[Federal Register Volume 86, Number 69 (Tuesday, April 13, 2021)]
[Proposed Rules]
[Pages 19480-19529]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2021-07433]
[[Page 19479]]
Vol. 86
Tuesday,
No. 69
April 13, 2021
Part III
Department of Health and Human Services
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Centers for Medicare & Medicaid Services
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42 CFR Part 412
Medicare Program; FY 2022 Inpatient Psychiatric Facilities Prospective
Payment System and Quality Reporting Updates for Fiscal Year Beginning
October 1, 2021 (FY 2022); Proposed Rule
Federal Register / Vol. 86 , No. 69 / Tuesday, April 13, 2021 /
Proposed Rules
[[Page 19480]]
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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.
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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
[email protected] 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.
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++ 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
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Total transfers & cost
Provision description reductions
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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.
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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/.
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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
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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-
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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 Sec. 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
[[Page 19485]]
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
[[Page 19486]]
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 Sec. 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 Sec. 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.
[[Page 19487]]
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://
[[Page 19488]]
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 Sec.
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
[[Page 19489]]
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 Sec. 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 Sec. 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
[[Page 19490]]
489.52. In this proposed rule, we are proposing to codify this
definition, as well as the definition of an IPF program closure, at
Sec. 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'' (Sec. 412.424(d)(1)(iii)(F)(1)(ii)) or `identifies the residents
``who have come from another IPF's closed program'' (Sec.
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'' (Sec. 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 Sec.
412.424(d)(1)(iii)(F)(1)(ii) and Sec. 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.
[[Page 19491]]
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
[[Page 19492]]
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
------------------------------------------------------------------------
FY 2022
FY 2018 through FY
Area through FY 2025
2021 (proposed)
------------------------------------------------------------------------
Alaska:
City of Anchorage and 80-kilometer 1.25 1.22
(50-mile) radius by road...........
City of Fairbanks and 80-kilometer 1.25 1.22
(50-mile) radius by road...........
City of Juneau and 80-kilometer (50- 1.25 1.22
mile) radius by road...............
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 Sec.
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 Sec.
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 Sec.
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
[[Page 19493]]
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 Sec.
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,
[[Page 19494]]
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.
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\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).
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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 (Sec. 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
Sec. 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
[[Page 19495]]
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.
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\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.
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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.
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\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.
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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
[[Page 19496]]
on gaps in quality through the following reports and programs:
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\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.
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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\
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\31\ https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities.
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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\
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\32\ https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.
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The Rural-Urban Disparities in Health Care in Medicare
Report, which details rural-urban differences in health care
experiences and clinical care.\33\
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\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.
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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\
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\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.
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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.
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\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.
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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.
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\37\ https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.
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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
[[Page 19497]]
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.
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\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.
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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.
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\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.
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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.
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\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.
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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
[[Page 19498]]
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.
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\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.
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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\
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\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/, https://
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.
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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\
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\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.
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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).\56\ 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.\57\
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\56\ 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.
\57\ 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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[[Page 19499]]
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.\58\ 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.\59\ \60\ The algorithms under
consideration are considerably less accurate for individuals who self-
identify as American Indian/Alaskan Native or multiracial.\61\ 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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\58\ 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.
\59\ 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.
\60\ 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.
\61\ 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.
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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.\62\ 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.\63\ 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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\62\ 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.
\63\ 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.
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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)
\64\ 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).\65\ 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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\64\ https://minorityhealth.hhs.gov/assets/pdf/checked/1/Fact_Sheet_Section_4302.pdf.
\65\ https://www.healthit.gov/sites/default/files/2020-08/2015EdCures_Update_CCG_USCDI.pdf.
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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.\66\
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\66\ 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.
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[[Page 19500]]
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
[[Page 19501]]
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) \67\ Measure for the FY2023 Payment Determination and
Subsequent Years
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\67\ This measure was previously titled, ``SARS-CoV-2
Vaccination Coverage among Healthcare Personnel.''.
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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).\68\ COVID-19 is a contagious
respiratory illness \69\ 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.\70\
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\68\ 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.
\69\ 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.
\70\ 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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As of April 2, 2021, the U.S. has reported over 30 million cases of
COVID-19 and over 550,000 COVID-19 deaths.\71\ Hospitals and health
systems saw significant surges of COVID-19 patients as community
infection levels increased.\72\ From December 2, 2020 through January
30, 2021, more than 100,000 Americans were in the hospital with COVID-
19 at the same time.\73\
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\71\ Centers for Disease Control and Prevention. (2020). CDC
COVID Data Tracker. Accessed on April 3, 2021 at: https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days.
