Medicare Program; FY 2022 Inpatient Psychiatric Facilities Prospective Payment System and Quality Reporting Updates for Fiscal Year Beginning October 1, 2021 (FY 2022)

DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Part 412

[CMS-1750-P]

RIN 0938-AU40

Medicare Program; FY 2022 Inpatient Psychiatric Facilities Prospective Payment System and Quality Reporting Updates for Fiscal Year Beginning October 1, 2021 (FY 2022)

AGENCY:

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

ACTION:

Proposed rule.

SUMMARY:

This proposed rule would update 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 Inpatient Prospective Payment System (IPPS) hospital or critical access hospital. This rule also proposes to update and clarify the IPF teaching policy with respect to IPF hospital closures and displaced residents and proposes a technical change to the 2016-based IPF market basket price proxies. In addition, this proposed rule would update quality measures and reporting requirements under the Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program. These changes would be effective for IPF discharges occurring during the Fiscal Year (FY) beginning October 1, 2021 through September 30, 2022 (FY 2022).

DATES:

To be assured consideration, comments must be received at one of the addresses provided below by June 7, 2021.

ADDRESSES:

In commenting, please refer to file code CMS-1750-P.

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

1.

Electronically.

You may submit electronic comments on this regulation to

http://www.regulations.gov.

Follow the “Submit a comment” instructions.

2.

By regular mail.

You may mail written comments to the following address ONLY: Centers for Medicare & Medicaid Services, Department of Health and Human Services, Attention: CMS-1750-P, P.O. Box 8010, Baltimore, MD 21244-8016.

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

3.

By express or overnight mail.

You may send written comments to the following address ONLY: Centers for Medicare & Medicaid Services, Department of Health and Human Services, Attention: CMS-1750-P, Mail Stop C4-26-05, 7500 Security Boulevard, Baltimore, MD 21244-1850.

For information on viewing public comments, see the beginning of the

SUPPLEMENTARY INFORMATION

section.

FOR FURTHER INFORMATION CONTACT:

The IPF Payment Policy mailbox at

IPFPaymentPolicy@cms.hhs.gov

for general information.

Mollie Knight (410) 786-7948 or Eric Laib (410) 786-9759, for information regarding the market basket update or the labor related share.

Nick Brock (410) 786-5148 or Theresa Bean (410) 786-2287, for information regarding the regulatory impact analysis.

Lauren Lowenstein, (410) 786-4507, for information regarding the inpatient psychiatric facilities quality reporting program.

SUPPLEMENTARY INFORMATION:

Inspection of Public Comments:

All comments received before the close of the comment period are available for viewing by the public, including any personally identifiable or confidential business information that is included in a comment. We post all comments received before the close of the comment period on the following website as soon as possible after they have been received:

http://www.regulations.gov.

Follow the search instructions on that website to view public comments.

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

Addendum A to this proposed rule summarizes the FY 2022 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 proposed rule shows the complete listing of ICD-10 Clinical Modification (CM) and Procedure Coding System codes underlying the Code First table, the FY 2022 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 2022 Wage Index for Urban Areas Based on Core-Based Statistical Area (CBSA) Labor Market Areas and the FY 2022 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 proposed rule would update 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 the FY 2022 beginning October 1, 2021 through September 30, 2022. This rule also proposes to update and clarify the IPF teaching policy with respect to IPF hospital closures and displaced residents and proposes a technical change to the 2016-based IPF market basket price proxies. In addition, the proposed rule would update quality measures and reporting requirements under the Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program.

B. Summary of the Major Provisions

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

For the IPF PPS, we are proposing to—

• Update IPF PPS teaching policy with respect to IPF hospital closures and displaced residents.

• Replace one of the price proxies currently used for the For-profit Interest cost category in the 2016-based IPF market basket with a similar price proxy.

• Adjust the 2016-based IPF market basket update (2.3 percent) for economy-wide productivity (0.2 percentage point) as required by section 1886(s)(2)(A)(i) of the Social Security Act (the Act), resulting in a proposed IPF payment rate update of 2.1 percent for FY 2022.

•

Make technical rate setting changes:

The IPF PPS payment rates would be adjusted annually for inflation, as well as statutory and other policy factors. This rule proposes to update:

++ The IPF PPS Federal per diem base rate from $815.22 to $833.50.

++ The IPF PPS Federal per diem base rate for providers who failed to report quality data to $817.18.

++ The Electroconvulsive therapy (ECT) payment per treatment from $350.97 to $358.84.

++ The ECT payment per treatment for providers who failed to report quality data to $351.81.

++ The labor-related share from 77.3 percent to 77.1 percent.

++ The wage index budget-neutrality factor to 1.0014.

++ The fixed dollar loss threshold amount from $14,630 to $14,030 to maintain estimated outlier payments at 2 percent of total estimated aggregate IPF PPS payments.

2. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program

In this proposed rule, we are proposing to:

• Adopt voluntary patient-level data reporting for data submitted for FY 2023 payment determination and mandatory patient-level data reporting for FY 2024 payment determination and subsequent years;

• Adopt the Coronavirus disease 2019 (COVID-19) Healthcare Personnel (HCP) Vaccination measure for the FY 2023 payment determination and subsequent years;

• Adopt the Follow-up After Psychiatric Hospitalization (FAPH) measure for the FY 2024 payment determination and subsequent years; and

• Remove the following four measures for FY 2024 payment determination and subsequent years:

++ Alcohol Use Brief Intervention Provided or Offered and Alcohol Use Brief Intervention Provided (SUB-2/2a) measure;

++ Tobacco Use Brief Intervention Provided or Offered and Tobacco Use Brief Intervention Provided (TOB-2/2a) measure;

++ Timely Transmission of Transition Record (Discharges from an Inpatient Facility to Home/Self Care or Any Other Site of Care) measure; and

++ Follow-up After Hospitalization for Mental Illness (FUH) measure.

C. Summary of Impacts

Provision description

Total transfers & cost reductions

FY 2022 IPF PPS payment update

The overall economic impact of this proposed rule is an estimated $90 million in increased payments to IPFs during FY 2022.

FY2023 IPFQR Program update

The overall economic impact of the IPFQR Program provisions of this proposed rule is an estimated $20,911,738 reduction in information collection burden.

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 Prospective Payment 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 in an inpatient prospective payment system (IPPS) hospital that is excluded from the IPPS, or a psychiatric unit in a Critical Access Hospital (CAH) that is excluded from the CAH payment system. These excluded psychiatric units would 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. As noted in our FY 2020 IPF PPS final rule with comment period, published in the

Federal Register

on August 6, 2019 (84 FR 38424 through 38482), for the RY beginning in 2019, the productivity adjustment currently in place was equal to 0.4 percentage point.

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; this 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 take into account such reduction in computing the payment amount for a subsequent RY. More information about the specifics of the current Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program is available in the FY 2020 IPF PPS and Quality Reporting Updates for Fiscal Year 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 Center for Medicare & Medicaid (CMS) website at

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html?redirect=/InpatientPsychFacilPPS/.

B. Overview of the IPF PPS

The November 2004 IPF PPS final rule (69 FR 66922) 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; additionally, there are 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 provides 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. Therefore, in the November 2004 IPF PPS final rule, 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 November 2004, we implemented the IPF PPS in a final rule that published on November 15, 2004 in the

Federal Register

(69 FR 66922). In developing the IPF PPS, and to ensure that the IPF PPS is able to 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 15, 2004 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 1. When proposing changes in IPF payment policy, a proposed rule would be issued in the spring, and the final rule in the summer to be effective on October 1. 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, 2020 in the

Federal Register

titled, “Medicare Program; FY 2021 Inpatient Psychiatric Facilities Prospective Payment System and Special Requirements for Psychiatric Hospitals for Fiscal Year Beginning October 1, 2020 (FY 2021)” (85 FR 47042), which updated the IPF PPS payment rates for FY 2021. That final rule updated the IPF PPS Federal per diem base rates that were published in the FY 2020 IPF PPS Rate Update final rule (84 FR 38424) in accordance with our established policies.

III. Provisions of the FY 2022 IPF PPS Proposed Rule

A. Proposed Update to the FY 2021 Market Basket 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. Accordingly, 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. Proposed FY 2022 IPF Market Basket Update

For FY 2022 (beginning October 1, 2021 and ending September 30, 2022), we are proposing to use an estimate of the 2016-based IPF market basket increase factor to update the IPF PPS base payment rate. Consistent with historical practice, we are proposing to estimate the market basket update for the IPF PPS based on IHS Global Inc.'s (IGI) forecast (see section III.A.3 of this proposed rule for a discussion of a proposed technical update to one price proxy that is part of the 2016-based IPF market basket). IGI is a nationally recognized economic and financial forecasting firm that contracts with the CMS to forecast the components of the market baskets and multifactor productivity (MFP). For the proposed rule, based on IGI's fourth quarter 2020 forecast with historical data through the third quarter of 2020, the 2016-based IPF market basket increase factor for FY 2022 is 2.3 percent. Therefore, we are proposing that the 2016-based IPF market basket update for FY 2022 would be 2.3 percent.

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 RY beginning in 2012 (a RY that coincides with a FY) and each subsequent RY. For this FY 2022 IPF PPS proposed rule, based on IGI's fourth quarter 2020 forecast, the proposed MFP adjustment for FY 2022 (the 10-year moving average of MFP for the period ending FY 2022) is projected to be 0.2 percent. We are proposing to reduce the proposed 2.3 percent IPF market basket update by this 0.2 percentage point productivity adjustment, as mandated by the Act. This results in a proposed estimated FY 2022 IPF PPS payment rate update of 2.1 percent (2.3 − 0.2 = 2.1). We are also proposing that if more recent data become available, we would use such data, if appropriate, to determine the FY 2022 IPF market basket update and MFP adjustment for the final rule. For more information on the productivity adjustment, we refer readers to the discussion in the FY 2016 IPF PPS final rule (80 FR 46675).

