment. We have broad statutory authority to establish health and safety regulations for several types of health care providers and suppliers, which include Conditions of Participation (CoPs), Conditions for Coverage (CfCs), and Requirements for LTC facilities. Section 1102 of the Act grants the Secretary authority to make and publish such rules and regulations, not inconsistent with the Act, as may be necessary to the efficient administration of the functions with which the Secretary is charged under the Act. Section 1871 of the Act grants the Secretary authority to prescribe regulations as may be necessary to carry out the administration of the Medicare program. Finally, section 1819 of the Act establishes requirements specifically with respect to skilled nursing facilities (SNFs), including, among other requirements, section 1819(b)(1)(A) of the Act, which requires that a SNF must care for its residents in such a manner and in such an environment as will promote maintenance or enhancement of the quality of life of each resident, section 1819(b)(4)(C)(i) of the Act, which requires that a SNF must provide 24-hour licensed nursing service sufficient to meet nursing needs of its residents, and must use the services of a registered professional nurse at least 8 consecutive hours a day. Section 1819(d)(4)(B) of the Act further states that a SNF must meet such other requirements relating to the health, safety, and well-being of residents or relating to the physical facilities thereof as the Secretary may find necessary. These provisions are largely paralleled in section 1919 of the Act for nursing facilities (NFs).

The regulatory requirements for SNFs and NFs, collectively referred to as LTC facilities and colloquially known as nursing homes, are codified at 42 CFR part 483. In this request for information, we are seeking public input on addressing direct care staffing requirements, especially those for registered nurses (RNs), licensed practical nurses (LPNs), or, in California and Texas, licensed vocational nurses (LVNs), and certified nursing assistants (CNAs), colloquially known as nurse aides, through the requirements for participation for LTC facilities. We also welcome input on which individuals should also be considered direct care staff, beyond nurses and CNAs.

Existing regulations at § 483.35 require that LTC facilities have sufficient nursing staff with the appropriate competencies and skill sets to provide nursing and related services to assure resident safety and attain or maintain the highest practicable physical, mental, and psychosocial well-being of each resident, as determined by resident assessments and individual plans of care and considering the number, acuity and diagnoses of the facility's resident population in accordance with a required facility assessment. Requirements at § 483.35(a) for sufficient staff mirror the statutory language at sections 1819(b)(4)(C)(i) and 1919(b)(4)(C)(i) of the Act, requiring (with certain exceptions) an RN to provide services in a facility 8 consecutive hours a day, 7 days a week as well as “sufficient numbers” of licensed nurses and other nursing personnel 24 hours a day to meet residents' needs. Certain nurse staffing requirements may be waived in accordance with the statute, under specific circumstances.

#### 1. Prior Staffing Studies

                        As indicated later in this section, there is research that associates increased RN staffing with improved quality of care. We have conducted prior studies that have been noted as potential sources for helping us assess minimum staffing levels, including the STM (1995 to 1997) and STRIVE (2006 to 2007) studies,[272]                                                  which determined the amount of nursing (RN, LVN, and nurse aide) time dedicated to residents classified under each RUG group. Both these studies measured the direct care time that was actually provided by the facilities and not nurse staffing levels necessary to provide adequate quality of care. Other studies as discussed later in this section, focus on the number of hours of nursing care a resident must receive to achieve certain quality objectives. At least one study noted that the relationship is not necessarily linear; that is, it takes more labor resources to achieve a certain level of improvement, but beyond that improvement slows.[273]                                                  Our own 2001 study conducted by Abt Associates reported that facilities with staffing levels below 4.1 hours per resident day (HPRD) for long stay residents (that is, those residents in the facility at least 90 days) may provide care that results in harm and jeopardy to residents.[274]                                                  A 2004 study by Schnelle and colleagues found that the highest-staffed nursing homes reported significantly lower resident care loads on all staffing reports and provided better care than all other homes.[275]                                                  In a more recent study involving 13,500 nursing homes, Schnelle et al. used a mathematical model to determine the CNA staffing necessary to provide activities of daily living (ADL) care to residents in accordance with their needs as identified in Minimum Data Set (MDS) data.[276]                                                  Based on their model, CNA staffing required for ADL care that would result in a rate of care omissions below 10 percent ranged from 2.8 HPRD to 3.6 HPRD. However, the nursing homes participating in the study reported actual CNA staffing that ranged                               ( print page 22791)                            from 2.3 HPRD to 2.5 HPRD. The rate of care omissions reported by the authors was intended for illustrative purposes, not necessarily as a desirable or acceptable level of staffing.                    

Despite these requirements and general understanding of the impacts of staffing on resident health and safety, understaffing continues to be an area of concern. We are aware of ongoing quality concerns and the association of RN staffing with quality of care. A staffing level of 4.1 HPRD is currently the most common number put forward as a potential minimum standard to ensure the adequacy of nursing staff, largely attributed to the 2001 Abt Associates study. As noted below, the care needs of, and the type of care provided to, LTC facility residents have changed. Therefore we are now reevaluating the evidence and conducting a new study.

#### 2. Trends in Resident Composition and Care Needs in LTC Facilities

                        Based on existing data analyses from Centers for Disease Control and Prevention's National Center for Health Statistics Vital and Health Statistics, Series 3, Number 43 (February 2019), the average hours of nursing care per resident per day for LTC facilities is 3 hours and 48 minutes 0.54 RN hours (up 0.02 hours from 2013), 0.85 LPN or LVN hours (same as 2013), and 2.41 Aide hours (down 0.05 hours from 2013), plus an additional 0.08 hours of Social Worker time and 0.19 hours activities staff time. This does not include therapist time, although virtually all LTC facilities (99.5 percent) offer at least some therapeutic services as therapeutic services are critical to helping residents “attain or maintain the highest practicable physical, mental, and psychosocial well-being” in order for a facility to achieve its statutory mandate that a nursing facility provide services and activities to attain or maintain the highest practicable physical, mental, and psychosocial well-being of each resident (see sections 1819(b)(2) and 1919(b)(2) of the Act). Very few LTC facilities (0.4 percent) were exclusive to dementia patients, who often require more care than the general LTC resident population; and only 14.9 percent offered a dedicated dementia care unit within the larger facility.[277] 

                        A study of trends in LTC facilities from 1985 to 2015 revealed changes in resident composition and increased acuity and care needs.[278]                                                  The percentage of residents with dementia increased from 39 to 45 percent. Prevalence of psychiatric diagnoses among residents almost tripled from 11 to 31 percent. The number of residents admitted from the hospital increased from 67 percent in 2000 to 85 percent in 2015 reflecting an increased percentage of residents being admitted for post-acute care with higher levels of acuity and functional impairments. Physical abilities decreased among residents from 1995 to 2015 with increased assistance among residents needed for bathing (89 to 96 percent), dressing (74 to 92 percent), transferring (60 to 85 percent), toileting (49 to 88 percent), and eating (38 to 56 percent). The study also found an overall decrease in the number of facilities nationwide by over 3,000, declining occupancy rates which fell from 87 to 81 percent, and overall increased staffing levels. Although the study found that overall direct care HPRD increased from 3.39 to 3.79, a breakdown by job title or discipline revealed that the increase was largely attributed to CNAs. CNA HPRD increased from 2.26 to 2.42 hours while nursing hours remained relatively stable for LPN/LVN hours (0.87 to 0.88) and decreased for RN hours (0.66 to 0.58).                    

                        An Issue Brief published by the Office of the Assistant Secretary for Planning and Evaluation (ASPE) in October 2020 revealed similar findings.[279]                                                  From 2002 to 2015, the proportion of older adults residing in LTC facilities declined. The age-standardized prevalence of dementia among older adults in the United States (U.S.) increased; however, the largest increase occurred among LTC facility residents. Moreover, the proportion of LTC facility residents with limitations in three or more activities of daily living was significantly higher than older adults living in other settings (that is, private home, apartment, or assisted living facility). Both of these studies suggest an overall decrease in census of LTC facilities occurred simultaneously with an increase in resident acuity and care needs while direct care responsibilities shifted from nursing personnel to CNAs. We welcome comment on these trends and their implications for staffing level requirements.                    

#### 3. Existing Data on Staffing in LTC Facilities

To ensure the availability of reliable and auditable data on LTC facility staffing, we developed a system to collect staffing information that is auditable back to payroll data, known as the Payroll Based Journal (PBJ). The Affordable Care Act (Pub. L. 111-148, March 23, 2010) added a new section 1128I to the Act to promote greater accountability for LTC facilities (defined under section 1128I(a) of the Act as SNFs). As added by the Affordable Care Act, section 1128I(g) of the Act pertains to the submission of staffing data by LTC facilities, and specifies that the Secretary, after consulting with State LTC ombudsman programs, consumer advocacy groups, provider stakeholder groups, employees and their representatives and other parties the Secretary deems appropriate, shall require a facility to electronically submit to the Secretary direct care staffing information, including information for agency and contract staff, based on payroll and other verifiable and auditable data in a uniform format according to specifications established by the Secretary in consultation with such programs, groups, and parties. Since July 2016, nursing homes have been submitting data electronically through the PBJ system as required under section 1128I(g) of the Act and § 483.70(q). The data submitted by facilities are the number of hours direct care staff are paid to work each day. All data submitted is auditable back to payroll and other verifiable sources.

In April 2018, we began using PBJ data to calculate staffing measures posted on Nursing Home Compare, and used in the Five Star Quality System. Staffing data is submitted quarterly and facilities are downgraded to a one-star staffing rating for a quarter if they meet either of the following criteria:
    - Facilities fail to submit any staffing data for the reporting quarter.
    - Facilities report four or more days in a quarter with zero registered nurse hours. [280]  

Facilities that report staffing below established thresholds are downgraded. LTC facilities with significant inaccuracies between the hours reported and the hours verified, or facilities who failed to submit any data by the required deadline would be presumed to have low levels of staffing. This results in these facilities being downgraded to a one-star rating in the staffing domain, which drops their overall (composite) star rating by one-star for a quarter.

                        In April 2019, we established new thresholds for staffing ratings and                               ( print page 22792)                            adjusted the staffing rating's grid to increase the weight RN staffing has on the staffing rating. We also reduced the number of days without an RN onsite that triggers an automatic downgrade to one-star from 7 days to 4 days.                    

In January 2022, we began posting on Care Compare the level of total nurse and RN staffing on weekends provided by each facility over a quarter and the percent of nursing staff and number of administrators that stopped working at the nursing home over a 12-month period. This data will be used in the Nursing Home Five Star Quality Rating System beginning in July 2022. We further anticipate using PBJ data to analyze the effects of LTC facility staffing on resident health and safety as we consider regulatory action. We are also considering a range of initiatives to further improve Care Compare.

#### 4. Considerations and Approaches To Address Staffing Concerns

                        States have implemented a variety of methods to attempt to address concerns about adequate staffing and care in LTC facilities. Some States have implemented a CNA hour-per-resident day model, with some including part or all of the hours of licensed nurses into this calculation). For example, the District of Columbia requires a minimum daily average of 4.1 hours of direct nursing care per resident per day (with opportunity to adjust the requirements above or below this level, as determined by the Director of Department of Health), an RN on site 24 hours a day 7 days a week, plus additional nursing and medical staffing requirements.[281]                                                  Some States have implemented a ratio of numbers of full-time equivalent CNAs per resident. For example, Maine requires 3.58 HPRD with at least 0.508 of those hours provided by an RN.[282]                                                  Arkansas requires at least 3.36 average HPRD each month to include licensed nurses; nurse aides; medication assistants; physicians; physician assistants; licensed physical or occupational therapists or licensed therapy assistants; registered respiratory therapists; licensed speech-language pathologists; infection preventionists; and other healthcare professionals licensed or certified in the State, plus requirements for minimum numbers of licensed nurses per residents per shift.[283] 

                        Research reporting on the outcomes of these State requirements is limited. A 2009 study that examined the impact of State staffing requirements in 16 States concluded that “[m]andated staffing standards affect only low-staff facilities facing potential for penalties, and effects are small. Selected facility-level outcomes may show improvement at all facilities due to a general response to increased standards or to other quality initiatives implemented at the same time as staffing standards.[284]                                                  However, Florida reported improved resident care outcomes and decreased deficiencies after increasing its nurse staffing levels. Specifically, Florida found “evidence that quality of care has substantially improved in Florida nursing homes since the introduction of increased nurse staffing levels and other quality standards since 2001. Average deficiencies per facility have decreased. Importantly, the citations for the more serious deficiencies have decreased dramatically and remain lower than the national average. Measures of resident care outcomes have improved in 2007 after the new staffing standards of 2.9 HPRD were instituted.” [285] 

                        An alternative or supplementary approach to mandating a specific number of direct care HPRD is to mandate the presence of an RN in a nursing home for more hours per day than is currently required, potentially 24 hours a day 7 days a week, subject to the statutory waiver. We note that a number of States already require this. Increased presence of RNs in nursing facilities would help address several issues. First, greater RN presence has been associated in research literature with higher quality of care and fewer deficiencies. Second, it has been reported in the literature that LPNs or LVNs may find themselves practicing outside of their scope of practice because, at least in part, there are not enough RNs providing direct patient care.[286]                                                  Increasing the number of hours per day that a LTC facility must have RNs in the nursing home would alleviate concerns about LPNs engaging in activities outside their scope of practice in the face of resident need during times when no RN is on site.                    