\72\ Associated Press. Tired to the Bone. Hospitals Overwhelmed
with Virus Cases. November 18, 2020. Accessed on December 16, 2020,
at https://apnews.com/article/hospitals-overwhelmed-coronavirus-cases-74a1f0dc3634917a5dc13408455cd895. Also see: New York Times.
Just how full are U.S. intensive care units? New data paints an
alarming picture. November 18, 2020. Accessed on December 16, 2020,
at: https://www.nytimes.com/2020/12/09/world/just-how-full-are-us-intensive-care-units-new-data-paints-an-alarming-picture.html.
\73\ U.S. Currently Hospitalized [bond] The COVID Tracking
Project https://covidtracking.com/data/charts/us-currently-hospitalized.
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Evidence indicates that COVID-19 primarily spreads when individuals
are in close contact with one another.\74\ The virus is typically
transmitted through respiratory droplets or small particles created
when someone who is infected with the virus coughs, sneezes, sings,
talks, or breathes.\75\ Thus, the CDC advises that infections mainly
occur through exposure to respiratory droplets when a person is in
close contact with someone who has COVID-19.\76\ Experts believe that
COVID-19 spreads less commonly through contact with a contaminated
surface (although that is not thought to be a common way that COVID-19
spreads),\77\ and that in certain circumstances, infection can occur
through airborne transmission.\78\ According to the CDC, those at
greatest risk of infection are persons who have had prolonged,
unprotected close contact (that is, within 6 feet for 15 minutes or
longer) with an individual with confirmed SARS-CoV-2 infection,
regardless of whether the individual has symptoms.\79\ Although
personal protective equipment (PPE) and other infection-control
precautions can reduce the likelihood of transmission in health care
settings, COVID-19 can spread between health care personnel (HCP) and
patients, or from patient to patient given the close contact that may
occur during the provision of care.\80\ The CDC has emphasized that
health care settings, including long-term care settings, can be high-
risk places for COVID-19 exposure and transmission.\81\
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\74\ Centers for Disease Control and Prevention. (2021). How
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
\75\ Centers for Disease Control and Prevention. (2021). How
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
\76\ Centers for Disease Control and Prevention. (2021). How
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
\77\ Centers for Disease Control and Prevention. (2021). How
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
\78\ Centers for Disease Control and Prevention. (2021). How
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
\79\ Centers for Disease Control and Prevention. (2021). When to
Quarantine. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/quarantine.html.
\80\ Centers for Disease Control and Prevention. (2020). Interim
U.S. Guidance for Risk Assessment and Work Restrictions for
Healthcare Personnel with Potential Exposure to COVID-19. Accessed
on April 2, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/hcp/faq.html#Transmission.
\81\ Dooling, K, McClung, M, et al. ``The Advisory Committee on
Immunization Practices' Interim Recommendations for Allocating
Initial Supplies of COVID-19 Vaccine--United States, 2020.'' Morb
Mortal Wkly Rep. 2020; 69(49): 1857-1859.
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Vaccination is a critical part of the nation's strategy to
effectively counter the spread of COVID-19 and ultimately help restore
societal functioning.\82\ On
[[Page 19502]]
December 11, 2020, the FDA issued the first Emergency Use Authorization
(EUA) for a COVID-19 vaccine in the U.S.\83\ Subsequently, the FDA
issued EUAs for additional COVID-19 vaccines.\84\
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\82\ Centers for Disease Control and Prevention. (2020). COVID-
19 Vaccination Program Interim Playbook for Jurisdiction Operations.
Accessed on April 3, 2021 at: https://www.cdc.gov/vaccines/imz-managers/downloads/COVID-19-Vaccination-Program-Interim_Playbook.pdf.
\83\ U.S. Food and Drug Administration. (2020). Pfizer-BioNTech
COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/144412/download.
\84\ U.S. Food and Drug Administration. (2020). Moderna COVID-19
Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/144636/download; U.S. Food and Drug
Administration. (2021). Janssen COVID-19 Vaccine EUA Letter of
Authorization. Available at https://www.fda.gov/media/146303/download.
