2022-16260. Medicare Program; FY 2023 Inpatient Psychiatric Facilities Prospective Payment System-Rate Update and Quality Reporting-Request for Information

AGENCY:

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

ACTION:

Final rule.

SUMMARY:

This final rule updates the prospective payment rates, the outlier threshold, and the wage index for Medicare inpatient hospital services provided by Inpatient Psychiatric Facilities (IPF), which include psychiatric hospitals and excluded psychiatric units of an acute care hospital or critical access hospital. This final rule establishes a permanent mitigation policy to smooth the impact of year-to-year changes in IPF payments related to decreases in the IPF wage index. In addition, this final rule includes responses to public comments received on the results of the data analysis of the IPF Prospective Payment System (PPS) adjustments. These changes will be effective for IPF discharges occurring during the Fiscal Year (FY) beginning October 1, 2022, through September 30, 2023 (FY 2023). Lastly, this final rule includes public comments received in response to requests for information that appeared in the FY 2023 IPF PPS proposed rule.

DATES:

Effective October 1, 2022.

FOR FURTHER INFORMATION CONTACT:

The IPF Payment Policy mailbox at [e-mail] for general information.

Mollie Knight [phone] or Eric Laib [phone], for information regarding the market basket update or the labor-related share.

Nick Brock [phone] or Theresa Bean [phone], for information regarding the regulatory impact analysis.

Lauren Lowenstein [phone], for information regarding the inpatient psychiatric facilities quality reporting program.

SUPPLEMENTARY INFORMATION:

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

Addendum A to this final rule summarizes the FY 2023 IPF PPS payment rates, outlier threshold, cost of living adjustment factors (COLA) for Alaska and Hawaii, national and upper limit cost-to-charge ratios, and adjustment factors. In addition, the B Addenda to this final rule shows the complete listing of ICD-10 Clinical Modification (CM) and Procedure Coding System (PCS) codes, the FY 2023 IPF PPS comorbidity adjustment, and electroconvulsive therapy (ECT) procedure codes. The A and B Addenda are available online at: https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html.

Tables setting forth the FY 2023 Wage Index for Urban Areas Based on Core-Based Statistical Area (CBSA) Labor Market Areas and the FY 2023 Wage Index Based on CBSA Labor Market Areas for Rural Areas are available exclusively through the internet, on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​IPFPPS/​WageIndex.html.

I. Executive Summary

A. Purpose

This final rule updates the prospective payment rates, the outlier threshold, and the wage index for Medicare inpatient hospital services provided by Inpatient Psychiatric Facilities (IPFs) for discharges occurring during Fiscal Year (FY) 2023 beginning October 1, 2022 through September 30, 2023. This final rule establishes a permanent mitigation policy to smooth the impact of year-to-year changes in IPF payments related to changes in the IPF wage index. In addition, this final rule includes responses to public comments received on the results of the data analysis of the IPF Prospective Payment System (PPS) adjustments. Lastly, this final rule includes public comments received in response to requests for information that appeared in the FY 2023 IPF PPS proposed rule.

B. Summary of the Major Provisions

1. Inpatient Psychiatric Facilities Prospective Payment System (IPF PPS)

For the IPF PPS, we are finalizing our proposal to—

• Establish a permanent mitigation policy in order to smooth the impact of year-to-year changes in IPF payments related to decreases to the IPF wage index.
• Adjust the 2016-based IPF market basket update (4.1 percent) for economy-wide productivity (0.3 percentage point) as required by section 1886(s)(2)(A)(i) of the Social Security Act (the Act), resulting in a final IPF payment rate update of 3.8 percent for FY 2023.

• Make technical rate setting changes: The IPF PPS payment rates will be adjusted annually for inflation, as well as statutory and other policy factors. This final rule updates:

++ The IPF PPS Federal per diem base rate from $832.94 to $865.63.

++ The IPF PPS Federal per diem base rate for providers who failed to report quality data to $848.95.

++ The ECT payment per treatment from $358.60 to $372.67.

++ The ECT payment per treatment for providers who failed to report quality data to $365.49.

++ The labor-related share from 77.2 percent to 77.4 percent.

++ The wage index budget-neutrality factor to 1.0012.

++ The fixed dollar loss threshold amount from $16,040 to $24,630 to maintain estimated outlier payments at 2 percent of total estimated aggregate IPF PPS payments.

2. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program

We did not propose any changes to the IPFQR Program and are not finalizing any changes to the IPFQR Program. We did receive many comments requesting that we add a patient experience of care measure to the IPFQR Program. Additionally, one commenter recommended that CMS adopt a patient and workforce safety measure for the IPF setting. We also received several comments recommending that CMS adopt a value-based purchasing program for the IPF setting. Finally, one commenter provided input about depression screening instruments for CMS's ongoing work to develop a measure of improvement of depression symptoms. We appreciate these comments but note that they fall outside the scope of this rulemaking. We will consider all these comments as we continue to evolve the IPFQR Program in the future.

We also included a request for information (RFI) on the Overarching Principles for Measuring Healthcare Quality Disparities Across CMS Quality Programs. Feedback provided will inform future efforts in all CMS Quality programs and, as applicable, may be introduced in the IPFQR as future RFIs or proposals.

C. Summary of Impacts

II. Background

A. Overview of the Legislative Requirements of the IPF PPS

Section 124 of the Medicare, Medicaid, and State Children's Health Insurance Program Balanced Budget Refinement Act of 1999 (BBRA) (Pub. L. 106-113) required the establishment and implementation of an IPF PPS. Specifically, section 124 of the BBRA mandated that the Secretary of the Department of Health and Human Services (the Secretary) develop a per diem payment perspective system (PPS) for inpatient hospital services furnished in psychiatric hospitals and excluded psychiatric units including an adequate patient classification system that reflects the differences in patient resource use and costs among psychiatric hospitals and excluded psychiatric units. “Excluded psychiatric unit” means a psychiatric unit of an acute care hospital or of a Critical Access Hospital (CAH), which is excluded from payment under the Inpatient Prospective Payment System (IPPS) or CAH payment system, respectively. These excluded psychiatric units will be paid under the IPF PPS.

Section 405(g)(2) of the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 (MMA) (Pub. L. 108-173) extended the IPF PPS to psychiatric distinct part units of CAHs.

Sections 3401(f) and 10322 of the Patient Protection and Affordable Care Act (Pub. L. 111-148) as amended by section 10319(e) of that Act and by section 1105(d) of the Health Care and Education Reconciliation Act of 2010 (Pub. L. 111-152) (hereafter referred to jointly as “the Affordable Care Act”) added subsection (s) to section 1886 of the Act.

Section 1886(s)(1) of the Act titled “Reference to Establishment and Implementation of System,” refers to section 124 of the BBRA, which relates to the establishment of the IPF PPS.

Section 1886(s)(2)(A)(i) of the Act requires the application of the productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of the Act to the IPF PPS for the rate year (RY) beginning in 2012 (that is, a RY that coincides with a FY) and each subsequent RY.

Section 1886(s)(2)(A)(ii) of the Act required the application of an “other adjustment” that reduced any update to an IPF PPS base rate by a percentage point amount specified in section 1886(s)(3) of the Act for the RY beginning in 2010 through the RY beginning in 2019. As noted in the FY 2020 IPF PPS final rule, for the RY beginning in 2019, section 1886(s)(3)(E) of the Act required that the other adjustment reduction be equal to 0.75 percentage point; that was the final year the statute required the application of this adjustment. Because FY 2021 was a RY beginning in 2020, FY 2021 was the first year section 1886(s)(2)(A)(ii) did not apply since its enactment.

Sections 1886(s)(4)(A) through (D) of the Act require that for RY 2014 and each subsequent RY, IPFs that fail to report required quality data with respect to such a RY will have their annual update to a standard Federal rate for discharges reduced by 2.0 percentage points. This may result in an annual update being less than 0.0 for a RY, and may result in payment rates for the upcoming RY being less than such payment rates for the preceding RY. Any reduction for failure to report required quality data will apply only to the RY involved, and the Secretary will not consider such reduction in computing the payment amount for a subsequent RY. Additional information about the specifics of the current IPFQR Program is available in the FY 2020 IPF PPS and Quality Reporting Updates for FY Beginning October 1, 2019 final rule (84 FR 38459 through 38468).

To implement and periodically update these provisions, we have published various proposed and final rules and notices in the Federal Register . For more information regarding these documents, see the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​index.html?​redirect=​/​InpatientPsychFacilPPS/​.

B. Overview of the IPF PPS

On November 15, 2004, we published the IPF PPS final rule in the Federal Register (69 FR 66922). The November 2004 IPF PPS final rule established the IPF PPS, as required by section 124 of the BBRA and codified at 42 CFR part 412, subpart N. The November 2004 IPF PPS final rule set forth the Federal per diem base rate for the implementation year (the 18-month period from January 1, 2005 through June 30, 2006), and provided payment for the inpatient operating and capital costs to IPFs for covered psychiatric services they furnish (that is, routine, ancillary, and capital costs, but not costs of approved educational activities, bad debts, and other services or items that are outside the scope of the IPF PPS). Covered psychiatric services include services for which benefits are provided under the fee-for-service Part A (Hospital Insurance Program) of the Medicare program.

The IPF PPS established the Federal per diem base rate for each patient day in an IPF derived from the national average daily routine operating, ancillary, and capital costs in IPFs in FY 2002. The average per diem cost was updated to the midpoint of the first year under the IPF PPS, standardized to account for the overall positive effects of the IPF PPS payment adjustments, and adjusted for budget-neutrality.

The Federal per diem payment under the IPF PPS is comprised of the Federal per diem base rate described previously and certain patient- and facility-level payment adjustments for characteristics that were found in the regression analysis to be associated with statistically significant per diem cost differences; with statistical significance defined as p less than 0.05. A complete discussion of the regression analysis that established the IPF PPS adjustment factors can be found in the November 2004 IPF PPS final rule (69 FR 66933 through 66936).

The patient-level adjustments include age, Diagnosis-Related Group (DRG) assignment, and comorbidities, as well as adjustments to reflect higher per diem costs at the beginning of a patient's IPF stay and lower costs for later days of the stay. Facility-level adjustments include adjustments for the IPF's wage index, rural location, teaching status, a cost-of-living adjustment for IPFs located in Alaska and Hawaii, and an adjustment for the presence of a qualifying emergency department (ED).

The IPF PPS has additional payment policies for outlier cases, interrupted stays, and a per treatment payment for patients who undergo electroconvulsive therapy (ECT). During the IPF PPS mandatory 3-year transition period, stop-loss payments were also provided; however, since the transition ended as of January 1, 2008, these payments are no longer available.

C. Annual Requirements for Updating the IPF PPS

Section 124 of the BBRA did not specify an annual rate update strategy for the IPF PPS and was broadly written to give the Secretary discretion in establishing an update methodology. In the November 2004 IPF PPS final rule (69 FR 66922), we implemented the IPF PPS using the following update strategy:

• Calculate the final Federal per diem base rate to be budget-neutral for the 18-month period of January 1, 2005 through June 30, 2006.
• Use a July 1 through June 30 annual update cycle.
• Allow the IPF PPS first update to be effective for discharges on or after July 1, 2006 through June 30, 2007.

In developing the IPF PPS, and to ensure that the IPF PPS can account adequately for each IPF's case-mix, we performed an extensive regression analysis of the relationship between the per diem costs and certain patient and facility characteristics to determine those characteristics associated with statistically significant cost differences on a per diem basis. That regression analysis is described in detail in our November 28, 2003 IPF proposed rule (68 FR 66923; 66928 through 66933) and our November 15, 2004 IPF final rule (69 FR 66933 through 66960). For characteristics with statistically significant cost differences, we used the regression coefficients of those variables to determine the size of the corresponding payment adjustments.

In the November 2004 IPF final rule, we explained the reasons for delaying an update to the adjustment factors, derived from the regression analysis, including waiting until we have IPF PPS data that yields as much information as possible regarding the patient-level characteristics of the population that each IPF serves. We indicated that we did not intend to update the regression analysis and the patient-level and facility-level adjustments until we complete that analysis. Until that analysis is complete, we stated our intention to publish a notice in the Federal Register each spring to update the IPF PPS (69 FR 66966).

On May 6, 2011, we published a final rule in the Federal Register titled, “Inpatient Psychiatric Facilities Prospective Payment System—Update for Rate Year Beginning July 1, 2011 (RY 2012)” (76 FR 26432), which changed the payment rate update period to a RY that coincides with a FY update. Therefore, final rules are now published in the Federal Register in the summer to be effective on October 1st. When proposing changes in IPF payment policy, a proposed rule is issued in the spring, and the final rule in the summer to be effective on October 1st. For a detailed list of updates to the IPF PPS, we refer readers to our regulations at 42 CFR 412.428.

The most recent IPF PPS annual update was published in a final rule on August 4, 2021 in the Federal Register titled, “Medicare Program; FY 2022 Inpatient Psychiatric Facilities Prospective Payment System and Quality Reporting Updates for Fiscal Year Beginning October 1, 2021 (FY 2022)” (86 FR 42608), which updated the IPF PPS payment rates for FY 2022. That final rule updated the IPF PPS Federal per diem base rates that were published in the FY 2021 IPF PPS Rate Update final rule (85 FR 47042) in accordance with our established policies.

III. Analysis of and Responses to Public Comments

We received 396 public comments, 27 of which pertained to proposed IPF PPS payment policies, 20 of which pertained to the request for comments on addressing healthcare disparities and advancing healthcare equity in the IPFQR Program, and the remainder were seeking to encourage the addition of a patient experience of care measure into the IPFQR Program. Comments were from health systems, national and state-level provider and patient advocacy organizations, MedPAC, and individuals. We reviewed each comment and grouped related comments, after which we placed them in categories based on subject matter or section(s) of the regulation affected. Summaries of the public comments received and our responses to those comments are provided in the appropriate sections in the preamble of this final rule.

IV. Provisions of the FY 2023 IPF PPS Final Rule and Responses to Comments

A. FY 2023 Market Basket Update and Productivity Adjustment for the IPF PPS

1. Background

Originally, the input price index that was used to develop the IPF PPS was the “Excluded Hospital with Capital” market basket. This market basket was based on 1997 Medicare cost reports for Medicare participating inpatient rehabilitation facilities (IRFs), IPFs, long-term care hospitals (LTCHs), cancer hospitals, and children's hospitals. Although “market basket” technically describes the mix of goods and services used in providing health care at a given point in time, this term is also commonly used to denote the input price index (that is, cost category weights and price proxies) derived from that market basket. The term market basket as used in this document, refers to an input price index.

Since the IPF PPS inception, the market basket used to update IPF PPS payments has been rebased and revised to reflect more recent data on IPF cost structures. We last rebased and revised the IPF market basket in the FY 2020 IPF PPS rule, where we adopted a 2016-based IPF market basket, using Medicare cost report data for both Medicare participating freestanding psychiatric hospitals and psychiatric units. We refer readers to the FY 2020 IPF PPS final rule for a detailed discussion of the 2016-based IPF PPS market basket and its development (84 FR 38426 through 38447). References to the historical market baskets used to update IPF PPS payments are listed in the FY 2016 IPF PPS final rule (80 FR 46656).

2. FY 2023 IPF Market Basket Update

For FY 2023 (beginning October 1, 2022 and ending September 30, 2023), we proposed to update the IPF PPS payments by a market basket increase factor with a productivity adjustment as required by section 1886(s)(2)(A)(i) of the Act. Consistent with historical practice, we proposed to estimate the market basket update for the IPF PPS based on the most recent forecast available at the time of rulemaking from IHS Global Inc. (IGI). IGI is a nationally recognized economic and financial forecasting firm with which CMS contracts to forecast the components of the market baskets and productivity adjustment. For the proposed rule, based on IGI's fourth quarter 2021 forecast with historical data through the third quarter of 2021, the proposed 2016-based IPF market basket increase factor for FY 2023 was 3.1 percent.

Section 1886(s)(2)(A)(i) of the Act requires that, after establishing the increase factor for a FY, the Secretary of the Department of Health and Human Services (the Secretary) shall reduce such increase factor for FY 2012 and each subsequent FY, by the productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of the Act. The statute defines the productivity adjustment to be equal to the 10-year moving average of changes in annual economy-wide, private nonfarm business multifactor productivity (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 “productivity adjustment”). The United States Department of Labor's Bureau of Labor Statistics (BLS) publishes the official measures of productivity for the United States economy. We note that previously the productivity measure referenced in section 1886(b)(3)(B)(xi)(II) of the Act was published by BLS as private nonfarm business MFP. Beginning with the November 18, 2021 release of productivity data, BLS replaced the term “multifactor productivity” with “total factor productivity” (TFP). BLS noted that this is a change in terminology only and will not affect the data or methodology. As a result of the BLS name change, the productivity measure referenced in section 1886(b)(3)(B)(xi)(II) of the Act is now published by BLS as private nonfarm business total factor productivity. However, as mentioned previously, the data and methods are unchanged. We refer readers to www.bls.gov for the BLS historical published TFP data. A complete description of IGI's TFP projection methodology is available on the CMS website at https://www.cms.gov/​Research-Statistics-Data-and-Systems/​Statistics-Trends-and-Reports/​MedicareProgramRatesStats/​MarketBasketResearch. In addition, in the FY 2022 IPF final rule (86 FR 42611), we noted that effective with FY 2022 and forward, CMS changed the name of this adjustment to refer to it as the productivity adjustment rather than the MFP adjustment.

For the FY 2023 IPF PPS proposed rule, based on IGI's fourth quarter 2021 forecast, the proposed productivity adjustment for FY 2023 (the 10-year moving average growth in TFP for the period ending FY 2023) was projected to be 0.4 percent. Accordingly, we proposed to reduce the proposed 3.1 percent IPF market basket update by this proposed 0.4 percentage point productivity adjustment, as mandated by the Act. This resulted in a proposed FY 2023 IPF PPS payment rate update of 2.7 percent (3.1−0.4 = 2.7). We also proposed that if more recent data became available, we would use such data, if appropriate, to determine the FY 2023 IPF market basket update and productivity adjustment for the final rule.