We recognize that RN presence alone would not address all these concerns. In addition to their clinical responsibilities, many RNs in LTC facilities appropriately carry out administrative duties as part or most of their routine work responsibilities. Further, that there are times of the day when nursing care demands may be less (such as during the night when most residents are sleeping); however, nursing care needs may occur at any time of the day and cannot be predicted or anticipated. Increases in resident acuity worsen this problem and safety should be maintained at all times.

                        With regard to whether there is an adequate supply of RNs, a December 2017 HRSA report on the future of the nursing workforce suggested that growth in RN supply would actually outpace demand in the period between 2012 and 2030.[287]                                                  The report noted that the national projections mask a distributional imbalance of RNs at the State level and that there is considerable variation in the geographic distribution of the growth in RN supply. Seven States were projected to have a shortage by 2030. Four States, California, Texas, New Jersey, and South Carolina, were projected to have the most significant deficiencies (>10,000 or more full-time employees), while South Dakota, Georgia, South Carolina, and Alaska were also projected to have shortages.[288] 

                        In looking at the employment of RNs in LTC facilities, the BLS reported in its May 2020 Occupational Employment and Wage Statistics [289]                                                  that 143,250 RNs were employed in nursing care facilities (SNFs); down from 151,300 in the May 2019 Occupational Employment Statistics 148,970.[290]                                                  At the same time, the number of LTC facilities has decreased somewhat from 15,844 based on FY 2012 to 15,691 in 2015, based on CASPER data. For CNAs, BLS reported in its May 2020 Occupational Employment and Wage Statistics [291]                                                  that 527,480 CNAs were employed in SNFs, down from 566,240 in the May 2019 Occupational Employment and Wage Statistics.[292] 

                        A 2022 analysis by Buerhaus et al. suggests that there is a tightening labor market for RNs, LPNs, and CNAs, marked by falling employment and rising wages through June 2021. Unemployment rates remained higher in                               ( print page 22793)                            nonhospital settings, including LTC facilities, and among RNs and CNAs who are members of racial and ethnic minority groups. The study notes that overall employment in LTC facilities has fallen more than in other nonhospital sectors.[293]                                                  In short, data indicate that there may be skilled direct care workers with experience in the LTC setting available.                    

                        There is concern that a facility can have sufficient numbers of staff, but if those staff do not have the skills and competencies to do the necessary work, quality will not improve. A 2011 review of the literature on nurse staffing and quality of care raises questions about the need to address issues beyond simply the numbers of nurses.[294]                                                  Specifically, the authors concluded that “[a] focus on numbers of nurses fails to address the influence of other staffing factors (for example, turnover and agency staff use), training and experience of staff, and care organization and management.” They note that the studies they reviewed presented 42 measures of quality and 52 ways of measuring staffing. They also note that it is “difficult to offer conclusions and recommendations about nurse staffing based on the existing research evidence.” An October 2011 research article by John R. Bowblis concluded that minimum direct care staffing requirements for LTC facilities “change staffing levels and skill mix, improve certain aspects of quality, but can lead to use of care practices associated with lower quality.” [295] 

                        The American Nurses Association (ANA), in its 2020 Principles for Nurse Staffing, describe appropriate nurse staffing as “a match of registered nurse expertise with the needs of the recipient of nursing care services in the context of the practice setting and situation.” [296]                                                  The ANA further notes that “staffing needs must be determined based on an analysis of healthcare consumer status (for example, degree of stability, intensity, and acuity), and the environment in which the care is provided. Other considerations to be included are: Professional characteristics, skill set, and mix of the staff and previous staffing patterns that have been shown to improve outcomes.” The International Council of Nurses (ICN) included similar considerations in its 2018 statement of principles of safe staffing levels.[297]                                                  The ICN policy statement notes that “Safe nurse staffing means that an appropriate number of nurses is available at all times across the continuum of care, with a suitable mix of education, skills and experience to ensure that patient care needs are met and that the working environment and conditions support staff to deliver quality care. This requires having an appropriate base staffing that includes a range of competencies which can be deployed to meet changing and fluctuating patient acuity in real time.” Nurses are not the only skilled workers who provide regular direct care to LTC facility residents. By a wide margin, the numbers of LPNs, home and personal care aides, CNAs, and other support staff working in SNFs far exceeded the numbers of registered nurses over the 5-year period 2014 to 2018.[298] 

#### 5. The Impact of the COVID-19 Pandemic on Staffing in LTC Facilities

                        While the adequacy of LTC staffing has been a topic of national interest for many years, the COVID-19 pandemic and associated Public Health Emergency (PHE) have had unprecedented impacts on staff and residents of LTC facilities, with evolving effects on staffing. A 2019 study by Geng et al.[299]                                                  assessed LTC facility staffing prior to the spread of COVID-19 using various data available from us. The study found that staffing levels for LPNs, CNAs especially RNs were stable during weekdays but dropped on weekends. On average, weekend RN staffing in terms of time spent per resident was 17 minutes (42 percent) less than weekday staffing, LPN staffing 9 minutes (17 percent) less, and nurse aide staffing 12 minutes (9 percent) less. Larger facilities, on average, had a larger decrease in staffing time per resident during weekends. Decreases were smaller among facilities with higher five-star overall ratings and with lower shares of Medicaid residents (who are more likely to be long-term residents without skilled care needs, thereby impacting nurse staffing needs to a lesser degree).                    

                        A 2020 study by McGarry et al.[300]                                                  examined access to personal protective equipment (PPE), staffing, and facility characteristics associated with shortages of PPE and staffing from May through the end of July 2020. Findings included the following:                    
    - One in five LTC facilities reported facing a severe shortage of PPE or staff shortage in early July 2020. Rates of both PPE shortages and staff did not meaningfully improve from May to July 2020.
    - PPE shortages were magnified in LTC facilities with COVID-19 cases among staff or residents and those with low quality scores.
    - Staff shortages were greater in LTC facilities with COVID-19 cases, particularly among those serving a high proportion of disadvantaged patients on Medicaid and those with lower quality scores, including pre-pandemic staffing score.
    - Most prominent staff shortages were for nurses and nursing aides as opposed to other providers or staff.

                        More recent research, using PBJ data, shows that LTC facility staffing (nurse staff HPRD) remained steady or increased slightly during the COVID-19 pandemic when adjusted for declining resident census.[301]                                                  Slight increases in staffing were concentrated in counties with high COVID-19 prevalence, low Medicaid census, and not-for profit facilities. Furthermore, an analysis of the incidence of COVID-19 among facilities with different staffing ratings found that facilities with 1 to 3 stars for nurse staffing had 18 to 22 percent more weeks with high COVID-19 incidence than 5-star staffed nursing homes.[302] 

                        The 2021 National Academy of Medicine Report, “The Future of Nursing 2020 to 2030: Charting a Path to Achieve Health Equity” specifically addressed nurse staffing in nursing homes since the onset of COVID-19.[303]                                                  As of 2020, there were 15,417 LTC facilities in the U.S.,[304]                                                  and in 2017, these facilities housed just over 1.3                               ( print page 22794)                            million residents.[305]                                                  As of the end of May 2020, there had been 95,515 cumulative confirmed cases of COVID-19 among LTC facility residents in the U.S. and 30.2 deaths per 1,000 residents. At that time, almost one-third (31,782) of the 103,700 people who had died from COVID-19 in the U.S. through the end of May were residents of LTC facilities.[306]                                                  As of mid-February 2022, approximately 150,000 deaths have occurred among U.S. LTC facility residents, and close to 2,300 staff have died.[307] 

                        A recent study of 4,254 LTC facilities across eight States found that those that were high-performing with respect to nurse staffing had fewer COVID-19 cases relative to their low-performing counterparts.[308]                                                  These findings suggest that poorly resourced LTC facilities with nurse staffing shortages may have been more susceptible to the spread of COVID-19. A 2020 study involving all 215 nursing homes in Connecticut revealed that a 20-minute increase in RN staffing HPRD was associated with 22 percent fewer confirmed cases of COVID-19 and 26 percent fewer COVID-19 deaths.[309] 

                        Evidence suggests that in addition to staffing quantity and composition, consistent staffing is an important consideration. A 2021 study by McGarry et al. examined the relationship between the number of unique staff members entering a facility daily, including direct care staff and staff members not involved resident care, direct care staff-to-resident ratios and skills mix, and the number of COVID-19 cases and deaths in the facility.[310]                                                  The study concluded that “[c]onventional staffing quality measures, including direct care staff-to-resident ratios and skills mix, were not significant predictors of COVID-19 cases or deaths.” The authors suggest that, moving forward, policy makers should encourage policies that not only maintain sufficient direct caregivers to provide safe and effective care for residents, but also promote the use of full-time and more consistent staff.                    

                        In considering resident health and safety issues associated with facility staffing, we must consider different levels of risk and benefit. We have reviewed the recommendations of the Institute of Medicine (IOM) in its 2004 report “Keeping Patients Safe: Transforming the Work Environment of Nurses.” [311]                                                  That report reiterates prior recommendations for a mandatory RN presence in LTC facilities and mandatory minimum staffing requirements, although it does not recommend a specific ratio. The report states, in part, that “[p]atient safety requires staff resources that are sufficient to prevent an inappropriately high rate of untoward events that could be avoided with adequate staffing levels. For such a standard to be reasonable, it must at least be based on the number of residents in the LTC facility and address NAs, who provide most of the care to LTC facility residents. Such minimum staffing standards are not a precise statement of how many staff are required to fully meet the needs of each specific group of residents on each unit, nor are they a quality improvement tool to optimize quality in each LTC facility. Rather, a minimum staffing level is one that avoids placing individual residents unnecessarily at risk because of insufficient numbers of staff to provide even the most basic care.” The report discusses our 2001 Report to Congress “Appropriateness of Minimum Nurse Staffing Ratios in Nursing Homes-Phase II Final Report” [312]                                                  and states: “With respect to the recommendation that DHHS specify staffing standards in regulations that would increase with the number of patients and be based on the findings and recommendations of the Phase II DHHS report to Congress on the appropriateness of minimum staffing ratios in nursing homes, the committee notes that the thresholds identified in that study above which no further benefit from staffing ratios could be identified are above the staffing levels of 75 to 90 percent of facilities, depending on the type of staff. However, a minimum standard set by DHHS need not approach the threshold level above which there is no further benefit. In fact, such a standard would go beyond the expectation for a minimum, which is intended to identify situations in which facilities unequivocally place residents at an unacceptable level of risk. The challenge is that there is no absolute minimum level of risk for untoward events that is considered acceptable.” The IOM report further states: “The study does not propose a specific minimum standard for RNs, licensed nurses, and NAs because agreement must first be reached about what is an unacceptable level of risk.”                    

                        A successor report [313]                                                  discussed that, ultimately, adequate staffing should involve direct care nurses in administrative decision making and consider both their levels of competence and unique organizational factors. The report asserts that nurse-staffing legislation is not a panacea for improving quality and safety.                    

Despite ongoing concern about LTC facility staffing, we have not yet directly addressed this issue in regulation. As discussed earlier in this section, while many studies indicate that consistent, adequate direct care facility staffing is vital to resident health and safety, we seek additional information to make fully informed policy proposals. We welcome your input on the topics addressed here, and others that you believe are relevant.

### B. Request for Information

Given the ongoing concerns related to adequate staffing discussed prior, we are considering options for future rulemaking and are seeking stakeholder input. Specifically, we are interested in the issues provided later on in this section, but also welcome input on other aspects of staffing in LTC facilities that we should consider as we evaluate future policy options.

1. Is there evidence (other than the evidence reviewed in this RFI) that establishes appropriate minimum threshold staffing requirements for both nurses and other direct care workers? To what extent do older studies remain relevant? What are the benefits of adequate staffing in LTC facilities to residents and quality of care?