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The FDA determined that it was reasonable to conclude that the
known and potential benefits of each vaccine, when used as authorized
to prevent COVID-19, outweighed its known and potential risks.\85\
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\85\ U.S. Food and Drug Administration. (2020). Pfizer-BioNTech
COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/144412/download and .S. Food and Drug
Administration. (2020). Moderna COVID-19 Vaccine EUA Letter of
Authorization. Available at https://www.fda.gov/media/144636/download; U.S. Food and Drug Administration. (2021). Janssen COVID-
19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/146303/download.
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As part of its national strategy to address COVID-19, the Biden
Administration stated that it would work with states and the private
sector to execute an aggressive vaccination strategy and has outlined a
goal of administering 200 million shots in 100 days,\86\ Although the
goal of the U.S. government is to ensure that every American who wants
to receive a COVID-19 vaccine can receive one, Federal agencies
recommended that early vaccination efforts focus on those critical to
the PHE response, including HCP providing direct care to patients with
COVID-19, and individuals at highest risk for developing severe illness
from COVID-19.\87\ For example, the CDC's Advisory Committee on
Immunization Practices (ACIP) recommended that HCP should be among
those individuals prioritized to receive the initial, limited supply of
the COVID-19 vaccination given the potential for transmission in health
care settings and the need to preserve health care system capacity.\88\
Research suggests most states followed this recommendation,\89\ and HCP
began receiving the vaccine in mid-December of 2020.\90\
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\86\ https://www.whitehouse.gov/briefing-room/speeches-remarks/2021/03/29/remarks-by-president-biden-on-the-covid-19-response-and-the-state-of-vaccinations/.
\87\ Health and Human Services, Department of Defense. (2020)
From the Factory to the Frontlines: The Operation Warp Speed
Strategy for Distributing a COVID-19 Vaccine. Accessed December 18
at: https://www.hhs.gov/sites/default/files/strategy-for-distributing-covid-19-vaccine.pdf; Centers for Disease Control
(2020). COVID-19 Vaccination Program Interim Playbook for
Jurisdiction Operations. Accessed December 18 at: https://www.cdc.gov/vaccines/imz-managers/downloads/COVID-19-Vaccination-Program-Interim_Playbook.pdf.
\88\ Dooling, K, McClung, M, et al. ``The Advisory Committee on
Immunization Practices' Interim Recommendations for Allocating
Initial Supplies of COVID-19 Vaccine--United States, 2020.'' Morb.
Mortal Wkly Rep. 2020; 69(49): 1857-1859. ACIP also recommended that
long-term care residents be prioritized to receive the vaccine,
given their age, high levels of underlying medical conditions, and
congregate living situations make them high risk for severe illness
from COVID-19.
\89\ Kates, J, Michaud, J, Tolbert, J. ``How Are States
Prioritizing Who Will Get the COVID-19 Vaccine First?'' Kaiser
Family Foundation. December 14, 2020. Accessed on December 16 at
https://www.kff.org/policy-watch/how-are-states-prioritizing-who-will-get-the-covid-19-vaccine-first/.
\90\ Associated Press. `Healing is Coming:' U.S. Health Workers
Start Getting Vaccine. December 15, 2020. Accessed on December 16
at: https://apnews.com/article/us-health-workers-coronavirus-vaccine-56df745388a9fc12ae93c6f9a0d0e81f.
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There are approximately 18 million healthcare workers in the
U.S.\91\ As of April 3, 2021 the CDC reported that over 162 million
doses of COVID-19 vaccine had been administered, and approximately 60
million people had received a complete vaccination course as described
in IV.1.b.i of this proposed rule.\92\ President Biden indicated on
March 2, 2021 that the U.S. is on track to have sufficient vaccine
supply for every adult by the end of May 2021.\93\
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\91\ https://www.cdc.gov/niosh/topics/healthcare/
default.html#:~:text=HEALTHCARE%20WORKERS,-
Related%20Pages&text=Healthcare%20is%20the%20fastest%2Dgrowing,of%20t
he%20healthcare%20work%20force.
\92\ CDC. COVID Data Tracker. COVID-19 Vaccinations in the
United States. Accessed on 2/18/21 at: https://covid.cdc.gov/covid-data-tracker/#vaccinations.