3. Proposed Update to IPF Market Basket Price Proxies

As discussed in section III.A.1. of this proposed rule, the IPF market basket is an input price index that consists of cost category weights and price proxies derived from the mix of goods and services used in providing health care. For FY 2022, for the For-profit Interest cost category of the 2016-based IPF market basket, we are proposing to use the iBoxx AAA Corporate Bond Yield index instead of the Moody's AAA Corporate Bond Yield index. Effective for December 2020, the Moody's AAA Corporate Bond series is no longer available for use under license to IGI, which is the nationally-recognized economic and financial forecasting firm with which we contract to forecast the components of the market baskets and MFP.

We compared the iBoxx AAA Corporate Bond Yield index with the Moody's AAA Corporate Bond Yield index and found that the average growth rates in the history of the two series are very similar. Over the historical time period of FY 2001 to FY 2020, the 4-quarter percent change moving average growth in the iBoxx series was approximately 0.1 percentage point higher, on average, than the Moody's series. However, given the relatively small weight for this cost category, replacing the Moody's series with the iBoxx series would not impact the historical top-line market basket increases when rounded to the nearest tenth of a percentage point over the past 10 fiscal years (FY 2011 to FY 2020). Therefore, because the iBoxx AAA Corporate Bond Yield index captures the same technical concept as the current corporate bond proxy and tracks similarly to the current measure that is no longer available, we believe that using the iBoxx AAA Corporate Bond Yield index is technically appropriate to use in the 2016-based IPF market basket.

4. Proposed FY 2022 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 are proposing to continue to classify a cost category as labor-related if the costs are labor-intensive and vary with the local labor market.

Based on our definition of the labor-related share and the cost categories in the 2016-based IPF market basket, we are proposing to continue 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; All Other: Labor-related Services; and a portion of the Capital-Related cost weight (46 percent) from the 2016-based IPF market basket. The relative importance reflects the different rates of price change for these cost categories between the base year (FY 2016) and FY 2022. Using IGI's fourth quarter 2020 forecast for the 2016-based IPF market basket, the proposed IPF labor-related share for FY 2022 is the sum of the FY 2022 relative importance of each labor-related cost category. 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). For FY 2022, the proposed labor-related share based on IGI's fourth quarter 2020 forecast of the 2016-based IPF PPS market basket is 77.1 percent. We are also proposing that if more recent data become available, we would use such data, if appropriate, to determine the FY 2022 labor-related share for the final rule.

B. Proposed Updates to the IPF PPS Rates for FY Beginning October 1, 2021

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, must 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. In addition, 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 proposed update to the ICD-10-PCS code set for FY 2022. Addendum B to this proposed rule shows the ECT procedure codes for FY 2022 and is available on our website at

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.

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

The current (FY 2021) Federal per diem base rate is $815.22 and the ECT payment per treatment is $350.97. For the proposed FY 2022 Federal per diem base rate, we applied the payment rate update of 2.1 percent—that is, the 2016-based IPF market basket increase for FY 2022 of 2.3 percent less the productivity adjustment of 0.2 percentage point—and the wage index budget-neutrality factor of 1.0014 (as discussed in section III.D.1 of this proposed rule) to the FY 2021 Federal per diem base rate of $815.22, yielding a proposed Federal per diem base rate of $833.50 for FY 2022. Similarly, we applied the 2.1 percent payment rate update and the 1.0014 wage index budget-neutrality factor to the FY 2021 ECT payment per treatment of $350.97, yielding a proposed ECT payment per treatment of $358.84 for FY 2022.

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 point reduction to the Federal per diem base rate and the ECT payment per treatment as follows:

• For IPFs that fail requirements under the IPFQR Program, we applied a 0.1 percent payment rate update—that is, the IPF market basket increase for FY 2022 of 2.3 percent less the productivity adjustment of 0.2 percentage point for an update of 2.1 percent, and further reduced by 2 percentage points in accordance with section 1886(s)(4)(A)(i) of the Act—and the wage index budget-neutrality factor of 1.0014 to the FY 2021 Federal per diem base rate of $815.22, yielding a Federal per diem base rate of $817.18 for FY 2022.

• For IPFs that fail to meet requirements under the IPFQR Program, we applied the 0.1 percent annual payment rate update and the 1.0014 wage index budget-neutrality factor to the FY 2021 ECT payment per treatment of $350.97, yielding an ECT payment per treatment of $351.81 for FY 2022.

C. Proposed 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 continue to use the existing regression-derived adjustment factors established in 2005 for FY 2022. 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. Proposed 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 2022, we are not proposing any changes to the 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 2022, we are proposing to continue to make 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 would still receive the Federal per diem base rate and all other applicable adjustments, but the payment would not include an MS-DRG adjustment.

The diagnoses for each IPF MS-DRG would be updated as of October 1, 2021, using the final IPPS FY 2022 ICD-10-CM/PCS code sets. The FY 2022 IPPS proposed rule includes tables of the proposed changes to the ICD-10-CM/PCS code sets, which underlie the FY 2022 IPF MS-DRGs. Both the FY 2022 IPPS proposed rule and the tables of proposed changes to the ICD-10-CM/PCS code sets, which underlie the FY 2022 MS-DRGs are available on the 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, the 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 would follow the instructions in the ICD-10-CM text. The submitted claim goes through the CMS processing system, which will identify the primary 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 our 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 2018, FY 2019 and FY 2020, there were no changes to the final ICD-10-CM/PCS codes in the IPF Code First table. For FY 2021, there were 18 ICD-10-PCS codes deleted from the final IPF Code First table. For FY 2022 there are 18 codes proposed for deletion from the ICD-10-CM/PCS codes in the IPF Code First table. The proposed FY 2022 Code First table is shown in Addendum B on our website at

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.

b. Proposed 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 would 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 2022, we are proposing to continue to use the same comorbidity adjustment factors in effect in FY 2021, which are found in Addendum A, available on our website at

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.

We have updated the ICD-10-CM/PCS codes, which are associated with the existing IPF PPS comorbidity categories, based upon the proposed FY 2022 update to the ICD-10-CM/PCS code set. The proposed FY 2022 ICD-10-CM/PCS updates include: 8 ICD-10-CM diagnosis codes added to the Poisoning comorbidity category, 4 codes deleted, and 4 changes to Poisoning comorbidity long descriptions; 2 ICD-10-CM diagnosis codes added to the Developmental Disabilities comorbidity category and 1 code deleted; and 3 ICD-10-PCS codes added to the Oncology Procedures comorbidity category. In addition, we are proposing to delete 18 ICD-10-PCS codes from the Code First Table. These updates are detailed in Addenda B of this proposed rule, which are available on our 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 2022 ICD-10-CM codes to remove codes that were site “unspecified” in terms of laterality from the FY 2022 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. None of the proposed additions to the FY 2022 ICD-10-CM/PCS codes were site “unspecified” by laterality, therefore, we are not removing any of the new codes.

c. Proposed 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 2022, we are proposing to continue to use the patient age adjustments currently in effect in FY 2021, as shown in Addendum A of this rule (see

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html

).

d. Proposed 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 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 III.D.4 of this rule.

For FY 2022, we are proposing to continue to use the variable per diem adjustment factors currently in effect, as shown in Addendum A of this rule (available 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. Proposed 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 15, 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 taking into account 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), requires us to 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 15, 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 to use 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 would be 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 would result in the most up-to-date wage data being the basis for the IPF wage index. It would also result in more consistency and parity in the wage index methodology used by other Medicare payment systems. The Medicare SNF PPS already used the concurrent IPPS hospital wage index data as the basis for the SNF PPS wage index. Thus, the wage adjusted Medicare payments of various provider types would be based upon wage index data from the same timeframe. CMS proposed 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. For FY 2022, we are proposing to continue to use the concurrent pre-floor, pre-reclassified IPPS hospital wage index as the basis for the IPF wage index.

We would 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 would change from 77.3 percent in FY 2021 to 77.1 percent in FY 2022. This percentage reflects the labor-related share of the 2016-based IPF market basket for FY 2022 (see section III.A.4 of this rule).

b. Office of Management and Budget (OMB) Bulletins

i. 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.

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).

On February 28, 2013, OMB issued OMB Bulletin No. 13-01 which established revised delineations for Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Combined Statistical Areas in the United States (U.S.) and Puerto Rico based on the 2010 Census, and provided guidance on the use of the delineations of these statistical areas using standards published in the June 28, 2010

Federal Register

(75 FR 37246 through 37252). These OMB Bulletin changes were reflected in the FY 2015 pre-floor, pre-reclassified IPPS hospital wage index, upon which the FY 2016 IPF wage index was based. We adopted these new OMB CBSA delineations in the FY 2016 IPF wage index and subsequent IPF wage indexes. We refer readers to the FY 2016 IPF PPS final rule (80 FR 46682 through 46689) for a full discussion of our implementation of the OMB labor market area delineations beginning with the FY 2016 wage index.

On July 15, 2015, OMB issued OMB Bulletin No. 15-01, which provided updates to and superseded OMB Bulletin No. 13-01 that was issued on February 28, 2013. The attachment to OMB Bulletin No. 15-01 provided detailed information on the update to statistical areas since February 28, 2013. The updates provided in OMB Bulletin No. 15-01 were based on the application of the 2010 Standards for Delineating Metropolitan and Micropolitan Statistical Areas to Census Bureau population estimates for July 1, 2012 and July 1, 2013. The complete list of statistical areas incorporating these changes is provided in OMB Bulletin No. 15-01. A copy of this bulletin may be obtained at

https://

www.whitehouse.gov/omb/information-for-agencies/bulletins/.

OMB Bulletin No. 15-01 established revised delineations for the Nation's Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Combined Statistical Areas. The bulletin also provided delineations of Metropolitan Divisions as well as delineations of New England City and Town Areas. As discussed in the FY 2017 IPPS/LTCH PPS final rule (81 FR 56913), the updated labor market area definitions from OMB Bulletin 15-01 were implemented under the IPPS beginning on October 1, 2016 (FY 2017). Therefore, we implemented these revisions for the IPF PPS beginning October 1, 2017 (FY 2018), consistent with our historical practice of modeling IPF PPS adoption of the labor market area delineations after IPPS adoption of these delineations (historically the IPF wage index has been based upon the pre-floor, pre-reclassified IPPS hospital wage index from the prior year).