2. What resident and facility factors should be considered in establishing a minimum staffing requirement for LTC facilities? How should the facility assessment of resident needs and acuity impact the minimum staffing requirement?

3. Is there evidence of the actual cost of implementing recommended thresholds, that accounts for current staffing levels as well as projected savings from reduced hospitalizations and other adverse events?

                        4. Is there evidence that resources that could be spent on staffing are instead                               ( print page 22795)                            being used on expenses that are not necessary to quality patient care?                    

5. What factors impact a facility's capability to successfully recruit and retain nursing staff? What strategies could facilities employ to increase nurse staffing levels, including successful strategies for recruiting and retaining staff? What risks are associated with these strategies, and how could nursing homes mitigate these risks?

6. What should CMS do if there are facilities that are unable to obtain adequate staffing despite good faith efforts to recruit workers? How would CMS define and assess what constitutes a good faith effort to recruit workers? How would CMS account for job quality, pay and benefits, and labor protections in assessing whether recruitment efforts were adequate and in good faith?

7. How should nursing staff turnover be considered in establishing a staffing standard? How should CMS consider the use of short-term (that is, travelling or agency) nurses?

8. What fields and professions should be considered to count towards a minimum staffing requirement? Should RNs, LPNs/LVAs, and CNAs be grouped together under a single nursing care expectation? How or when should they be separated out? Should mental health workers be counted as direct care staff?

9. How should administrative nursing time be considered in establishing a staffing standard? Should a standard account for a minimum time for administrative nursing, in addition to direct care? If so, should it be separated out?

10. What should a minimum staffing requirement look like, that is, how should it be measured? Should there be some combination of options? For example, options could include establishing minimum nurse HPRD, establishing minimum nurse to resident ratios, requiring that an RN be present in every facility either 24 hours a day or 16 hours a day, and requiring that an RN be on-call whenever an RN was not present in the facility. Should it include any non-nursing requirements? Is there data that supports a specific option?

11. How should any new quantitative direct care staffing requirement interact with existing qualitative staffing requirements? We currently require that facilities have “sufficient nursing staff” based on a facility assessment and patient needs, including but not limited to the number of residents, resident acuity, range of diagnoses, and the content of care plans. We welcome comments on how facilities have implemented this qualitative requirement, including both successes and challenges and if or how this standard should work concurrently with a minimum staffing requirement. We would also welcome comments on how State laws limiting or otherwise restricting overtime for health care workers would interact with minimum staffing requirements.

12. Have minimum staffing requirements been effective at the State level? What were facilities' experiences transitioning to these requirements? We note that States have implemented a variety of these options, discussed in section VIII.A. of this proposed rule, and would welcome comment on experiences with State minimum staffing requirements.

13. Are any of the existing State approaches particularly successful? Should CMS consider adopting one of the existing successful State approaches or specific parts of successful State approaches? Are there other approaches to consider in determining adequate direct care staffing? We invite information regarding research on these approaches which indicate an association of a particular approach or approaches and the quality of care and/or quality of life outcomes experienced by resident, as well as any efficiencies that might be realized through such approaches.

14. The IOM has recommended in several reports that we require the presence of at least one RN within every facility at all times. Should CMS concurrently require the presence of an RN 24 hours a day 7 days a week? We also invite comment on the costs and benefits of a mandatory 24-hour RN presence, including savings from improved resident outcomes, as well as any unintended consequences of implementing this requirement.

15. Are there unintended consequences we should consider in implementing a minimum staffing ratio? How could these be mitigated? For example, how would a minimum staffing ratio impact and/or account for the development of innovative care options, particularly in smaller, more home-like settings, for a subset of residents who might benefit from and be appropriate for such a setting? Are there concerns about shifting non-nursing tasks to nursing staff in order to offset additions to nursing staff by reducing other categories of staff?

16. Does geographic disparity in workforce numbers make a minimum staffing requirement challenging in rural and underserved areas? If yes, how can that be mitigated?

17. What constitutes “an unacceptable level of risk of harm?” What outcomes and care processes should be considered in determining the level of staffing needed?

We welcome public input from a broad range of commenters including, but not limited to nursing home residents and caretakers, nursing staff, nurse aides, physicians, nursing home administrators, owners and operators, and researchers. We are particularly interested in data, evidence, and experience on the issues identified above and any others that are relevant to defining and ensuring adequate staffing in LTC facilities.

## VIII. Collection of Information Requirements

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

                        With regard to the SNF QRP, in section VI.C.1. of this proposed rule, we propose that SNFs submit data on the Influenza Vaccination Coverage among HCP measure beginning with the FY 2025 SNF QRP. We note that the CDC has a PRA waiver for the collection and reporting of vaccination data under section 321 of the National Childhood Vaccine Injury Act (NCVIA) (Pub. L. 99-660, enacted November 14, 1986).[314]                                                  Since the burden is waived from the requirements of the PRA, we have set out such burden under the economic analysis section (see section X.A.5.) of this proposed rule. While the waiver is specific to the PRA's requirements (“Chapter 35 of Title 44, United States Code”), our economic analysis requirements are not waived by any such statutes. We refer readers to section X.A.5. of this proposed rule, where we have provided an estimate of the burden to SNFs.                    

                        In section VI.C.2. of this proposed rule, we propose to revise the compliance date for certain SNF QRP reporting requirements including the Transfer of Health information measures and certain standardized patient assessment data elements (including race, ethnicity, preferred language, need for interpreter, health literacy, and social isolation). The proposed change in compliance date would have no                               ( print page 22796)                            impact on any requirements or burden estimates; both proposals are active and accounted for under OMB control number 0938-1140 (CMS-10387). Consequently, we are not proposing any changes under that control number.                    

In section VI.C.3. of this proposed rule, we discuss our proposed revisions to the regulatory text. The proposed revisions have no collection of information implications.

With regard to the SNF VBP Program, in section VII.B.1.b. of this proposed rule, we propose to suppress the SNFRM for scoring and payment purposes for the FY 2023 SNF VBP program year. This measure is calculated using Medicare FFS claims data, and our proposal to suppress data on this measure for the FY 2023 program year would not create any new reporting burden for SNFs. We note that, if our proposals described in section VII.B.1.b. of this proposed rule are finalized, we would publicly report the SNFRM rates for the FY 2023 program year, and we would make clear in the public presentation of those data that we are suppressing the use of those data for purposes of scoring and payment adjustments in the FY 2023 SNF VBP Program given the significant changes in SNF patient case volume and facility-level case mix described in that section of this proposed rule. In addition, as we describe in sections VII.B.3.b. and VII.B.3.c. of this proposed rule, we are proposing to adopt two additional measures (the SNF Healthcare-Associated Infections (HAI) Requiring Hospitalization and the Total Nursing Hours per Resident Day/Payroll-Based Journal (PBJ) measures) beginning with the FY 2026 Program. The SNF HAI measure would be calculated using Medicare FFS claims data, therefore, our proposal to add the measure to the SNF VBP measure set would not create any new reporting burden for SNFs. The PBJ measure would be calculated using data that SNFs currently report to CMS under the Nursing Home Five-Star Quality Rating System, and therefore, our proposal to add the measure to the SNF VBP measure set would not create new reporting burden for SNFs.

In section VII.B.3.d. of this proposed rule, we are proposing to adopt the DTC PAC Measure for SNFs beginning with the FY 2027 Program. The DTC PAC SNF measure would be calculated using Medicare FFS claims data; therefore, our proposal to add the measure to the SNF VBP measure set would not create a new reporting burden for SNFs.

The aforementioned FFS-related claims submission requirements and burden are active and approved by OMB under control number 0938-1140 (CMS-10387). This rule's proposed changes would have no impact on the requirements and burden that are currently approved under that control number.

## IX. Response to Comments

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

## X. Economic Analyses

### A. Regulatory Impact Analysis

#### 1. Statement of Need

#### a. Statutory Provisions

                        This proposed rule updates the FY 2023 SNF prospective payment rates as required under section 1888(e)(4)(E) of the Act. It also responds to section 1888(e)(4)(H) of the Act, which requires the Secretary to provide for publication in the                         **Federal Register**                         before the August 1 that precedes the start of each FY, the unadjusted Federal per diem rates, the case-mix classification system, and the factors to be applied in making the area wage adjustment. These are statutory provisions that prescribe a detailed methodology for calculating and disseminating payment rates under the SNF PPS, and we do not have the discretion to adopt an alternative approach on these issues.                    

With respect to the SNF QRP, the proposed rule updates the FY 2025 SNF QRP requirements. Section 1888(e)(6) of the Act authorizes the SNF QRP and applies to freestanding SNFs, SNFs affiliated with acute care facilities, and all non-critical access hospital (CAH) swing-bed rural hospitals. We propose one new measure which we believe will encourage healthcare personnel to receive the influenza vaccine, resulting in fewer cases, less hospitalizations, and lower mortality associated with the virus. We propose to revise the compliance date for certain SNF QRP reporting requirements to improve data collection to allow for better measurement and reporting on equity across post-acute care programs and policies. For consistency in our regulations, we are also proposing conforming revisions to the Requirements under the SNF QRP at § 413.360.

With respect to the SNF VBP Program, the proposed rule updates SNF VBP Program requirements for FY 2023 and subsequent years. Section 1888(h)(3) of the Act requires the Secretary to establish and announce performance standards for SNF VBP Program measures no later than 60 days before the performance period, and this rule proposes numerical values of the performance standards for the all-cause, all-condition hospital readmission measure required by section 1888(g)(1) of the Act.

#### b. Discretionary Provisions

In addition, this proposed rule proposes the following discretionary provisions:

#### (1) Recalibrating the Patient Driven Payment Model (PDPM) Parity Adjustment

As a policy decision to ensure on-going budget neutral implementation of the new case mix system, the PDPM, we recommend proposing a recalibration of the PDPM parity adjustment. Since October 1, 2019, we have been monitoring the implementation of PDPM and our analysis of FY 2020 and FY 2021 data reveals that the PDPM implementation led to an increase in Medicare Part A SNF spending, even after accounting for the effects of the COVID-19 PHE. We believe that proposing recalibration and reducing SNF spending by 4.6 percent, or $1.7 billion, in FY 2023 with no delayed implementation or phase-in period would allow for the most rapid establishment of payments at the appropriate level. This would work to ensure that PDPM will be budget-neutral as intended and prevent continuing accumulation of excess SNF payments, which we cannot recoup.

#### (2) SNF Forecast Error Adjustment

                        Each year, we evaluate the market basket forecast error for the most recent year for which historical data is available. The forecast error is determined by comparing the projected market basket increase in a given year with the actual market basket increase in that year. In evaluating the data for FY 2021, we found that the forecast error for that year was 1.5 percentage point, exceeding the 0.5 percentage point threshold we established in regulation for proposing adjustments to correct for forecast error. Given that the forecast error exceeds the 0.5 percentage threshold, current regulations require that the SNF market basket for FY 2022 be increased by 1.5 percentage point.                              ( print page 22797)    

#### (3) Proposed Permanent Cap on Wage Index Decreases

The Secretary has broad authority to establish appropriate payment adjustments under the SNF PPS, including the wage index adjustment. As discussed earlier in this section, the SNF PPS regulations require us to use an appropriate wage index based on the best available data. For the reasons discussed earlier in this section, we believe that a 5-percent cap on wage index decreases would be appropriate for the SNF PPS. Therefore, for FY 2023 and subsequent years, we 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, regardless of the circumstances causing the decline.

#### (4) Technical Updates to ICD-10 Mappings

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

#### 2. Introduction

We have examined the impacts of this proposed rule as required by Executive Order 12866 on Regulatory Planning and Review (September 30, 1993), Executive Order 13563 on Improving Regulation and Regulatory Review (January 18, 2011), the Regulatory Flexibility Act (RFA, September 19, 1980, Pub. L. 96-354), section 1102(b) of the Act, section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA, March 22, 1995; Pub. L. 104-4), Executive Order 13132 on Federalism (August 4, 1999), and the Congressional Review Act (5 U.S.C. 804(2)).

Executive Orders 12866 and 13563 direct agencies to assess all costs and benefits of available regulatory alternatives and, if regulation is necessary, to select regulatory approaches that maximize net benefits (including potential economic, environmental, public health and safety effects, distributive impacts, and equity). Executive Order 13563 emphasizes the importance of quantifying both costs and benefits, of reducing costs, of harmonizing rules, and of promoting flexibility. Based on our estimates, OMB's Office of Information and Regulatory Affairs has determined this rulemaking is “economically significant” as measured by the $100 million threshold. Accordingly, we have prepared a regulatory impact analysis (RIA) as further discussed below. Also, the rule has been reviewed by OMB.