\93\ The White House. Remarks by President Biden on the
Administration's COVID-19 Vaccination Efforts. Accessed March 18,
2021 at: https://www.whitehouse.gov/briefing-room/speeches-remarks/2021/03/02/remarks-by-president-biden-on-the-administrations-covid-19-vaccination-efforts/.
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We believe it is important to require that IPFs report HCP
vaccination in their facilities in order to assess whether they are
taking steps to protect health care workers and to help sustain the
ability of IPFs to continue serving their communities throughout the
PHE and beyond. Therefore, we are proposing a new measure, COVID-19
Vaccination Coverage Among HCP, beginning with the FY 2023 program
year. For that program year, IPFs would be required to report data on
the measure for the fourth quarter of 2021 (October 1, 2021 through
December 31, 2021). For more information about the proposed reporting
period, see section V.E.2.c of this proposed rule. The measure would
assess the proportion of an IPF's health care workforce that has been
vaccinated against COVID-19.
Although at this time data to show the effectiveness of COVID-19
vaccines to prevent asymptomatic infection or transmission of SARS-CoV-
2 are limited, we believe IPFs should report the level of vaccination
among their HCP as part of their efforts to assess and reduce the risk
of transmission of COVID-19 within their facilities. HCP vaccination
can potentially reduce illness that leads to work absence and limit
disruptions to care.\94\ Data from influenza vaccination demonstrates
that provider uptake of the vaccine is associated with that provider
recommending vaccination to patients,\95\ and we believe HCP COVID-19
vaccination in IPFs could similarly increase uptake among that patient
population. We also believe that publishing the HCP vaccination rates
will be helpful to many patients, including those who are at high-risk
for developing serious complications from COVID-19, as they choose
facilities from which to seek treatment. Under CMS' Meaningful Measures
Framework, the COVID-19 measure addresses the quality priority of
``Promote Effective Prevention and Treatment of Chronic Disease''
through the Meaningful Measure Area of ``Preventive Care.''
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\94\ Centers for Disease Control and Prevention. Overview of
Influenza Vaccination among Health Care Personnel. October 2020.
(2020) Accessed March 16, 2021 at: https://www.cdc.gov/flu/toolkit/long-term-care/why.htm.
\95\ Measure Application Committee Coordinating Committee
Meeting Presentation. March 15, 2021. (2021) Accessed March 16, 2021
at: http://www.qualityforum.org/Project_Pages/MAP_Coordinating_Committee.aspx.
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b. Overview of Measure
The COVID-19 Vaccination Coverage Among HCP measure (``COVID-19 HCP
vaccination measure'') is a process measure developed by the CDC to
track COVID-19 vaccination coverage among HCP in facilities such as
IPFs.
(1). Measure Specifications
The denominator is the number of HCP eligible to work in the IPF
for at least 1 day during the reporting period, excluding persons with
contraindications to COVID-19 vaccination that are described by the
CDC.\96\
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\96\ Centers for Disease Control and Prevention.
Contraindications and precautions. https://www.cdc.gov/vaccines/covid-19/info-by-product/clinical-considerations.html#Contraindications.
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The numerator is the cumulative number of HCP eligible to work in
the health care facility for at least 1 day
[[Page 19503]]
during the reporting period and who received a completed vaccination
course against COVID-19 since the vaccine was first available or on a
repeated interval if revaccination on a regular basis is needed.\97\
Vaccination coverage for the purposes of this measure is defined as the
estimated percentage of HCP eligible to work at the IPF for at least 1
day who received a completed vaccination course. A completed
vaccination course may require one or more doses depending on the EUA
for the specific vaccine used.
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\97\ Measure Application Partnership Coordinating Committee
Meeting Presentation. March 15, 2021. (2021) Accessed March 16, 2021
at: http://www.qualityforum.org/Project_Pages/MAP_Coordinating_Committee.aspx.
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The finalized specifications for this measure will be available at
https://www.cdc.gov/nhsn/nqf/index.html.
(2). Review by the Measure Applications Partnership
The COVID-19 HCP vaccination measure was included on the publicly
available ``List of Measures under Consideration for December 21,
2020,'' \98\ a list of measures under consideration for use in various
Medicare programs. When the Measure Applications Partnership (MAP)
Hospital Workgroup convened on January 11, 2021, it reviewed the MUC
List and the COVID-19 HCP vaccination measure. The MAP recognized that
the proposed measure represents a promising effort to advance
measurement for an evolving national pandemic and that it would bring
value to the IPFQR Program measure set by providing transparency about
an important COVID-19 intervention to help prevent infections in HCP
and patients.\99\ The MAP also stated that collecting information on
COVID-19 vaccination coverage among HCP and providing feedback to
facilities will allow facilities to benchmark coverage rates and
improve coverage in their facility, and that reducing rates of COVID-19
in HCP may reduce transmission among patients and reduce instances of
staff shortages due to illness.\100\
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\98\ https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=94212.