Comment: Commenters appreciated the positive proposed update to the IPF market basket for FY 2023; however, many commenters expressed concern that the proposed 2.7 percent market basket update (reflecting a 3.1 percent market basket update less 0.4 percentage point productivity adjustment) was inadequate, particularly noting the historically high inflation rates. The commenters acknowledged that CMS will refresh the market basket update in the final rule but were deeply concerned the revised update would continue to be insufficient relative to input cost inflation. They stated that hospitals on the front lines of the “coronavirus disease 2019” (abbreviated “COVID-19”) Public Health Emergency (PHE) during the past 2 years continue to weather a number of market pressures such as labor shortages (which have led to use of more contract labor) and supply chain issues. One commenter stated that the rate update does not account for the many issues that their system encounters, including higher acuity patients, additional staffing to meet acuity needs and care for underserved patients. Another commenter stated that unlike many of the other hospitals and providers, IPFs did not receive any targeted funding allocation from the Provider Relief Fund to address their increased costs as well as the increased need for mental healthcare and addiction treatment during this pandemic.

Many commenters believe CMS's current methodology for updating the market basket is ill-suited to adequately adjust Medicare payments in a highly inflationary environment. Therefore, they recommended that CMS consider other methods and data sources to calculate the final rule market basket update and an alternative approach to better align the market basket increases with increases in cost to treat patients, including using the authority under section 1886(s) of the Act to further increase IPF rates to better adjust FY 2023 payments to IPFs to account for inflation.

Response: We believe the 2016-based IPF market basket increase adequately reflects the average change in the price of goods and services hospitals purchase in order to provide IPF medical services, and is appropriate to use as the IPF payment update factor. As described in the FY 2020 IPF final rule (84 FR 38426 through 38447), the IPF market basket is a fixed-weight, Laspeyres-type index that measures price changes over time and would not reflect increases in costs associated with changes in the volume or intensity of input goods and services. As such, the IPF market basket update would reflect the prospective price pressures described by the commenters as increasing during a high inflation period (such as faster wage growth or higher energy prices), but would inherently not reflect other factors that might increase the level of costs, such as the quantity of labor used or any shifts between contract and staff nurses. We note that cost changes (that is, the product of price and quantities) would only be reflected when a market basket is rebased and the base year weights are updated to a more recent time period.

We agree with the commenters that recent higher inflationary trends have impacted the outlook for price growth over the next several quarters. Based on IGI's fourth quarter 2021 forecast with historical data through the third quarter of 2021, the proposed 2016-based IPF market basket update for FY 2023 was 3.1 percent, reflecting forecasted compensation price growth of 3.5 percent (by comparison, compensation price growth in the IPF market basket averaged 2.2 percent from 2012-2021). In the FY 2023 IPF PPS proposed rule, we proposed that if more recent data became available, we would use such data, if appropriate, to derive the final FY 2023 IPF market basket update for the final rule. For this final rule, we now have an updated forecast of the price proxies underlying the market basket that incorporates more recent historical data and reflects a revised outlook regarding the United States economy and expected price inflation for FY 2023 for IPFs. Based on IGI's second quarter 2022 forecast with historical data through the first quarter of 2022, the final FY 2023 IPF market basket update is 4.1 percent (reflecting forecasted compensation price growth of 4.5 percent) and the final FY 2023 productivity adjustment is 0.3 percentage point. Therefore, for FY 2023, the final IPF productivity-adjusted market basket update is 3.8 percent (4.1 percent less 0.3 percentage point), compared to the proposed 2.7 percent productivity-adjusted market basket update. We note that the final FY 2023 IPF market basket growth rate of 4.1 percent would be the highest market basket update we have implemented in a final rule since the beginning of the IPF PPS.

With respect to the comment about the lack of a targeted funding allocation for IPFs from the Provider Relief Fund, we do not agree with the commenter and note that IPFs were included in the types of eligible specialty hospitals for rural targeted distribution payments.^[1]

Lastly, regarding commenters' request that CMS consider other methods and data sources to calculate the final rule market basket update, including the authority under section 1886(s) of the Act, while we generally agree that the Secretary has broad authority under the statute to establish the methodology for updating the IPF PPS base rate, our longstanding policy since the inception of the IPF PPS has been to update IPF PPS payments based on an appropriate market basket. As discussed earlier in this section of this final rule, the market basket used to update IPF PPS payments has been rebased and revised over the history of the IPF PPS to reflect more recent data on IPF cost structures, and we believe it continues to appropriately reflect IPF cost structures. We did not propose to use other methods or data sources to calculate the final market basket update for FY 2023, and we are not finalizing such an approach for this final rule. Consistent with our proposal, we have used more recent data to calculate a final IPF productivity-adjusted market basket update of 3.8 percent for FY 2023.

Comment: One commenter stated that the market basket updates in FY 2021 and FY 2022 are currently estimated to underinflate the base IPF rate by 1.9 percent, which means the base rate for FY 2023 is 1.9 percent too low.

Response: The IPF market basket updates are set prospectively, which means that the update relies on a mix of both historical data for part of the period for which the update is calculated and forecasted data for the remainder. For instance, the FY 2023 market basket update in this final rule reflects historical data through the first quarter of CY 2022 and forecasted data through the third quarter of CY 2023. While there is no precedent to adjust for market basket forecast error in the IPF payment update, a forecast error can be calculated by comparing the actual market basket increase for a given year less the forecasted market basket increase. Due to the uncertainty regarding future price trends, forecast errors can be both positive and negative. This was the case for the FY 2020 IPF forecast error, which was -0.7 percentage point, and the FY 2021 IPF forecast error, which was +0.7 percentage point; FY 2022 historical data is not yet available to calculate a forecast error for FY 2022. Regarding the comment that the FY 2023 IPF base rate is 1.9 percent too low, we disagree with this assertion as it does not consider years in which the base rates may have been overinflated. For this final rule, we have incorporated more recent historical data and forecasts to capture the price and wage pressures facing IPFs. We believe it is the best available projection of inflation to determine the applicable percentage increase for the IPF payments in FY 2023.

Comment: One commenter stated that with the significant increase in inflation that has already taken place in 2022, they did not support using 2021 historical data to set the FY 2023 rates. The commenter stated that an additional increase should be added to the 2021 historical data to help offset the significant increased costs that providers are currently experiencing.

Response: In determining the FY 2023 IPF market basket update of 4.1 percent, a combination of observed and forecasted trends were used. Actual experience is incorporated through first quarter 2022, and forecasted trends through the remaining quarters of FY 2022 and all of FY 2023. Likewise, the FY 2024 market basket update would reflect not only historical data through 2022 but also forecasted trends through FY 2024.

Comment: Several commenters disagreed with the assumptions underpinning the productivity adjustment. They stated that the productivity adjustment to the market basket update assumes IPFs can increase overall productivity at the same rate as increases in the broader economy, and referenced CMS Office of the Actuary analysis that compares private non-farm total factor productivity growth measure and a hospital-specific measure ( https://www.cms.gov/​files/​document/​productivity-memo.pdf). The commenters stated that IPF services are highly labor-intensive, and therefore, IPFs cannot improve productivity using strategies like offshoring or automation that are commonly deployed in other sectors of the economy. The commenters claimed that during the PHE productivity fell as result of having to use temporary staffing due to labor shortages.

In addition, the commenters stated that although CMS is required by statute to implement a productivity adjustment to the market basket update, they requested that CMS work with the Congress to permanently eliminate the productivity adjustment. Furthermore, the commenters recommended that CMS use its Section 1135 waiver authority to remove the productivity adjustment for any FY that was covered under the PHE determination (that is, 2020, 2021, and 2022) from the calculation of market basket for FY 2023 and any year thereafter that the PHE continues.

Response: Section 1886(s)(2)(A)(i) of the Act requires the application of a productivity adjustment to the IPF PPS market basket increase factor. As required by statute, the FY 2023 productivity adjustment is derived based on the 10-year moving average growth in economy-wide productivity for the period ending FY 2023. Regarding the suggestion that CMS consider section 1135 waiver authority to suspend application of the productivity adjustment, such authority is unavailable in this circumstance. Section 1135 of the Act authorizes the Secretary to waive or modify only those statutory provisions and regulations described at section 1135(b) of the Act, such as conditions of participation or providers' regulatory deadlines. Payment requirements, such as the application of the productivity adjustment under the IPF PPS, are not one of the types of requirements set out under this subsection.

Final Decision: After consideration of the comments we received, we are finalizing a FY 2023 IPF productivity-adjusted market basket update equal to 3.8 percent based on the more recent data available. This 3.8 percent update is based on a more recent forecast of the FY 2023 IPF market basket update of 4.1 percent reduced by a statutorily required productivity adjustment of 0.3 percentage point.

3. FY 2023 IPF Labor-Related Share

Due to variations in geographic wage levels and other labor-related costs, we believe that payment rates under the IPF PPS should continue to be adjusted by a geographic wage index, which would apply to the labor-related portion of the Federal per diem base rate (hereafter referred to as the labor-related share). The labor-related share is determined by identifying the national average proportion of total costs that are related to, influenced by, or vary with the local labor market. We proposed to continue to classify a cost category as labor-related if the costs are labor-intensive and vary with the local labor market.

Based on our definition of the labor-related share and the cost categories in the 2016-based IPF market basket, we proposed to include in the labor-related share the sum of the relative importance of Wages and Salaries; Employee Benefits; Professional Fees: Labor-related; Administrative and Facilities Support Services; Installation, Maintenance, and Repair Services; All Other: Labor-related Services; and a portion of the Capital-Related relative importance from the 2016-based IPF market basket. For more details regarding the methodology for determining specific cost categories for inclusion in the 2016-based IPF labor-related share, see the FY 2020 IPF PPS final rule (84 FR 38445 through 38447).

The relative importance reflects the different rates of price change for these cost categories between the base year (FY 2016) and FY 2023. Based on IGI's fourth quarter 2021 forecast of the 2016-based IPF market basket, the sum of the FY 2023 relative importance moving average of Wages and Salaries; Employee Benefits; Professional Fees: Labor-related; Administrative and Facilities Support Services; Installation, Maintenance, and Repair Services; All Other: Labor-related Services was 74.4 percent. We proposed, consistent with prior rulemaking, that the portion of Capital-Related costs that are influenced by the local labor market is 46 percent. Since the relative importance for Capital-Related costs was 6.6 percent of the 2016-based IPF market basket for FY 2023, we proposed to take 46 percent of 6.6 percent to determine a labor-related share of Capital-Related costs for FY 2023 of 3.0 percent. Therefore, we proposed a total labor-related share for FY 2023 of 77.4 percent (the sum of 74.4 percent for the labor-related share of operating costs and 3.0 percent for the labor-related share of Capital-Related costs). We also proposed that if more recent data became available, we would use such data, if appropriate to determine the FY 2023 labor-related share for the final rule. For more information on the labor-related share and its calculation, we refer readers to the FY 2020 IPF PPS final rule (84 FR 38445 through 38447).

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

Comment: One commenter did not support CMS's proposal to increase the labor-related share from 77.2 percent in FY 2022 to 77.4 percent in FY 2023, stating that any increase to the labor-related share penalizes facilities that have a wage index less than 1.0. The commenter also stated that there is a growing disparity between high-wage and low-wage states that harms hospitals in many rural and underserved communities. In addition, the commenter stated that they believe CMS should consider excluding the labor portion of capital related costs for FY 2023 and going forward.

Response: We proposed to use the FY 2023 relative importance values for the labor-related cost categories from the 2016-based IPF market basket because it accounts for more recent data regarding price pressures and cost structure of IPFs. This methodology is consistent with the determination of the labor-related share since the implementation of the IPF PPS in 2007. The labor-related cost categories reflect IPF costs that are related to, influenced by, or vary with the local labor market, which would include a portion of the capital-related costs. Therefore, we disagree with the commenter that we should exclude the labor portion of capital-related costs for FY 2023 and going forward. As stated in the FY 2023 IPF proposed rule, we also proposed that if more recent data became available, we would use such data, if appropriate, to determine the FY 2023 labor-related share for the final rule. Based on IHS Global Inc.'s second quarter 2022 forecast with historical data through the first quarter of 2022, the FY 2023 labor-related share for the final rule is 77.4 percent, unchanged from the proposed rule.

Final Decision: After consideration of the comments we received, we are finalizing a FY 2023 labor-related share equal to 77.4 percent based on the latest available IGI forecast.

Table 1 shows the FY 2023 labor-related share and the final FY 2022 labor-related share using the 2016-based IPF market basket relative importance.

B. Updates to the IPF PPS Rates for FY Beginning October 1, 2022

The IPF PPS is based on a standardized Federal per diem base rate calculated from the IPF average per diem costs and adjusted for budget-neutrality in the implementation year. The Federal per diem base rate is used as the standard payment per day under the IPF PPS and is adjusted by the patient-level and facility-level adjustments that are applicable to the IPF stay. A detailed explanation of how we calculated the average per diem cost appears in the November 2004 IPF PPS final rule (69 FR 66926).

1. Determining the Standardized Budget-Neutral Federal Per Diem Base Rate

Section 124(a)(1) of the BBRA required that we implement the IPF PPS in a budget-neutral manner. In other words, the amount of total payments under the IPF PPS, including any payment adjustments, had to be projected to be equal to the amount of total payments that would have been made if the IPF PPS were not implemented. Therefore, we calculated the budget-neutrality factor by setting the total estimated IPF PPS payments to be equal to the total estimated payments that would have been made under the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) (Pub. L. 97-248) methodology had the IPF PPS not been implemented. A step-by-step description of the methodology used to estimate payments under the TEFRA payment system appears in the November 2004 IPF PPS final rule (69 FR 66926).

Under the IPF PPS methodology, we calculated the final Federal per diem base rate to be budget-neutral during the IPF PPS implementation period (that is, the 18-month period from January 1, 2005 through June 30, 2006) using a July 1 update cycle. We updated the average cost per day to the midpoint of the IPF PPS implementation period (October 1, 2005), and this amount was used in the payment model to establish the budget-neutrality adjustment.

Next, we standardized the IPF PPS Federal per diem base rate to account for the overall positive effects of the IPF PPS payment adjustment factors by dividing total estimated payments under the TEFRA payment system by estimated payments under the IPF PPS. The information concerning this standardization can be found in the November 2004 IPF PPS final rule (69 FR 66932) and the RY 2006 IPF PPS final rule (71 FR 27045). We then reduced the standardized Federal per diem base rate to account for the outlier policy, the stop loss provision, and anticipated behavioral changes. A complete discussion of how we calculated each component of the budget-neutrality adjustment appears in the November 2004 IPF PPS final rule (69 FR 66932 through 66933) and in the RY 2007 IPF PPS final rule (71 FR 27044 through 27046). The final standardized budget-neutral Federal per diem base rate established for cost reporting periods beginning on or after January 1, 2005 was calculated to be $575.95.

The Federal per diem base rate has been updated in accordance with applicable statutory requirements and § 412.428 through publication of annual notices or proposed and final rules. A detailed discussion on the standardized budget-neutral Federal per diem base rate and the electroconvulsive therapy (ECT) payment per treatment appears in the FY 2014 IPF PPS update notice (78 FR 46738 through 46740). These documents are available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​index.html.

IPFs must include a valid procedure code for ECT services provided to IPF beneficiaries in order to bill for ECT services, as described in our Medicare Claims Processing Manual, Chapter 3, Section 190.7.3 (available at https://www.cms.gov/​Regulations-and-Guidance/​Guidance/​Manuals/​Downloads/​clm104c03.pdf.) There were no changes to the ECT procedure codes used on IPF claims as a result of the final update to the ICD-10-PCS code set for FY 2023. Addendum B to this final rule shows the ECT procedure codes for FY 2023 and is available on our website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html.

2. Update of the Federal Per Diem Base Rate and Electroconvulsive Therapy Payment per Treatment

The current (FY 2022) Federal per diem base rate is $832.94 and the ECT payment per treatment is $358.60. For the final FY 2023 Federal per diem base rate, we applied the payment rate update of 3.8 percent—that is, the 2016-based IPF market basket increase for FY 2023 of 4.1 percent less the productivity adjustment of 0.3 percentage point—and the wage index budget-neutrality factor of 1.0012 (as discussed in section IV.D.1 of this final rule) to the FY 2022 Federal per diem base rate of $832.94, yielding a final Federal per diem base rate of $865.63 for FY 2023. Similarly, we applied the 3.8 percent payment rate update and the 1.0012 wage index budget-neutrality factor to the FY 2022 ECT payment per treatment of $358.60, yielding a final ECT payment per treatment of $372.67 for FY 2023.

Section 1886(s)(4)(A)(i) of the Act requires that for RY 2014 and each subsequent RY, in the case of an IPF that fails to report required quality data with respect to such RY, the Secretary will reduce any annual update to a standard Federal rate for discharges during the RY by 2.0 percentage points. Therefore, we are applying a 2.0 percentage points reduction to the Federal per diem base rate and the ECT payment per treatment as follows:

• For IPFs that fail to report required data under the IPFQR Program, we applied a 1.8 percent payment rate update—that is, the IPF market basket increase for FY 2023 of 4.1 percent less the productivity adjustment of 0.3 percentage point for an update of 3.8 percent, and further reduced by 2.0 percentage points in accordance with section 1886(s)(4)(A)(i) of the Act—and the wage index budget-neutrality factor of 1.0012 to the FY 2022 Federal per diem base rate of $832.94, yielding a Federal per diem base rate of $848.95 for FY 2023.
• For IPFs that fail to report required data under the IPFQR Program, we applied the 1.8 percent annual payment rate update and the final 1.0012 wage index budget-neutrality factor to the FY 2022 ECT payment per treatment of $358.60, yielding an ECT payment per treatment of $365.49 for FY 2023.