#### 3. Overall Impacts

This rule updates the SNF PPS rates contained in the SNF PPS final rule for FY 2022 (86 FR 42424). We estimate that the aggregate impact would be a decrease of approximately $320 million (0.9 percent) in Part A payments to SNFs in FY 2023. This reflects a $1.4 billion (3.9 percent) increase from the proposed update to the payment rates and a $1.7 billion (4.6 percent) decrease from the proposed reduction to the SNF payment rates to account for the recalibrated parity adjustment. We note that these impact numbers do not incorporate the SNF VBP Program reductions that we estimate would total $185.55 million in FY 2023. We would note that events may occur to limit the scope or accuracy of our impact analysis, as this analysis is future-oriented, and thus, very susceptible to forecasting errors due to events that may occur within the assessed impact time period.

In accordance with sections 1888(e)(4)(E) and (e)(5) of the Act and implementing regulations at § 413.337(d), we are proposing to update the FY 2022 payment rates by a factor equal to the market basket index percentage change increased by the forecast error adjustment and reduced by the productivity adjustment to determine the payment rates for FY 2023. The impact to Medicare is included in the total column of Table 19. When proposing the SNF PPS rates for FY 2023, we proposed a number of standard annual revisions and clarifications mentioned elsewhere in this proposed rule.

The annual update in this rule applies to SNF PPS payments in FY 2023. Accordingly, the analysis of the impact of the annual update that follows only describes the impact of this single year. Furthermore, in accordance with the requirements of the Act, we will publish a rule or notice for each subsequent FY that will provide for an update to the payment rates and include an associated impact analysis.

#### 4. Detailed Economic Analysis

The FY 2023 SNF PPS payment impacts appear in Table 19. Using the most recently available data, in this case FY 2021 we apply the current FY 2022 CMIs, wage index and labor-related share value to the number of payment days to simulate FY 2022 payments. Then, using the same FY 2021 data, we apply the FY 2023 CMIs, wage index and labor-related share value to simulate FY 2023 payments. We would note that, given that this same data is being used for both parts of this calculation, as compared to other analyses discussed in this proposed rule which compare data from FY 2020 to data from other fiscal years, any issues discussed throughout this proposed rule with regard to data collected in FY 2020 will not cause any difference in this economic analysis. We tabulate the resulting payments according to the classifications in Table 19 (for example, facility type, geographic region, facility ownership), and compare the simulated FY 2022 payments to the simulated FY 2023 payments to determine the overall impact. The breakdown of the various categories of data in Table 19 is as follows:
    - The first column shows the breakdown of all SNFs by urban or rural status, hospital-based or freestanding status, census region, and ownership.
    - The first row of figures describes the estimated effects of the various proposed changes on all facilities. The next six rows show the effects on facilities split by hospital-based, freestanding, urban, and rural categories. The next nineteen rows show the effects on facilities by urban versus rural status by census region. The last three rows show the effects on facilities by ownership (that is, government, profit, and non-profit status).
    - The second column shows the number of facilities in the impact database.
    - The third column shows the effect of the proposed parity adjustment recalibration discussed in section V.C. of this proposed rule.
    - The fourth column shows the effect of the proposed annual update to the wage index. This represents the effect of using the most recent wage data available as well as accounts for the proposed 5 percent cap on wage index transitions, discussed in section V.A of this proposed rule. The total impact of this change is 0.0 percent; however, there are distributional effects of the proposed change.
    - The fifth column shows the effect of all of the changes on the FY 2023 payments. The update of 3.9 percent is       ( print page 22798)                              constant for all providers and, though not shown individually, is included in the total column. It is projected that aggregate payments would increase by 3.9 percent, assuming facilities do not change their care delivery and billing practices in response. The figures in this column are calculated by multiplying the percentage change. For example, the Total Change figure for the Total Group Category is −0.9%, which is (1−4.6%) * (1 + 0.0%) * (1 + 3.9%).                    

As illustrated in Table 19, the combined effects of all of the changes vary by specific types of providers and by location. For example, due to changes in this proposed rule, rural providers would experience a 1.0 percent decrease in FY 2023 total payments.

#### 5. Impacts for the Skilled Nursing Facility Quality Reporting Program (SNF QRP) for FY 2023

Estimated impacts for the SNF QRP are based on analysis discussed in section IX.B. of this proposed rule.

In accordance with section 1888(e)(6)(A)(i) of the Act, the Secretary must reduce by 2 percentage points the annual payment update applicable to a SNF for a fiscal year if the SNF does not comply with the requirements of the SNF QRP for that fiscal year. In section VI.A. of this proposed rule, we discuss the method for applying the 2-percentage point reduction to SNFs that fail to meet the SNF QRP requirements.

                        As discussed in section VI.C.1. of this proposed rule, we are proposing the adoption of one new measure to the SNF QRP beginning with the FY 2025 SNF QRP, the Influenza Vaccination Coverage among HCP (NQF #0431) measure. We believe that the burden associated with the SNF QRP is the time and effort associated with complying with the non-claims-based measures requirements of the SNF QRP. Although the burden associated with the Influenza Vaccination Coverage among HCP (NQF #0431) measure is not accounted for under the Centers for Diseases Control and Prevention Paperwork Reduction Act (CDC PRA) package due to the NCVIA waiver discussed in section IX. of this proposed                               ( print page 22799)                            rule, the cost and burden is discussed here.                    

                        Consistent with the CDC's experience of collecting data using the NHSN, we estimate that it would take each SNF an average of 15 minutes per month to collect data for the Influenza Vaccination Coverage among HCP (NQF #0431) measure and enter it into NHSN. We do not estimate that it will take SNFs additional time to input their data into NHSN, once they have logged onto the system for the purpose of submitting their monthly COVID-19 vaccine report. We believe it would take an administrative assistant 15 minutes to enter this data into NHSN. For the purposes of calculating the costs associated with the collection of information requirements, we obtained mean hourly wages from the U.S. Bureau of Labor Statistics' May 2020 National Occupational Employment and Wage Estimates.[315]                                                  To account for overhead and fringe benefits, we have doubled the hourly wage. These amounts are detailed in Table 20.                    

Based on this time range, it would cost each SNF an average cost of $9.38 each year. We believe the data submission for the Influenza Vaccination Coverage among HCP (NQF #0431) measure would cause SNFs to incur additional average burden of 15 minutes per year for each SNF and a total annual burden of 3,868 hours across all SNFs. The estimated annual cost across all 15,472 SNFs in the U.S. for the submission of the Influenza Vaccination Coverage among HCP (NQF #0431) measure would be an average of $145,127.36.

As discussed in section VII.C.2. of this proposed rule, we are proposing that SNFs would begin collecting data on two quality measures and certain standardized patient assessment data elements beginning with discharges on October 1, 2023. CMS estimated the impacts for collecting the new data elements in the FY 2020 SNF PPS final rule (84 FR 38829). When we delayed the compliance date for certain reporting requirements under the SNF QRP in the May 8th COVID-19 IFC, we did not remove the impacts for the new reporting requirements. However, we are providing updated impact information.

                        For these two quality measures, we are adding 4 data elements on discharge which would require an additional 1.2 minutes of nursing staff time per discharge. We estimate these data elements for these quality measures would be completed by registered nurses (25 percent of the time or 0.30 minutes) and by licensed practical and vocational nurses (75 percent of the time or 0.90 minutes). For the purposes of calculating the costs associated with the collection of information requirements, we obtained mean hourly wages from the U.S. Bureau of Labor Statistics' May 2020 National Occupational Employment and Wage Estimates.[316]                                                  To account for overhead and fringe benefits, we have doubled the hourly wage. These amounts are detailed in Table 21.                    

With 2,406,401 discharges from 15,472 SNFs annually, we estimate an annual burden of 48,128 additional hours (2,406,401 discharges × 1.2 min/60) at a cost of $2,664,127 (2,406,401 × [(0.30/60 × $76.94/hr) + (0.90/60 × $48.16/hr)]). For each SNF we estimate an annual burden of 3.11 hours (48,128 hr/15,472 SNFs) at a cost of $172.19 ($2,664,127/15,472 SNFs).

                        We are also proposing SNFs would begin collecting data on certain standardized patient assessment data elements, beginning with admissions and discharges (except for the preferred language, need for interpreter services, hearing, vision, race, and ethnicity standardized patient assessment data elements, which would be collected at admission only) on October 1, 2023. If finalized as proposed, SNFs would use the MDS 3.0 V1.18.11 to submit SNF QRP data. We are finalizing requirements to collect 55.5 standardized patient assessment data elements consisting of 8 data elements on admission and 47.5 data elements on discharge beginning with the FY 2025 SNF QRP. We estimate that the data elements would take an additional 12.675 minutes of nursing staff time consisting of 1.725 minutes to report on each admission and 10.95 minutes to report on each discharge. We assume the added data elements would be performed by both registered nurses (25 percent of the time or 3.169 minutes) and licensed practical and vocational (75 percent of the time or 9.506                               ( print page 22800)                            minutes). We estimate the reporting of these assessment items will impose an annual burden of 508,352 total hours (2,406,401 discharges × 12.675 min/60) at a cost of $28,139,825 ((508,352 hr × 0.25 × $76.94/hr) + (508,352 hr × 0.75 × $48.16/hr)). For each SNF the annual burden is 32.86 hours (508,352 hr/15,472 SNFs) at a cost of $1,818.76 ($28,139,825/15,472 SNFs). The overall annual cost of the finalized changes associated with the newly added 59.5 assessment items is estimated at $1,990.95 per SNF annually ($172.19 + $1,818.76), or $30,803,952 ($2,664,127 + $28,139,825) for all 15,472 SNFs annually.                    

We propose in section VI.C.3. of this proposed rule to make certain revisions in the regulation text itself at § 413.360 to include new paragraph (f) to reflect all the data completion thresholds required for SNFs to meet the compliance threshold for the annual payment update, as well as certain conforming revisions. As discussed in section IX. of this proposed rule, this proposal would not affect the information collection burden for the SNF QRP.

We welcome comments on the estimated time to collect influenza vaccination data and enter it into NHSN.

#### 6. Impacts for the SNF VBP Program

The estimated impacts of the FY 2023 SNF VBP Program are based on historical data and appear in Table 22. We modeled SNF performance in the Program using SNFRM data from FY 2019 as the baseline period and FY 2021 as the performance period. Additionally, we modeled a logistic exchange function with a payback percentage of 60 percent, as we finalized in the FY 2018 SNF PPS final rule (82 FR 36619 through 36621).

However, in section VII.B.1 of this proposed rule, we are proposing to suppress the SNFRM for the FY 2023 program year. If finalized, we will award each participating SNF 60 percent of their 2 percent withhold. Additionally, we are proposing to apply a case minimum requirement for the SNFRM in section VII.E.3.b. of this proposed rule. In section VII.E.5. of this proposed rule, we are proposing to remove the Low-Volume Adjustment policy beginning with the FY 2023 Program year. As a result of these provisions, SNFs that do not meet the case minimum specified for the FY 2023 program year would be excluded from the program and would receive their full Federal per diem rate for that fiscal year. If finalized, this policy would maintain the overall payback percentage at 60 percent.

Based on the 60 percent payback percentage, we estimated that we will redistribute approximately $278.32 million (of the estimated $463.87 million in withheld funds) in value-based incentive payments to SNFs in FY 2023, which means that the SNF VBP Program is estimated to result in approximately $185.55 million in savings to the Medicare Program in FY 2023.

Our detailed analysis of the impacts of the FY 2023 SNF VBP Program is shown in Table 22.
      ( print page 22801)    
                        In section VII.B.2. of this proposed rule, we are also proposing to adopt two additional measures (the SNF HAI and Total Nurse Staffing measures) beginning with the FY 2026 program year. Additionally, we are proposing to apply a case minimum requirement for the SNF HAI and Total Nurse Staffing measures in section VII.E.3.c. of this proposed rule. In section VII.E.3.d. of this proposed rule, we are proposing to adopt a measure minimum policy for the FY 2026 program year. Therefore, we are providing estimated impacts of the FY 2026 SNF VBP Program, which are based on historical data and appear in Table 23. We modeled SNF performance in the Program using measure data from FY 2018 as the baseline period and FY 2019 as the performance period for the SNFRM, SNF HAI, and Total Nurse Staffing measures. Additionally, we modeled a logistic exchange function with a payback percentage of 60 percent, as we finalized in the FY 2018 SNF PPS final rule (82 FR 36619 through 36621), though we note that the logistic exchange function and payback percentage policies could be reconsidered in a future rulemaking. Based on the 60 percent payback percentage, we estimate that we will redistribute approximately $296.44 million (of the estimated $494.07 million in withheld funds) in value-based incentive payments to SNFs in FY 2026, which means that the SNF VBP Program is estimated to result in                               ( print page 22802)                            approximately $197.63 million in savings to the Medicare Program in FY 2026.                    