\99\ Measure Applications Partnership. MAP Preliminary
Recommendations 2020-2021. Accessed on January 24, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
\100\ Measure Applications Partnership. MAP Preliminary
Recommendations 2020-2021. Accessed on January 24, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
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In its preliminary recommendations, the MAP Hospital Workgroup did
not support this measure for rulemaking, subject to potential for
mitigation.\101\ To mitigate its concerns, the MAP believed that the
measure needed well-documented evidence, finalized specifications,
testing, and NQF endorsement prior to implementation.\102\
Subsequently, the MAP Coordinating Committee met on January 25, 2021,
and reviewed the COVID-19 Vaccination Coverage Among HCP measure. In
the 2020-2021 MAP Final Recommendations, the MAP offered conditional
support for rulemaking contingent on CMS bringing the measures back to
MAP once the specifications are further refined.\103\ The MAP
specifically stated, ``the incomplete specifications require immediate
mitigation and further development should continue.'' \104\ The
spreadsheet of final recommendations no longer cited concerns regarding
evidence, testing, or NQF endorsement.\105\ In response to the MAP
final recommendation request that CMS bring the measure back to the MAP
once the specifications are further refined, CMS and the CDC met with
MAP Coordinating committee on March 15th. Additional information was
provided to address vaccine availability, alignment of the COVID-19
Vaccination Coverage Among HCP as closely as possible with the data
collection for the Influenza HCP vaccination measure (NQF 0431) and
clarification related to how HCP are defined. At this meeting, CMS and
the CDC presented preliminary findings from the testing of the
numerator of COVID-19 Vaccination Coverage Among HCP, which is
currently in process. These preliminary findings show numerator data
should be feasible and reliable. Testing of the numerator of the number
of healthcare personnel vaccinated involves a comparison of the data
collected through NHSN and independently reported through the Federal
pharmacy partnership program for delivering vaccination to LTC
facilities. These are two completely independent data collection
systems. In initial analyses of the first month of vaccination, the
number of healthcare workers vaccinated in approximately 1,200
facilities, which had data from both systems the number of healthcare
personnel vaccinated was highly correlated between these 2 systems with
a correlation coefficient of nearly 90 percent in the second two weeks
of reporting.\106\ The MAP further noted that the measure would add
value to the program measure set by providing visibility into an
important intervention to limit COVID-19 infections in healthcare
personnel and the patients for whom they provide care.\107\
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\101\ Measure Applications Partnership. MAP Preliminary
Recommendations 2020-2021. Accessed on January 24, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
\102\ Measure Applications Partnership. MAP Preliminary
Recommendations 2020-2021. Accessed on January 24, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
\103\ Measure Applications Partnership. 2020-2021 MAP Final
Recommendations. Accessed on February 3, 2021 at: http://www.qualityforum.org/Setting_Priorities/Partnership/Measure_Applications_Partnership.aspx.
\104\ Measure Applications Partnership. 2020-2021 MAP Final
Recommendations. Accessed on February 23, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
\105\ Ibid.
\106\ For more information on testing results and other measure
updates, please see the Meeting Materials (including Agenda,
Recording, Presentation Slides, Summary, and Transcript) of the
March 15, 2021 meeting available at https://www.qualityforum.org/ProjectMaterials.aspx?projectID=75367.
\107\ Measure Applications Partnership. 2020-2021 MAP Final
Recommendations. Accessed on February 23, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
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We value the recommendations of the MAP and considered these
recommendations carefully. Section 1890A(a)(4) of the Act requires the
Secretary to take into consideration input from multi-stakeholder
groups in selecting certain quality and efficiency measures. While we
value input from the MAP, we believe it is important to propose the
measure as quickly as possible to address the urgency of the COVID-19
PHE and its impact on vulnerable populations, including IPFs. CMS
continues to engage with the MAP to mitigate concerns and appreciates
the MAP's conditional support for the measure.