C. Updates to the IPF PPS Patient-Level Adjustment Factors

1. Overview of the IPF PPS Adjustment Factors

The IPF PPS payment adjustments were derived from a regression analysis of 100 percent of the FY 2002 Medicare Provider and Analysis Review (MedPAR) data file, which contained 483,038 cases. For a more detailed description of the data file used for the regression analysis, see the November 2004 IPF PPS final rule (69 FR 66935 through 66936). We proposed to continue to use the existing regression-derived adjustment factors established in 2005 for FY 2023. However, we have used more recent claims data to simulate payments to finalize the outlier fixed dollar loss threshold amount and to assess the impact of the IPF PPS updates.

2. IPF PPS Patient-Level Adjustments

The IPF PPS includes payment adjustments for the following patient-level characteristics: Medicare Severity Diagnosis Related Groups (MS-DRGs) assignment of the patient's principal diagnosis, selected comorbidities, patient age, and the variable per diem adjustments.

a. Update to MS-DRG Assignment

We believe it is important to maintain for IPFs the same diagnostic coding and Diagnosis Related Group (DRG) classification used under the IPPS for providing psychiatric care. For this reason, when the IPF PPS was implemented for cost reporting periods beginning on or after January 1, 2005, we adopted the same diagnostic code set (ICD-9-CM) and DRG patient classification system (MS-DRGs) that were utilized at the time under the IPPS. In the RY 2009 IPF PPS notice (73 FR 25709), we discussed CMS' effort to better recognize resource use and the severity of illness among patients. CMS adopted the new MS-DRGs for the IPPS in the FY 2008 IPPS final rule with comment period (72 FR 47130). In the RY 2009 IPF PPS notice (73 FR 25716), we provided a crosswalk to reflect changes that were made under the IPF PPS to adopt the new MS-DRGs. For a detailed description of the mapping changes from the original DRG adjustment categories to the current MS-DRG adjustment categories, we refer readers to the RY 2009 IPF PPS notice (73 FR 25714).

The IPF PPS includes payment adjustments for designated psychiatric DRGs assigned to the claim based on the patient's principal diagnosis. The DRG adjustment factors were expressed relative to the most frequently reported psychiatric DRG in FY 2002, that is, DRG 430 (psychoses). The coefficient values and adjustment factors were derived from the regression analysis discussed in detail in the November 28, 2003 IPF proposed rule (68 FR 66923; 66928 through 66933) and the November 15, 2004 IPF final rule (69 FR 66933 through 66960). Mapping the DRGs to the MS-DRGs resulted in the current 17 IPF MS-DRGs, instead of the original 15 DRGs, for which the IPF PPS provides an adjustment. For FY 2023, we did not propose any changes to the IPF MS-DRG adjustment factors. Therefore, we are retaining the existing IPF MS-DRG adjustment factors.

In the FY 2015 IPF PPS final rule published August 6, 2014 in the Federal Register titled, “Inpatient Psychiatric Facilities Prospective Payment System—Update for FY Beginning October 1, 2014 (FY 2015)” (79 FR 45945 through 45947), we finalized conversions of the ICD-9-CM-based MS-DRGs to ICD-10-CM/PCS-based MS-DRGs, which were implemented on October 1, 2015. Further information on the ICD-10-CM/PCS MS-DRG conversion project can be found on the CMS ICD-10-CM website at https://www.cms.gov/​Medicare/​Coding/​ICD10/​ICD-10-MS-DRG-Conversion-Project.html.

For FY 2023, we proposed to continue making the existing payment adjustment for psychiatric diagnoses that group to one of the existing 17 IPF MS-DRGs listed in Addendum A. Addendum A is available on our website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html. Psychiatric principal diagnoses that do not group to one of the 17 designated MS-DRGs will still receive the Federal per diem base rate and all other applicable adjustments; however, the payment will not include an MS-DRG adjustment. The diagnoses for each IPF MS-DRG will be updated as of October 1, 2022, using the final IPPS FY 2023 ICD-10-CM/PCS code sets. The FY 2023 IPPS/LTCH PPS final rule includes tables of the changes to the ICD-10-CM/PCS code sets, which underlie the FY 2023 IPF MS-DRGs. Both the FY 2023 IPPS final rule and the tables of final changes to the ICD-10-CM/PCS code sets, which underlie the FY 2023 MS-DRGs, are available on the CMS IPPS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​AcuteInpatientPPS/​index.html.

Code First

As discussed in the ICD-10-CM Official Guidelines for Coding and Reporting, certain conditions have both an underlying etiology and multiple body system manifestations due to the underlying etiology. For such conditions, ICD-10-CM has a coding convention that requires the underlying condition be sequenced first followed by the manifestation. Wherever such a combination exists, there is a “use additional code” note at the etiology code, and a “code first” note at the manifestation code. These instructional notes indicate the proper sequencing order of the codes (etiology followed by manifestation). In accordance with the ICD-10-CM Official Guidelines for Coding and Reporting, when a primary (psychiatric) diagnosis code has a “code first” note, the provider will follow the instructions in the ICD-10-CM Tabular List. The submitted claim goes through the CMS processing system, which will identify the principal diagnosis code as non-psychiatric and search the secondary codes for a psychiatric code to assign a DRG code for adjustment. The system will continue to search the secondary codes for those that are appropriate for comorbidity adjustment.

For more information on the code first policy, we refer readers to the November 2004 IPF PPS final rule (69 FR 66945) and see sections I.A.13 and I.B.7 of the FY 2020 ICD-10-CM Coding Guidelines, available at https://www.cdc.gov/​nchs/​data/​icd/​10cmguidelines-FY2020_​final.pdf. In the FY 2015 IPF PPS final rule, we provided a code first table for reference that highlights the same or similar manifestation codes where the code first instructions apply in ICD-10-CM that were present in ICD-9-CM (79 FR 46009). In FY 2022 there were 18 codes finalized for deletion from the ICD-10-CM codes in the IPF Code First table. For FY 2023, we proposed to delete 2 ICD-10-PCS codes and add 48 ICD-10-PCS codes to the IPF Code First table. For this FY 2023 IPF PPS final rule, we are finalizing our proposal to delete 2 ICD-10-PCS codes to add 48 ICD-10-PCS codes to the IPF Code First table. The FY 2023 Code First table is shown in Addendum B on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html.

b. Payment for Comorbid Conditions

The intent of the comorbidity adjustments is to recognize the increased costs associated with comorbid conditions by providing additional payments for certain existing medical or psychiatric conditions that are expensive to treat. In our RY 2012 IPF PPS final rule (76 FR 26451 through 26452), we explained that the IPF PPS includes 17 comorbidity categories and identified the new, revised, and deleted ICD-9-CM diagnosis codes that generate a comorbid condition payment adjustment under the IPF PPS for RY 2012 (76 FR 26451).

Comorbidities are specific patient conditions that are secondary to the patient's principal diagnosis and that require treatment during the stay. Diagnoses that relate to an earlier episode of care and have no bearing on the current hospital stay are excluded and must not be reported on IPF claims. Comorbid conditions must exist at the time of admission or develop subsequently, and affect the treatment received, length of stay (LOS), or both treatment and LOS.

For each claim, an IPF may receive only one comorbidity adjustment within a comorbidity category, but it may receive an adjustment for more than one comorbidity category. Current billing instructions for discharge claims, on or after October 1, 2015, require IPFs to enter the complete ICD-10-CM codes for up to 24 additional diagnoses if they co-exist at the time of admission, or develop subsequently and impact the treatment provided.

The comorbidity adjustments were determined based on the regression analysis using the diagnoses reported by IPFs in FY 2002. The principal diagnoses were used to establish the DRG adjustments and were not accounted for in establishing the comorbidity category adjustments, except where ICD-9-CM code first instructions applied. In a code first situation, the submitted claim goes through the CMS processing system, which will identify the principal diagnosis code as non-psychiatric and search the secondary codes for a psychiatric code to assign an MS-DRG code for adjustment. The system will continue to search the secondary codes for those that are appropriate for comorbidity adjustment.

As noted previously, it is our policy to maintain the same diagnostic coding set for IPFs that is used under the IPPS for providing the same psychiatric care. The 17 comorbidity categories formerly defined using ICD-9-CM codes were converted to ICD-10-CM/PCS in our FY 2015 IPF PPS final rule (79 FR 45947 through 45955). The goal for converting the comorbidity categories is referred to as replication, meaning that the payment adjustment for a given patient encounter is the same after ICD-10-CM implementation as it will be if the same record had been coded in ICD-9-CM and submitted prior to ICD-10-CM/PCS implementation on October 1, 2015. All conversion efforts were made with the intent of achieving this goal. For FY 2023, we proposed to continue to use the same comorbidity adjustment factors in effect in FY 2022. The FY 2023 comorbidity adjustment factors are found in Addendum A, available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html.

For FY 2023, we proposed to add 10 ICD-10-CM/PCS codes and remove 1 ICD-10-CM/PCS code from the Coagulation Factor category; proposed to add 3 ICD-10-CM/PCS codes and remove 11 ICD-10-CM/PCS codes from the Oncology Treatment comorbidity category; and proposed to add 4 ICD-10-CM/PCS codes to the Poisoning comorbidity category.

Comment: One commenter expressed concerns that the proposed FY 2023 comorbidity codes detailed in Addenda B were not displayed on the CMS website at the time the proposed rule was posted.

Response: We appreciate the concern that this commenter raised. Due to unanticipated technical issues, we were unable to post the B addenda until a few days after the display of the proposed rule. We apologize for any inconvenience that this delay caused, and will continue to work to ensure that addenda are posted as soon as possible after the display of the proposed rule for each FY. We encourage readers to contact the IPF Payment Policy mailbox at [e-mail] in order to bring issues like this to our attention as soon as possible.

The proposed FY 2023 comorbidity codes are shown in Addenda B, available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html.

In accordance with the policy established in the FY 2015 IPF PPS final rule (79 FR 45949 through 45952), we reviewed all new FY 2023 ICD-10-CM codes to remove codes that were site “unspecified” in terms of laterality from the FY 2023 ICD-10-CM/PCS codes in instances where more specific codes are available. As we stated in the FY 2015 IPF PPS final rule, we believe that specific diagnosis codes that narrowly identify anatomical sites where disease, injury, or a condition exists should be used when coding patients' diagnoses whenever these codes are available. We finalized in the FY 2015 IPF PPS rule, that we would remove site “unspecified” codes from the IPF PPS ICD-10-CM/PCS codes in instances when laterality codes (site specified codes) are available, as the clinician should be able to identify a more specific diagnosis based on clinical assessment at the medical encounter. There were no proposed changes to the FY 2023 ICD-10-CM/PCS codes, therefore, we did not propose to remove any of the new codes.

c. Patient Age Adjustments

As explained in the November 2004 IPF PPS final rule (69 FR 66922), we analyzed the impact of age on per diem cost by examining the age variable (range of ages) for payment adjustments. In general, we found that the cost per day increases with age. The older age groups are costlier than the under 45 age group, the differences in per diem cost increase for each successive age group, and the differences are statistically significant. For FY 2023, we proposed continuing to use the patient age adjustments currently in effect in FY 2022, as shown in Addendum A of this rule (see https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html). We did not receive any comments on this proposal and are finalizing it as proposed.

d. Variable Per Diem Adjustments

We explained in the November 2004 IPF PPS final rule (69 FR 66946) that the regression analysis indicated that per diem cost declines as the length of stay (LOS) increases. The variable per diem adjustments to the Federal per diem base rate account for ancillary and administrative costs that occur disproportionately in the first days after admission to an IPF. As discussed in the November 2004 IPF PPS final rule, we used a regression analysis to estimate the average differences in per diem cost among stays of different lengths (69 FR 66947 through 66950). As a result of this analysis, we established variable per diem adjustments that begin on day 1 and decline gradually until day 21 of a patient's stay. For day 22 and thereafter, the variable per diem adjustment remains the same each day for the remainder of the stay. However, the adjustment applied to day 1 depends upon whether the IPF has a qualifying ED. If an IPF has a qualifying ED, it receives a 1.31 adjustment factor for day 1 of each stay. If an IPF does not have a qualifying ED, it receives a 1.19 adjustment factor for day 1 of the stay. The ED adjustment is explained in more detail in section IV.D.4 of this final rule.

For FY 2023, we proposed to continue to use the variable per diem adjustment factors currently in effect, as shown in Addendum A to this rule, which is available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html. A complete discussion of the variable per diem adjustments appears in the November 2004 IPF PPS final rule (69 FR 66946).

D. Updates to the IPF PPS Facility-Level Adjustments

The IPF PPS includes facility-level adjustments for the wage index, IPFs located in rural areas, teaching IPFs, cost of living adjustments for IPFs located in Alaska and Hawaii, and IPFs with a qualifying ED.

1. Wage Index Adjustment

a. Background

As discussed in the RY 2007 IPF PPS final rule (71 FR 27061), RY 2009 IPF PPS (73 FR 25719) and the RY 2010 IPF PPS notices (74 FR 20373), in order to provide an adjustment for geographic wage levels, the labor-related portion of an IPF's payment is adjusted using an appropriate wage index. Currently, an IPF's geographic wage index value is determined based on the actual location of the IPF in an urban or rural area, as defined in § 412.64(b)(1)(ii)(A) and (C).

Due to the variation in costs and because of the differences in geographic wage levels, in the November 2004 IPF PPS final rule, we required that payment rates under the IPF PPS be adjusted by a geographic wage index. We proposed and finalized a policy to use the unadjusted, pre-floor, pre-reclassified IPPS hospital wage index to account for geographic differences in IPF labor costs. We implemented use of the pre-floor, pre-reclassified IPPS hospital wage data to compute the IPF wage index since there was not an IPF- specific wage index available. We believe that IPFs generally compete in the same labor market as IPPS hospitals so the pre-floor, pre-reclassified IPPS hospital wage data should be reflective of labor costs of IPFs. We believe this pre-floor, pre-reclassified IPPS hospital wage index to be the best available data to use as proxy for an IPF specific wage index. As discussed in the RY 2007 IPF PPS final rule (71 FR 27061 through 27067), under the IPF PPS, the wage index is calculated using the IPPS wage index for the labor market area in which the IPF is located, without considering geographic reclassifications, floors, and other adjustments made to the wage index under the IPPS. For a complete description of these IPPS wage index adjustments, we refer readers to the FY 2019 IPPS/LTCH PPS final rule (83 FR 41362 through 41390). Our wage index policy at § 412.424(a)(2) provides that we use the best Medicare data available to estimate costs per day, including an appropriate wage index to adjust for wage differences.

When the IPF PPS was implemented in the November 2004 IPF PPS final rule, with an effective date of January 1, 2005, the pre-floor, pre-reclassified IPPS hospital wage index that was available at the time was the FY 2005 pre-floor, pre-reclassified IPPS hospital wage index. Historically, the IPF wage index for a given RY has used the pre-floor, pre-reclassified IPPS hospital wage index from the prior FY as its basis. This has been due in part to the pre-floor, pre-reclassified IPPS hospital wage index data that were available during the IPF rulemaking cycle, where an annual IPF notice or IPF final rule was usually published in early May. This publication timeframe was relatively early compared to other Medicare payment rules because the IPF PPS follows a RY, which was defined in the implementation of the IPF PPS as the 12-month period from July 1 to June 30 (69 FR 66927). Therefore, the best available data at the time the IPF PPS was implemented was the pre-floor, pre-reclassified IPPS hospital wage index from the prior FY (for example, the RY 2006 IPF wage index was based on the FY 2005 pre-floor, pre-reclassified IPPS hospital wage index).

In the RY 2012 IPF PPS final rule, we changed the reporting year timeframe for IPFs from a RY to the FY, which begins October 1 and ends September 30 (76 FR 26434 through 26435). In that FY 2012 IPF PPS final rule, we continued our established policy of using the pre-floor, pre-reclassified IPPS hospital wage index from the prior year (that is, from FY 2011) as the basis for the FY 2012 IPF wage index. This policy of basing a wage index on the prior year's pre-floor, pre-reclassified IPPS hospital wage index has been followed by other Medicare payment systems, such as hospice and inpatient rehabilitation facilities. By continuing with our established policy, we remained consistent with other Medicare payment systems.

In FY 2020, we finalized the IPF wage index methodology to align the IPF PPS wage index with the same wage data timeframe used by the IPPS for FY 2020 and subsequent years. Specifically, we finalized the use of the pre-floor, pre-reclassified IPPS hospital wage index from the FY concurrent with the IPF FY as the basis for the IPF wage index. For example, the FY 2020 IPF wage index was based on the FY 2020 pre-floor, pre-reclassified IPPS hospital wage index rather than on the FY 2019 pre-floor, pre-reclassified IPPS hospital wage index.

We explained in the FY 2020 proposed rule (84 FR 16973), that using the concurrent pre-floor, pre-reclassified IPPS hospital wage index will result in the most up-to-date wage data being the basis for the IPF wage index. We noted that it would also result in more consistency and parity in the wage index methodology used by other Medicare payment systems. We indicated that the Medicare skilled nursing facility (SNF) PPS already used the concurrent IPPS hospital wage index data as the basis for the SNF PPS wage index. CMS proposed and finalized similar policies to use the concurrent pre-floor, pre-reclassified IPPS hospital wage index data in other Medicare payment systems, such as hospice and inpatient rehabilitation facilities. Thus, the wage adjusted Medicare payments of various provider types are based upon wage index data from the same timeframe. For FY 2023, we proposed to continue to use the concurrent pre-floor, pre-reclassified IPPS hospital wage index as the basis for the IPF wage index.

Comment: One commenter recommended we revise our policy so that the post-reclassification and post-floor hospital inpatient PPS wage index is used to calculate the wage index for IPFs. The commenter believe that the continued use of the pre-reclassification and pre-floor hospital inpatient wage index is unreasonable because it places IPFs at a disadvantage in the labor markets in which they operate relative to hospitals in the same markets. Another commenter recommended the application of a non-budget neutral wage index floor along with an annual cap on CBSAs with high wage indices and asserted that that the impact of certain wage index changes could be eliminated by allowing IPFs to reclassify to another CBSA as they are permitted to do under the IPPS.