On August 15, 2017, OMB issued OMB Bulletin No. 17-01, which provided updates to and superseded OMB Bulletin No. 15-01 that was issued on July 15, 2015. The attachments to OMB Bulletin No. 17-01 provide detailed information on the update to statistical areas since July 15, 2015, and are based on the application of the 2010 Standards for Delineating Metropolitan and Micropolitan Statistical Areas to Census Bureau population estimates for July 1, 2014 and July 1, 2015. In the FY 2020 IPF PPS final rule (84 FR 38453 through 38454), we adopted the updates set forth in OMB Bulletin No. 17-01 effective October 1, 2019, beginning with the FY 2020 IPF wage index. Given that the loss of the rural adjustment was mitigated in part by the increase in wage index value, and that only a single IPF was affected by this change, we did not believe it was necessary to transition this provider from its rural to newly urban status. We refer readers to the FY 2020 IPF PPS final rule (84 FR 38453 through 38454) for a more detailed discussion about the decision to forego a transition plan in FY 2020.

On April 10, 2018, OMB issued OMB Bulletin No. 18-03, which superseded the August 15, 2017 OMB Bulletin No. 17-01, and on September 14, 2018, OMB issued, OMB Bulletin No. 18-04, which superseded the April 10, 2018 OMB Bulletin No. 18-03. These bulletins established revised delineations for Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Combined Statistical Areas, and provided guidance on the use of the delineations of these statistical areas. A copy of OMB Bulletin No. 18-04 may be obtained at

https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf.

In the FY 2021 IPF PPS final rule (85 FR 47051 through 47059), we adopted the updates set forth in OMB Bulletin No. 18-04 effective October 1, 2020, beginning with the FY 2021 IPF wage index. These updates included material changes to the OMB statistical area delineations which included 34 urban counties that became rural, 47 rural counties that became urban, and 19 counties that moved to a new or modified CBSA.

Given the scope of changes involved in adopting the CBSA delineations for FY 2021, we finalized a 2-year transition policy 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 III.D.1.b.ii, 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 are not proposing to adopt OMB Bulletin 20-01.

ii. 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 the reader to the FY 2007 IPF PPS final rule (71 FR 27064 through 27065) for a complete discussion regarding treating Micropolitan Areas as rural.

c. Proposed 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 2022, we are proposing 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).

d. Proposed Budget Neutrality Adjustment

Changes to the wage index are made in a budget-neutral manner so that updates do not increase expenditures. Therefore, for FY 2022, we are proposing 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 2022 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 use the following steps to ensure that the rates reflect the FY 2022 update to the wage indexes (based on the FY 2018 hospital cost report data) and the labor-related share in a budget-neutral manner:

Step 1:

Simulate estimated IPF PPS payments, using the FY 2021 IPF wage index values (available on the CMS website) and labor-related share (as published in the FY 2021 IPF PPS final rule (85 FR 47043).

Step 2:

Simulate estimated IPF PPS payments using the proposed FY 2022 IPF wage index values (available on the CMS website) and proposed FY 2022 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

2022 budget-neutral wage adjustment factor of 1.0014.

Step

4:

Apply the FY 2022 budget-neutral wage adjustment factor from step 3 to the FY 2021 IPF PPS Federal per diem base rate after the application of the market basket update described in section III.A of this rule, to determine the FY 2022 IPF PPS Federal per diem base rate.

2. Proposed Teaching Adjustment

a. Background

In the November 2004 IPF PPS final rule, 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).

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. The 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 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 this 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 section III.D.2.b of this proposed rule, we discuss proposed updates to the IPF policy on temporary adjustment to the FTE cap.

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 2022 proposed rule, we are proposing to continue to retain the coefficient value of 0.5150 for the teaching adjustment to the Federal per diem base rate.

b. Proposed Update to IPF Teaching Policy on IPF Program Closures and Displaced Residents

For FY 2022, we are proposing to change the IPF policy regarding displaced residents from IPF closures and closures of IPF teaching programs. Specifically, we are proposing to adopt conforming changes to the IPF PPS teaching policy to align with the policy changes that the IPPS finalized in the FY 2021 IPPS final rule (85 FR 58865 through 58870). We believe that the IPF IME policy relating to hospital closure and displaced students is susceptible to the same vulnerabilities as IPPS GME policy. Hence, if an IPF with a large number of residents training in its residency program announces it is closing, these residents will become displaced and will need to find alternative positions at other IPF hospitals or risk being unable to become Board certified. Although we propose to adopt a policy under the IPF PPS that is consistent with an applicable policy under the IPPS, the actual caps under the two payment systems may not be commingled. In other words, the resident cap applicable under the IPPS is separate from the resident cap applicable under the IPF PPS; moreover, a provider cannot add its IPF resident cap to its IPPS resident cap in order to increase the number of residents it receives payment for under either payment system.

Section 124 of the BBRA gives the Secretary broad discretion to determine the appropriate adjustment factors for the IPF PPS. We are proposing to implement the policy discussed in this section to remain consistent with the way that the IPPS teaching policy calculates FTE resident caps in the case of a receiving hospital that obtains a temporary IME and direct GME cap adjustment for assuming the training of displaced residents due to another hospital or residency program's closure. We are also proposing that in the future, we would deviate from IPPS teaching policy as it pertains to counting displaced residents for the purposes of the IPF teaching adjustment only when it is necessary and appropriate for the IPF PPS.

As stated 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 facility-level adjustment we are providing for teaching hospitals under IPF PPS parallels the IME payments paid under the IPPS. Both payments are add on adjustments to the amount per case and both are based in part on the number of full-time equivalent (FTE) residents training at the facility.

The regulation at 42 CFR 412.424(d)(1)(iii)(F) permits an IPF to temporarily adjust its FTE cap to reflect residents added because of another hospital or program's closure. We first implemented regulations regarding residents displaced by teaching hospital and program closures in the May 6, 2011 IPF PPS final rule (76 FR 26431). In that final rule, we adopted the IPPS definition of “closure of a hospital” at 42 CFR 413.79(h)(1)(i) to apply to IPF closures as well, and to mean that the IPF terminates its Medicare provider agreement as specified in 42 CFR

489.52. In this proposed rule, we are proposing to codify this definition, as well as the definition of an IPF program closure, at § 412.402.

Although not explicitly stated in regulations text, our current policy is that a displaced resident is one that is physically present at the hospital training on the day prior to or the day of hospital or program closure. This longstanding policy derived from the fact that in the regulations text, there are requirements that the receiving hospital identifies the residents' “who have come from the closed IPF” (§ 412.424(d)(1)(iii)(F)(

1

)(

ii

)) or `identifies the residents “who have come from another IPF's closed program” (§ 412.424(d)(1)(iii)(F)(

2

)(

i

)), and that the IPF that closed its program identifies ” the residents who were in training at the time of the program's closure” (§ 412.424(d)(1)(iii)(F)(

2

)(

ii

)). We considered the residents who were physically present at the IPF to be those residents who were “training at the time of the program's closure,” thereby granting them the status of “displaced residents.” Although we did not want to limit the “displaced residents” to only those physically present at the time of closure, it becomes much more administratively challenging for the following groups of residents at closing IPFs/programs to continue their training: (1) Residents who leave the program after the closure is publicly announced to continue training at another IPF, but before the actual closure; (2) residents assigned to and training at planned rotations at other IPFs who will be unable to return to their rotations at the closing IPF or program; and (3) individuals (such as medical students or would-be fellows) who matched into resident programs at the closing IPF or program but have not yet started training at the closing IPF or program. Other groups of residents who, under current policy, are already considered “displaced residents” include—(1) residents who are physically training in the IPF on the day prior to or day of program or IPF closure; and (2) residents who would have been at the closing IPF or IPF program on the day prior to or of closure but were on approved leave at that time, and are unable to return to their training at the closing IPF or IPF program.

We are proposing to amend the IPF policy with regard to closing teaching IPFs and closing residency programs to address the needs of residents attempting to find alternative IPFs in which to complete their training. Additionally, this proposal addresses the incentives of originating and receiving IPFs with regard to ensuring we appropriately account for their indirect teaching costs by way of an appropriate IPF teaching adjustment based on each program's resident FTEs. We are proposing to change two aspects of the current IPF policy, which are discussed in the following section.

First, rather than link the status of displaced residents for the purpose of the receiving IPF's request to increase their FTE cap to the resident's presence at the closing IPF or program on the day prior to or the day of program or IPF closure, we propose that the ideal day would be the day that the closure was publicly announced, (for example, via a press release or a formal notice to the Accreditation Council on Graduate Medical Education (ACGME)). This would provide greater flexibility for the residents to transfer while the IPF operations or residency programs were winding down, rather than waiting until the last day of IPF or program operation. This would address the needs of the first group of residents as previously described: Residents who would leave the IPF program after the closure was publicly announced to continue training at another IPF, but before the day of actual closure.

Second, by removing the link between the status of displaced residents and their presence at the closing IPF or program on the day prior to or the day of program or IPF closure, we propose to also allow the second and third group of residents who are not physically at the closing IPF/closing program, but had intended to train at (or return to training at, in the case of residents on rotation) to be considered a displaced resident. Thus, we are proposing to revise our teaching policy with regard to which residents can be considered “displaced” for the purpose of the receiving IPF's request to increase their FTE cap in the situation where an IPF announces publicly that it is closing, and/or that it is closing an IPF residency program(s). Specifically, we are proposing to adopt the definitions of “closure of a hospital”, “closure of a hospital residency training program”, and “displaced resident” as defined at 42 CFR 413.79(h) but with respect to IPFs and for the purposes of accounting for indirect teaching costs.

In addition, we are proposing to change another detail of the IPF teaching policy specific to the requirements for the receiving IPF. To apply for the temporary increase in the FTE resident cap, the receiving IPF would have to submit a letter to its Medicare Administrative Contractor (MAC) within 60 days of beginning the training of the displaced residents. As established under existing regulation at § 412.424(d)(1)(iii)(F)(

1

)(

ii

) and § 412.424(d)(1)(iii)(F)(

2

)(

i

), this letter must identify the residents who have come from the closed IPF or program and have caused the receiving IPF to exceed its cap, and must specify the length of time the adjustment is needed. Moreover, we want to propose clarifications on how the information would be delivered in this letter. Consistent with IPPS teaching policy, we are proposing that the letter from the receiving IPF would have to include: (1) The name of each displaced resident; (2) the last four digits of each displaced resident's social security number; (3) the IPF and program in which each resident was training previously; and (4) the amount of the cap increase needed for each resident (based on how much the receiving IPF is in excess of its cap and the length of time for which the adjustments are needed). We are proposing to require the receiving hospital to only supply the last four digits of each displaced resident's social security number to reduce the amount of personally identifiable information (PII) included in these agreements.