(3). NQF Endorsement
Under section 1886(s)(4)(D)(i) of the Act, unless the exception of
clause (ii) applies, measures selected for the quality reporting
program must have been endorsed by the entity with a contract under
section 1890(a) of the Act. The NQF currently holds this contract.
Section 1886(s)(4)(D)(ii) of the Act provides an exception to the
requirement for NQF endorsement of measures: In the case of a specified
area or medical topic determined appropriate by the Secretary for which
a feasible and practical measure has not been endorsed by the entity
with a contract under section 1890(a) of the Act, the Secretary may
specify a measure that is not so endorsed as long as due consideration
is given to measures that have been endorsed or adopted by a consensus
organization identified by the Secretary.
This measure is not NQF endorsed and has not been submitted to NQF
for
[[Page 19504]]
endorsement consideration. CMS will consider the potential for future
NQF endorsement as part of its ongoing work with the MAP.
Because this measure is not NQF-endorsed, we considered other
available measures. We found no other feasible and practical measures
on the topic of COVID-19 vaccination among HCP, therefore, we believe
the exception in Section 1186(s)(4)(D)(ii) of the Act applies.
c. Data Collection, Submission and Reporting
Given the time-sensitive nature of this measure considering the
PHE, we are proposing that IPFs would be required to begin reporting
data on the proposed COVID-19 Vaccination Coverage Among HCP measure
beginning October 1, 2021 for the FY 2023 IPFQR Program year.
Thereafter, we propose annual reporting periods.
To report this measure facilities would report COVID-19 vaccination
data to the NHSN for at least one week each month, beginning in October
2021 for the October 1, 2021 through December 31, 2021 reporting period
affecting FY 2023 payment determination and continuing for each quarter
in subsequent years. For more details on data submission, we refer
readers to section V.J.2.a of this proposed rule.
If our proposal to adopt this measure is finalized, IPFs would
report the measure through the CDC National Healthcare Safety Network
(NHSN) web-based surveillance system.\108\ While the IPFQR Program does
not currently require use of the NHSN web-based surveillance system, we
have previously required use of this system. We refer readers to the FY
2015 IPF PPS final rule in which we adopted the Influenza Vaccination
Coverage Among Healthcare Personnel (NQF #0431) measure for additional
information on reporting through the NHSN web-based surveillance system
(79 FR 45968 through 45970).
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\108\ Centers for Disease Control and Prevention. Surveillance
for Weekly HCP COVID-19 Vaccination. Accessed at: https://www.cdc.gov/nhsn/hps/weekly-covid-vac/index.html. on February 10,
2021.
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IPFs would report COVID-19 vaccination data in the NHSN Healthcare
Personnel Safety (HPS) Component by reporting the number of HCP
eligible to have worked at the facility that week (denominator) and the
number of those HCP who have received a completed vaccination course of
a COVID-19 vaccination (numerator). For additional information about
the data reporting requirements, see V.J.4. of this proposed rule.
We invite public comment on our proposal to add a new measure,
COVID-19 Vaccination Coverage Among HCP, to the IPFQR Program for the
FY 2023 payment determination and subsequent years.
3. Proposed Adoption of the Follow-Up After Psychiatric Hospitalization
(FAPH) Measure for the FY 2024 Payment Determination and Subsequent
Years
a. Background
We are proposing one new measure, Follow-Up After Psychiatric
Hospitalization (FAPH), for the FY 2024 payment determination and
subsequent years. The FAPH measure would use Medicare fee-for-service
(FFS) claims to determine the percentage of inpatient discharges from
an inpatient psychiatric facility (IPF) stay with a principal diagnosis
of select mental illness or substance use disorders (SUDs) for which
the patient received a follow-up visit for treatment of mental illness
or SUD. Two rates would be calculated for this measure: (1) The
percentage of discharges for which the patient received follow-up
within 7 days of discharge, and; (2) the percentage of discharges for
which the patient received follow-up within 30 days of discharge.