Response: We appreciate the commenters' recommendations. We did not propose the specific policies suggested by commenters, but we will take them into consideration to potentially inform future rulemaking. We do not believe that the continued use of the pre-reclassification and pre-floor hospital inpatient wage index for FY 2023 is unreasonable or that this policy puts IPFs at a disadvantage relative to hospitals in the labor markets in which they operate. As we have previously discussed in the RY 2007 final rule (71 FR 27066), we believe that the actual location of an IPF (as opposed to the location of affiliated providers) is most appropriate for determining the wage adjustment because the prevailing wages in the area in which the IPF is located influence the cost of a case. In that same RY 2007 final rule (71 FR 27066), we also stated that we believe the “rural floor” is required only for the acute care hospital payment system, because section 4410 of the Balanced Budget Act of 1997 (Pub. L. 105-33) applies specifically to acute care hospitals and not excluded hospitals and excluded units. Therefore, we believe using the pre-floor, pre-reclassified IPPS hospital wage index is the best available data to use as a proxy for an IPF wage index because it best reflects the variation in local labor costs of IPFs in the various geographic areas in which they are located and uses the most recent IPPS hospital wage data without any geographic reclassifications, floors, or other adjustments.

Final Decision: After consideration of the comments received, we are finalizing our proposal for FY 2023 to continue to use the concurrent pre-floor, pre-reclassified IPPS hospital wage index as the basis for the IPF wage index.

We will apply the IPF wage index adjustment to the labor-related share of the national base rate and ECT payment per treatment. The labor-related share of the national rate and ECT payment per treatment will change from 77.2 percent in FY 2022 to 77.4 percent in FY 2023. This percentage reflects the labor-related share of the 2016-based IPF market basket for FY 2023 (see section IV.A of this rule).

b. Office of Management and Budget (OMB) Bulletins

1. Background

The wage index used for the IPF PPS is calculated using the unadjusted, pre-reclassified and pre-floor IPPS wage index data and is assigned to the IPF on the basis of the labor market area in which the IPF is geographically located. IPF labor market areas are delineated based on the Core-Based Statistical Area (CBSAs) established by the OMB.

Generally, OMB issues major revisions to statistical areas every 10 years, based on the results of the decennial census. However, OMB occasionally issues minor updates and revisions to statistical areas in the years between the decennial censuses through OMB Bulletins. These bulletins contain information regarding CBSA changes, including changes to CBSA numbers and titles. OMB bulletins may be accessed online at https://www.whitehouse.gov/​omb/​information-for-agencies/​bulletins/​. In accordance with our established methodology, the IPF PPS has historically adopted any CBSA changes that are published in the OMB bulletin that corresponds with the IPPS hospital wage index used to determine the IPF wage index and, when necessary and appropriate, has proposed and finalized transition policies for these changes.

In the RY 2007 IPF PPS final rule (71 FR 27061 through 27067), we adopted the changes discussed in the 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 OMB CBSA geographic designations in RY 2007, we did not provide a separate transition for the CBSA-based wage index since the IPF PPS was already in a transition period from TEFRA payments to PPS payments.

In the RY 2009 IPF PPS notice, we incorporated the CBSA nomenclature changes published in the most recent OMB bulletin that applied to the IPPS hospital wage index used to determine the current IPF wage index and stated that we expected to continue to do the same for all the OMB CBSA nomenclature changes in future IPF PPS rules and notices, as necessary (73 FR 25721).

Subsequently, CMS adopted the changes that were published in past OMB bulletins in the FY 2016 IPF PPS final rule (80 FR 46682 through 46689), the FY 2018 IPF PPS rate update (82 FR 36778 through 36779), the FY 2020 IPF PPS final rule (84 FR 38453 through 38454), and the FY 2021 IPF PPS final rule (85 FR 47051 through 47059). We direct readers to each of these rules for more information about the changes that were adopted and any associated transition policies.

In part due to the scope of changes involved in adopting the CBSA delineations for FY 2021, we finalized a 2-year transition policy in the FY 2021 IPF PPS final rule consistent with our past practice of using transition policies to help mitigate negative impacts on hospitals of certain wage index policy changes. We applied a 5-percent cap on wage index decreases to all IPF providers that had any decrease in their wage indexes, regardless of the circumstance causing the decline, so that an IPF's final wage index for FY 2021 would not be less than 95 percent of its final wage index for FY 2020, regardless of whether the IPF was part of an updated CBSA. We refer readers to the FY 2021 IPF PPS final rule (85 FR 47058 through 47059) for a more detailed discussion about the wage index transition policy for FY 2021.

On March 6, 2020, OMB issued OMB Bulletin 20-01 (available on the web at https://www.whitehouse.gov/​wp-content/​uploads/​2020/​03/​Bulletin-20-01.pdf). In considering whether to adopt this bulletin, we analyzed whether the changes in this bulletin would have a material impact on the IPF PPS wage index. This bulletin creates only one Micropolitan statistical area. As discussed in further detail in section IV.D.1.b.ii of this final rule since Micropolitan areas are considered rural for the IPF PPS wage index, this bulletin has no material impact on the IPF PPS wage index. That is, the constituent county of the new Micropolitan area was considered rural effective as of FY 2021 and would continue to be considered rural if we adopted OMB Bulletin 20-01. Therefore, we did not propose to adopt OMB Bulletin 20-01 in the FY 2022 IPF PPS proposed rule.

2. Micropolitan Statistical Areas

OMB defines a “Micropolitan Statistical Area” as a CBSA associated with at least one urban cluster that has a population of at least 10,000, but less than 50,000 (75 FR 37252). We refer to these as Micropolitan Areas. After extensive impact analysis, consistent with the treatment of these areas under the IPPS as discussed in the FY 2005 IPPS final rule (69 FR 49029 through 49032), we determined the best course of action would be to treat Micropolitan Areas as “rural” and include them in the calculation of each state's IPF PPS rural wage index. We refer readers to the FY 2007 IPF PPS final rule (71 FR 27064 through 27065) for a complete discussion regarding treating Micropolitan Areas as rural.

c. Permanent Cap on Wage Index Decreases

As discussed in section IV.D.1.b.(1) of this final rule, we have proposed and finalized temporary transition policies in the past to mitigate significant changes to payments due to changes to the IPF PPS wage index. Specifically, for FY 2016 (80 FR 46652), 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 47059), we implemented a 2-year transition to mitigate any negative effects of wage index changes by applying a 5-percent cap on any decrease in an IPF's wage index from the IPF's final wage index from FY 2020. We explained that we believe 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 the second year, that is, FY 2022, and that this transition approach struck an appropriate balance by providing a transition period to mitigate the resulting short-term instability and negative impacts on providers and time for them to adjust to their new labor market area delineations and wage index values.

In FY 2022 (86 FR 42616 through 42617), a couple of commenters recommended CMS extend the transition period adopted in the FY 2021 IPF PPS final rule. We did not propose to modify the transition policy that was finalized in the FY 2021 IPF PPS final rule, and we did not extend the transition period for FY 2022. In the FY 2022 IPF PPS final rule, we stated that we continued to believe that applying the 5-percent cap transition policy in year one provided an adequate safeguard against any significant payment reductions associated with the adoption of the revised CBSA delineations in FY 2021, allowed for sufficient time to make operational changes for future FYs, and provided a reasonable balance between mitigating some short-term instability in IPF payments and improving the accuracy of the payment adjustment for differences in area wage levels. However, we acknowledged that certain changes to wage index policy may significantly affect Medicare payments. In addition, we reiterated that our policy principles with regard to the wage index include generally using the most current data and information available and providing that data and information, as well as any approaches to addressing any significant effects on Medicare payments resulting from these potential scenarios, in notice and comment rulemaking. With these policy principles in mind, we considered for the FY 2023 proposed rule how best to address the potential scenarios about which commenters raised concerns; that is, scenarios in which changes to wage index policy may significantly affect Medicare payments.

In the past, we have established transition policies of limited duration to phase in significant changes to labor market areas. In taking this approach in the past, we sought to mitigate short-term instability and fluctuations that can negatively impact providers due to wage index changes. In accordance with the requirements of the IPF PPS wage index regulations at § 412.424(a)(2), we use an appropriate wage index based on the best available data, including the best available labor market area delineations, to adjust IPF PPS payments for wage differences. We have previously stated that, because the wage index is a relative measure of the value of labor in prescribed labor market areas, we believe it is important to implement new labor market area delineations with as minimal a transition as is reasonably possible. However, we recognize that changes to the wage index have the potential to create instability and significant negative impacts on certain providers even when labor market areas do not change. In addition, year-to-year fluctuations in an area's wage index can occur due to external factors beyond a provider's control, such as the COVID-19 PHE, and for an individual provider, these fluctuations can be difficult to predict. We also recognize that predictability in Medicare payments is important to enable providers to budget and plan their operations.

In light of these considerations, we proposed a permanent approach to smooth year-to-year changes in providers' wage indexes. We proposed a policy that we believe increases the predictability of IPF PPS payments for providers and mitigates instability and significant negative impacts to providers resulting from changes to the wage index. As previously discussed, we believe 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 believe this methodology mitigated short-term instability and fluctuations that can negatively impact providers due to wage index changes. Lastly, we believe the 5-percent cap applied to all wage index decreases for FY 2021 provided an adequate safeguard against significant payment reductions related to the adoption of the revised CBSAs. However, as discussed earlier in this section of the proposed rule, we recognize there are circumstances that a 2-year mitigation policy, like the one adopted for FY 2021, would not effectively address future years in which providers continue to be negatively affected by significant wage index decreases.

We explained in the FY 2023 IPF PPS proposed rule (87 FR 19424) that typical year-to-year variation in the IPF PPS wage index has historically been within 5 percent, and we expected this will continue to be the case in future years. Because providers are usually experienced with this level of wage index fluctuation, we stated that 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 IPF PPS payments due to any significant wage index decreases that may affect providers in a year. Therefore, we believe this approach would address concerns about instability that commenters raised in the FY 2022 IPF PPS rule. In addition, we noted that we believe applying a 5-percent cap on all wage index decreases would support increased predictability about IPF PPS payments for providers, enabling them to more effectively budget and plan their operations. Lastly, because applying a 5-percent cap on all wage index decreases would represent a small overall impact on the labor market area wage index system, we believe it would ensure the wage index is a relative measure of the value of labor in prescribed labor market areas. As discussed in further detail in section IV.D.1.e of this final rule, we estimated that applying a 5-percent cap on all wage index decreases would 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 explained that we anticipated that in the absence of proposed policy changes most providers would not experience year-to-year wage index declines greater than 5 percent in any given year. Therefore, we anticipated that the impact to the wage index budget neutrality factor in future years would continue to be minimal. We also stated that we believe that the 5-percent cap would likely be applied similarly to all IPFs 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) will be similar. We explained that, while this policy may result in IPFs 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.

The Secretary has broad authority under section 1886(s)(1) of the Act and Section 124 of the BBRA to establish appropriate payment adjustments under the IPF PPS, including the wage index adjustment. As discussed earlier in this section, the IPF PPS regulations specify that we use an appropriate wage index based on the best available data. For the reasons discussed in this section, we stated in the proposed rule that we believe a 5-percent cap on wage index decreases would be appropriate for the IPF PPS (87 FR 19424). Therefore, for FY 2023 and subsequent years, we proposed to apply a 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, regardless of the circumstances causing the decline. That is, we proposed that an IPF'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 IPF is part of an updated CBSA, and that for subsequent years, a provider's wage index would not be less than 95 percent of its wage index calculated in the prior FY. This also means that if an IPF's prior FY wage index is calculated with the application of the 5-percent cap, the following year's wage index would not be less than 95 percent of the IPF's capped wage index in the prior FY. For example, if an IPF's wage index for FY 2023 is calculated with the application of the 5-percent cap, then its wage index for FY 2024 would not be less than 95 percent of its capped wage index in FY 2023. Lastly, we proposed that a new IPF 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 IPF would not have a wage index in the prior FY. We proposed to reflect the permanent cap on wage index decreases at § 412.424(d)(1)(i).

Comment: We received 11 comments supporting the proposal of a permanent cap on wage index decreases. One commenter recommended that CMS consider a more gradual reduction of the wage index cap, such as between 1 and 2 percent.

Response: We appreciate commenters' support for the proposed permanent cap on wage index decreases. We also appreciate the suggestion to consider a lower threshold for the permanent cap; however, we are not finalizing a lower threshold for the cap. Furthermore, as we discussed in the FY 2023 IPF PPS proposed rule (87 FR 19424), we believe applying a 5-percent cap on wage index decreases would be appropriate for the IPF PPS, because it would effectively mitigate instability in IPF PPS payments due to any significant wage index decreases, and would also represent a small overall impact on the labor market area wage index system and would therefore ensure the wage index is a relative measure of the value of labor in prescribed labor market areas. Based on the data used for this FY 2023 IPF PPS final rule, we estimate that only 1.3 percent of providers will experience wage index changes of more than 5 percent. In contrast, we estimate that approximately 12.2 percent of providers will experience wage index decreases of more than 2 percent, and 32.1 percent will experience wage index decreases of more than 1 percent. Therefore, if we were to cap wage index decreases at a lower threshold, for example 1 or 2 percent as the commenter suggested, the wage index cap would affect more providers and, accordingly, would result in a larger budget neutrality effect. Furthermore, the wage index cap policy would represent a relatively larger overall impact on the labor market area wage index system, since more IPFs in a greater number of labor market areas would be affected by the cap. We therefore do not believe it would be appropriate to apply a 1 or 2 percent cap on wage index decreases as the commenter suggested.

Comment: MedPAC supported the proposal to cap wage index decreases at 5 percent, but suggested also applying a cap to increases of more than 5 percent.

Response: We appreciate MedPAC's suggestion that the cap on wage index changes of more than 5 percent should also be applied to increases in the wage index. However, as we discussed in the proposed rule, one purpose of the proposed policy is to help mitigate the significant negative impacts of certain wage index changes. As we noted in the FY 2023 IPF PPS proposed rule (87 FR 19424), we believe applying a 5-percent cap on all wage index decreases would support increased predictability about IPF PPS payments for providers, enabling them to more effectively budget and plan their operations. That is, we proposed to cap decreases because we believe that a provider would be able to more effectively budget and plan when there is predictability about its expected minimum level of IPF PPS payments in the upcoming fiscal year. We did not propose to limit wage index increases because we do not believe such a policy is needed to enable IPFs to more effectively budget and plan their operations. Therefore, we believe it is appropriate for providers that experience an increase in their wage index value to receive that wage index value.

Comment: Several commenters recommended that CMS apply the wage index cap in a non-budget neutral manner.

Response: In accordance with our longstanding policy under the IPF PPS, we updated the wage index in such a way that total estimated payments to IPFs for FY 2023 are the same with or without the changes (that is, in a budget-neutral manner) by applying a budget neutrality factor to the IPF PPS rates. We proposed to apply the wage index cap in a budget-neutral manner in accordance with this overall budget neutrality policy for the IPF PPS wage index so that wage index changes do not increase aggregate Medicare spending. In the FY 2023 IPF PPS proposed rule, we noted that applying a 5-percent cap on all wage index decreases would have a very small effect on the wage index budget neutrality factor for FY 2023. We explained that 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 and that we expect the impact to the wage index budget neutrality factor in future years will continue to be minimal.

Comment: Two commenters opposed the proposal to pay any new provider the wage index for the area in which it is geographically located for its first full or partial FY with no cap applied. One commenter expressed concern that this policy will create an unnecessary inequity in Medicare payments for IPFs in the same market. Another commenter asserted that new facilities will struggle to fill hospital beds and recruit staff if their wage index is lower than other IPFs in the same CBSA. This commenter further noted that ultimately, the addition of a new facility will most likely increase the region's wage index in the future.

Response: We appreciate the concerns that commenters raised, but we do not agree that this proposal would create an unnecessary inequity in IPF PPS payments or make it more difficult for new facilities to fill hospital beds and recruit staff. As we discussed in the FY 2023 IPF PPS proposed rule (87 FR 19424), while this policy may result in IPFs 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 because, over time, wage levels in a CBSA will converge to the same level. In addition, as we have previously stated, we believe the IPF PPS wage index accurately reflects the cost of labor in a prescribed labor market area. Therefore, we believe the IPF PPS wage index would accurately reflect the labor costs that a new provider would face. As we noted earlier in this section, we proposed to apply the permanent 5-percent cap on wage index decreases in order to mitigate instability, support increased predictability about IPF PPS payments, and enable providers to more effectively budget and plan their operations. We do not believe that changes to the wage index in a labor market area would represent a change for a new provider in that labor market area. In contrast to other providers in the same area, a new provider would not have a prior year wage index against which to compare the current year wage index. Therefore, we do not believe that applying the cap to new providers would be appropriate.

Comment: A commenter recommended that CMS retroactively apply the 5-percent cap policy to the FY 2022 wage index for providers that experienced wage index decreases due to their transition to a new CBSA based on the new OMB delineations that were finalized for FY 2021.

Response: As noted previously, in FY 2021, we implemented a 2-year transition to mitigate any negative effects of wage index changes by applying a 5-percent cap on any decrease in an IPF's wage index from the IPF's final wage index from FY 2020; we indicated that no cap would be applied to the reduction in the second year, FY 2022. In the FY 2023 IPF PPS proposed rule, we did not propose to modify that transition policy to extend the transition period for FY 2022. We have historically implemented transitions of limited duration, as discussed in the FY 2016 (80 FR 46652) final rule, to address CBSA changes due to substantial updates to OMB delineations. In accordance with our policy principles that we use the most updated data and information available with regard to the wage index, as noted in the FY 2022 IPF PPS final rule (86 FR 42617), we proposed that the FY 2023 IPF PPS 5-percent cap wage index policy would be prospective to mitigate any significant decreases beginning in FY 2023.

Final Decision: After consideration of the comments received, we are finalizing as proposed a permanent 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, which we will apply in a budget-neutral manner. We are also finalizing as proposed that a new IPF will 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 IPF would not have a wage index in the prior FY. We are reflecting the permanent cap on wage index decreases at § 412.424(d)(1)(i).