Our detailed analysis of the impacts of the FY 2026 SNF VBP Program is shown in Table 23.

                        In section VII.B.2. of this proposed rule, we are also proposing to adopt one additional measure (the DTC PAC SNF measure) beginning with the FY 2027 program year. Additionally, we are proposing to apply a case minimum requirement for the DTC PAC SNF measure in section VII.E.3.c. of this proposed rule. In section VII.E.3.d, of this proposed rule, we are proposing to adopt a measure minimum policy for the FY 2027 program year. Therefore, we are providing estimated impacts of the FY 2027 SNF VBP Program, which are based on historical data and appear                               ( print page 22803)                            in Table 24. We modeled SNF performance in the Program using measure data from FY 2018 (the SNFRM, SNF HAI, and Total Nurse Staffing measures) and FY 2017-FY 2018 (the DTC PAC SNF measure) as the baseline period and FY 2019 (the SNFRM, SNF HAI, and Total Nurse Staffing measures) and FY 2019-FY 2020 (the DTC PAC SNF measure) as the performance period. Additionally, we modeled a logistic exchange function with a payback percentage of 60 percent, as we finalized in the FY 2018 SNF PPS final rule (82 FR 36619 through 36621), though we note that the logistic exchange function and payback percentage policies could be reconsidered in a future rule. Based on the 60 percent payback percentage, we estimate that we will redistribute approximately $294.67 million (of the estimated $491.12 million in withheld funds) in value-based incentive payments to SNFs in FY 2027, which means that the SNF VBP Program is estimated to result in approximately $196.45 million in savings to the Medicare Program in FY 2027.                    

Our detailed analysis of the impacts of the FY 2027 SNF VBP Program is shown in Table 24.
      ( print page 22804)    

#### 7. Alternatives Considered

As described in this section, we estimate that the provisions in this proposed rule would result in an estimated net decrease in SNF payments of $320 million for FY 2023. This reflects a $1.4 billion increase from the proposed update to the payment rates of 3.9 percent and a $1.7 billion decrease from the proposed reduction to the SNF payment rates to account for the recalibrated parity adjustment.

                        Section 1888(e) of the Act establishes the SNF PPS for the payment of Medicare SNF services for cost reporting periods beginning on or after July 1, 1998. This section of the statute prescribes a detailed formula for calculating base payment rates under the SNF PPS, and does not provide for the use of any alternative methodology. It specifies that the base year cost data                               ( print page 22805)                            to be used for computing the SNF PPS payment rates must be from FY 1995 (October 1, 1994, through September 30, 1995). In accordance with the statute, we also incorporated a number of elements into the SNF PPS (for example, case-mix classification methodology, a market basket index, a wage index, and the urban and rural distinction used in the development or adjustment of the Federal rates). Further, section 1888(e)(4)(H) of the Act specifically requires us to disseminate the payment rates for each new FY through the                         **Federal Register**                        , and to do so before the August 1 that precedes the start of the new FY; accordingly, we are not pursuing alternatives for this process.                    

With regard to the alternatives considered related to the methodology for calculating the proposed parity adjustment to the rates, we considered numerous alternative approaches to the methodology, including alternative data sets, applying the parity adjustment to targeted components of the payment system, and delaying or phasing-in the parity adjustment. These alternatives are described in full detail in section V.C. of this proposed rule.

With regard to the proposal to add the HCP Influenza Vaccine measure to the SNF QRP Program, the COVID-19 pandemic has exposed the importance of implementing infection prevention strategies, including the promotion of HCP influenza vaccination. We believe this measure will encourage healthcare personnel to receive the influenza vaccine, resulting in fewer cases, less hospitalizations, and lower mortality associated with the virus, but were unable to identify any alternative methods for collecting the data. A compelling public need exists to target quality improvement among SNF providers and this proposed measure has the potential to generate actionable data on HCP vaccination rates.

With regard to the proposal to revise the compliance date for the MDS v1.18.11, section 1888(d)(6)(B)(i)(III) of the Act requires that, for fiscal years 2019 and each subsequent year, SNFs must report standardized patient assessment data required under section 1899B(b)(1) of the Act. Section 1899(a)(1)(C) of the Act requires, in part, the Secretary to modify the PAC assessment instruments in order for PAC providers, including SNFs, to submit standardized patient assessment data under the Medicare program. Further delay of collecting this data would delay compliance with the current regulations.

As discussed previously the burden for these proposals is minimal, and we believe the importance of the information necessitates these provisions.

With regard to the proposals for the SNF VBP Program, we discuss alternatives considered within those sections. In section VII.B.2. of this proposed rule, we considered 4 options to adjust for COVID-19 in a technical update to the SNFRM. None of the alternatives would change the analysis of the impacts of the FY 2023 SNF VBP Program described in section X.A.6. of this proposed rule. In section VII.C.2. of this proposed rule, we propose to revise the baseline period for the FY 2025 SNF VBP Program to FY 2019. We considered using alternative baseline periods, including FY 2020 and FY 2022, but these options are operationally infeasible. We will provide estimated impacts of the FY 2025 SNF VBP Program in future rulemaking. In section

In section VII.E.3.c. of this proposed rule, we are proposing that SNFs must have a minimum of 25 residents, on average, across all available quarters during the applicable 1-year performance period in order to be eligible to receive a score on the Total Nurse Staffing measure. We tested three alternative case minimums for this measure: A 25-resident minimum, a minimum of one quarter of PBJ data, and a minimum of two quarters of PBJ data. After considering these alternatives, we determined that the proposed 25-resident minimum best balances quality measure reliability with our desire to score as many SNFs as possible on this measure.

In section VII.E.3.d. of this proposed rule, we proposed measure minimums for the FY 2026 and FY 2027 SNF VBP programs. SNFs that do not meet these minimum requirements would be excluded from the program and would receive their full Federal per diem rate for that fiscal year. We also discussed alternatives, which are detailed below, that would result in more SNFs being excluded from the program.

We are proposing that for FY 2026, SNFs must have the minimum number of cases for two of these three measures during the performance period to receive a performance score and value-based incentive payment. Under these proposed minimum requirements for the FY 2026 program year, we estimate that approximately 14 percent of SNFs would be excluded from the FY 2026 Program. Specifically, if we required SNFs to have the minimum number of cases for all three measures during the performance period, approximately 21 percent of SNFs would be excluded from the FY 2026 Program. We also assessed the consistency of incentive payment multipliers (IPMs) between time periods as a proxy for performance score reliability under the different measure minimum options. The testing results indicated that the reliability of the SNF performance score would be relatively consistent across the different measure minimum requirements. Specifically, for the FY 2026 program year, we estimate that under the proposed minimum of two measures, 82 percent of SNFs receiving a net-negative IPM in the first testing period also received a net-negative IPM in the second testing period. Alternatively, under a minimum of three measures for the FY 2026 program year, we found that the consistency was 81 percent. Based on these testing results, we believe the proposed minimum of two out of three measures for FY 2026 best balances SNF performance score reliability with our desire to ensure that as many SNFs as possible can receive a performance score and value-based incentive payment.

                        We are proposing that for FY 2027, SNFs must have the minimum number of cases for three of the four measures during a performance period to receive a performance score and value-based incentive payment. Under these proposed minimum requirements, we estimate that approximately 16 percent of SNFs would be excluded from the FY 2027 Program. Alternatively, if we required SNFs to report the minimum number of cases for all four measures, we estimate that approximately 24 percent of SNFs would be excluded from the FY 2027 Program. We also assessed the consistency of incentive payment multipliers (IPMs) between time periods as a proxy for performance score reliability under the different measure minimum options. The testing results indicated that the reliability of the SNF performance score for the FY 2027 program year would be relatively consistent across the different measure minimum requirements. That is, among the different measure minimums for the FY 2027 program year, a strong majority (between 85 and 87 percent) of the SNFs receiving a net-negative IPM for the first testing period also received a net-negative IPM for the second testing period. These findings indicate that increasing the measure minimum requirements does not meaningfully increase the consistency of the performance score. Based on these testing results, we believe the propose minimum of three out of four measures for FY 2027 best balances SNF performance score reliability with our desire to ensure that as many SNFs as possible can receive a performance                               ( print page 22806)                            score and value-based incentive payment.                    

#### 8. Accounting Statement

                        As required by OMB Circular A-4 (available online at                         *https://obamawhitehouse.archives.gov/​omb/​circulars_​a004_​a-4/​*), in Tables 25 through 27, we have prepared an accounting statement showing the classification of the expenditures associated with the provisions of this proposed rule for FY 2023. Tables 19 and 25 provide our best estimate of the possible changes in Medicare payments under the SNF PPS as a result of the policies in this proposed rule, based on the data for 15,472 SNFs in our database. Table 26 provides our best estimate of the possible changes in Medicare payments under the SNF VBP as a result of the policies we have proposed for this program. Tables 20 and 27 provide our best estimate of the additional cost to SNFs to submit the data for the SNF QRP as a result of the policies in this proposed rule.                    

#### 9. Conclusion

This rule updates the SNF PPS rates contained in the SNF PPS final rule for FY 2022 (86 FR 42424). Based on the above, we estimate that the overall payments for SNFs under the SNF PPS in FY 2023 are projected to decrease by approximately $320 million, or 0.9 percent, compared with those in FY 2022. We estimate that in FY 2023, SNFs in urban and rural areas would experience, on average, a 0.9 percent decrease and 1.0 percent decrease, respectively, in estimated payments compared with FY 2022. Providers in the rural Pacific region would experience the largest estimated decrease in payments of approximately 2.3 percent. Providers in the urban Pacific region would experience the smallest estimated decrease in payments of 0.1 percent.

### B. Regulatory Flexibility Act Analysis

                        The RFA requires agencies to analyze options for regulatory relief of small entities, if a rule has a significant impact on a substantial number of small entities. For purposes of the RFA, small entities include small businesses, non-profit organizations, and small governmental jurisdictions. Most SNFs and most other providers and suppliers are small entities, either by reason of their non-profit status or by having revenues of $30 million or less in any 1 year. We utilized the revenues of individual SNF providers (from recent Medicare Cost Reports) to classify a small business, and not the revenue of a larger firm with which they may be affiliated. As a result, for the purposes of the RFA, we estimate that almost all SNFs are small entities as that term is used in the RFA, according to the Small Business Administration's latest size standards (NAICS 623110), with total revenues of $30 million or less in any 1 year. (For details, see the Small Business Administration's website at                         *https://www.sba.gov/​category/​navigation-structure/​contracting/​contracting-officials/​eligibility-size-standards*) In addition, approximately 20 percent of SNFs classified as small entities are non-profit organizations. Finally, individuals and states are not                               ( print page 22807)                            included in the definition of a small entity.                    

This rule would update the SNF PPS rates contained in the SNF PPS final rule for FY 2022 (86 FR 42424). Based on the above, we estimate that the aggregate impact for FY 2023 would be a decrease of $320 million in payments to SNFs, resulting from the SNF market basket update to the payment rates, reduced by the proposed parity adjustment discussed in section IV.D. While it is projected in Table 19 that all providers would experience a net decrease in payments, we note that some individual providers within the same region or group may experience different impacts on payments than others due to the distributional impact of the FY 2023 wage indexes and the degree of Medicare utilization.

                        Guidance issued by the Department of Health and Human Services on the proper assessment of the impact on small entities in rulemakings, utilizes a cost or revenue impact of 3 to 5 percent as a significance threshold under the RFA. In their March 2022 Report to Congress (available at                         *https://www.medpac.gov/​wp-content/​uploads/​2022/​03/​Mar22_​MedPAC_​ReportToCongress_​Ch7_​SEC.pdf*), MedPAC states that Medicare covers approximately 10 percent of total patient days in freestanding facilities and 17 percent of facility revenue (March 2022 MedPAC Report to Congress, 238). As indicated in Table 19, the effect on facilities is projected to be an aggregate negative impact of 0.9 percent for FY 2023. As the overall impact on the industry as a whole, and thus on small entities specifically, is less than the 3 to 5 percent threshold discussed previously, the Secretary has determined that this proposed rule will not have a significant impact on a substantial number of small entities for FY 2023.                    