The proposed FAPH measure is an expanded and enhanced version of
the Follow-Up After Hospitalization for Mental Illness (FUH, NQF #0576)
measure currently in the IPFQR Program. We are proposing to adopt the
FAPH measure and replace the FUH measure and refer readers to section
IV.F.2.d of this proposed rule for our proposal to remove the FUH
measure contingent on adoption of the FAPH measure. The FUH (NQF #0576)
measure uses Medicare FFS claims to determine the percentage of
inpatient discharges from an IPF stay with a principal diagnosis of
select mental illness diagnoses for which the patient received a
follow-up visit for treatment of mental illness, and it excludes
patients with primary substance use diagnoses. During the 2017
comprehensive review of NQF #0576, the NQF Behavioral Health Standing
Committee (BHSC) recommended expanding the measure population to
include patients hospitalized for drug and alcohol disorders, because
these patients also require follow-up care after they are discharged.
In 2018, CMS began development of a measure to expand the IPFQR FUH
population to include patients with principal SUD diagnoses to address
the NQF BHSC recommendation and the CMS Meaningful Measures priority to
promote treatment of SUDs. The FAPH measure would expand the number of
discharges in the denominator by about 35 percent over the current FUH
measure by adding patients with SUD or dementia, populations that also
benefit from timely follow-up care.
Furthermore, compared to the criteria for provider type in the
current FUH measure, the FAPH measure does not limit the provider type
for the follow-up visit if it is billed with a diagnosis of mental
illness or SUD. During the measure's testing, the most frequent
provider types for the FAPH measure were family or general practice
physicians, internal medicine physicians, nurse practitioners, and
physician assistants. The technical expert panel (TEP) convened by our
contractor agreed that these provider types should be credited by the
measure for treating mental illness and SUD and confirmed that this is
aligned with integrated care models that aim to treat the whole
patient. The TEP further noted that in areas where there are shortages
of mental health or SUD clinicians, other types of providers are often
the only choice for follow-up treatment. Allowing visits to these types
of providers to count towards the numerator allows the measure to
capture the rates of appropriate follow-up care more accurately in
areas with provider shortages.
Performance on the FAPH measure indicates that follow-up rates for
patients hospitalized with mental illness or SUD are less than optimal
and that room for improvement is ample. The clinical benefits of timely
follow-up care after hospitalization, including reduced risk of
readmission and improved adherence to medication, are well-documented
in the published literature.109 110 111 112 113 114 115
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\109\ Tong, L., Arnold, T., Yang, J., Tian, X., Erdmann, C., &
Esposito, T. (2018). The association between outpatient follow-up
visits and all-cause non-elective 30-day readmissions: A
retrospective observational cohort study. PloS one, 13(7), e0200691.
https://doi.org/10.1371/journal.pone.0200691.
\110\ Terman, S.W., Reeves, M.J., Skolarus, L.E., & Burke, J.F.
(2018). Association Between Early Outpatient Visits and Readmissions
After Ischemic Stroke. Circulation. Cardiovascular quality and
outcomes, 11(4), e004024. https://doi.org/10.1161/CIRCOUTCOMES.117.004024.
\111\ First Outpatient Follow-Up After Psychiatric
Hospitalization: Does One Size Fit All? (2014). Psychiatric
Services, 66(6), 364-372. https://doi.org/10.1176/appi.ps.201400081.
\112\ Terman, S.W., Reeves, M.J., Skolarus, L.E., & Burke, J.F.
(2018). Association Between Early Outpatient Visits and Readmissions
After Ischemic Stroke. Circulation. Cardiovascular quality and
outcomes, 11(4), e004024. https://doi.org/10.1161/CIRCOUTCOMES.117.004024.
\113\ Jackson, C., Shahsahebi, M., Wedlake, T., & DuBard, C.A.
(2015). Timeliness of outpatient follow-up: An evidence-based
approach for planning after hospital discharge. Annals of family
medicine, 13(2), 115-122. https://doi.org/10.1370/afm.1753.
\114\ Hernandez, A.F., Greiner, M.A., Fonarow, G.C., Hammill,
B.G., Heidenreich, P.A., Yancy, C.W., Peterson, E.D., & Curtis, L.H.
(2010). Relationship between early physician follow-up and 30-day
readmission among Medicare beneficiaries hospitalized for heart
failure. JAMA, 303(17), 1716-1722. https://doi.org/10.1001/jama.2010.533.
\115\ Nadereh Pourat, Xiao Chen, Shang-Hua Wu and Anna C. Davis.
Timely Outpatient Follow-up Is Associated with Fewer Hospital
Readmissions among Patients with Behavioral Health Conditions. The
Journal of the American Board of Family Medicine. May 2019, 32 (3)
353-361; DOI: https://doi.org/10.3122/jabfm.2019.03.180244.