As previously discussed, we believe this methodology will maintain the IPF PPS wage index as a relative measure of the value of labor in prescribed labor market areas, increase predictability of IPF PPS payments for providers, and mitigate instability and significant negative impacts to providers resulting from significant changes to the wage index. In section VIII.C.2 of this final rule, we estimate the impact to payments for providers in FY 2023 based on this policy. We also note that we will examine the effects of this policy on an ongoing basis in the future in order to assess its appropriateness.

d. Adjustment for Rural Location

In the November 2004 IPF PPS final rule, (69 FR 66954) we provided a 17-percent payment adjustment for IPFs located in a rural area. This adjustment was based on the regression analysis, which indicated that the per diem cost of rural facilities was 17-percent higher than that of urban facilities after accounting for the influence of the other variables included in the regression. This 17-percent adjustment has been part of the IPF PPS each year since the inception of the IPF PPS. For FY 2023, we proposed to continue to apply a 17-percent payment adjustment for IPFs located in a rural area as defined at § 412.64(b)(1)(ii)(C) (see 69 FR 66954 for a complete discussion of the adjustment for rural locations). We did not receive any comments on this proposal, and we are finalizing it as proposed.

e. Budget Neutrality Adjustment

Changes to the wage index are made in a budget-neutral manner so that updates do not increase expenditures. For FY 2023, we proposed to continue to apply a budget-neutrality adjustment in accordance with our existing budget-neutrality policy. This policy requires us to update the wage index in such a way that total estimated payments to IPFs for FY 2023 are the same with or without the changes (that is, in a budget-neutral manner) by applying a budget neutrality factor to the IPF PPS rates. As discussed in section IV.E.2 of this final rule, we used the March 2022 update of the FY 2021 IPF claims to calculate the final FY 2023 IPF PPS wage index budget neutrality factor. We used the following steps, which include the 5-percent cap on decreases to a provider's wage index, to ensure that the rates reflect the FY 2023 update to the wage indexes (based on the FY 2019 hospital cost report data) and the labor-related share in a budget-neutral manner:

Step 1: Simulate estimated IPF PPS payments, using the FY 2022 IPF wage index values (available on the CMS website) and labor-related share (as published in the FY 2022 IPF PPS final rule (86 FR 42608).

Step 2: Simulate estimated IPF PPS payments using the final FY 2023 IPF wage index values (available on the CMS website), including application of the 5-percent cap on wage index decreases, and the final FY 2023 labor-related share (based on the latest available data as discussed previously).

Step 3: Divide the amount calculated in step 1 by the amount calculated in step 2. The resulting quotient is the FY 2023 budget-neutral wage adjustment factor of 1.0012.

Step4: Apply the FY 2023 budget-neutral wage adjustment factor from step 3 to the FY 2022 IPF PPS Federal per diem base rate after the application of the market basket update described in section IV.A of this final rule, to determine the FY 2023 IPF PPS Federal per diem base rate.

As discussed in section IV.D.1.c of this final rule, we also followed these steps to separately calculate the budget neutrality factor associated with the 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year. First, we calculated the budget neutrality factor associated with the FY 2023 IPF wage index and FY 2023 labor-related share. We divided the amount of simulated payments using the FY 2022 IPF wage index and labor-related share by the amount of simulated payments using the FY 2023 wage index and FY 2023 labor-related share. The resulting quotient is 1.0013.

Next, we calculated the budget neutrality factor associated with the 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year. We divided the amount of simulated payments using the FY 2023 wage index and FY 2023 labor-related share by the amount of simulated payments using the FY 2023 wage index, the 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, and the FY 2023 labor-related share. The resulting quotient is 0.9999. The combined budget neutrality factor, which is the FY 2023 budget-neutral wage adjustment factor as discussed earlier in this section, is 1.0012.

2. Teaching Adjustment

In the November 2004 IPF PPS final rule (69 FR 66922), we implemented regulations at § 412.424(d)(1)(iii) to establish a facility-level adjustment for IPFs that are, or are part of, teaching hospitals. The teaching adjustment accounts for the higher indirect operating costs experienced by hospitals that participate in graduate medical education (GME) programs. The payment adjustments are made based on the ratio of the number of full-time equivalent (FTE) interns and residents training in the IPF and the IPF's average daily census (ADC).

Under the IPPS, Medicare makes direct GME payments (for direct costs such as resident and teaching physician salaries, and other direct teaching costs) to all teaching hospitals including those paid under a PPS, and those paid under the TEFRA rate-of-increase limits. These direct GME payments are made separately from payments for hospital operating costs and are not part of the IPF PPS. In addition, direct GME payments do not address the estimated higher indirect operating costs teaching hospitals may face.

The results of the regression analysis of FY 2002 IPF data established the basis for the payment adjustments included in the November 2004 IPF PPS final rule. The results showed that the indirect teaching cost variable is significant in explaining the higher costs of IPFs that have teaching programs. We calculated the teaching adjustment based on the IPF's “teaching variable,” which is (1 + (the number of FTE residents training in the IPF/the IPF's ADC)). The teaching variable is then raised to the 0.5150 power to result in the teaching adjustment. This formula is subject to the limitations on the number of FTE residents, which are described in this section of the final rule.

We established the teaching adjustment in a manner that limited the incentives for IPFs to add FTE residents for the purpose of increasing their teaching adjustment. We imposed a cap on the number of FTE residents that may be counted for purposes of calculating the teaching adjustment. The cap limits the number of FTE residents that teaching IPFs may count for the purpose of calculating the IPF PPS teaching adjustment, not the number of residents teaching institutions can hire or train. We calculated the number of FTE residents that trained in the IPF during a “base year” and used that FTE resident number as the cap. An IPF's FTE resident cap is ultimately determined based on the final settlement of the IPF's most recent cost report filed before November 15, 2004 (publication date of the IPF PPS final rule). A complete discussion of the temporary adjustment to the FTE cap to reflect residents due to hospital closure or residency program closure appears in the RY 2012 IPF PPS proposed rule (76 FR 5018 through 5020) and the RY 2012 IPF PPS final rule (76 FR 26453 through 26456).

In the regression analysis, the logarithm of the teaching variable had a coefficient value of 0.5150. We converted this cost effect to a teaching payment adjustment by treating the regression coefficient as an exponent and raising the teaching variable to a power equal to the coefficient value. We note that the coefficient value of 0.5150 was based on the regression analysis holding all other components of the payment system constant. A complete discussion of how the teaching adjustment was calculated appears in the November 2004 IPF PPS final rule (69 FR 66954 through 66957) and the RY 2009 IPF PPS notice (73 FR 25721). As with other adjustment factors derived through the regression analysis, we do not plan to rerun the teaching adjustment factors in the regression analysis until we more fully analyze IPF PPS data. Therefore, in this FY 2023 final rule, we will continue to retain the coefficient value of 0.5150 for the teaching adjustment to the Federal per diem base rate.

3. Cost of Living Adjustment for IPFs Located in Alaska and Hawaii

The IPF PPS includes a payment adjustment for IPFs located in Alaska and Hawaii based upon the area in which the IPF is located. As we explained in the November 2004 IPF PPS final rule, the FY 2002 data demonstrated that IPFs in Alaska and Hawaii had per diem costs that were disproportionately higher than other IPFs. Other Medicare prospective payment systems (for example, the IPPS and LTCH PPS) adopted a COLA to account for the cost differential of care furnished in Alaska and Hawaii.

We analyzed the effect of applying a COLA to payments for IPFs located in Alaska and Hawaii. The results of our analysis demonstrated that a COLA for IPFs located in Alaska and Hawaii will improve payment equity for these facilities. As a result of this analysis, we provided a COLA in the November 2004 IPF PPS final rule.

A COLA for IPFs located in Alaska and Hawaii is made by multiplying the non-labor-related portion of the Federal per diem base rate by the applicable COLA factor based on the COLA area in which the IPF is located.

The COLA factors through 2009 were published by the Office of Personnel Management (OPM), and the OPM memo showing the 2009 COLA factors is available at https://www.chcoc.gov/​content/​nonforeign-area-retirement-equity-assurance-act.

We note that the COLA areas for Alaska are not defined by county as are the COLA areas for Hawaii. In 5 CFR 591.207, the OPM established the following COLA areas:

• City of Anchorage, and 80-kilometer (50-mile) radius by road, as measured from the Federal courthouse.
• City of Fairbanks, and 80-kilometer (50-mile) radius by road, as measured from the Federal courthouse.
• City of Juneau, and 80-kilometer (50-mile) radius by road, as measured from the Federal courthouse.
• Rest of the state of Alaska.

As stated in the November 2004 IPF PPS final rule, we update the COLA factors according to updates established by the OPM. However, sections 1911 through 1919 of the Non-foreign Area Retirement Equity Assurance Act, as contained in subtitle B of title XIX of the National Defense Authorization Act (NDAA) for FY 2010 (Pub. L. 111-84, October 28, 2009), transitions the Alaska and Hawaii COLAs to locality pay. Under section 1914 of NDAA, locality pay was phased in over a 3-year period beginning in January 2010, with COLA rates frozen as of the date of enactment, October 28, 2009, and then proportionately reduced to reflect the phase-in of locality pay.

When we published the proposed COLA factors in the RY 2012 IPF PPS proposed rule (76 FR 4998), we inadvertently selected the FY 2010 COLA rates, which had been reduced to account for the phase-in of locality pay. We did not intend to propose the reduced COLA rates because that would have understated the adjustment. Since the 2009 COLA rates did not reflect the phase-in of locality pay, we finalized the FY 2009 COLA rates for RY 2010 through RY 2014.

In the FY 2013 IPPS/LTCH final rule (77 FR 53700 through 53701), we established a new methodology to update the COLA factors for Alaska and Hawaii, and adopted this methodology for the IPF PPS in the FY 2015 IPF final rule (79 FR 45958 through 45960). We adopted this new COLA methodology for the IPF PPS because IPFs are hospitals with a similar mix of commodities and services. We believe it is appropriate to have a consistent policy approach with that of other hospitals in Alaska and Hawaii. Therefore, the IPF COLAs for FY 2015 through FY 2017 were the same as those applied under the IPPS in those years. As finalized in the FY 2013 IPPS/LTCH PPS final rule (77 FR 53700 and 53701), the COLA updates are determined every 4 years, when the IPPS market basket labor-related share is updated. Because the labor-related share of the IPPS market basket was most recently updated for FY 2022, the COLA factors were updated in FY 2022 IPPS/LTCH rulemaking (86 FR 45547). As such, we also updated the IPF PPS COLA factors for FY 2022 (86 FR 42621 through 42622) to reflect the updated COLA factors finalized in the FY 2022 IPPS/LTCH rulemaking. Table 2 shows the IPF PPS COLA factors effective for FY 2022 through FY 2025.

We did not receive any comments about the proposed COLA factors for FY 2023, and are finalizing them as proposed. The IPF PPS COLA factors for FY 2023 are also shown in Addendum A to this final rule, and is available on the CMS website at https://www.cms.gov/​Medicare/​Medicare-Fee-for-Service-Payment/​InpatientPsychFacilPPS/​tools.html.

4. Adjustment for IPFs With a Qualifying Emergency Department (ED)

The IPF PPS includes a facility-level adjustment for IPFs with qualifying EDs. We provide an adjustment to the Federal per diem base rate to account for the costs associated with maintaining a full-service ED. The adjustment is intended to account for ED costs incurred by a psychiatric hospital with a qualifying ED or an excluded psychiatric unit of an IPPS hospital or a CAH, for preadmission services otherwise payable under the Medicare Hospital Outpatient Prospective Payment System (OPPS), furnished to a beneficiary on the date of the beneficiary's admission to the hospital and during the day immediately preceding the date of admission to the IPF (see § 413.40(c)(2)), and the overhead cost of maintaining the ED. This payment is a facility-level adjustment that applies to all IPF admissions (with one exception which we described), regardless of whether a particular patient receives preadmission services in the hospital's ED.

The ED adjustment is incorporated into the variable per diem adjustment for the first day of each stay for IPFs with a qualifying ED. Those IPFs with a qualifying ED receive an adjustment factor of 1.31 as the variable per diem adjustment for day 1 of each patient stay. If an IPF does not have a qualifying ED, it receives an adjustment factor of 1.19 as the variable per diem adjustment for day 1 of each patient stay.

The ED adjustment is made on every qualifying claim except as described in this section of the final rule. As specified in § 412.424(d)(1)(v)(B), the ED adjustment is not made when a patient is discharged from an IPPS hospital or CAH and admitted to the same IPPS hospital's or CAH's excluded psychiatric unit. We clarified in the November 2004 IPF PPS final rule (69 FR 66960) that an ED adjustment is not made in this case because the costs associated with ED services are reflected in the DRG payment to the IPPS hospital or through the reasonable cost payment made to the CAH.

Therefore, when patients are discharged from an IPPS hospital or CAH and admitted to the same hospital's or CAH's excluded psychiatric unit, the IPF receives the 1.19 adjustment factor as the variable per diem adjustment for the first day of the patient's stay in the IPF. For FY 2023, we proposed to continue to retain the 1.31 adjustment factor for IPFs with qualifying EDs. We did not receive any comments on this proposal, and we are finalizing it as proposed. A complete discussion of the steps involved in the calculation of the ED adjustment factors are in the November 2004 IPF PPS final rule (69 FR 66959 through 66960) and the RY 2007 IPF PPS final rule (71 FR 27070 through 27072).

E. Other Final Payment Adjustments and Policies

1. Outlier Payment Overview

The IPF PPS includes an outlier adjustment to promote access to IPF care for those patients who require expensive care and to limit the financial risk of IPFs treating unusually costly patients. In the November 2004 IPF PPS final rule, we implemented regulations at § 412.424(d)(3)(i) to provide a per-case payment for IPF stays that are extraordinarily costly. Providing additional payments to IPFs for extremely costly cases strongly improves the accuracy of the IPF PPS in determining resource costs at the patient and facility level. These additional payments reduce the financial losses that would otherwise be incurred in treating patients who require costlier care, and therefore, reduce the incentives for IPFs to under-serve these patients. We make outlier payments for discharges in which an IPF's estimated total cost for a case exceeds a fixed dollar loss threshold amount (multiplied by the IPF's facility-level adjustments) plus the Federal per diem payment amount for the case.

In instances when the case qualifies for an outlier payment, we pay 80 percent of the difference between the estimated cost for the case and the adjusted threshold amount for days 1 through 9 of the stay (consistent with the median LOS for IPFs in FY 2002), and 60 percent of the difference for day 10 and thereafter. The adjusted threshold amount is equal to the outlier threshold amount adjusted for wage area, teaching status, rural area, and the COLA adjustment (if applicable), plus the amount of the Medicare IPF payment for the case. We established the 80 percent and 60 percent loss sharing ratios because we were concerned that a single ratio established at 80 percent (like other Medicare PPSs) might provide an incentive under the IPF per diem payment system to increase LOS in order to receive additional payments.

After establishing the loss sharing ratios, we determined the current fixed dollar loss threshold amount through payment simulations designed to compute a dollar loss beyond which payments are estimated to meet the 2-percent outlier spending target. Each year when we update the IPF PPS, we simulate payments using the latest available data to compute the fixed dollar loss threshold so that outlier payments represent 2 percent of total estimated IPF PPS payments.

2. Update to the Outlier Fixed Dollar Loss Threshold Amount

In accordance with the update methodology described in § 412.428(d), we proposed to update the fixed dollar loss threshold amount used under the IPF PPS outlier policy. Based on the regression analysis and payment simulations used to develop the IPF PPS, we established a 2-percent outlier policy, which strikes an appropriate balance between protecting IPFs from extraordinarily costly cases while ensuring the adequacy of the Federal per diem base rate for all other cases that are not outlier cases.

Our longstanding methodology for updating the outlier fixed dollar loss threshold involves using the best available data, which is typically the most recent available data. Last year for the FY 2022 IPF PPS final rule, we finalized the use of FY 2019 claims rather than the more recent FY 2020 claims for updating the outlier fixed dollar loss threshold (86 FR 42623). We noted that our use of the FY 2019 claims to set the final outlier fixed dollar loss threshold for FY 2022 deviated from our longstanding practice of using the most recent available year of claims, but remained otherwise consistent with the established outlier update methodology. We explained that we finalized our proposal to deviate from our longstanding practice of using the most recent available year of claims only because, and to the extent that, the COVID-19 PHE appeared to have significantly impacted the FY 2020 IPF claims. We further stated that we intended to continue to analyze further data in order to better understand both the short-term and long-term effects of the COVID-19 PHE on IPFs (86 FR 42624).

For the FY 2023 IPF PPS proposed rule, consistent with our longstanding practice, we analyzed the most recent available data for simulating IPF PPS payments in FY 2023. We observed a continuation of two main trends that we noted in our analysis of FY 2020 claims for FY 2022—that is, an overall increase in average cost per day and an overall decrease in the number of covered days. However, we also identified that some providers had significant increases in their charges, resulting in higher than normal estimated cost per day that would skew our estimate of outlier payments for FY 2022 and FY 2023.

In the proposed rule (87 FR 19428), we noted that historically, we have applied statistical trims under the IPF PPS in order to improve the statistical validity of the data used for ratesetting. In the November 2004 final rule, we explained that we applied a 3 standard deviation trim on cost per day prior to calculating the average per diem cost used to calculate the IPF PPS Federal per diem base rate (69 FR 66927). Furthermore, as discussed in section IV.E.3 of this final rule, our longstanding policy applies a ceiling on a provider's cost-to-charge ratio when it exceeds 3 standard deviations from the mean cost-to-charge ratio for urban or rural providers. We proposed a similar approach in order to address the skew in estimated cost per day that we observed in the FY 2021 claims. Specifically, we proposed for FY 2023 to exclude providers from our simulation of IPF PPS payments for FY 2022 and FY 2023 if their change in estimated average cost per day is outside 3 standard deviations from the mean.