We are also clarifying that, as we previously discussed in the May 6, 2011 IPF PPS final rule (76 FR 26455), the maximum number of FTE resident cap slots that could be transferred to all receiving IPFs is the number of FTE resident cap slots belonging to the IPF that has the closed program or that is closing. Therefore, if the originating IPF is training residents in excess of its cap, then being a displaced resident does not guarantee that a cap slot will be transferred along with that resident. Therefore, we are proposing that if there are more IPF displaced residents than available cap slots, the slots may be apportioned according to the closing IPF's discretion. The decision to transfer a cap slot if one is available would be voluntary and made at the sole discretion of the originating IPF. However, if the originating IPF decides to do so, then it would be the originating IPF's responsibility to determine how much of an available cap slot would go with a particular resident (if any). We also note that, as we previously discussed in the May 6, 2011 IPF PPS final rule (76 FR 25455), only to the extent a receiving IPF would exceed its FTE cap by training displaced residents would it be eligible for a temporary adjustment to its resident FTE cap. Displaced residents are factored into the receiving IPF's ratio of resident FTEs to the facility's average daily census.

3. Proposed 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 would 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 think 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 updated for FY 2018, the COLA factors were updated in FY 2018 IPPS/LTCH rulemaking (82 FR 38529). As such, we also updated the IPF PPS COLA factors for FY 2018 (82 FR 36780 through 36782) to reflect the updated COLA factors finalized in the FY 2018 IPPS/LTCH rulemaking.

For FY 2022, we are proposing to update the COLA factors published by OPM for 2009 (as these are the last COLA factors OPM published prior to transitioning from COLAs to locality pay) using the methodology that we finalized in the FY 2013 IPPS/LTCH PPS final rule and adopted for the IPF PPS in the FY 2015 IPF final rule. Specifically, we are proposing to update the 2009 OPM COLA factors by a comparison of the growth in the Consumer Price Indices (CPIs) for the areas of Urban Alaska and Urban Hawaii, relative to the growth in the CPI for the average U.S. city as published by the Bureau of Labor Statistics (BLS). We note that for the prior update to the COLA factors, we used the growth in the CPI for Anchorage and the CPI for Honolulu. Beginning in 2018, these indexes were renamed to the CPI for Urban Alaska and the CPI for Urban Hawaii due to the BLS updating its sample to reflect the data from the 2010 Decennial Census on the distribution of the urban population (

https://www.bls.gov/regions/west/factsheet/2018cpirevisionwest.pdf,

accessed January 22, 2021). The CPI for Urban Alaska area covers Anchorage and Matanuska-Susitna Borough in the State of Alaska and the CPI for Urban Hawaii covers Honolulu in the State of Hawaii. BLS notes that the indexes are considered continuous over time, regardless of name or composition changes.

Because BLS publishes CPI data for only Urban Alaska and Urban Hawaii, using the methodology we finalized in the FY 2013 IPPS/LTCH PPS final rule and adopted for the IPF PPS in the FY 2015 IPF final rule, we are proposing to use the comparison of the growth in the overall CPI relative to the growth in the CPI for those areas to update the COLA factors for all areas in Alaska and Hawaii, respectively. We believe that the relative price differences between these urban areas and the U.S. (as measured by the CPIs mentioned above) are appropriate proxies for the relative price differences between the “other areas” of Alaska and Hawaii and the U.S.

BLS publishes the CPI for All Items for Urban Alaska, Urban Hawaii, and for the average U.S. city. However, consistent with our methodology finalized in the FY 2013 IPPS/LTCH PPS final rule and adopted for the IPF PPS in the FY 2015 IPF final rule, we are proposing to create reweighted CPIs for each of the respective areas to reflect the underlying composition of the IPPS market basket nonlabor-related share. The current composition of the CPI for All Items for all of the respective areas is approximately 40 percent commodities and 60 percent services. However, the IPPS nonlabor-related share is comprised of a different mix of commodities and services. Therefore, we are proposing to create reweighted indexes for Urban Alaska, Urban Hawaii, and the average U.S. city using the respective CPI commodities index and CPI services index and proposed shares of 57 percent commodities/43 percent. We created reweighted indexes using BLS data for 2009 through 2020—the most recent data available at the time of this proposed rulemaking. In the FY 2018 IPPS/LTCH PPS final rule (82 FR 38530), we created reweighted indexes based on the 2014-based IPPS market basket (which was adopted for the FY 2018 IPPS update) and BLS data for 2009 through 2016 (the most recent BLS data at the time of the FY 2018

IPPS/LTCH PPS rulemaking), and we updated the IPF PPS COLA factors accordingly for FY 2018.

We continue to believe this methodology is appropriate because we continue to make a COLA for hospitals located in Alaska and Hawaii by multiplying the nonlabor-related portion of the standardized amount by a COLA factor. We note that OPM's COLA factors were calculated with a statutorily mandated cap of 25 percent. As stated in the FY 2018 IPPS/LTCH PPS final rule (82 FR 38530), under the COLA update methodology we finalized in the FY 2013 IPPS/LTCH PPS final rule, we exercised our discretionary authority to adjust payments to hospitals in Alaska and Hawaii by incorporating this cap. In applying this finalized methodology for updating the COLA factors, for FY 2022, we are proposing to continue to use such a cap, as our policy is based on OPM's COLA factors (updated by the methodology described above).

Applying this methodology, the COLA factors that we are proposing to establish for FY 2022 to adjust the nonlabor-related portion of the standardized amount for IPFs located in Alaska and Hawaii are shown in the Table 1 below. For comparison purposes, we also are showing the COLA factors effective for FY 2018 through FY 2021.

Table 1—Comparison of IPF PPS Cost-of-Living Adjustment Factors: IPFs Located in Alaska and Hawaii

Area

FY 2018

through

FY 2021

FY 2022

through

FY 2025

(proposed)

Alaska:

City of Anchorage and 80-kilometer (50-mile) radius by road

1.25

1.22

City of Fairbanks and 80-kilometer (50-mile) radius by road

1.25

1.22

City of Juneau and 80-kilometer (50-mile) radius by road

1.25

1.22

Rest of Alaska

1.25

1.24

Hawaii:

City and County of Honolulu

1.25

1.25

County of Hawaii

1.21

1.22

County of Kauai

1.25

1.25

County of Maui and County of Kalawao

1.25

1.25

The proposed IPF PPS COLA factors for FY 2022 are also shown in Addendum A to this proposed rule, and is available at

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.

4. Proposed 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 proposed 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 2022, we are proposing to continue to retain the 1.31 adjustment factor for IPFs with qualifying EDs. 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).

F. Other Proposed 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. Proposed Update to the Outlier Fixed Dollar Loss Threshold Amount

In accordance with the update methodology described in § 412.428(d), we are proposing 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. For this proposed rulemaking, the most recent available data would be the FY 2020 claims. However, during FY 2020, the U.S. healthcare system undertook an unprecedented response to the Public Health Emergency (PHE) declared by the Health and Human Services Secretary on January 31, 2020 in response to the outbreak of respiratory disease caused by a novel (new) coronavirus that has been named “SARS CoV 2” and the disease it causes, which has been named “coronavirus disease 2019” (abbreviated “COVID-19”). Therefore, as discussed in section VI.C.3 of this proposed rule, we considered whether the most recent available year of claims, FY 2020, or the prior year, FY 2019, would be the best for estimating IPF PPS payments in FY 2021 and FY 2022. We compared the two years' claims distributions as well as the impact results, and based on that analysis determined that the FY 2019 claims appear to be the best available data at this time. We refer the reader to section VI.C.3 of this proposed rule for a detailed discussion of that analysis.

Based on an analysis of the June 2020 update of FY 2019 IPF claims and the FY 2021 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 are proposing to update the IPF outlier threshold amount for FY 2022 using FY 2019 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 2021. Based on an analysis of these updated data, we estimate that IPF outlier payments as a percentage of total estimated payments are approximately 1.8 percent in FY 2021. Therefore, we are proposing to update the outlier threshold amount to $14,030 to maintain estimated outlier payments at 2 percent of total estimated aggregate IPF payments for FY 2022. This proposed update is a decrease from the FY 2021 threshold of $14,630. In contrast, using the FY 2020 claims to estimate payments, the proposed outlier fixed dollar loss threshold for FY 2022 would be $19,840, an increase from the FY 2021 threshold of $14,630. We refer the reader to section VI.C.3 of this proposed rule for a detailed discussion of the estimated impacts of the proposed update to the outlier fixed dollar loss threshold, and we invite comments on this analysis.

We note that our proposed use of the FY 2019 claims to set the proposed outlier fixed dollar loss threshold for FY 2022 would deviate from what has been our longstanding practice of using the most recent available year of claims, which is FY 2020 data. However, this proposal remains consistent with the established outlier update methodology. As discussed in this section and in section VI.C.3 of this proposed rule, we are proposing to update the outlier fixed dollar loss threshold based on FY 2019 IPF claims in order to maintain the appropriate outlier percentage in FY 2022. We are proposing to deviate from our longstanding practice of using the most recent available year of claims only because and only to the extent that the COVID-19 PHE appears to have significantly impacted the FY 2020 IPF claims. As we are able to analyze more recent available IPF claims data and better understand both the short-term and long-term effects of the COVID-19 PHE on IPFs, we intend to re-assess the appropriateness of using FY 2019 IPF claims rather than FY 2020 IPF claims for the FY 2022 update.

3. Proposed 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 cost-to-charge ratio (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 2022, we are proposing to continue to follow this methodology.

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 2022 is 2.0398 for rural IPFs, and 1.6126 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 are proposing to continue to update the FY 2022 national median and ceiling CCRs for urban and rural IPFs based on the CCRs entered in the latest available IPF PPS PSF. Specifically, for FY 2022, to be used in each of the three situations listed previously, using the most recent CCRs entered in the CY 2021 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).