In addition, section 1102(b) of the Act requires us to prepare a regulatory impact analysis if a rule may have a significant impact on the operations of a substantial number of small rural hospitals. This analysis must conform to the provisions of section 603 of the RFA. For purposes of section 1102(b) of the Act, we define a small rural hospital as a hospital that is located outside of an MSA and has fewer than 100 beds. This proposed rule will affect small rural hospitals that: (1) Furnish SNF services under a swing-bed agreement or (2) have a hospital-based SNF. We anticipate that the impact on small rural hospitals would be similar to the impact on SNF providers overall. Moreover, as noted in previous SNF PPS final rules (most recently, the one for FY 2022 (86 FR 42424)), the category of small rural hospitals is included within the analysis of the impact of this proposed rule on small entities in general. As indicated in Table 19, the effect on facilities for FY 2023 is projected to be an aggregate negative impact of 0.9 percent. As the overall impact on the industry as a whole is less than the 3 to 5 percent threshold discussed above, the Secretary has determined that this proposed rule will not have a significant impact on a substantial number of small rural hospitals for FY 2023.

### C. Unfunded Mandates Reform Act Analysis

Section 202 of the Unfunded Mandates Reform Act of 1995 also requires that agencies assess anticipated costs and benefits before issuing any rule whose mandates require spending in any 1 year of $100 million in 1995 dollars, updated annually for inflation. In 2022, that threshold is approximately $165 million. This proposed rule will impose no mandates on State, local, or tribal governments or on the private sector.

### D. Federalism Analysis

Executive Order 13132 establishes certain requirements that an agency must meet when it issues a proposed rule (and subsequent final rule) that imposes substantial direct requirement costs on State and local governments, preempts State law, or otherwise has federalism implications. This proposed rule will have no substantial direct effect on State and local governments, preempt State law, or otherwise have federalism implications.

### E. Regulatory Review Costs

If regulations impose administrative costs on private entities, such as the time needed to read and interpret this proposed rule, we should estimate the cost associated with regulatory review. Due to the uncertainty involved with accurately quantifying the number of entities that will review the rule, we assume that the total number of unique commenters on last year's proposed rule will be the number of reviewers of this year's proposed rule. We acknowledge that this assumption may understate or overstate the costs of reviewing this rule. It is possible that not all commenters reviewed this year's proposed rule in detail, and it is also possible that some reviewers chose not to comment on that proposed rule. For these reasons, we believe that the number of commenters on last year's proposed rule is a fair estimate of the number of reviewers of this year's 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.

                        Using the national mean hourly wage data from the May 2020 BLS Occupational Employment Statistics (OES) for medical and health service managers (SOC 11-9111), we estimate that the cost of reviewing this rule is $114.24 per hour, including overhead and fringe benefits                         *https://www.bls.gov/​oes/​current/​oes_​nat.htm.*                         Assuming an average reading speed, we estimate that it would take approximately 4 hours for the staff to review half of the proposed rule. For each SNF that reviews the rule, the estimated cost is $456.96 (4 hours × $114.24). Therefore, we estimate that the total cost of reviewing this regulation is $156,280.32 ($442.96 × 342 reviewers).                    

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

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

# List of Subjects in 42 CFR Part 413

      - Diseases
      - 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 413—PRINCIPLES OF REASONABLE COST REIMBURSEMENT; PAYMENT FOR END-STAGE RENAL DISEASE SERVICES; PROSPECTIVELY DETERMINED PAYMENT RATES FOR SKILLED NURSING FACILITIES; PAYMENT FOR ACUTE KIDNEY INJURY DIALYSIS

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

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

2. Amend § 413.337 by revising paragraph (b)(4) to read as follows: 

§ 413.337

* * * * *

(b) * * *

                            (4)                             *Standardization of data for variation in area wage levels and case-                                      ( print page 22808)                                    mix.*                             The cost data described in paragraph (b)(2) of this section are standardized to remove the effects of geographic variation in wage levels and facility variation in case-mix.                        

(i) The cost data are standardized for geographic variation in wage levels using the wage index. The application of the wage index is made on the basis of the location of the facility in an urban or rural area as defined in § 413.333.

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

(iii) The cost data are standardized for facility variation in case-mix using the case-mix indices and other data that indicate facility case-mix.

* * * * *

3. Amend § 413.338 by— 

a. Revising paragraphs (a)(1) and (4) through (17); 

b. Revising paragraphs (b) and (c)(2)(i), the paragraph (d) heading, and paragraph (d)(3); 

c. Adding paragraphs (d)(5) and (6); 

d. Redesignating paragraphs (e) through (g) as paragraphs (f) through (h); 

e. Adding a new paragraph (e); 

f. Revising newly redesignated paragraph (f)(1) and paragraph (f)(3) introductory text; and 

g. Adding paragraphs (f)(4), (i), and (j). 

The revisions and additions read as follows:

§ 413.338

(a) * * *

                            (1)                             *Achievement threshold (or achievement performance standard)*                             means the 25th percentile of SNF performance on a measure during the baseline period for a fiscal year.                        

* * * * *

                            (4)                             *Baseline period*                             means the time period used to calculate the achievement threshold, benchmark, and improvement threshold that apply to a measure for a fiscal year.                        

                            (5)                             *Benchmark*                             means, for a fiscal year, the arithmetic mean of the top decile of SNF performance on a measure during the baseline period for that fiscal year.                        

                            (6)                             *Eligible stay*                             means, for purposes of the SNF readmission measure, an index SNF admission that would be included in the denominator of that measure.                        

                            (7)                             *Improvement threshold (or improvement performance standard)*                             means an individual SNF's performance on a measure during the applicable baseline period for that fiscal year.                        

                            (8)                             *Logistic exchange function*                             means the function used to translate a SNF's performance score into a value-based incentive payment percentage.                        

                            (9)                             *Low-volume SNF*                             means a SNF with fewer than 25 eligible stays included in the SNF readmission measure denominator during the performance period for each of fiscal years 2019 through 2022.                        

                            (10)                             *Performance period*                             means the time period during which SNF performance on a measure is calculated for a fiscal year.                        

                            (11)                             *Performance score*                             means the numeric score ranging from 0 to 100 awarded to each SNF based on its performance under the SNF VBP Program for a fiscal year.                        

                            (12)                             *Performance standards*                             are the levels of performance that SNFs must meet or exceed to earn points on a measure under the SNF VBP Program for a fiscal year.                        

                            (13)                             *Ranking*                             means the ordering of SNFs based on each SNF's                             *Performance score*                             under the SNF VBP Program for a fiscal year.                        

                            (14)                             *SNF readmission measure*                             means, prior to October 1, 2019, the all-cause all-condition hospital readmission measure (SNFRM) or the all-condition risk-adjusted potentially preventable hospital readmission rate (SNFPPR) specified by CMS for application in the SNF Value-Based Purchasing Program. Beginning October 1, 2019, the term                             *SNF readmission measure*                             means the all-cause all-condition hospital readmission measure (SNFRM) or the all-condition risk-adjusted potentially preventable hospital readmission rate (Skilled Nursing Facility Potentially Preventable Readmissions after Hospital Discharge measure) specified by CMS for application in the SNF VBP Program.                        

                            (15)                             *SNF Value-Based Purchasing (VBP) Program*                             means the program required under section 1888(h) of the Social Security Act.                        

                            (16)                             *Value-based incentive payment adjustment factor*                             is the number that will be multiplied by the adjusted Federal per diem rate for services furnished by a SNF during a fiscal year, based on its performance score for that fiscal year, and after such rate is reduced by the applicable percent.                        

                            (17)                             *Value-based incentive payment amount*                             is the portion of a SNF's adjusted Federal per diem rate that is attributable to the SNF VBP Program.                        

                            (b)                             *Applicability of the SNF VBP Program.*                             The SNF VBP Program applies to SNFs, including facilities described in section 1888(e)(7)(B) of the Act. Beginning with fiscal year 2023, the SNF VBP Program does not include a SNF, with respect to a fiscal year, if:                        

(1) The SNF does not have the minimum number of cases that applies to each measure for the fiscal year, as specified by CMS; or

(2) The SNF does not have the minimum number of measures for the fiscal year, as specified by CMS.

(c) * * *

(2) * * *

                            (i)                             *Total amount available for a fiscal year.*                             The total amount available for value-based incentive payments for a fiscal year is at least 60 percent of the total amount of the reduction to the adjusted SNF PPS payments for that fiscal year, as estimated by CMS, and will be increased as appropriate for each fiscal year to account for the assignment of a performance score to low-volume SNFs under paragraph (d)(3) of this section. Beginning with the FY 2023 SNF VBP, the total amount for value-based incentive payments for a fiscal year is 60 percent of the total amount of the reduction to the adjusted SNF PPS payments for that fiscal year, as estimated by CMS.                        

* * * * *

                            (d)                             *Performance scoring under the SNF VBP Program (applicable, as described in this paragraph, to fiscal year 2019 through and including fiscal year 2025).*                             * * *                        

(3) If, with respect to a fiscal year beginning with fiscal year 2019 through and including fiscal year 2022, CMS determines that a SNF is a low-volume SNF, CMS will assign a performance score to the SNF for the fiscal year that, when used to calculate the value-based incentive payment amount (as defined in paragraph (a)(17) of this section), results in a value-based incentive payment amount that is equal to the adjusted Federal per diem rate (as defined in paragraph (a)(2) of this section) that would apply to the SNF for the fiscal year without application of § 413.337(f).

* * * * *

(5) CMS will specify the measures for application in the SNF VBP Program for a given fiscal year.

(6)(i) Performance standards are announced no later than 60 days prior to the start of the performance period that applies to that measure for that fiscal year.

                            (ii) Beginning with the performance standards that apply to FY 2021, if CMS discovers an error in the performance standard calculations subsequent to publishing their numerical values for a fiscal year, CMS will update the numerical values to correct the error. If CMS subsequently discovers one or more other errors with respect to the same fiscal year, CMS will not further update the numerical values for that fiscal year.                                  ( print page 22809)    

                            (e)                             *Performance scoring under the SNF VBP Program beginning with fiscal year 2026—*                            (1)                             *Points awarded based on SNF performance.*                             CMS will award points to SNFs based on their performance on each measure for which the SNF reports the applicable minimum number of cases during the performance period applicable to that fiscal year as follows:                        

(i) CMS will award from 1 to 9 points for achievement to each SNF whose performance on a measure during the applicable performance period meets or exceeds the achievement threshold for that measure but is less than the benchmark for that measure.

(ii) CMS will award 10 points for achievement to a SNF whose performance on a measure during the applicable performance period meets or exceeds the benchmark for that measure.

(iii) CMS will award from 0 to 9 points for improvement to each SNF whose performance on a measure during the applicable performance period exceeds the improvement threshold but is less than the benchmark for that measure.

(iv) CMS will not award points for improvement to a SNF that does not meet the case minimum for a measure for the applicable baseline period.

(v) The highest of the SNF's achievement and improvement score for a given measure will be the SNF's score on that measure for the applicable fiscal year.

                            (2)                             *Calculation of the SNF performance score.*                             The SNF performance score for a fiscal year is calculated as follows:                        

(i) CMS will sum all points awarded to a SNF as described in paragraph (e) of this section for each measure applicable to a fiscal year to calculate the SNF's point total.

(ii) CMS will normalize the point total such that the resulting SNF performance score is expressed as a number of points earned out of a total of 100.

(f) * * *

(1) CMS will provide quarterly confidential feedback reports to SNFs on their performance on each measure specified for the fiscal year. Beginning with the baseline period and performance period quality measure quarterly reports issued on or after October 1, 2021, which contain the baseline period and performance period measure rates, respectively, SNFs will have 30 days following the date CMS provides each of these reports to review and submit corrections to the measure rates contained in that report. The administrative claims data used to calculate measure rates are not subject to review and correction under paragraph (e)(1) of this section. All correction requests must be accompanied by appropriate evidence showing the basis for the correction to each of the applicable measure rates.

* * * * *

(3) CMS will publicly report the information described in paragraphs (f)(1) and (2) of this section on the Nursing Home Compare website or a successor website. Beginning with information publicly reported on or after October 1, 2019, and ending with information publicly reported on September 30, 2022 the following exceptions apply:

* * * * *

(4) Beginning with the information publicly reported on or after October 1, 2022, the following exceptions apply:

(i) If a SNF does not have the minimum number of cases during the baseline period that applies to a measure for a fiscal year, CMS will not publicly report the SNF's baseline period measure rate for that particular measure, although CMS will publicly report the SNF's performance period measure rate and achievement score if the SNF had the minimum number of cases for the measure during the performance period of the same program year;

(ii) If a SNF does not have the minimum number of cases during the performance period that applies to a measure for a fiscal year, CMS will not publicly report any information with respect to the SNF's performance on that measure for the fiscal year;

(iii) If a SNF does not have the minimum number of measures during the performance period for a fiscal year, CMS will not publicly report any data for that SNF for the fiscal year.