In the proposed rule (87 FR 19428), we stated that based on an analysis of the December 2021 update of FY 2021 IPF claims and the FY 2022 rate increases, we believe it is necessary to update the fixed dollar loss threshold amount to maintain an outlier percentage that equals 2 percent of total estimated IPF PPS payments. We proposed to update the IPF outlier threshold amount for FY 2023 using FY 2021 claims data and the same methodology that we used to set the initial outlier threshold amount in the RY 2007 IPF PPS final rule (71 FR 27072 and 27073), which is also the same methodology that we used to update the outlier threshold amounts for years 2008 through 2022. However, as discussed earlier in this section, we also proposed for FY 2023 to exclude providers from our impact simulations whose change in simulated cost per day is outside 3 standard deviations from the mean. Based on an analysis of the data available for the proposed rule, we estimated that IPF outlier payments as a percentage of total estimated payments were approximately 3.2 percent in FY 2022. Therefore, we proposed to update the outlier threshold amount to $24,270 to maintain estimated outlier payments at 2 percent of total estimated aggregate IPF payments for FY 2023. This proposed update was an increase from the FY 2022 threshold of $16,040.

Comment: Several commenters expressed concern about using CY 2021 data because of the impact of the COVID-19 PHE and suggested that CMS consider alternative methodologies for estimating the outlier percentage and setting the outlier fixed dollar loss threshold amount. Some commenters expressed their belief that the proposed trimming methodology is not sufficient to blunt COVID-19's overstated impact on the IPF PPS outlier calculation. These commenters encouraged CMS to use an alternative inflation factor from a period before the COVID-19 PHE and to adjust cost-to-charge ratios (CCRs) to reflect the CCRs from prior to the COVID-19 PHE. Another commenter suggested that CMS estimate the outlier percentage using multiple years of claims, or set the outlier fixed dollar loss threshold amount based on an average of outlier thresholds from multiple years. Another commenter suggested that the percent increase to the outlier fixed dollar loss threshold amount should be limited to no more than the market basket update percentage.

Response: We appreciate the suggestions from commenters regarding these alternative methodologies. We believe that the proposed trimming methodology sufficiently mitigates the significant increases in charges that we observed in the FY 2021 claims, which we noted would skew our estimate of outlier payments for FY 2022. We believe this methodology also appropriately accounts for the ongoing trends that we noted in previous analysis of FY 2020 claims for FY 2022—that is, an overall increase in average cost per day and an overall decrease in the number of covered days. In the FY 2022 IPF PPS final rule (86 FR 42624), we explained that we believed these trends were related to the COVID-19 PHE and noted that we would continue to analyze further data in order to better understand both the short-term and long-term effects of the COVID-19 PHE on IPFs. Because we observed these continued trends in FY 2021, we believe it is reasonable to expect that they will continue to some extent in FY 2023.

Regarding the recommendation to use an inflation factor from a different time period, we do not believe it would be appropriate to do so for this FY 2023 IPF PPS final rule. We note that whereas the IPPS uses a charge inflation factor calculated based on historical IPPS charge data, the longstanding IPF PPS methodology uses a charge inflation factor calculated based on the latest available forecast of the IPF PPS market basket price proxies. As discussed in section IV.A.2 of this final rule, we believe the 2016-based IPF market basket increase adequately reflects the average change in the price of goods and services hospitals purchase in order to provide IPF medical services. Furthermore, as discussed in that same section of this final rule, the updated forecast for this FY 2023 final rule incorporates more recent historical data and reflects a revised outlook regarding the United States economy and expected price inflation for FY 2023 for IPFs. Therefore, we believe it is more appropriate to use an inflation factor that is based on the latest available forecast of input price growth for IPFs, rather than a factor based on data from an earlier time period, as the commenters suggested.

Regarding the alternative methodologies that commenters suggested for calculating the outlier threshold, we do not believe that averaging the proposed FY 2023 outlier fixed dollar loss threshold amount with the amounts from prior years, or limiting the increase to the outlier fixed dollar loss threshold amount, would be appropriate for this FY 2023 IPF PPS final rule. As discussed earlier in this section, the longstanding IPF PPS 2-percent outlier policy was established based on the regression analysis and payment simulations used to develop the IPF PPS. We have previously explained that the 2-percent outlier policy strikes an appropriate balance between protecting IPFs from extraordinarily costly cases while ensuring the adequacy of the Federal per diem base rate for all other cases that are not outlier cases. Each year when we update the IPF PPS, we simulate payments using the latest available data to compute the fixed dollar loss threshold so that outlier payments represent 2 percent of total estimated IPF PPS payments. For this FY 2023 IPF PPS final rule, we have simulated payments using the latest available data, and these payment simulations indicate that an increase to the outlier fixed dollar loss threshold is necessary in order to maintain outlier payments at 2 percent of total payments. We are concerned that the alternative methodologies that commenters suggested would not appropriately target outlier payments such that they remain at 2 percent of total IPF PPS payments. Regarding the suggestion that CMS use multiple years of claims to determine the outlier fixed dollar loss threshold amount, we reiterate that our longstanding methodology uses the best available data, which is typically the most recent available data, to update the outlier fixed dollar loss threshold amount. We believe the proposed methodology appropriately accounts for the trends in average cost per day and the number of covered days reflected in the IPF PPS claims, which we expect are likely to continue to some extent into FY 2023. We believe the proposed methodology also incorporates more recent historical data and reflects a revised outlook regarding the United States economy and expected price inflation for FY 2023 for IPFs. Therefore, we are finalizing the use of the proposed methodology to calculate the FY 2023 IPF PPS outlier fixed dollar loss threshold amount.

Comment: MedPAC encouraged CMS to provide additional data about the increase to the outlier fixed dollar loss threshold amount for FY 2023.

Response: As we noted in the proposed rule, two main trends that we observed in the FY 2020 claims continued in the FY 2021 claims. First, we observed that average cost per day increased approximately 12 percent when comparing the simulated FY 2021 IPF PPS payments from the FY 2022 IPF PPS final rule to the simulated FY 2022 IPF PPS payments that we used to estimate the outlier percentage for this FY 2023 IPF PPS final rule. In the FY 2022 IPF PPS proposed rule (86 FR 19526), we explained that we estimate the costs per case based on the covered charges on each IPF claim and the IPF's most recent CCR. In that proposed rule, we noted that laboratory charges, which make up roughly one-third of the covered charges per IPF claim, increased approximately 6.8 percent between FY 2019 and FY 2020. We found that laboratory charges continued to increase for the FY 2021 claims analyzed for this FY 2023 IPF PPS final rule. We found that laboratory charges per day in 2021 were approximately 12.7 percent higher than laboratory charges per day in 2019. We believe these increased laboratory charges are likely in response to the COVID-19 PHE, and as stated earlier, we believe it is reasonable to expect that these increased laboratory charges will continue to some extent in FY 2023.

The second continued trend that we observed was that the number of covered days decreased in the FY 2021 claims. As we discussed in the FY 2022 IPF PPS proposed rule (86 FR 19524), we observed a decrease in covered days of approximately 15 percent from the FY 2019 claims to the FY 2020 claims. Before applying the statistical trim for this FY 2023 IPF PPS final rule, the number of covered days in the FY 2021 claims was approximately 28 percent lower than the number of covered days in the FY 2019 claims used for FY 2022 final rulemaking. This decrease in covered days corresponds with a decrease of approximately 27 percent in the total simulated FY 2022 IPF PPS payments compared to total simulated FY 2021 IPF PPS payments used for FY 2022 final rulemaking. After applying the statistical trim, covered days were approximately 32 percent lower than FY 2019, and total simulated FY 2022 IPF PPS payments that we used to estimate the outlier percentage for this FY 2023 IPF PPS final rule were approximately 30 percent lower than total simulated FY 2021 IPF PPS payments. Because we calculate the outlier fixed dollar loss threshold amount so that outlier payments represent 2 percent of total estimated IPF PPS payments, the decrease to the number of days and total estimated IPF PPS payments increases the percentage of outlier payments relative to total payments, which contributes to the upward trend in the outlier fixed dollar loss threshold amount.

In our simulated FY 2022 outlier payments using the FY 2022 IPF PPS outlier fixed dollar loss threshold of $16,040, we estimated that 9,169 cases will receive outlier payments, with a mean outlier payment amount per outlier case of $10,057.59. We observed that the distribution of simulated FY 2022 outlier payments is skewed right, which means that a large number of outlier cases receive relatively small amounts of outlier payments, and a smaller number of outlier cases receive relatively large outlier payments. Consequently, half of all simulated outlier cases receive outlier payments of $5,490.11 or less, and 1,231 cases receive outlier payments of $1,000 or less. We also observed that outlier payments are concentrated among certain types of IPFs. As shown in Table 3, in section VIII.C.2 of this final rule, teaching IPFs with more than 10 percent interns and residents to beds are projected to experience the largest decreases in estimated payments as a result of the increase to the outlier fixed dollar loss threshold amount, because these providers had a larger share of outlier cases than other provider types. We did not observe that changes in case mix appear to be driving the increase in the outlier percentage. In the simulated FY 2022 IPF PPS payments, we observed that approximately 79 percent of outlier cases are for DRG 885 (Psychoses), which aligns with the proportion of IPF PPS cases that typically receive that DRG. We estimate that the average outlier payment for cases with DRG 885 is $10,600.21, which is comparable to the average outlier payment for all cases.

Final Decision: After consideration of the comments received, we are finalizing our proposal to use the latest available FY 2021 claims, in accordance with our longstanding practice, to simulate payments for determining the final FY 2023 IPF PPS outlier fixed dollar loss threshold amount. We are also finalizing our proposal to exclude providers from our impact simulations whose change in simulated cost per day is outside 3 standard deviations from the mean.

Based on an analysis of the March 2022 update of FY 2021 IPF claims and the FY 2022 rate increases, we continue to believe it is necessary to update the fixed dollar loss threshold amount to maintain an outlier percentage that equals 2 percent of total estimated IPF PPS payments. We estimate that IPF outlier payments as a percentage of total estimated payments were approximately 3.2 percent in FY 2022. Therefore, we are updating the outlier threshold amount to $24,630 to maintain estimated outlier payments at 2 percent of total estimated aggregate IPF payments for FY 2023. This update is an increase from the FY 2022 threshold of $16,040.

3. Update to IPF Cost-to-Charge Ratio Ceilings

Under the IPF PPS, an outlier payment is made if an IPF's cost for a stay exceeds a fixed dollar loss threshold amount plus the IPF PPS amount. In order to establish an IPF's cost for a particular case, we multiply the IPF's reported charges on the discharge bill by its overall CCR. This approach to determining an IPF's cost is consistent with the approach used under the IPPS and other PPSs. In the FY 2004 IPPS final rule (68 FR 34494), we implemented changes to the IPPS policy used to determine CCRs for IPPS hospitals, because we became aware that payment vulnerabilities resulted in inappropriate outlier payments. Under the IPPS, we established a statistical measure of accuracy for CCRs to ensure that aberrant CCR data did not result in inappropriate outlier payments.

As we indicated in the November 2004 IPF PPS final rule (69 FR 66961), we believe that the IPF outlier policy is susceptible to the same payment vulnerabilities as the IPPS; therefore, we adopted a method to ensure the statistical accuracy of CCRs under the IPF PPS. Specifically, we adopted the following procedure in the November 2004 IPF PPS final rule:

• Calculated two national ceilings, one for IPFs located in rural areas and one for IPFs located in urban areas.
• Computed the ceilings by first calculating the national average and the standard deviation of the CCR for both urban and rural IPFs using the most recent CCRs entered in the most recent Provider Specific File (PSF) available.

For FY 2023, we proposed to continue to follow this methodology. We did not receive any comments on this proposal, and we are finalizing it as proposed.

To determine the rural and urban ceilings, we multiplied each of the standard deviations by 3 and added the result to the appropriate national CCR average (either rural or urban). The upper threshold CCR for IPFs in FY 2023 is 2.0412 for rural IPFs, and 1.7437 for urban IPFs, based on CBSA-based geographic designations. If an IPF's CCR is above the applicable ceiling, the ratio is considered statistically inaccurate, and we assign the appropriate national (either rural or urban) median CCR to the IPF.

We apply the national median CCRs to the following situations:

• New IPFs that have not yet submitted their first Medicare cost report. We continue to use these national median CCRs until the facility's actual CCR can be computed using the first tentatively or final settled cost report.
• IPFs whose overall CCR is in excess of three standard deviations above the corresponding national geometric mean (that is, above the ceiling).
• Other IPFs for which the MAC obtains inaccurate or incomplete data with which to calculate a CCR.

We proposed to continue to update the FY 2023 national median and ceiling CCRs for urban and rural IPFs based on the CCRs entered in the latest available IPF PPS PSF. We did not receive any comments on this proposal, and we are finalizing it as proposed. Specifically, for FY 2023, to be used in each of the three situations listed previously, using the most recent CCRs entered in the CY 2022 PSF, we provide an estimated national median CCR of 0.5720 for rural IPFs and a national median CCR of 0.4200 for urban IPFs. These calculations are based on the IPF's location (either urban or rural) using the CBSA-based geographic designations. A complete discussion regarding the national median CCRs appears in the November 2004 IPF PPS final rule (69 FR 66961 through 66964).

V. Comment Solicitation on Analysis of IPF PPS Adjustments

In the FY 2023 IPF PPS proposed rule (87 FR 19428 through 19429), we discussed the background of the current IPF PPS patient-level and facility-level adjustment factors, which are the regression-derived adjustment factors from the November 15, 2004 IPF PPS final rule. We briefly discussed past analyses and areas of concern for future refinement, about which we previously solicited comments. Finally, we described the results of the latest analysis of the IPF PPS and solicited comments on certain topics from the report.

As we discussed in the proposed rule, we have undertaken further analysis of more recent IPF cost and claim information. In conjunction with the FY 2023 IPF PPS proposed rule, we posted a report on the CMS website,^[2] which summarizes the results of the latest analysis. We noted that this updated analysis finds that the existing IPF PPS model continues to be generally appropriate in terms of effectively aligning IPF PPS payments with the cost of providing IPF services, but suggests that certain updates to the codes, categories, adjustment factors, and ECT payment amount per treatment could improve payment accuracy. We requested comments on the results of our latest analysis as summarized in the report. In particular, we requested comments about the following topics, which are discussed in detail in the report:

• The report summarizes results of the analysis regarding patient-level characteristics, about which we requested comments:

++ The updated regression analysis suggests that certain technical changes to the DRG and comorbidity adjustment factors, consolidation of the age categories for the patient age adjustment, and changes to the adjustment factors for age and length of stay could be appropriate.

++ The analysis of ancillary costs for IPF stays with ECT suggests that a higher ECT payment amount per treatment could better align IPF PPS payments with the costs of furnishing ECT.

++ The analysis of the outlier percentage suggests that fewer IPF cases qualify for outliers under the current 2 percent outlier target than were estimated when the IPF PPS was established. We estimate that increasing the outlier percentage will increase the number of IPF cases that qualify for outliers, but will have distributional effects due to budget neutrality.

• The report summarizes the results of analysis regarding facility-level characteristics, about which we requested comments:

++ The updated regression analysis suggests that updating the adjustment factors for teaching facilities, rural facilities, and facilities with an ED could improve payment accuracy; however, we estimate such changes could have positive and negative effects on payments for different types of IPFs.

++ The analysis of occupancy-related control variables included in the regression model indicates that these control variables are correlated with the rural adjustment factor, and that removal of these control variables from the model could result in an increase to the rural adjustment factor in the regression model.

• The report summarizes certain areas where we believe additional research is needed. We requested comments about the results summarized in the report. We also requested comments about additional analyses that we should undertake to better understand how these issues affect the cost of providing IPF services, and how the IPF PPS could better account for these costs:

++ We analyzed the costs associated with social determinants of health, but found that our analysis was confounded by a low frequency of IPF claims reporting the applicable ICD-10 diagnosis codes. We solicited public comments on the results of this analysis, and whether there are additional patient characteristics that affect the cost of providing IPF services that may not be consistently reported on claims. Additionally, we solicited public comments about how we could better identify such patient characteristics and their effects on costs.

++ We analyzed the costs associated with the percentage of low-income patients that IPFs treat, based on a construction of the Disproportionate Share Hospitals (DSH) percentage that is used in other payment systems using the data currently available for IPFs. We solicited public comments about the results of this analysis, which suggest that the addition of an adjustment factor for disproportionate share intensity could improve the accuracy of IPF PPS payments.

We received 10 comments in response to the FY 2023 IPF PPS pertaining to the report, the analysis of patient-level and facility-level adjustment factors, and areas of interest for further research. Commenters included MedPAC, state-level and national provider and patient advocacy organizations, and individual IPF hospitals and health systems. We thank commenters for their detailed responses to this comment solicitation. We will take these comments into consideration to potentially inform future rulemaking.

VI. Inpatient Psychiatric Facility Quality Reporting (IPFQR) Program

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

Significant and persistent disparities in healthcare outcomes exist in the United States. Belonging to an underserved community is often associated with worse health outcomes.^{[3]  [4]  [5]  [6]  [7]  [8]  [9]  [10]  [11]} With this in mind, CMS aims to advance health equity, by which we mean the attainment of the highest level of health for all people, where everyone has a fair and just opportunity to attain their optimal health regardless of race, ethnicity, disability, sexual orientation, gender identity, socioeconomic status, geography, preferred language, or other factors that affect access to care and health outcomes. CMS is working to advance health equity by designing, implementing, and operationalizing policies and programs that support health for all the people served by our programs, eliminating avoidable differences in health outcomes experienced by people who are disadvantaged or underserved, and providing the care and support that our beneficiaries need to thrive.^[12]

We are committed to advancing equity in healthcare outcomes for our beneficiaries by supporting healthcare providers' quality improvement activities to reduce health disparities, enabling them to make more informed decisions, and promoting healthcare provider accountability for healthcare disparities.^[13] Measuring healthcare disparities in quality measures is a cornerstone of our approach to advancing health equity. Hospital performance results that illustrate differences in outcomes between patient populations have been reported to hospitals confidentially since 2018.