IV. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program

A. Background and Statutory Authority

We refer readers to the FY 2019 IPF PPS final rule (83 FR 38589) for a discussion of the background and statutory authority

1

of the IPFQR Program.

1

We note that the statute uses the term “rate year” (RY). However, beginning with the annual update of the inpatient psychiatric facility prospective payment system (IPF PPS) that took effect on July 1, 2011 (RY 2012), we aligned the IPF PPS update with the annual update of the ICD codes, effective on October 1 of each year. This change allowed for annual payment updates and the ICD coding update to occur on the same schedule and appear in the same

Federal Register

document, promoting administrative efficiency. To reflect the change to the annual payment rate update cycle, we revised the regulations at 42 CFR 412.402 to specify that, beginning October 1, 2012, the RY update period would be the 12-month period from October 1 through September 30, which we refer to as a “fiscal year” (FY) (76 FR 26435). Therefore, with respect to the IPFQR Program, the terms “rate year,” as used in the statute, and “fiscal year” as used in the regulation, both refer to the period from October 1 through September 30. For more information regarding this terminology change, we refer readers to section III. of the RY 2012 IPF PPS final rule (76 FR 26434 through 26435).

B. Covered Entities

In the FY 2013 IPPS/LTCH PPS final rule (77 FR 53645), we established that the IPFQR Program's quality reporting requirements cover those psychiatric hospitals and psychiatric units paid under Medicare's IPF PPS (§ 412.404(b)). Generally, psychiatric hospitals and psychiatric units within acute care and critical access hospitals that treat Medicare patients are paid under the IPF PPS. Consistent with previous regulations, we continue to use the terms “facility” or IPF to refer to both inpatient psychiatric hospitals and psychiatric units. This usage follows the terminology in our IPF PPS regulations at § 412.402. For more information on covered entities, we refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 53645).

C. Previously Finalized Measures and Administrative Procedures

The current IPFQR Program includes 14 measures. For more information on these measures, we refer readers to Table 4 of this proposed rule and the following final rules:

• The FY 2013 IPPS/LTCH PPS final rule (77 FR 53646 through 53652);

• The FY 2014 IPPS/LTCH PPS final rule (78 FR 50889 through 50897);

• The FY 2015 IPF PPS final rule (79 FR 45963 through 45975);

• The FY 2016 IPF PPS final rule (80 FR 46695 through 46714);

• The FY 2017 IPPS/LTCH PPS final rule (81 FR 57238 through 57247);

• The FY 2019 IPF PPS final rule (83 FR 38590 through 38606); and

• The FY 2020 IPF PPS final rule (84 FR 38459 through 38467).

For more information on previously adopted procedural requirements, we refer readers to the following rules:

• The FY 2013 IPPS/LTCH PPS final rule (77 FR 53653 through 53660);

• The FY 2014 IPPS/LTCH PPS final rule (78 FR 50897 through 50903);

• The FY 2015 IPF PPS final rule (79 FR 45975 through 45978);

• The FY 2016 IPF PPS final rule (80 FR 46715 through 46719);

• The FY 2017 IPPS/LTCH PPS final rule (81 FR 57248 through 57249);

• The FY 2018 IPPS/LTCH PPS final rule (82 FR 38471 through 38474);

• The FY 2019 IPF PPS final rule (83 FR 38606 through 38608); and

• The FY 2020 IPF PPS final rule (84 FR 38467 through 38468).

D. Closing the Health Equity Gap in CMS Quality Programs—Request for Information (RFI)

Persistent inequities in health care outcomes exist in the U.S., including among Medicare patients. In recognition of persistent health disparities and the importance of closing the health equity gap, we request information on revising several CMS programs to make reporting of health disparities based on social risk factors and race and ethnicity more comprehensive and actionable for facilities, providers, and patients. The following is part of an ongoing effort across CMS to evaluate appropriate initiatives to reduce health disparities. Feedback will be used to inform the creation of a future, comprehensive, RFI focused on closing the health equity gap in CMS programs and policies.

This RFI contains four parts:

•

Background:

This section provides information describing our commitment to health equity, and existing initiatives with an emphasis on reducing health disparities.

•

Current CMS Disparity Methods:

This section describes the methods, measures, and indicators of social risk currently used with the CMS Disparity Methods.

•

Future potential stratification of quality measure results:

This section describes four potential future expansions of the CMS Disparity Methods, including (1) Stratification of Quality Measure Results—Dual Eligibility; (2) Stratification of Quality Measure Results—Race and Ethnicity; (3) Improving Demographic Data Collection; and (4) Potential Creation of a Facility Equity Score to Synthesize Results Across Multiple Social Risk Factors.

•

Solicitation of public comment:

This section specifies 12 requests for feedback on the above topics. We look forward to receiving feedback on these topics, and note our intention for an additional RFI or rulemaking on this topic in the future.

1. Background

Significant and persistent inequities in health care outcomes exist in the U.S. Belonging to a racial or ethnic minority group; living with a disability; being a member of the lesbian, gay, bisexual, transgender, and queer (LGBTQ+) community; living in a rural area; or being near or below the poverty level, is often associated with worse health outcomes.

2 3 4 5 6 7 8 9

Such disparities in

health outcomes are the result of number of factors, but importantly for CMS programs, although not the sole determinant, poor access and provision of lower quality health care contribute to health disparities. For instance, numerous studies have shown that among Medicare beneficiaries, racial and ethnic minority individuals often receive lower quality of care, report lower experiences of care, and experience more frequent hospital readmissions and operative complications.

10 11 12 13 14 15

Readmission rates for common conditions in the Hospital Readmissions Reduction Program are higher for Black Medicare beneficiaries and higher for Hispanic Medicare beneficiaries with Congestive Heart Failure and Acute Myocardial Infarction.

16 17 18 19 20

Studies have also shown that African Americans are significantly more likely than white Americans to die prematurely from heart disease and stroke.

21

The COVID-19 pandemic has further illustrated many of these longstanding health inequities with higher rates of infection, hospitalization, and mortality among Black, Latino, and Indigenous and Native American persons relative to White persons.

22 23

As noted by the Centers for Disease Control “long-standing systemic health and social inequities have put many people from racial and ethnic minority groups at increased risk of getting sick and dying from COVID-19.”

24

One important strategy for addressing these important inequities is improving data collection to allow for better measurement and reporting on equity across our programs and policies.

2

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

3

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

4

Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and Equity of Care in U.S. Hospitals. New

England Journal of Medicine. 2014;371(24):2298-2308.

5

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

6

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

7

https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.

8

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

9

Poteat TC, Reisner SL, Miller M, Wirtz AL. COVID-19 Vulnerability of Transgender Women With and Without HIV Infection in the Eastern and Southern U.S. Preprint.

medRxiv.

2020;2020.07.21.20159327. Published 2020 Jul 24. doi:10.1101/2020.07.21.20159327.

10

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

11

Guide to Reducing Disparities in Readmissions. CMS Office of Minority Health. Revised August 2018. Available at:

https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf.

12

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

13

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

14

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

15

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

16

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

17

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

18

Guide to Reducing Disparities in Readmissions. CMS Office of Minority Health. Revised August 2018. Available at:

https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf.

19

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

20

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

21

HHS. Heart disease and African Americans. (March 29, 2021).

https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19.

22

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

23

Ochieng N, Cubanski J, Neuman T, Artiga S, and Damico A. Racial and Ethnic Health Inequities and Medicare. Kaiser Family Foundation. Februray 2021. Available at:

https://www.kff.org/medicare/report/racial-and-ethnic-health-inequities-and-medicare/.

24

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

We are committed to achieving equity in health care outcomes for our beneficiaries by supporting providers in quality improvement activities to reduce health inequities, enabling them to make more informed decisions, and promoting provider accountability for health care disparities.

25

For the purposes of this proposed rule, we are using a definition of equity established in Executive Order 13985, as “the consistent and systematic fair, just, and impartial treatment of all individuals, including individuals who belong to underserved communities that have been denied such treatment, such as Black, Latino, and Indigenous and Native American persons, Asian Americans and Pacific Islanders and other persons of color; members of religious minorities; lesbian, gay, bisexual, transgender, and queer (LGBTQ+) persons; persons with disabilities; persons who live in rural areas; and persons otherwise adversely affected by persistent poverty or inequality.”

26

We note that this definition was recently established by the current administration, and provides a useful, common definition for equity across different areas of government, although numerous other definitions of equity exist.

25

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

26

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

Our ongoing commitment to closing the equity gap in CMS quality programs is demonstrated by a portfolio of programs aimed at making information on the quality of health care providers and services, including disparities, more transparent to consumers and providers. The CMS Equity Plan for Improving Quality in Medicare outlines a path to equity which aims to support Quality Improvement Networks and Quality Improvement Organizations (QIN-QIOs); Federal, state, local, and tribal organizations; providers; researchers; policymakers; beneficiaries and their families; and other stakeholders in activities to achieve health equity.

27

The CMS Equity Plan for Improving Quality in Medicare focuses on three core priority areas which inform our policies and programs: (1) Increasing understanding and awareness of health disparities; (2) developing and disseminating solutions to achieve health equity; and (3) implementing sustainable actions to achieve health equity.

28

The CMS Quality Strategy

29

and Meaningful Measures Framework

30

include elimination of racial and ethnic disparities as a central principle. Our efforts aimed at closing the health equity gap to date have included both providing transparency about health disparities, supporting providers with evidence-informed solutions to achieve health equity, and reporting to providers

on gaps in quality through the following reports and programs:

27

Centers for Medicare and Medicaid Services Office of Minority Health. The CMS Equity Plan for Improving Quality in Medicare. 2015.

https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf.

28

Centers for Medicare and Medicaid Services Office of Minority Health. The CMS Equity Plan for Improving Quality in Medicare. 2015.

https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf.

29

Centers for Medicare Services. CMS Quality Strategy. 2016.

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

30

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

• The

CMS Mapping Medicare Disparities Tool,

which is an interactive map that identifies areas of disparities and a starting point to understand and investigate geographical, racial and ethnic differences in health outcomes for Medicare patients.

31

31

https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities.