* * * * *

                            (i)                             *Special rules for the FY 2023 SNF VBP Program.*                             (1) CMS will calculate a SNF readmission measure rate for each SNF based on its performance on the SNF readmission measure during the performance period specified by CMS for fiscal year 2023, but CMS will not calculate a performance score for any SNF using the methodology described in paragraphs (d)(1) and (2) of this section. CMS will instead assign a performance score of zero to each SNF.                        

(2) CMS will calculate the value-based incentive payment adjustment factor for each SNF using a performance score of zero and will then calculate the value-based incentive payment amount for each SNF using the methodology described in paragraph (c)(2)(ii) of this section.

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

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

                            (j)                             *Validation.*                             (1) Beginning with the FY 2023 Program year, for the SNFRM measure, information reported through claims for the SNFRM measure are validated for accuracy by Medicare Administrative Contractors (MACs) to ensure accurate Medicare payments.                        

(2) [Reserved]

4. Amend § 413.360 by— 

a. Removing paragraph (b)(2) and redesignating paragraph (b)(3) as paragraph (b)(2); and 

b. Adding paragraph (f). 

The addition reads as follows:

§ 413.360

* * * * *

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

(2) These thresholds (80 percent for completion of required quality measures data and standardized patient assessment data on the MDS; 100 percent for CDC NHSN data) will apply to all measures and standardized patient assessment data requirements adopted into the SNF QRP.

(3) A SNF must meet or exceed both thresholds to avoid receiving a 2-percentage point reduction to their annual payment update for a given fiscal year.

Dated: April 8, 2022.

Xavier Becerra,

Secretary, Department of Health and Human Services.

# Footnotes

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

2.                                 HL7 FHIR. PACIO Functional Status Implementation Guide. Available at                             *https://paciowg.github.io/​functional-status-ig/​.*
        Back to Citation      

3.                                 PACIO Project. Available at                             *http://pacioproject.org/​about/​.*
        Back to Citation      

4.                                 Centers for Medicare & Medicaid Services. Newsroom. Fact sheet: CMS Data Element Library Fact Sheet. June 21, 2018. Available at                             *https://www.cms.gov/​newsroom/​fact-sheets/​cms-data-element-library-fact-sheet.*
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5.                                 Sections 4001 through 4008 of Public Law 114-255. Available at                             *https://www.govinfo.gov/​content/​pkg/​PLAW-114publ255/​html/​PLAW-114publ255.htm.*
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6.                                 The Trusted Exchange Framework (TEF): Principles for Trusted Exchange (Jan. 2022). Available at                             *https://www.healthit.gov/​sites/​default/​files/​page/​2022-01/​Trusted_​Exchange_​Framework_​0122.pdf.*
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7.                                 Common Agreement for Nationwide Health Information Interoperability Version 1 (Jan. 2022). Available at                             *https://www.healthit.gov/​sites/​default/​files/​page/​2022-01/​Common_​Agreement_​for_​Nationwide_​Health_​Information_​Interoperability_​Version_​1.pdf.*
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8.                                 The Common Agreement defines Individual Access Services (IAS) as “with respect to the Exchange Purposes definition, the services provided utilizing the Connectivity Services, to the extent consistent with Applicable Law, to an Individual with whom the QHIN, Participant, or Subparticipant has a Direct Relationship to satisfy that Individual's ability to access, inspect, or obtain a copy of that Individual's Required Information that is then maintained by or for any QHIN, Participant, or Subparticipant.” The Common Agreement defines “IAS Provider” as: “Each QHIN, Participant, and Subparticipant that offers Individual Access Services.”                             *See*                             Common Agreement for Nationwide Health Information Interoperability Version 1, at 7 (Jan. 2022),                             *https://www.healthit.gov/​sites/​default/​files/​page/​2022-01/​Common_​Agreement_​for_​Nationwide_​Health_​Information_​Interoperability_​Version_​1.pdf.*
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9.                                 CMS Measures Inventory Tool. (2022). Influenza Vaccination Coverage among Healthcare Personnel. Retrieved from                             *https://cmit.cms.gov/​CMIT_​public/​ReportMeasure?​measureId=​854.*
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10.                                 Grohskopf, L.A., Alyanak, E., Broder, K.R., Walter, E.B., Fry, A.M., & Jernigan, D.B. (2019). Prevention and Control of Seasonal Influenza with Vaccines: Recommendations of the Advisory Committee on Immunization Practices—United States, 2019-20 Influenza Season.                             *MMWR Recomm Rep,*                             68(No. RR-3), 1-21.                             *https://www.cdc.gov/​mmwr/​volumes/​68/​rr/​rr6803a1.htm?​s_​cid=​rr6803a1_​w.*
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11.                                 Centers for Disease Control and Prevention (CDC). (2021). Disease Burden of Flu. Retrieved from                             *https://www.cdc.gov/​flu/​about/​burden/​index.html?​CDC_​AA_​refVal=​https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdisease%2Fus_​flu-related_​deaths.htm.*

12.                                 Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & Infection Advisory Subcommittee of AMDA, The Society for Post-Acute and Long-Term Care Medicine (2020). Recommendations for Mandatory Influenza Vaccinations for Health Care Personnel From AMDA's Infection Advisory Subcommittee.                             *Journal of the American Medical Directors Association,*                             21(1), 25-28.e2.                             *https://doi.org/​10.1016/​j.jamda.2019.11.008.*
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13.                                 Centers for Disease Control and Prevention (CDC). (2021). Estimated Flu-Related Illnesses, Medical visits, Hospitalizations, and Deaths in the United States—2019-2020 Flu Season. Retrieved from                             *https://www.cdc.gov/​flu/​about/​burden/​2019-2020.html.*
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14.                                 Centers for Disease Control and Prevention (CDC). (2021). Retrieved from Flu & People 65 Years and Older:                             *https://www.cdc.gov/​flu/​highrisk/​65over.htm?​CDC_​AA_​refVal=​https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdisease%2F65over.htm.*

15.                                 Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & Infection Advisory Subcommittee of AMDA, The Society for Post-Acute and Long-Term Care Medicine (2020). Recommendations for Mandatory Influenza Vaccinations for Health Care Personnel From AMDA's Infection Advisory Subcommittee.                             *Journal of the American Medical Directors Association,*                             21(1), 25-28.e2.                             *https://doi.org/​10.1016/​j.jamda.2019.11.008.*
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16.                                 Centers for Disease Control and Prevention (CDC). (2021). Retrieved from Flu & People 65 Years and Older:                             *https://www.cdc.gov/​flu/​highrisk/​65over.htm?​CDC_​AA_​refVal=​https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdisease%2F65over.htm.*
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17.                                 Lansbury, L.E., Brown, C.S., & Nguyen‐Van‐Tam, J.S. (2017). Influenza in long‐term care facilities.                             *Influenza Other Respir Viruses,*                             11(5), 356-366.                             *https://dx.doi.org/​10.1111%2Firv.12464.*

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20.                                 Wilde, J.A., McMillan, J.A., Serwint, J., Butta, J., O'Riordan, M.A., & Steinhoff, M.C. (1999). Effectiveness of influenza vaccine in health care professionals: a randomized trial.                             *JAMA,*                             281(10), 908-913.                             *https://doi.org/​10.1001/​jama.281.10.908.*
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21.                                 Harriman K, Rosenberg J, Robinson S, et al. (2009). Novel influenza A (H1N1) virus infections among health-care personnel—United States, April-May 2009.                             *MMWR Morb Mortal Wkly Rep,*                             58(23), 641-645. Retrieved from                             *https://www.cdc.gov/​mmwr/​preview/​mmwrhtml/​mm5823a2.htm.*
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22.                                 Black, C.L., Yue, X., Ball, S.W., Fink, R.V., de Perio, M.A., Laney, A.S., Williams, W.W., Graitcer, S.B., Fiebelkorn, A.P., Lu, P.J., & Devlin, R. (2018). Influenza Vaccination Coverage Among Health Care Personnel—United States, 2017-18 Influenza Season. MMWR.                             *Morbidity and mortality weekly report,*                             67(38), 1050-1054.                             *https://doi.org/​10.15585/​mmwr.mm6738a2.*

23.                                 Jaklevic M.C. (2020). Flu Vaccination Urged During COVID-19 Pandemic.                             *JAMA.*                             324(10),926-927.                             *https://doi.org/​10.1001/​jama.2020.15444.*
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24.                                 Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & Infection Advisory Subcommittee of AMDA, The Society for Post-Acute and Long-Term Care Medicine (2020). Recommendations for Mandatory Influenza Vaccinations for Health Care Personnel From AMDA's Infection Advisory Subcommittee.                             *Journal of the American Medical Directors Association,*                             21(1), 25-28.e2.                             *https://doi.org/​10.1016/​j.jamda.2019.11.008.*
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25.                                 Kenny, E., McNamara, Á., Noone, C., & Byrne, M. (2020). Barriers to seasonal influenza vaccine uptake among health care workers in long-term care facilities: A cross-sectional analysis.                             *British journal of health psychology,*                             25(3), 519-539.                             *https://doi.org/​10.1111/​bjhp.12419.*
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26.                                 Kose, S., Mandiracioglu, A., Sahin, S., Kaynar, T., Karbus, O., & Ozbel, Y. (2020). Vaccine hesitancy of the COVID-19 by health care personnel.                             *Int J Clin Pract,*                             75(5), e13917.                             *https://doi.org/​10.1111/​ijcp.13917.*
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27.                                 Ofstead, C.L., Amelang, M.R., Wetzler, H.P., & Tan, L. (2017). Moving the needle on nursing staff influenza vaccination in long-term care: Results of an evidence-based intervention.                             *Vaccine,*                             35(18), 2390-2395.                             *https://doi.org/​10.1016/​j.vaccine.2017.03.041.*
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28.                                 Centers for Disease Control and Prevention (CDC). (2021). Retrieved from Vaccine Effectiveness: How Well Do Flu Vaccines Work?:                             *https://www.cdc.gov/​flu/​vaccines-work/​vaccineeffect.htm.*
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29.                                 Centers for Disease Control and Prevention (CDC). (2021). Retrieved from Vaccine Effectiveness: How Well Do Flu Vaccines Work?:                             *https://www.cdc.gov/​flu/​vaccines-work/​vaccineeffect.htm.*
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30.                                 Ferdinands, J.M., Thompson, M.G., Blanton, L., Spencer, S., Grant, L., & Fry, A.M. (2021). Does influenza vaccination attenuate the severity of breakthrough infections? A narrative review and recommendations for further research.                             *Vaccine,*                             39(28), 3678-3695.                             *https://doi.org/​10.1016/​j.vaccine.2021.05.011.*
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31.                                 Carman, W.F., Elder, A.G., Wallace, L.A., McAulay, K., Walker, A., Murray, G.D., & Stott, D.J. (2000). Effects of influenza vaccination of health-care workers on mortality of elderly people in long-term care: a randomised controlled trial.                             *Lancet (London, England), 355*                            (9198), 93-97.                             *https://doi.org/​10.1016/​S0140-6736(99)05190-9.*

32.                                 Hayward, A.C., Harling, R., Wetten, S., Johnson, A.M., Munro, S., Smedley, J., Murad, S., & Watson, J.M. (2006). Effectiveness of an influenza vaccine programme for care home staff to prevent death, morbidity, and health service use among residents: cluster randomised controlled trial.                             *BMJ (Clinical research ed.), 333*                            (7581), 1241.                             *https://doi.org/​10.1136/​bmj.39010.581354.55.*

33.                                 Lemaitre, M., Meret, T., Rothan-Tondeur, M., Belmin, J., Lejonc, J.L., Luquel, L., Piette, F., Salom, M., Verny, M., Vetel, J.M., Veyssier, P., & Carrat, F. (2009). Effect of influenza vaccination of nursing home staff on mortality of residents: a cluster-randomized trial.                             *Journal of the American Geriatrics Society, 57*                            (9), 1580-1586.                             *https://doi.org/​10.1111/​j.1532-5415.2009.02402.x.*