The RFI in the proposed rule (87 FR 19429 through 19437) consisted of three sections. The first section discussed a general framework that could be utilized across CMS quality programs to assess disparities in healthcare quality. The next section outlined approaches that could be used in the IPFQR Program to assess drivers of healthcare quality disparities in the IPFQR Program. Additionally, this section discussed measures of health equity that could be adapted for use in the IPFQR Program. Finally, the third section solicited public comment on the principles and approaches listed in the first two sections as well as sought other thoughts about disparity measurement guidelines for the IPFQR Program.

1. Cross-Setting Framework To Assess Healthcare Quality Disparities

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

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

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

There are several different conceptual approaches to reporting health disparities in the acute care setting, including two complementary approaches that are already used to confidentially provide disparity information to hospitals for a subset of existing measures. The first approach, referred to as the “within-hospital disparity method,” compares measure performance results for a single measure between subgroups of patients with and without a given factor. This type of comparison directly estimates disparities in outcomes between subgroups and can be helpful to identify potential disparities in care. This type of approach can be used with most measures that include patient-level data. The second approach, referred to as the “between-hospital disparity methodology,” provides performance on measures for only the subgroup of patients with a particular social risk factor. These approaches can be used by a healthcare provider to compare their own measure performance on a particular subgroup of patients against subgroup-specific state and national benchmarks. Alone, each approach may provide an incomplete picture of disparities in care for a particular measure, but when reported together with overall quality performance, these approaches may provide detailed information about where differences in care may exist or where additional scrutiny may be appropriate. For example, the between-provider disparity method may indicate that an IPF underperformed (when compared to other facilities on average) for patients with a given social risk factor, which would signal the need to improve care for this population. However, if the IPF also underperformed for patients without that social risk factor, the measured difference, or disparity in care, (the “within-hospital” disparity, as described above) could be negligible even though performance for the group that has been historically marginalized remains poor. We refer readers to the technical report describing the CMS Disparity Methods in detail as well as the FY 2018 IPPS/LTCH PPS final rule (82 FR 38405 through 38407) and the posted Disparity methods Updates and Specifications Report posted on the QualityNet website.^[14]

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

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

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

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

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

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

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

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

Social risk factors are the wide array of non-clinical drivers of health known to negatively impact patient outcomes. These include factors such as socioeconomic status, housing availability, and nutrition (among others), often inequitably affecting historically marginalized communities on the basis of race and ethnicity, rurality, sexual orientation and gender identity, religion, and disability.^{[15 16 17 18 19 20 21 22]}

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

• Patient-reported data are considered to be the gold standard for evaluating quality of care for patients with social risk factors.^[23] While data sources for many social risk factors and demographic variables are still developing among several CMS settings, the IPFQR Program will begin collecting mandatory patient-level data for certain chart-abstracted measures the FY 2024 payment determination and subsequent years (86 FR 42608).

• 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.^[24] There are, however, limitations in these data's usability for stratification analysis.

• Area-based indicators of social risk create approximations of patient risk based on the neighborhood or context that a patient resides in. Several indexes, such as Agency for Healthcare Research and Quality (AHRQ) Socioeconomic Status (SES) Index,^[25] Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI),^[26] and Health Resources and Services Administration (HRSA) Area Deprivation Index (ADI),^[27] provide multifaceted contextual information about an area and may be considered as an efficient way to stratify measures that include many social risk factors.

• Imputed data sources use statistical techniques to estimate patient-reported factors, including race and ethnicity. One such tool is the Medicare Bayesian Improved Surname Geocoding (MBISG) method (currently in version 2.1), which combines information from administrative data, surname, and residential location to estimate patient race and ethnicity.^[28]

d. Identifying Meaningful Performance Differences

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

• Statistical approaches could be used to reliably group results, such as using confidence intervals, creating cut points based on standard deviations, or using a clustering algorithm.
• Programs could use a ranked ordering and percentile approach, ordering healthcare providers in a ranked system based on their performance on disparity measures to quickly allow them to compare their performance to other similar healthcare providers.
• Healthcare providers could be categorized into groups based on their performance using defined thresholds, such as fixed intervals of results of disparity measures, indicating different levels of performance.
• Benchmarking, or comparing individual results to state or national average, is another potential reporting strategy.
• Finally, a ranking system may not be appropriate for all programs and care settings, and some programs may only report disparity results.

e. Guiding Principles for Reporting Disparity Measures

Reporting of the results discussed above can be employed in several ways to drive improvements in quality. Confidential reporting, or reporting results privately to healthcare providers, is generally used for new programs or new measures recently adopted for programs through notice and comment rulemaking to give healthcare providers an opportunity to become more familiar with calculation methods and to improve before other forms of reporting are used. In addition, many results are reported publicly, in accordance with the statute. This method provides all stakeholders with important information on healthcare provider quality, and in turn, relies on market forces to incentivize healthcare providers to improve and become more competitive in their markets without directly influencing payment from CMS. One important consideration is to assess differential impact on IPFs, 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.

B. Approaches to Assessing Drivers of Healthcare Quality Disparities and Developing Measures of Healthcare Equity in the IPFQR Program

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

a. Performance Disparity Decomposition

In response to the FY 2022 IPF PPS proposed rule's RFI (86 FR 19494 through 19500), “Closing the Health Equity Gap in CMS Quality Programs,” some stakeholders noted that identifying which factors are contributing to the performance gaps may not always be straightforward, especially if the IPF has limited information or resources to determine the extent to which a patient's driver of health or other mediating factors (for example: health histories) explain a given disparity. An additional complicating factor is the reality that there are likely multiple social determinants of health (SDOH) and other mediating factors responsible for a given disparity, and it may not be obvious to the IPF which of these factors are the primary drivers.

Consequently, CMS may consider methods to use the data already available in enrollment, claims, and assessment data to estimate the extent to which various SDOH (for example, transportation, health literacy) and other mediating factors drive disparities in an effort to provide more actionable information. Researchers have utilized decomposition techniques to examine inequality in health care and, specifically, as a way to understand and explain the underlying causes of inequality.^[29] At a high level, regression decomposition is a method that allows one to estimate the extent to which disparities (that is, differences) in measure performance between subgroups of patient populations are due to specific factors. These factors can be either non-clinical (for example, SDOH) or clinical. Similarly, CMS may utilize regression decomposition to identify and calculate the specific contribution of SDOHs and other mediating factors to observed disparities. This approach may better inform our understanding of the extent to which providers and policy-makers may be able to narrow the gap in healthcare outcomes. Additionally, provider-specific decomposition results could be shared through confidential results so that IPFs 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 IPFs 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 IPF. Figures 1 through 3 depict an example (using hypothetical data) of how a disparity in a measure of Medicare Spending Per Beneficiary (MSPB) between dual eligible beneficiaries (that is, those enrolled in Medicare and Medicaid) and non-dual eligible beneficiaries (that is, those with Medicare only) could be decomposed among two mediating factors, one SDOH and one clinical factor: (1) low health literacy and (2) high volume of emergency department (ED) use. These examples were selected because they are factors the healthcare provider could mitigate the effects of, if they were shown to be drivers of disparity in their IPF. Additionally, high volume ED use is used as a potential mediating factor that could be difficult for IPFs to determine on their own, as it will require having longitudinal data for patients across multiple facilities.

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

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

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

(1) Health Equity Summary Score

The HESS measure was developed by the CMS OMH ^[30 31] to identify and to reward healthcare providers (that is, Medicare Advantage [MA] plans) that perform relatively well on measures of care provided to beneficiaries with social risk factors (SRFs), as well as to discourage the non-treatment of patients who are potentially high-risk, in the context of value-based purchasing. Additionally, a version of the HESS is under consideration for the Hospital Inpatient Quality Reporting (HIQR) program.^[32] The HESS composite measure provides a summary of equity of care delivery by combining performance and improvement across multiple measures and multiple at-risk groups. The HESS was developed with the following goals: allow for “multiple grouping variables, not all of which will be measurable for all plans,” allow for “disaggregation by grouping variable for nuanced insights,” and allow for the future usage of additional and different SRFs for grouping.^[33]

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

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

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

CMS has 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 organizational leadership and culture can play an essential role in advancing equity goals. This structural measure, entitled the Hospital Commitment to Health Equity measure (MUC2021-106) was included on the 2021 CMS List of Measures Under Consideration (MUC List) ^[34] for acute inpatient hospitals 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 would include five attestation-based questions, each representing a separate domain of commitment. A hospital would receive a point for each domain where they attest to the corresponding statement (for a total of 5 points). At a high level, the five domains cover the following areas: (1) strategic plan to reduce health disparities; (2) approach to collecting valid and reliable demographic and SDOH data; (3) analyses performed to assess disparities; (4) engagement in quality improvement activities; ^[35] and (5) leadership involvement in activities designed to reduce disparities. The specific questions requested 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 hospital could receive a point for each domain where data are submitted through a CMS portal to reflect actions taken by the hospital for each corresponding domain (for a point total). If we were to consider this measure for the IPFQR Program, we would include it for this program on a future MUC list.

CMS believes this type of organizational commitment structural measure may complement the health disparities approach described in previous sections, and support IPFs 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.” ^[36] We acknowledge that collection of this structural measure may impose administrative and/or reporting requirements for IPFs.

We requested feedback from stakeholders on conceptual and measurement priorities for the IPFQR Program to better illuminate organizational commitment to health equity.

C. Solicitation of Public Comment

We requested information with the goal to describe key principles and approaches that we will consider when advancing the use of quality measure development and stratification to address healthcare disparities and advance health equity across our programs.

We invited general comments on the principles and approaches described previously in this section of the rule, as well as additional thoughts about disparity measurement or stratification guidelines suitable for overarching consideration across CMS' QRP programs. Specifically, we invited 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 IPFs to present stratified measure results

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

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

• Guiding Principles for Selecting and Prioritizing Measures for Disparity Reporting

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

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

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

• Identification of Meaningful Performance Differences

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

• Guiding Principles for Reporting Disparity Measures

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

• Measures Related to Health Equity

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

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

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

++ Other options for measures that address health equity.

Consistent with what we stated in the proposed rule, we will not be responding to specific comments submitted in response to this RFI in this final rule, we will actively consider all input as we develop future policies that address these issues. 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. Below is a summary of the comments we received in response to this request for information.

We received the following comments in response to our request for information.

Comment: Many commenters expressed support for reporting stratified IPF measures, specifically recommending providing these data in confidential reports prior to public reporting. Some commenters described potential benefits of public reporting including improved transparency, increased provider accountability, and use of market forces to drive improvement. Several commenters provided recommendations for developing a stratified reporting strategy, including focusing on data that cannot be calculated independently by IPFs, providing support to the public in interpreting the data, and analyzing the effects of potential confounders when developing reports. One commenter recommended that IPFs only be compared to other IPFs in between-provider analyses.

Some commenters expressed concerns regarding stratified data reporting. One commenter expressed that publicly reported stratified data could lead to the perception that it is acceptable for some subgroups to experience worse care. This commenter recommended the use of performance benchmarks or national thresholds instead of the between-provider disparity method. Several commenters expressed concern that the burden of collecting data for stratifying the chart-based measure outweighs the potential benefit of stratifying these measures, especially given small numbers of patients in each stratum and high overall performance on the measures. Some of these commenters specifically stated that IPFs do not have widespread electronic health technology to support this data collection. Several commenters were concerned that there may be unintended consequences of reporting data based on a small sample and recommended that CMS establish a minimum sample size for subgroup reporting. Another commenter recommended using estimates of variability (that is, confidence intervals) when reporting data. Another commenter observed that while stratification of claims-based measures is less burdensome, this reporting would exclude patients with private insurance coverage and rely on data, which are not self-reported. Some commenters recommended that CMS analyze the predictive power of drivers of health compared to the predictive power of the diagnosis requiring treatment prior to stratifying any measures by drivers of health. Another commenter recommended further analysis of regression decomposition prior to considering this technique in data reporting. Some commenters expressed that stratification based on dual-eligibility creates bias due to state-level variation in Medicaid eligibility. One commenter recommended stratifying based on eligibility for the low-income subsidy (LIS) instead. One commenter cautioned CMS to ensure patient privacy is safeguarded, especially when reporting on small samples.

Many commenters expressed support for the collection of data (including race, ethnicity, language, and other factors) to support increased reporting of stratified data, though these commenters observed that there are not currently industry standards for most of these data and recommended developing standard terminology prior to proceeding. One commenter expressed that this data collection could improve provider interventions and performance in providing care. Some commenters recommended that CMS partner with other entities such as states and private payors to align data collection requirements. Some commenters recommended that CMS evaluate use of claims to identify drivers of health, such as by using payment programs to incentive the use of ICD-10 Z Codes. One commenter observed that if CMS were to adopt a patient experience of care measure in this setting the same collection instrument could be used to collect self-reported demographic data. Other commenters supported use of proxy variables, such as indices or other data sets, when self-reported data are unavailable. Some commenters supported further research into statistical imputation prior to use in stratification.

Many commenters expressed concerns about potentially adapting the HESS for this setting. Some commenters observed that an aggregated score may not be actionable for many facilities, with one commenter recommending only reporting such a measure with all its component scores. One commenter cautioned that in using a composite score a single risk factor could mask the effects of other risk factors. Another commenter stated that HESS scoring may not be practical for many smaller facilities, or facilities whose enrolled populations differ in drivers of health distribution patterns compared to typical MA plans. Several commenters expressed the belief that the measures underlying the HESS (HEDIS and CAHPS) are not applicable for the IPF settings. Another commenter observed that calculation of a HESS-type measure would require standardized demographic data collection for all patients. One commenter recommended that if CMS were to develop a summary measure for quality reporting programs for settings other than IPFs, it should include behavioral health measures in the composite because socially at-risk groups often experience poor mental health outcomes.

Many commenters supported the Degree of Hospital Leadership Engagement in Health Equity Performance Data measure concept. Some of these commenters recommended that CMS adopt this structural measure before process or outcome measures related to health equity. However, several commenters provided recommendations or expressed concerns about this measure. Several commenters observed that the measure as specified would be difficult for many IPFs to report due to the requirement to use certified electronic health record technology (CEHRT). One commenter expressed that there is no evidence that performance on this measure is associated with improved patient outcomes. One commenter recommended adopting an audit procedure along with this measure. Another commenter recommended adding a different attestation measure on other efforts to gauge hospital data collection efforts (for example, the Leapfrog Hospital Survey).

Many commenters observed that there are measures of patient experience of care for other settings and that having such a measure in the IPF setting would improve public accountability and quality of care. A few commenters stated that a patient experience of care measure is necessary to improve the equity of care provided by IPFs.

Several commenters stated that improving health equity would require government investment in addressing social needs, such as reducing financial barriers to access. One commenter observed that having such an investment would reduce provider frustration with data collection requirements.

Several commenters recommended linking payment to equity performance; these commenters specifically recommended the use of incentives to avoid unintended consequences for socially at-risk patients. One commenter recommended the use of peer grouping (that is, comparing each provider's performance with providers with similar mixes of patients, that is, its “peers,” to determine rewards or penalties based on performance) within value based purchasing (VBP) programs.

Several commenters supported the suggested criteria for prioritizing equity measures and recommended additional criteria including building on existing health equity strategies, balancing administrative burden, allowing flexibility, relying on existing data sources, relying on measures that include self-reported data in the measure structure, providing timely feedback, expanding to include resource use measures, and aligning with states and other payors.

Some commenters provided general feedback on the concept of using quality reporting programs to reduce healthcare disparities. Several commenters observed that quality improvement initiatives are often initiated at the system level and therefore measurement should be at the system level to avoid duplicative reporting requirements. Another commenter expressed the belief that it would be appropriate to update the conditions of participation to address health equity. Other commenters recommended that any effort to use quality reporting to reduce healthcare disparities should include detailed definitions of all variables (for example, health outcomes, hospital leadership).

Response: We appreciate all of the comments and interest in this topic. We believe that this input is very valuable in the continuing development of the CMS health equity quality measurement efforts. We note that in the FY 2023 IPPS/LTCH PPS proposed rule, we proposed several measures related to health equity for the Hospital Inpatient Quality Reporting (IQR) program. Specifically, we proposed the Hospital Commitment to Health Equity measure (87 FR 28492 through 28497) and two social drivers of health measures (87 FR 28497 through 28506). and we may consider these or similar measures for other quality reporting programs, such as the IPFQR Program in the future. Additionally, we refer readers to the FY 2022 IPF PPS final rule in which we described our initial request for information on the concept of an equity summary score for the IPF setting and summarized the input we received (86 FR 42625 through 42632). We will continue to take all concerns, comments, and suggestions into account for future development and expansion of our health equity quality measurement efforts. If we determine that a measure, including a patient experience of care measure, a health equity measure, or any other measure is appropriate for the IPFQR program we will follow the pre-rulemaking process as described on our website ( https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​QualityMeasures/​Pre-Rulemaking).

For more information on our ongoing effort to address health equity, we refer readers to our recently released updated CMS Quality Strategy ( https://www.cms.gov/​Medicare/​Quality-Initiatives-Patient-Assessment-Instruments/​Value-Based-Programs/​CMS-Quality-Strategy) and our Framework for Health Equity ( https://www.cms.gov/​About-CMS/​Agency-Information/​OMH/​equity-initiatives/​framework-for-health-equity) in which we describe our five priorities for advancing health equity.

VII. Collection of Information Requirements

This final rule updates the prospective payment rates, outlier threshold, and wage index for Medicare inpatient hospital services provided by IPFs. It also establishes a permanent mitigation policy for providers negatively affected by changes to the IPF PPS wage index. While discussed in section IV (Comment Solicitation on Analysis of IPF PPS Adjustments) of this preamble, the active requirements and burden associated with our hospital cost report form CMS-2552-10 (OMB control number 0938-0050) are unaffected by this rule.

Therefore, this document does not impose information collection requirements, that is, reporting, recordkeeping or third-party disclosure requirements. Consequently, there is no need for review by the Office of Management and Budget under the authority of the Paperwork Reduction Act of 1995 (44 U.S.C. 3501 et seq.).