• The

Racial, Ethnic, and Gender Disparities in Health Care in Medicare Advantage Stratified Report,

which highlights racial and ethnic differences in health care experiences and clinical care, compares quality of care for women and men, and looks at racial and ethnic differences in quality of care among women and men separately for Medicare Advantage plans.

32

32

https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.

•

The Rural-Urban Disparities in Health Care in Medicare Report,

which details rural-urban differences in health care experiences and clinical care.

33

33

Centers for Medicare and Medicaid Services. Rural-Urban Disparities in Health Care in Medicare. 2019.

https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Rural-Urban-Disparities-in-Health-Care-in-Medicare-Report.pdf.

• The

Standardized Patient Assessment Data Elements

for certain post-acute care Quality Reporting Programs, which now includes data reporting for race and ethnicity and preferred language, in addition to screening questions for social needs (84 FR 42536 through 42588).

• The

CMS Innovation Center's Accountable Health Communities Model,

which include standardized data collection of health-related social needs data.

• The

Guide to Reducing Disparities

which provides an overview of key issues related to disparities in readmissions and reviews sets of activities that can help hospital leaders reduce readmissions in diverse populations.

34

34

Guide to Reducing Disparities in Readmissions. CMS Office of Minority Health. Revised August 2018. Available at:

https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf.

• The

CMS Disparity Methods,

which provide hospital-level confidential results stratified by dual eligibility for condition-specific readmission measures currently included in the Hospital Readmission Reduction Program (84 FR 42496 through 42500).

These programs are informed by reports by the National Academies of Science, Engineering and Medicine (NASEM)

35

and the Office of the Assistant Secretary for Planning and Evaluation (ASPE)

36

which have examined the influence of social risk factors on several of our quality programs. In this RFI, we address only the sixth initiative listed, the CMS Disparity Methods, which we have implemented for measures in the Hospital Readmissions Reduction Program and are considering in other programs, including the IPFQR Program. We discuss the implementation of these methods to date and present considerations for continuing to improve and expand these methods to provide providers and ultimately consumers with actionable information on disparities in health care quality to support efforts at closing the equity gap.

35

National Academies of Sciences, Engineering, and Medicine. 2016. Accounting for Social Risk Factors in Medicare Payment: Identifying Social Risk Factors. Washington, DC: The National Academies Press.

https://doi.org/10.17226/21858.

36

https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.

2. Current CMS Disparity Methods

We first sought public comment on potential confidential and public reporting of IPFQR program measure data stratified by social risk factors in the FY 2018 IPPS/LTCH PPS proposed rule (82 FR 20121). We initially focused on stratification by dual eligibility, which is consistent with recommendations from ASPE's First Report to Congress which was required by the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014 (Pub. L. 113-185).

37

This report found that in the context of value-based purchasing (VBP) programs, dual eligibility was among the most powerful predictors of poor health outcomes among those social risk factors that ASPE examined and tested.

37

https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.

In the FY 2018 IPPS/LTCH PPS final rule we also solicited feedback on two potential methods for illuminating differences in outcomes rates among patient groups within a provider's patient population that would also allow for a comparison of those differences, or disparities, across providers for the Hospital IQR Program (82 FR 38403 through 38409). The first method (the Within-Hospital disparity method) promotes quality improvement by calculating differences in outcome rates among patient groups within a hospital while accounting for their clinical risk factors. This method also allows for a comparison of the magnitude of disparity across hospitals, permitting hospitals to assess how well they are closing disparity gaps compared to other hospitals. The second methodological approach (the Across-Hospital method) is complementary and assesses hospitals' outcome rates for dual-eligible patients only, across hospitals, allowing for a comparison among hospitals on their performance caring for their patients with social risk factors. In the FY 2018 IPPS/LTCH PPS proposed rule under the IPFQR Program (82 FR 20121), we also specifically solicited feedback on which social risk factors provide the most valuable information to stakeholders. Overall, comments supported the use of dual eligibility as a proxy for social risk, although commenters also suggested investigation of additional social risk factors, and we continue to consider which risk factors provide the most valuable information to stakeholders.

Concurrent with our comment solicitation on stratification in the IPFQR Program, we have considered methods for stratifying measure results for other quality reporting programs. For example, FY 2019 IPPS/LTCH PPS final rule (82 FR 41597 through 41601), we finalized plans to provide confidential hospital-specific reports (HSRs) containing stratified results of the Pneumonia Readmission (NQF #0506) and Pneumonia Mortality (NQF #0468) measures including both the Across-Hospital Disparity Method and the Within-Hospital Disparity Method (disparity methods), stratified by dual eligibility. In the FY 2019 IPPS/LTCH PPS final rule (83 FR 41554 through 41556), we also removed six condition/procedure specific readmissions measures, including the Pneumonia Readmission measure (NQF #0506) and five mortality measures, including the Pneumonia Mortality measure (NQF #0468) (83 FR 41556 through 41558) from the Hospital IQR Program. However, the Pneumonia Readmission (NQF #0506) and the other condition/procedure measures remained in the Hospital Readmissions Reduction Program. In 2019, we provided hospitals with results of the Pneumonia Readmission measure (NQF #0506) stratified using dual eligibility. We provided this information in annual confidential HSRs for claims-based measures.

We then, in the FY 2020 IPPS/LTCH PPS Final Rule (84 FR 42388 through 42390), finalized the proposal to provide confidential hospital specific reports (HSRs) containing data stratified by dual-eligible status for all six readmission measures included in the

Hospital Readmission Reduction Program.

3. Potential Expansion of the CMS Disparity Methods

We are committed to advancing health equity by improving data collection to better measure and analyze disparities across programs and policies.

38

As we previously noted, we have been considering, among other things, expanding our efforts to provide stratified data for additional social risk factors and measures, optimizing the ease-of-use of the results, enhancing public transparency of equity results, and building towards provider accountability for health equity. We are seeking public comment on the potential stratification of quality measures in the IPFQR Program across two social risk factors: Dual eligibility and race/ethnicity.

38

Centers for Medicare Services. CMS Quality Strategy. 2016.

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

a. Stratification of Quality Measure Results—Dual Eligibility

As described above, landmark reports by the National Academies of Science, Engineering and Medicine (NASEM)

39

and the Office of the Assistant Secretary for Planning and Evaluation (ASPE),

40

which have examined the influence of social risk factors on several of our quality programs, have shown that in the context of value-based purchasing (VBP) programs, dual eligibility, as an indicator of social risk, is a powerful predictor of poor health outcomes. We note that the patient population of IPFs has a higher percentage of dually eligible patients than the general Medicare population. Specifically, over half (56 percent) of Medicare patients in IPFs are dually eligible

41

while approximately 20 percent of all Medicare patients are dually eligible.

42

We are considering stratification of quality measure results in the IPFQR Program and are considering which measures would be most appropriate for stratification and if dual eligibility would be a meaningful social risk factor for stratification.

39

National Academies of Sciences, Engineering, and Medicine. 2016. Accounting for Social Risk Factors in Medicare Payment: Identifying Social Risk Factors. Washington, DC: The National Academies Press.

https://doi.org/10.17226/21858.

40

https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.

41

https://aspe.hhs.gov/basic-report/transitions-care-and-service-use-among-medicare-beneficiaries-inpatient-psychiatric-facilities-issue-brief.

42

https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/DataStatisticalResources/Downloads/MedicareMedicaidDualEnrollmentEverEnrolledTrendsDataBrief2006-2018.pdf.

For the IPFQR Program, we would consider disparity reporting using two disparity methods derived from the Within-Hospital and Across-Hospital methods, described above. The first method (based on the Within-Facility disparity method) would aim to promote quality improvement by calculating differences in outcome rates between dual and non-dual eligible patient groups within a facility while accounting for their clinical risk factors. This method would allow for a comparison of those differences, or disparities, across facilities, so facilities could assess how well they are closing disparity gaps compared to other facilities. The second approach (based the Across-Facility method) would be complementary and assesses facilities' outcome rates for subgroups of patients, such as dual eligible patients, across facilities, allowing for a comparison among facilities on their performance caring for their patients with social risk factors.

b. Stratification of Quality Measure Results—Race and Ethnicity

The Administration's

Executive Order on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government

directs agencies to assess potential barriers that underserved communities and individuals may face to enrollment in and access to benefits and services in Federal Programs. As summarized above, studies have shown that among Medicare beneficiaries, racial and ethnic minority persons often experience worse health outcomes, including more frequent hospital readmissions and operative complications. An important part of identifying and addressing inequities in health care is improving data collection to allow us to better measure and report on equity across our programs and policies. We are considering stratification of quality measure results in the IPFQR Program by race and ethnicity and are considering which measures would be most appropriate for stratification.

As outlined in the 1997 Office of Management and Budget (OMB) Revisions to the Standards for the Collection of Federal Data on Race and Ethnicity, the racial and ethnic categories, which may be used for reporting the disparity methods are considered to be social and cultural, not biological or genetic.

43

The 1997 OMB Standard lists five minimum categories of race: (1) American Indian or Alaska Native; (2) Asian; (3) Black or African American; (4) Native Hawaiian or Other Pacific Islander; (5) and White. In the OMB standards, Hispanic or Latino is the only ethnicity category included, and since race and ethnicity are two separate and distinct concepts, persons who report themselves as Hispanic or Latino can be of any race.

44

Another example, the “Race & Ethnicity—CDC” code system in Public Health Information Network (PHIN) Vocabulary Access and Distribution System (VADS)

45

permits a much more granular structured recording of a patient's race and ethnicity with its inclusion of over 900 concepts for race and ethnicity. The recording and exchange of patient race and ethnicity at such a granular level can facilitate the accurate identification and analysis of health disparities based on race and ethnicity. Further, the “Race & Ethnicity—CDC” code system has a hierarchy that rolls up to the OMB minimum categories for race and ethnicity and, thus, supports aggregation and reporting using the OMB standard. ONC includes both the CDC and OMB standards in its criterion for certified health IT products.

46

For race and ethnicity, a certified health IT product must be able to express both detailed races and ethnicities using

any

of the 900 plus concepts in the “Race & Ethnicity—CDC” code system in the PHIN VADS, as well as aggregate each one of a patient's races and ethnicities to the categories in the OMB standard for race and ethnicity. This approach can reduce burden on providers recording demographics using certified products.