34.                                 Potter, J., Stott, D.J., Roberts, M.A., Elder, A.G., O'Donnell, B., Knight, P.V., & Carman, W.F. (1997). Influenza vaccination of health care workers in long-term-care hospitals reduces the mortality of elderly patients.                             *The Journal of infectious diseases, 175*                            (1), 1-6.                             *https://doi.org/​10.1093/​infdis/​175.1.1.*
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35.                                 Bechini, A., Lorini, C., Zanobini, P., Mandò Tacconi, F., Boccalini, S., Grazzini, M., Bonanni, P., & Bonaccorsi, G. (2020). Utility of Healthcare System-Based Interventions in Improving the Uptake of Influenza Vaccination in Healthcare Workers at Long-Term Care Facilities: A Systematic Review.                             *Vaccines,*                             8(2), 165.                             *https://doi.org/​10.3390/​vaccines8020165.*

36.                                 Ofstead, C.L., Amelang, M.R., Wetzler, H.P., & Tan, L. (2017). Moving the needle on nursing staff influenza vaccination in long-term care: Results of an evidence-based intervention.                             *Vaccine,*                             35(18), 2390-2395.                             *https://doi.org/​10.1016/​j.vaccine.2017.03.041.*

37.                                 Yue, X., Black, C., Ball, S., Donahue, S., de Perio, M.A., Laney, A.S., & Greby, S. (2019). Workplace Interventions and Vaccination-Related Attitudes Associated With Influenza Vaccination Coverage Among Healthcare Personnel Working in Long-Term Care Facilities, 2015-2016 Influenza Season.                             *Journal of the American Medical Directors Association,*                             20(6), 718-724.                             *https://doi.org/​10.1016/​j.jamda.2018.11.029.*
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38.                                 Cai, S., Feng, Z., Fennell, M.L., & Mor, V. (2011). Despite small improvement, black nursing home residents remain less likely than whites to receive flu vaccine.                             *Health affairs (Project Hope),*                             30(10), 1939-1946.                             *https://doi.org/​10.1377/​hlthaff.2011.0029.*

39.                                 Luo, H., Zhang, X., Cook, B., Wu, B., & Wilson, M.R. (2014). Racial/Ethnic Disparities in Preventive Care Practice Among U.S. Nursing Home Residents.                             *Journal of Aging and Health,*                             26(4), 519-539.                             *https://doi.org/​10.1177/​0898264314524436.*

40.                                 Mauldin, R.L., Sledge, S.L., Kinney, E.K., Herrera, S., & Lee, K. (2021). Addressing Systemic Factors Related to Racial and Ethnic Disparities among Older Adults in Long-Term Care Facilities. IntechOpen.                        

41.                                 Travers, J.L., Dick, A.W., & Stone, P.W. (2018). Racial/Ethnic Differences in Receipt of Influenza and Pneumococcal Vaccination among Long-Stay Nursing Home Residents.                             *Health services research,*                             53(4), 2203-2226.                             *https://doi.org/​10.1111/​1475-6773.12759.*
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42.                                 Riester, M.R., Bosco, E., Bardenheier, B.H., Moyo, P., Baier, R.R., Eliot, M., Silva, J.B., Gravenstein, S., van Aalst, R., Chit, A., Loiacono, M.M., & Zullo, A.R. (2021). Decomposing Racial and Ethnic Disparities in Nursing Home Influenza Vaccination.                             *Journal of the American Medical Directors Association,*                             22(6), 1271-1278.e3.                             *https://doi.org/​10.1016/​j.jamda.2021.03.003.*
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43.                                 Hall, L.L., Xu, L., Mahmud, S.M., Puckrein, G.A., Thommes, E.W., & Chit, A. (2020). A Map of Racial and Ethnic Disparities in Influenza Vaccine Uptake in the Medicare Fee-for-Service Program.                             *Advances in therapy,*                             37(5), 2224-2235.                             *https://doi.org/​10.1007/​s12325-020-01324-y.*

44.                                 Inactivated vaccines use the killed version of the germ that causes a disease. Inactivated vaccines usually don't provide immunity (protection) that is as strong as the live vaccines. For more information regarding inactivated vaccines we refer readers to the following web page:                             *https://hhs.gov/​immunization/​basics/​types/​index.html.*

45.                                 High dose flu vaccines contain four times the amount of antigen (the inactivated virus that promotes a protective immune response) as a regular flu shot. It is associated with a stronger immune response following vaccination. For more information regarding high dose flu vaccines, we refer readers to the following web page:                             *https://www.cdc.gov/​flu/​highrisk/​65over.htm.*
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46.                                 Wang, X., Kulkarni, D., Dozier, M., Hartnup, K., Paget, J., Campbell, H., Nair, H., & Usher Network for COVID-19 Evidence Reviews (UNCOVER) group                                                         (2020). Influenza vaccination strategies for 2020-21 in the context of COVID-19.                             *Journal of global health,*                             10(2), 021102.                             *https://www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC7719353/​.*
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47.                                 Del Riccio, M., Lorini, C., Bonaccorsi, G., Paget, J., & Caini, S. (2020). The Association between Influenza Vaccination and the Risk of SARS-CoV-2 Infection, Severe Illness, and Death: A Systematic Review of the Literature.                             *International journal of environmental research and public health,*                             17(21), 7870.                             *https://doi.org/​10.3390/​ijerph17217870.*
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48.                                 Libby T.E., Lindley M.C., Lorick S.A., MacCannell T., Lee S.J., Smith C, Geevarughese A., Makvandi M., Nace D.A., Ahmed F. (2013). Reliability and validity of a standardized measure of influenza vaccination coverage among healthcare personnel.                             *Infect Control Hosp Epidemiol,*                             34(4),335-45.                             *https://doi.org/​10.1086/​669859.*

49.                                 The Libby et al. (2013) article (preceding footnote) is referenced throughout the entirety of section VI.C.1.b. of this rule.                        
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50.                                 For a full list of case study categorization results, please refer to the following study: Libby T.E., Lindley M.C., Lorick S.A., MacCannell T., Lee S.J., Smith C., Geevarughese A., Makvandi M., Nace D.A., Ahmed F. (2013). Reliability and validity of a standardized measure of influenza vaccination coverage among healthcare personnel.                             *Infect Control Hosp Epidemiol,*                             34(4),335-45.                             *https://doi.org/​10.1086/​669859.*
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51.                                 Libby T.E,. Lindley M.C., Lorick S.A., MacCannell T, Lee S.J., Smith C., Geevarughese A., Makvandi M., Nace D.A., Ahmed F. (2013). Reliability and validity of a standardized measure of influenza vaccination coverage among healthcare personnel.                             *Infect Control Hosp Epidemiol,*                             34(4),335-45.                             *https://doi.org/​10.1086/​669859.*
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52.                                 Centers for Medicare and Medicaid Services. (2021). List of Measures Under Consideration for December 1, 2021. CMS.gov.                             *https://www.cms.gov/​files/​document/​measures-under-consideration-list-2020-report.pdf.*
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53.                                 Refer to the proposed measure's specifications in The National Healthcare Safety Network (NSHN) Manual Healthcare Personnel Safety Component Protocol—Healthcare Personnel Vaccination Module: Influenza Vaccination Summary linked at                             *https://www.cdc.gov/​nhsn/​pdfs/​hps-manual/​vaccination/​hps-flu-vaccine-protocol.pdf*                             for an exhaustive list of those included in the licensed independent practitioners definition.                        
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54.                                 78 FR 47906.                        
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55.                                 Centers for Disease Control and Prevention (CDC). (2021)                             *https://www.cdc.gov/​nhsn/​hps/​weekly-covid-vac/​index.html.*                             Healthcare Personnel Safety Component (HPS).                             *CDC.gov.*
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56.                                 The MDS version referred to in IFC-2 was MDS 3.0 v1.18.1. This version number, MDS 3.0 v1.18.11, reflects the version which would be implemented if the proposal is finalized.                        
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57.                                 Bhumbra S, Malin S, Kirkpatrick L, et al. Clinical Features of Critical Coronavirus Disease 2019 in Children. Pediatric Critical Care Medicine. 2020;02:02. DOI:                             *https://doi.org/​10.1097/​PCC.0000000000002511.*

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59.                                 Gold JAW, Wong KK, Szablewski CM, Patel PR, Rossow J, da Silva J, et al. Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19—Georgia, March 2020. MMWR Morb Mortal Wkly Rep. 2020;69(18):545-50. DOI:                             *http://dx.doi.org/​10.15585/​mmwr.mm6918e1.*

60.                                 Hsu HE, Ashe EM, Silverstein M, Hofman M, Lange SJ, Razzaghi H, et al. Race/Ethnicity, Underlying Medical Conditions, Homelessness, and Hospitalization Status of Adult Patients with COVID-19 at an Urban Safety-Net Medical Center—Boston, Massachusetts, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(27):864-9. DOI:                             *http://dx.doi.org/​10.15585/​mmwr.mm6927a3.*

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63.                                 Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and Mortality among Black Patients and White Patients with Covid-19. New England Journal of Medicine. 2020;382(26):2534-43. DOI:                             *https://doi.org/​10.1056/​NEJMsa2011686.*
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64.                                 Centers for Medicare and Medicaid Services. COVID-19 Emergency Declaration Blanket waivers for Health Care Providers. Accessed 11/23/2021. Retrieved from                             *https://www.cms.gov/​files/​document/​covid-19-emergency-declaration-waivers.pdf.*
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70.                                 COVID-19 Treatment Guidelines. National Institutes of Health. Updated October 27, 2021. Retrieved from                             *https://www.covid19treatmentguidelines.nih.gov/​whats-new/​.*
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71.                                 Here's Exactly Where We are with Vaccine and Treatments for COVID-19. Healthline. November 9, 2021. Retrieved from                             *https://www.healthline.com/​health-news/​heres-exactly-where-were-at-with-vaccines-and-treatments-forcovid-19.*

72.                                 U.S. Food and Drug Administration (2021). Janssen Biotech, Inc. COVID-19 Vaccine EUA Letter of Authorization. Available at                             *https://www.fda.gov/​media/​146303/​download.*                             Accessed 9/9/2021.                        

73.                                 On January 31, 2021, FDA approved a second COVID-19 vaccine. Available at                             *https://www.fda.gov/​news-events/​press-announcements/​coronavirus-covid-19-update-fda-takes-key-action-approving-second-covid-19-vaccine.*                             Accessed 3/02/22. The Moderna COVID-19 Vaccine also continues to be available under EUA. U.S. Food and Drug Administration (2022). Spikevax and Moderna COVID-19 Vaccine.                             *https://www.fda.gov/​emergency-preparedness-and-response/​coronavirus-disease-2019-covid-19/​spikevax-and-moderna-covid-19-vaccineAccessed3/​02/​22.*

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75.                                 FDA Approves First Treatment for COVID-19. October 22, 2020. Available at                             *https://www.fda.gov/​newsevents/​press-announcements/​fda-approves-first-treatment-covid-19.*                             Accessed 9/9/2021.                        
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76.                                 M. Domi, M. Leitson, D. Gifford, A. Nicolaou, K. Sreenivas, C. Bishnoi. The BNT162b2 vaccine is associated with lower new COVID-19 cases in nursing home residents and staff.                             *Journal of the American Geriatrics Society*                             (2021), 10.1111/jgs.17224.                        

77.                                 American Health Care Association and National Center for Assisted Living. COVID-19 Vaccines Helping Long Term Care Facilities Rebound From The Pandemic. May 25, 2021. Retrieved from                             *https://www.ahcancal.org/​News-and-Communications/​Press-Releases/​Pages/​COVID-19-Vaccines-Helping-Long-Term-Care-Facilities-Rebound-From-The-Pandemic.aspx.*
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78.                                 COVID-19 Health Equity Interactive Dashboard. Emory University. Accessed January 12, 2022. Retrieved from                             *https://covid19.emory.edu/​.*

79.                                 COVID-19 is affecting Black, Indigenous, Latinx, and other people of color the most. The COVID Tracking Project. March 7, 2021. Accessed January 12, 2022. Retrieved from                             *https://covidtracking.com/​race.*

80.                                 Centers for Medicare & Medicaid Services. CMS Quality Strategy. 2016. Available at                             *https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​QualityInitiativesGenInfo/​Downloads/​CMS-Quality-Strategy.pdf.*

81.                                 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.*

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314.                                 Section 321 of the NCVIA provides the PRA waiver for activities that come under the NCVIA, including those in the NCVIA at section 2102 of the Public Health Service Act (42 U.S.C. 300aa-2). Section 321 is not codified in the U.S.C., but can be found in a note at 42 U.S.C. 300aa-1.                        
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BILLING CODE 4120-01-P

BILLING CODE 4120-01-C

[FR Doc. 2022-07906 Filed 4-11-22; 4:15 pm]