VIII. Regulatory Impact Analysis

A. Statement of Need

This rule finalizes updates to the prospective payment rates for Medicare inpatient hospital services provided by IPFs for discharges occurring during FY 2023 (October 1, 2022 through September 30, 2023). We are finalizing our proposal to apply the 2016-based IPF market basket increase of 4.1 percent, less the productivity adjustment of 0.3 percentage point as required by section 1886(s)(2)(A)(i) of the Act for a total FY 2023 payment rate update of 3.8 percent. In this final rule, we are finalizing our proposal to update the outlier fixed dollar loss threshold amount, update the IPF labor-related share, and update the IPF wage index to reflect the FY 2023 hospital inpatient wage index. Lastly, for FY 2023 and subsequent years, we will apply a 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, regardless of the circumstances causing the decline.

B. Overall Impact

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

Executive Orders 12866 and 13563 direct agencies to assess all costs and benefits of available regulatory alternatives and, if regulation is necessary, to select regulatory approaches that maximize net benefits (including potential economic, environmental, public health and safety effects, distributive impacts, and equity). Section 3(f) of Executive Order 12866 defines a “significant regulatory action” as an action that is likely to result in a rule: (1) having an annual effect on the economy of $100 million or more in any 1 year, or adversely and materially affecting a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or state, local or tribal governments or communities (also referred to as “economically significant”); (2) creating a serious inconsistency or otherwise interfering with an action taken or planned by another agency; (3) materially altering the budgetary impacts of entitlement grants, user fees, or loan programs or the rights and obligations of recipients thereof; or (4) raising novel legal or policy issues arising out of legal mandates, the President's priorities, or the principles set forth in the Executive Order.

A regulatory impact analysis (RIA) must be prepared for major rules with significant regulatory action/s and/or with economically significant effects ($100 million or more in any 1 year). We estimate that the total impact of these changes for FY 2023 payments compared to FY 2022 payments will be a net increase of approximately $90 million. This reflects a $130 million increase from the update to the payment rates (+$140 million from the second quarter 2022 IGI forecast of the 2016-based IPF market basket of 4.1 percent, and −$10 million for the productivity adjustment of 0.3 percentage point), as well as a $40 million decrease as a result of the update to the outlier threshold amount. Outlier payments are estimated to change from 3.2 percent in FY 2022 to 2.0 percent of total estimated IPF payments in FY 2023.

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, and hence also a “major” rule under Subtitle E of the Small Business Regulatory Enforcement Fairness Act of 1996 (also known as the Congressional Review Act). Accordingly, we have prepared a Regulatory Impact Analysis that to the best of our ability presents the costs and benefits of the rulemaking. Therefore, OMB has reviewed these final regulations, and the Departments have provided the following assessment of their impact.

C. Detailed Economic Analysis

In this section, we discuss the historical background of the IPF PPS and the impact of this final rule on the Federal Medicare budget and on IPFs.

1. Budgetary Impact

As discussed in the November 2004 and RY 2007 IPF PPS final rules, we applied a budget neutrality factor to the Federal per diem base rate and ECT payment per treatment to ensure that total estimated payments under the IPF PPS in the implementation period would equal the amount that would have been paid if the IPF PPS had not been implemented. This budget neutrality factor included the following components: outlier adjustment, stop-loss adjustment, and the behavioral offset. As discussed in the RY 2009 IPF PPS notice (73 FR 25711), the stop-loss adjustment is no longer applicable under the IPF PPS.

As discussed in section IV.D.1 of this final rule, we are updating the wage index and labor-related share, as well as applying the 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, in a budget neutral manner, by applying a wage index budget neutrality factor to the Federal per diem base rate and ECT payment per treatment. Therefore, the budgetary impact to the Medicare program of this final rule will be due to the market basket update for FY 2023 of 4.1 percent (see section IV.A.2 of this final rule) less the productivity adjustment of 0.3 percentage point required by section 1886(s)(2)(A)(i) of the Act and the update to the outlier fixed dollar loss threshold amount.

We estimate that the FY 2023 impact will be a net increase of $90 million in payments to IPF providers. This reflects an estimated $130 million increase from the update to the payment rates and a $40 million decrease due to the update to the outlier threshold amount to set total estimated outlier payments at 2.0 percent of total estimated payments in FY 2023. This estimate does not include the implementation of the required 2.0 percentage point reduction of the productivity-adjusted market basket update factor for any IPF that fails to meet the IPF quality reporting requirements (as discussed in section IV.B.2. of this final rule).

2. Impact on Providers

To show the impact on providers of the changes to the IPF PPS discussed in this final rule, we compare estimated payments under the IPF PPS rates and factors for FY 2023 versus those under FY 2022. We determined the percent change in the estimated FY 2023 IPF PPS payments compared to the estimated FY 2022 IPF PPS payments for each category of IPFs. In addition, for each category of IPFs, we have included the estimated percent change in payments resulting from the update to the outlier fixed dollar loss threshold amount; the updated wage index data including the labor-related share and the 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year; and the market basket update for FY 2023, as reduced by the productivity adjustment according to section 1886(s)(2)(A)(i) of the Act. To illustrate the impacts of the FY 2023 changes in this final rule, our analysis begins with FY 2021 IPF PPS claims (based on the 2021 MedPAR claims, March 2022 update). As discussed in section IV.E.2 of this final rule, we are excluding providers from our impact simulations whose change in estimated cost per day is outside 3 standard deviations from the mean. We estimate FY 2022 IPF PPS payments using these 2021 claims, the finalized FY 2022 IPF PPS Federal per diem base rates, and the finalized FY 2022 IPF PPS patient and facility level adjustment factors (as published in the FY 2022 IPF PPS final rule (86 FR 42608)). We then estimate the FY 2022 outlier payments based on these simulated FY 2022 IPF PPS payments using the same methodology as finalized in the FY 2022 IPF PPS final rule (86 FR 42623 through 42624) where total outlier payments are maintained at 2 percent of total estimated FY 2022 IPF PPS payments. Each of the following changes is added incrementally to this baseline model in order to isolate the effects of each change:

• The final update to the outlier fixed dollar loss threshold amount.
• The final FY 2023 IPF wage index, the 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, and the FY 2023 labor-related share.
• The final market basket update for FY 2023 of 4.1 percent less the productivity adjustment of 0.3 percentage point in accordance with section 1886(s)(2)(A)(i) of the Act for a payment rate update of 3.8 percent.

Our column comparison in Table 3 illustrates the percent change in payments from FY 2022 (that is, October 1, 2021, to September 30, 2022) to FY 2023 (that is, October 1, 2022, to September 30, 2023) including all the payment policy changes.

3. Impact Results

Table 3 displays the results of our analysis. The table groups IPFs into the categories listed here based on characteristics provided in the Provider of Services file, the IPF PSF, and cost report data from the Healthcare Cost Report Information System:

• Facility Type.
• Location.
• Teaching Status Adjustment.
• Census Region.
• Size.

The top row of the table shows the overall impact on the 1,417 IPFs included in the analysis. In column 2, we present the number of facilities of each type that had information available in the PSF, had claims in the MedPAR dataset for FY 2021, and were not excluded due to the trim on providers whose change in estimated cost per day is outside 3 standard deviations from the mean.

In column 3, we present the effects of the update to the outlier fixed dollar loss threshold amount. We estimate that IPF outlier payments as a percentage of total IPF payments are 3.2 percent in FY 2022. Therefore, we adjusted the outlier threshold amount to set total estimated outlier payments equal to 2.0 percent of total payments in FY 2023. The estimated change in total IPF payments for FY 2023, therefore, includes an approximate 1.2 percent decrease in payments because we expect the outlier portion of total payments to decrease from approximately 3.2 percent to 2.0 percent.

The overall impact of the estimated decrease to payments due to updating the outlier fixed dollar loss threshold (as shown in column 3 of Table 3), across all hospital groups, is a 1.2 percent decrease. The largest decrease in payments due to this change is estimated to be 3.4 percent for teaching IPFs with 10 percent to 30 percent interns and residents to beds.

In column 4, we present the effects of the budget-neutral update to the IPF wage index, the labor-related share (LRS), and the 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year discussed in section IV.D.2 of this final rule. This represents the effect of using the concurrent hospital wage data as discussed in section IV.D.1.a of this final rule. That is, the impact represented in this column reflects the update from the FY 2022 IPF wage index to the FY 2023 IPF wage index, which includes basing the FY 2023 IPF wage index on the FY 2023 pre-floor, pre-reclassified IPPS hospital wage index data, applying a 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year, and updating the LRS from 77.2 percent in FY 2022 to 77.4 percent in FY 2023. We note that there is no projected change in aggregate payments to IPFs, as indicated in the first row of column 4; however, there are distributional effects among different categories of IPFs. For example, we estimate the largest increase in payments to be 0.8 percent for Pacific IPFs, and the largest decrease in payments to be 0.5 percent for New England IPFs.

Overall, IPFs are estimated to experience a net increase in payments of 2.5 percent as a result of the updates in this final rule. IPF payments are therefore estimated to increase by 2.5 percent in urban areas and 2.9 percent in rural areas. The largest payment increases are estimated at 3.7 percent for freestanding urban for-profit IPFs, IPFs located in the West South Central region, and IPF hospitals with 25 to 49 beds.

4. Effect on Beneficiaries

Under the FY 2023 IPF PPS, IPFs will continue to receive payment based on the average resources consumed by patients for each day. Our longstanding payment methodology reflects the differences in patient resource use and costs among IPFs, as required under section 124 of the BBRA. We expect that updating IPF PPS rates in this final rule will improve or maintain beneficiary access to high quality care by ensuring that payment rates reflect the best available data on the resources involved in inpatient psychiatric care and the costs of these resources. We continue to expect that paying prospectively for IPF services under the FY 2023 IPF PPS will enhance the efficiency of the Medicare program.

5. Regulatory Review Costs

If regulations impose administrative costs on private entities, such as the time needed to read and interpret this final rule, we estimate the cost associated with regulatory review. Due to the uncertainty involved with accurately quantifying the number of entities that will be directly impacted and will review this final rule, we assume that the total number of unique commenters on the most recent IPF proposed rule will be the number of reviewers of this final rule. For the FY 2023 IPF PPS final rule, the most recent IPF proposed rule was the FY 2023 IPF PPS proposed rule, and we received 396 unique comments on this proposed rule. We believe that the number of past commenters on the most recent IPF proposed rule would be a fair estimate of the number of reviewers of this final rule. We acknowledge that this assumption may understate or overstate the costs of reviewing this final rule. It is possible that not all commenters reviewed the FY 2023 IPF PPS proposed rule in detail, and it is also possible that some reviewers chose not to comment on that proposed rule. We solicited comments on this assumption and did not receive any comments on it.

We also recognize that different types of entities are in many cases affected by mutually exclusive sections of the proposed rule, and therefore for the purposes of our estimate we assume that each reviewer reads approximately 50 percent of the rule. Using the May 2021 mean (average) wage information from the BLS for medical and health service managers (Code 11-9111), we estimate that the cost of reviewing this final rule is $115.22 per hour, including fringe benefits and other indirect costs ( https://www.bls.gov/​oes/​current/​oes119111). Assuming an average reading speed of 250 words per minute, we estimate that it would take approximately 64 minutes (1.07 hours) for the staff to review half of this final rule, which contains a total of approximately 32,000 words. For each IPF that reviews the final rule, the estimated cost is $123.29 (1.07 × $115.22). Therefore, we estimate that the total cost of reviewing this final rule is $48,822.84 ($123.29 × 396 reviewers).

D. Alternatives Considered

The statute does not specify an update strategy for the IPF PPS and is broadly written to give the Secretary discretion in establishing an update methodology. We continue to believe it is appropriate to routinely update the IPF PPS so that it reflects the best available data about differences in patient resource use and costs among IPFs as required by the statute. Therefore, we are finalizing our proposal to update the IPF PPS using the methodology published in the November 2004 IPF PPS final rule; applying the 2016-based IPF PPS market basket update for FY 2023 of 4.1 percent, reduced by the statutorily required productivity adjustment of 0.3 percentage point along with the wage index budget neutrality adjustment to update the payment rates; and finalizing a FY 2023 IPF wage index which uses the FY 2023 pre-floor, pre-reclassified IPPS hospital wage index as its basis. Additionally, we are applying a 5-percent cap on any decrease to a provider's wage index from its wage index in the prior year. Lastly, we are excluding providers from our simulation of IPF PPS payments for FY 2022 and FY 2023 if their change in estimated cost per day is outside 3 standard deviations from the mean.

E. Accounting Statement

As required by OMB Circular A-4 (available at https://www.whitehouse.gov/​wp-content/​uploads/​legacy_​drupal_​files/​omb/​circulars/​A4/​a-4.pdf), in Table 4, we have prepared an accounting statement showing the classification of the expenditures associated with the updates to the IPF wage index and payment rates in this final rule. Table 4 provides our best estimate of the increase in Medicare payments under the IPF PPS as a result of the changes presented in this final rule and based on the data for 1,417 IPFs with data available in the PSF, with claims in our FY 2021 MedPAR claims dataset, and which were not excluded due to the trim on providers whose change in estimated cost per day is outside 3 standard deviations from the mean. Lastly, Table 4 also includes our best estimate of the costs of reviewing and understanding this final rule.

F. Regulatory Flexibility Act

The RFA requires agencies to analyze options for regulatory relief of small entities, if a rule has a significant impact on a substantial number of small entities. For purposes of the RFA, small entities include small businesses, nonprofit organizations, and small governmental jurisdictions. Most IPFs and most other providers and suppliers are small entities, either by nonprofit status or having revenues of $8 million to $41.5 million or less in any 1 year. Individuals and states are not included in the definition of a small entity.

Because we lack data on individual hospital receipts, we cannot determine the number of small proprietary IPFs or the proportion of IPFs' revenue derived from Medicare payments. Therefore, we assume that all IPFs are considered small entities.

The Department of Health and Human Services generally uses a revenue impact of 3 to 5 percent as a significance threshold under the RFA. As shown in Table 3, we estimate that the overall revenue impact of this final rule on all IPFs is to increase estimated Medicare payments by approximately 2.5 percent. As a result, the estimated impact of this final rule is a net increase in revenue across almost all categories of IPFs. Therefore, the Secretary has determined that this final rule will have a positive revenue impact on a substantial number of small entities.

In addition, section 1102(b) of the Social Security Act requires us to prepare a regulatory impact analysis if a rule may have a significant impact on the operations of a substantial number of small rural hospitals. This analysis must conform to the provisions of section 604 of the RFA. For purposes of section 1102(b) of the Act, we define a small rural hospital as a hospital that is located outside of a metropolitan statistical area and has fewer than 100 beds. As discussed in section VIII.C.2 of this final rule, the rates and policies set forth in this rule will not have an adverse impact on the rural hospitals based on the data of the 210 rural excluded psychiatric units and 57 rural psychiatric hospitals in our database of 1,417 IPFs for which data were available. Therefore, the Secretary has certified that this final rule will not have a significant impact on the operations of a substantial number of small rural hospitals.

G. Unfunded Mandates Reform Act (UMRA)

Section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA) also requires that agencies assess anticipated costs and benefits before issuing any rule whose mandates require spending in any 1 year of $100 million in 1995 dollars, updated annually for inflation. In 2022, that threshold is approximately $165 million. This final rule does not mandate any requirements for state, local, or tribal governments, or for the private sector. This final rule will not impose a mandate that will result in the expenditure by state, local, and Tribal Governments, in the aggregate, or by the private sector, of more than $165 million in any 1 year.

H. Federalism

Executive Order 13132 establishes certain requirements that an agency must meet when it promulgates a proposed rule (and subsequent final rule) that imposes substantial direct requirement costs on state and local governments, preempts state law, or otherwise has Federalism implications. This final rule does not impose substantial direct costs on state or local governments or preempt state law.

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

Chiquita Brooks-LaSure, Administrator of the Centers for Medicare & Medicaid Services, approved this document on July 25, 2022.

List of Subjects in 42 CFR Part 412

• Administrative practice and procedure
• Health facilities
• Medicare
• Puerto Rico
• Reporting and recordkeeping requirements

For the reasons set forth in the preamble, the Centers for Medicare & Medicaid Services amends 42 CFR part 412 as set forth below:

PART 412—PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL SERVICES

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

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

2. Section 412.424 is amended by revising paragraph (d)(1)(i) to read as follows:

§ 412.424

Methodology for calculating the Federal per diem payment amount.

* * * * *

(d) * * *

(1) * * *

(i) Adjustment for wages. CMS adjusts the labor portion of the Federal per diem base rate to account for geographic differences in the area wage levels using an appropriate wage index.

(A) The application of the wage index is made on the basis of the location of the inpatient psychiatric facility in an urban or rural area as defined in § 412.402.

(B) Beginning October 1, 2022, CMS applies a cap on decreases to the wage index, such that the wage index applied to an inpatient psychiatric facility is not less than 95 percent of the wage index applied to that inpatient psychiatric facility in the prior fiscal year.

* * * * *

Dated: July 25, 2022.

Xavier Becerra,

Secretary, Department of Health and Human Services.

Footnotes

1.   https://www.hrsa.gov/​sites/​default/​files/​hrsa/​provider-relief/​provider-relief-fund-faq-complete.pdf.

2.  The report can be accessed directly via the following link: https://www.cms.gov/​files/​document/​technical-report-medicare-program-inpatient-psychiatric-facilities-prospective-payment-system.pdf

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

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

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

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

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

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

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

10.  Poteat TC, Reisner SL, Miller M, Wirtz AL. (2020). COVID-19 vulnerability of transgender women with and without HIV infection in the Eastern and Southern U.S. preprint. medRxiv. 2020;2020.07.21. 20159327. doi:10.1101/2020.07.21.20159327.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

31.  2021 Quality Conference. Health Equity as a “New Normal”: CMS Efforts to Address the Causes of Health Disparities. Available at https://s3.amazonaws.com/​bizzabo.file.upload/​83kO1DYXTs6mKHjVtuk8_​1%20-%20Session%2023%20Health%20Equity%20New%20Normal%20FINAL_​508.pdf. Accessed March 2, 2022.

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

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

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

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

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