43

Executive Office of the President Office of Management and Budget, Office of Information and Regulatory Affairs. Revisions to the standards for the classification of Federal data on race and ethnicity. Vol 62.

Federal Register

. 1997:58782-58790.

44

https://www.census.gov/topics/population/hispanic-origin/about.html.

45

https://phinvads.cdc.gov/vads/ViewValueSet.action?id=67D34BBC-617F-DD11-B38D-00188B398520.

46

ONC criteria for certified health IT products:

https://www.healthit.gov/isa/representing-patient-race-and-ethnicity.

Self-reported race and ethnicity data remain the gold standard for classifying an individual according to race or ethnicity. However, CMS does not consistently collect self-reported race and ethnicity for the Medicare program, but instead gets the data from the Social Security Administration (SSA) and the data accuracy and comprehensiveness have proven challenging despite capabilities in the marketplace via

certified health IT products. Historical inaccuracies in Federal data systems and limited collection classifications have contributed to the limited quality of race and ethnicity information in Medicare's administrative data systems.

47

In recent decades, to address these data quality issues, we have undertaken numerous initiatives, including updating to data taxonomies and conducting direct mailings to some beneficiaries to enable more comprehensive race and ethnic identification.

48

49

Despite those efforts, studies reveal varying data accuracy in identification of racial and ethnic groups in Medicare administrative data, with higher sensitivity for correctly identifying White and Black individuals, and lower sensitivity for correctly identifying individuals of Hispanic ethnicity or of Asian/Pacific Islander and American Indian/Alaskan Native race.

50

Incorrectly classified race or ethnicity may result in overestimation or underestimation in the quality of care received by certain groups of beneficiaries.

47

Eicheldinger, C., & Bonito, A. (2008). More accurate racial and ethnic codes for Medicare administrative data.

Health Care Financing Review, 29

(3), 27-42.

48

Filice CE, Joynt KE. Examining Race and Ethnicity Information in Medicare Administrative Data. Med Care. 2017;55(12):e170-e176. doi:10.1097/MLR.0000000000000608.

49

Eicheldinger, C., & Bonito, A. (2008). More accurate racial and ethnic codes for Medicare administrative data. Health Care Financing Review, 29(3), 27-42.

50

Centers for Medicare and Medicaid Services. Building an Organizational Response to Health Disparities Inventory of Resources for Standardized Demographic and Language Data Collection. 2020.

https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf.

We continue to work with Federal and private partners to better collect and leverage data on social risk to improve our understanding of how these factors can be better measured in order to close the health equity gap. Among other things, we have developed an Inventory of Resources for Standardized Demographic and Language Data Collection

51

and supported collection of specialized International Classification of Disease, 10th Edition, Clinical Modification (ICD-10-CM) codes for describing the socioeconomic, cultural, and environmental determinants of health, and sponsored several initiatives to statistically estimate race and ethnicity information when it is absent.

52

The Office of the National Coordinator for Health Information Technology (ONC) included social, psychological, and behavioral standards in the 2015 Edition health information technology (IT) certification criteria (2015 Edition), providing interoperability standards (LOINC (Logical Observation Identifiers Names and Codes) and SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms)) for financial strain, education, social connection and isolation, and others. Additional stakeholder efforts underway to expand capabilities to capture additional social determinants of health data elements include the Gravity Project to identify and harmonize social risk factor data for interoperable electronic health information exchange for EHR fields, as well as proposals to expand the ICD-10 (International Classification of Diseases, Tenth Revision) z-codes, the alphanumeric codes used worldwide to represent diagnoses.

53

51

Centers for Medicare and Medicaid Services. Building an Organizational Response to Health Disparities Inventory of Resources for Standardized Demographic and Language Data Collection. 2020.

https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf.

52

https://pubmed.ncbi.nlm.nih.gov/18567241/,

h

ttp

s

://pubmed.ncbi.nlm.nih.gov/30506674/,

Eicheldinger C, Bonito A. More accurate racial and ethnic codes for Medicare administrative data.

Health Care Finance Rev.

2008;29(3):27-42. Haas A, Elliott MN, Dembosky JW, et al. Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity.

Health Serv Res.

2019;54(1):13-23. doi:10.1111/1475-6773.13099.

53

https://aspe.hhs.gov/pdf-report/second-impact-report-to-congress.

While development of sustainable and consistent programs to collect data on social determinants of health can be considerable undertakings, we recognize that another method to identify better race and ethnicity data is needed in the short term to address the need for reporting on health equity. In working with our contractors, two algorithms have been developed to

indirectly estimate

the race and ethnicity of Medicare beneficiaries (as described further in the following paragraphs). We feel that using indirect estimation can help to overcome the current limitations of demographic information and enable timelier reporting of equity results until longer term collaborations to improve demographic data quality across the health care sector materialize. The use of indirectly estimated race and ethnicity for conducting stratified reporting does not place any additional collection or reporting burdens on facilities as these data are derived using existing administrative and census-linked data.

Indirect estimation relies on a statistical imputation method for inferring a missing variable or improving an imperfect administrative variable using a related set of information that is more readily available.

54

Indirectly estimated data are most commonly used at the population level (such as the facility or health plan-level), where aggregated results form a more accurate description of the population than existing, imperfect data sets. These methods often estimate race and ethnicity using a combination of other data sources which are predictive of self-identified race and ethnicity, such as language preference, information about race and ethnicity in our administrative records, first and last names matched to validated lists of names correlated to specific national origin groups, and the racial and ethnic composition of the surrounding neighborhood. Indirect estimation has been used in other settings to support population-based equity measurement when self-identified data are not available.

55

54

IOM. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press.

55

IOM. 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press.

As described above, we have previously supported the development of two such methods of indirect estimation of race and ethnicity of Medicare beneficiaries. One indirect estimation approach, developed by our contractor, uses Medicare administrative data, first name and surname matching, derived from the U.S. Census and other sources, with beneficiary language preference, state of residence, and the source of the race and ethnicity code in Medicare administrative data to reclassify some beneficiaries as Hispanic or Asian/Pacific Islander (API).

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In recent years, we have also worked with another contractor to develop a new approach, the Medicare Bayesian Improved Surname Geocoding (MBISG), which combines Medicare administrative data, first and surname matching, geocoded residential address linked to the 2010 U.S. Census, and uses both Bayesian updating and multinomial logistic regression to estimate the probability of belonging to each of six racial/ethnic groups.

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Bonito AJ, 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.

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Haas, A., Elliott, M. et al (2018). Imputation of race/ethnicity to enable measurement of HEDIS

performance by race/ethnicity.

Health Services Research,

54:13-23.

The MBISG model is currently used to conduct the national, contract-level, stratified reporting of Medicare Part C & D performance data for Medicare Advantage Plans by race and ethnicity.

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Validation testing reveals concordances of 0.88 through 0.95 between indirectly estimated and self-report among individuals who identify as White, Black, Hispanic and API for the MIBSG version 2.0 and concordances with self-reported race and ethnicity of 0.96 through 0.99 for these same groups for MBISG version 2.1.

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The algorithms under consideration are considerably less accurate for individuals who self-identify as American Indian/Alaskan Native or multiracial.

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Indirect estimation can be a statistically reliable approach for calculating population-level equity results for groups of individuals (such as the facility-level) and is not intended, nor being considered, as an approach for inferring the race and ethnicity of an individual.

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The Office of Minority Health (2020). Racial, Ethnic, and Gender Disparities in Health Care in Medicare Advantage, The Centers for Medicare and Medicaid Services, (pg vii).

https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.

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Haas, A., Elliott, M. et al (2018). Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity. Health Services Research, 54:13-23.

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MBISG 2.1 validation results performed under contract #GS-10F-0012Y/HHSM-500-2016-00097G). Pending public release of the 2021 Part C and D Performance Data Stratified by Race, Ethnicity, and Gender Report, available at:

https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.

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Haas, A., Elliott, M. et al (2018). Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity.

Health Services Research,

54:13-23 and Bonito AJ, 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.

However, despite the high degree of statistical accuracy of the indirect estimation algorithms under consideration there remains the small risk of unintentionally introducing bias. For example, if the indirect estimation is not as accurate in correctly estimating race and ethnicity in certain geographies or populations it could lead to some bias in the method results. Such bias might result in slight overestimation or underestimation of the quality of care received by a given group. We feel this amount of bias is considerably less than would be expected if stratified reporting was conducted using the race and ethnicity currently contained in our administrative data. Indirect estimation of race and ethnicity is envisioned as an intermediate step, filling the pressing need for more accurate demographic information for the purposes of exploring inequities in service delivery, while allowing newer approaches, as described in the next section, for improving demographic data collection to progress. We are interested in learning more about, and soliciting comments about, the potential benefits and challenges associated with measuring facility equity using an imputation algorithm to enhance existing administrative data quality for race and ethnicity until self-reported information is sufficiently available.

c. Improving Demographic Data Collection

Stratified facility-level reporting using dual eligibility and indirectly estimated race and ethnicity would represent an important advance in our ability to provide equity reports to facilities. However, self-reported race and ethnicity data remain the gold standard for classifying an individual according to race or ethnicity. The CMS Quality Strategy outlines our commitment to strengthening infrastructure and data systems by ensuring that standardized demographic information is collected to identify disparities in health care delivery outcomes.

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Collection and sharing of a standardized set of social, psychological, and behavioral data by facilities, including race and ethnicity, using electronic data definitions which permit nationwide, interoperable health information exchange, can significantly enhance the accuracy and robustness of our equity reporting.

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This could potentially include expansion to additional social risk factors, such as disability status, where accuracy of administrative data is currently limited. We are mindful that additional resources, including data collection and staff training may be necessary to ensure that conditions are created whereby all patients are comfortable answering all demographic questions, and that individual preferences for non-response are maintained.

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The Centers for Medicare & Medicaid Services. CMS Quality Strategy. 2016.

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

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The Office of the National Coordinator for Health Information Technology. United State Core Data for Interoperability Draft Version 2. 2021.

https://www.healthit.gov/isa/sites/isa/files/2021-01/Draft-USCDI-Version-2-January-2021-Final.pdf.

We are also interested in learning about and are soliciting comments on current data collection practices by facilities to capture demographic data elements (such as race, ethnicity, sex, sexual orientation and gender identity (SOGI), primary language, and disability status). Further, we are interested in potential challenges facing facility collection, at the time of admission, of a minimum set of demographic data elements in alignment with national data collection standards (such as the standards finalized by the Affordable Care Act)

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and standards for interoperable exchange (such as the U.S. Core Data for Interoperability incorporated into certified health IT products as part of the 2015 Edition of health IT certification criteria).

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Advancing data interoperability through collection of a minimum set of demographic data collection, and incorporation of this demographic information into quality measure specifications, has the potential for improving the robustness of the disparity method results, potentially permitting reporting using more accurate, self-reported information, such as race and ethnicity, and expanding reporting to additional dimensions of equity, including stratified reporting by disability status.

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https://minorityhealth.hhs.gov/assets/pdf/checked/1/Fact_Sheet_Section_4302.pdf.

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https://www.healthit.gov/sites/default/files/2020-08/2015EdCures_Update_CCG_USCDI.pdf.

d. Potential Creation of a Facility Equity Score To Synthesize Results Across Multiple Social Risk Factors

As we describe in section IV.D.3.a of this proposed rule, we are considering expanding the disparity methods to IPFs and to include two social risk factors (dual eligibility and race/ethnicity). This approach would improve the comprehensiveness of health equity information provided to facilities. Aggregated results from multiple measures and multiple social risk factors, from the CMS Disparity Methods, in the format of a summary score, can improve the usefulness of the equity results. In working with our contractors, we recently developed an equity summary score for Medicare Advantage contract/plans, the Health Equity Summary Score (HESS), with application to stratified reporting using two social risk factors: dual eligibility and race and as described in

Incentivizing Excellent Care to At-Risk Groups with a Health Equity Summary Score

.

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Agniel D, Martino SC, Burkhart Q, et al. Incentivizing Excellent Care to At-Risk Groups with

a Health Equity Summary Score. J Gen Intern Med. Published online November 11, 2019 Nov 11. doi: 10.1007/s11606-019-05473-x.

The HESS calculates standardized and combined performance scores blended across the two social risk factors. The HESS also combines results of the within-plan (similar to the Within-Facility method) and across-plan method (similar to the Across-Facility method) across multiple performance measures.

We are considering building a “Facility Equity Score,” not yet developed, which would be modeled off the HESS but adapted to the context of risk-adjusted facility outcome measures and potentially other IPF quality measures. We envision that the Facility Equity Score would synthesize results for a range of measures and using multiple social risk factors, using measures and social risk factors, which would be reported to facilities as part of the CMS Disparity Methods. We believe that creation of the Facility Equity Score has the potential to supplement the overall measure data already reported on the

Care Compare

or successor website, by providing easy to interpret information regarding disparities measured within individual facilities and across facilities nationally. A summary score would decrease burden by minimizing the number of measure results provided and providing an overall indicator of equity.

The Facility Equity Score under consideration would potentially:

• Summarize facility performance across multiple social risk factors (initially dual eligibility and indirectly estimated race and ethnicity, as described above); and

• Summarize facility performance across the two disparity methods (that is, the Within-Facility Disparity Method and the Across-Facility Disparity Method) and potentially for multiple measures.

Prior to any future public reporting, if we determine that a Facility Equity Score can be feasibly and accurately calculated, we would provide results of the Facility Equity Score, in confidential facility specific reports, which facilities and their QIN-QIOs would be able to download. Any potential future proposal to display the Facility Equity Score on the Care Compare or successor website would be made through future RFI or rulemaking.

c. Solicitation of Public Comment

We are soliciting public comments on the possibility of stratifying IPFQR Program measures by dual eligibility and race and ethnicity. We are also soliciting public comments on mechanisms of incorporating co-occurring disability status into such stratification as well. We are soliciting public comments on the application of the within-facility or across-facility disparities methods IPFQR Program measures if we were to stratify IPFQR Program measures. We are also seeking comment on the possibility of facility collection of standardized demographic information for the purposes of potential future quality reporting and measure stratification. In addition, we are soliciting public comments on the potential design of a facility equity score for calculating results across multiple social risk factors and measures, including race and disability. Any data pertaining to these areas that are recommended for collection for measure reporting for a CMS program and any potential public disclosure on

Care Compare

or successor website would be addressed through a separate and future notice- and-comment rulemaking. We plan to continue working with ASPE, facilities, the public, and other key stakeholders on this important issue to identify policy solutions that achieve the goals of attaining health equity for all patients and minimizing unintended consequences. We look forward to receiving feedback on these topics. We also note our intention for additional RFIs or rulemaking on this topic in the future.

Specifically, we are soliciting public comment on the following:

Future Potential Stratification of Quality Measure Results

• The possible stratification of facility-specific reports for IPFQR program measure data by dual-eligibility status given that over half of the patient population in IPFs are dually eligible, including, which measures would be most appropriate for stratification;

• The potential future application of indirect estimation of race and ethnicity to permit stratification of measure data for reporting facility-level disparity results until more accurate forms of self-identified demographic information are available;

• Appropriate privacy safeguards with respect to data produced from the indirect estimation of race and ethnicity to ensure that such data are properly identified if/when it is shared with providers.

• Ways to address the challenges of defining and collecting accurate and standardized self-identified demographic information, including information on race and ethnicity and disability, for the purposes of reporting, measure stratification and other data collection efforts relating to quality.

• Recommendations for other types of readily available data elements for measuring disadvantage and discrimination for the purposes of reporting, measure stratification and other data collection efforts relating to quality, in addition, or in combination with race and ethnicity.

• Recommendations for types of quality measures or measurement domains to prioritize for stratified reporting by dual eligibility, race and ethnicity, and disability.

• Examples of approaches, methods, research, and/or considerations for use of data-driven technologies that do not facilitate exacerbation of health inequities, recognizing that biases may occur in methodology or be encoded in datasets.

Improving Demographic Data Collection

• Experiences of users of certified health IT regarding local adoption of practices for collection of social, psychological, and behavioral data elements, the perceived value of using these data for improving decision-making and care delivery, and the potential challenges and benefits of collecting more granular, structured demographic information, such as the “Race & Ethnicity—CDC” code system.

• The possible collection of a minimum set of social, psychological, and behavioral data elements by hospitals at the time of admission using structured, interoperable data standards, for the purposes of reporting, measure stratification and other data collection efforts relating to quality.

Potential Creation of a Facility Equity Score To Synthesize Results Across Multiple Social Risk Factors

• The possible creation and confidential reporting of a Facility Equity Score to synthesize results across multiple social risk factors and disparity measures.

• Interventions facilities could institute to improve a low facility equity score and how improved demographic data could assist with these efforts.

E. Measure Adoption

We strive to put consumers and caregivers first, ensuring they are empowered to make decisions about their own healthcare along with their clinicians using information from data-driven insights that are increasingly aligned with meaningful quality measures. We support technology that reduces burden and allows clinicians to focus on providing high-quality healthcare for their patients. We also

support innovative approaches to improve quality, accessibility, and affordability of care while paying particular attention to improving clinicians' and beneficiaries' experiences when interacting with our programs. In combination with other efforts across the Department of Health and Human Services (HHS), we believe the IPFQR Program helps to incentivize facilities to improve healthcare quality and value while giving patients and providers the tools and information needed to make the best decisions for them. Consistent with these goals, our objective in selecting quality measures is to balance the need for information on the full spectrum of care delivery and the need to minimize the burden of data collection and reporting. We have primarily focused on measures that evaluate critical processes of care that have significant impact on patient outcomes and support CMS and HHS priorities for improved quality and efficiency of care provided by IPFs. When possible, we also propose to incorporate measures that directly evaluate patient outcomes and experience. We refer readers to section VIII.F.4.a. of the FY 2013 IPPS/LTCH PPS final rule (77 FR 53645 through 53646) for a detailed discussion of the considerations taken into account in selecting quality measures.

1. Measure Selection Process

Before being proposed for inclusion in the IPFQR Program, measures are placed on a list of measures under consideration (MUC), which is published annually on behalf of CMS by the National Quality Forum (NQF). Following publication on the MUC list, the Measure Applications Partnership (MAP), a multi-stakeholder group convened by the NQF, reviews the measures under consideration for the IPFQR Program, among other Federal programs, and provides input on those measures to the Secretary. We consider the input and recommendations provided by the MAP in selecting all measures for the IPFQR Program. In our evaluation of the IPFQR Program measure set, we identified two measures that we believe are appropriate to propose for the IPFQR Program.

2. Proposed Adoption of COVID-19 Vaccination Coverage Among Health Care Personnel (HCP)

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Measure for the FY2023 Payment Determination and Subsequent Years

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This measure was previously titled, “SARS-CoV-2 Vaccination Coverage among Healthcare Personnel.”.

a. Background

On January 31, 2020, the Secretary declared a public health emergency (PHE) for the U.S. in response to the global outbreak of SARS-CoV-2, a novel (new) coronavirus that causes a disease named “coronavirus disease 2019” (COVID-19).

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COVID-19 is a contagious respiratory illness

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that can cause serious illness and death. Older individuals and those with underlying medical conditions are considered to be at higher risk for more serious complications from COVID-19.

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U.S. Dept of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response. (2020). Determination that a Public Health Emergency Exists. Available at:

https://www.phe.gov/emergency/news/healthactions/phe/Pages/2019-nCoV.aspx.

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Centers for Disease Control and Prevention. (2020). Your Health: Symptoms of Coronavirus. Available at:

https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.

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Centers for Disease Control and Prevention. (2020). Your Health: Symptoms of Coronavirus. Available at

https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.

As of April 2, 2021, the U.S. has reported over 30 million cases of COVID-19 and over 550,000 COVID-19 deaths.

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Hospitals and health systems saw significant surges of COVID-19 patients as community infection levels increased.

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From December 2, 2020 through January 30, 2021, more than 100,000 Americans were in the hospital with COVID-19 at the same time.

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Centers for Disease Control and Prevention. (2020). CDC COVID Data Tracker. Accessed on April 3, 2021 at:

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