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[[Page 19505]]
Behavioral health patients in particular have a number of risk
factors that underscore the need for timely follow-up and continuity of
care: Behavioral health patients have higher baseline hospitalization
rates, higher hospital readmission rates, and higher health care costs
as compared with the general population of patients.116 117
Among patients with serious mental illness, 90 percent have comorbid
clinical conditions such as hypertension, cardiovascular disease,
hyperlipidemia, or diabetes.\118\ Among patients hospitalized for
general medical conditions, those who also have a mental illness are 28
percent more likely to be readmitted within 30 days than their
counterparts without a psychiatric comorbidity.\119\ The high
prevalence of clinical comorbidities among behavioral health patients,
combined with the compounding effect of mental illness on patients with
general medical conditions, suggests that behavioral health patients
are uniquely vulnerable and supports the intent of the measure to
increase follow-up after hospitalization.
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\116\ Germack, H.D., et al. (2019, January). Association of
comorbid serious mental illness diagnosis with 30-day medical and
surgical readmissions. JAMA Psychiatry.
\117\ First Outpatient Follow-Up After Psychiatric
Hospitalization: Does One Size Fit All? (2014). Psychiatric
Services, 66(6), 364-372. https://doi.org/10.1176/appi.ps.201400081.
\118\ First Outpatient Follow-Up After Psychiatric
Hospitalization: Does One Size Fit All? (2014). Psychiatric
Services, 66(6), 364-372. https://doi.org/10.1176/appi.ps.201400081.
\119\ Benjenk, I., & Chen, J. (2018). Effective mental health
interventions to reduce hospital readmission rates: A systematic
review. Journal of hospital management and health policy, 2, 45.
https://doi.org/10.21037/jhmhp.2018.08.05.
---------------------------------------------------------------------------
In addition, clinical practice guidelines stress the importance of
continuity of care between settings for patients with mental illness
and SUD. For the treatment of SUD patients, the 2010 guidelines of the
American Psychiatric Association (APA) state: ``It is important to
intensify the monitoring for substance use during periods when the
patient is at a high risk of relapsing, including during the early
stages of treatment, times of transition to less intensive levels of
care, and the first year after active treatment has ceased.'' \120\
This statement is accompanied by a grade of [I], which indicates the
highest level of APA endorsement: ``recommended with substantial
clinical evidence.''
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\120\ American Psychiatric Association. Practice guideline for
the treatment of patients with substance use disorders. 2010. http://psychiatryonline.org/pb/assets/raw/sitewide/practice_guidelines/guidelines/substanceuse.pdf.
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Evidence supports that outpatient follow-up care and interventions
after hospital discharges are associated with a decreased risk of
readmissions for patients with mental illness.121 122 IPFs
can influence rates of follow-up care for patients hospitalized for
mental illness or SUD. Three studies reported that with certain
interventions--such as pre-discharge transition interviews, appointment
reminder letters or reminder phone calls, meetings with outpatient
clinicians before discharge, and meetings with inpatient staff familiar
to patients at the first post-discharge appointment--facilities
achieved 30-day follow-up rates of 88 percent or
more.123 124 125 This is substantially higher than the
national rate of about 52 percent observed in the current FUH measure
for Medicare FFS discharges between July 1, 2016, and June 30,
2017.\126\ Medicare FFS data from July 1, 2016, to June 30, 2017, show
the national 7-day follow-up rate to be 35.5 percent and the 30-day
rate to be 61.0 percent. These data reveal wide variation in follow-up
rates across facilities, with a 16.9 percent absolute difference
between the 25th and 75th percentiles for the 7-day rate and a 17.4
percent absolute difference for the 30-day rate. If all facilities
achieved the benchmark follow-up rates for their Medicare FFS patients
(as calculated using the AHRQ Achievable Benchmarks of Care
method),\127\ 53,841 additional discharges would have a 7-day follow-up
visit, and 47,552 would have a 30-day follow-up visit.\128\
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\121\ Kurdyak P, Vigod SN, Newman A, Giannakeas V, Mulsant BH,
Stukel T. Impact of Physician Follow-Up Care on Psychiatric
Readmission Rates in a Population-Based Sample of Patients With
Schizophrenia. Psychiatr Serv. 2018;69(1):61-68. doi: 10.1176/
appi.ps.201600507.
\122\ Marcus SC, Chuang CC, Ng-Mak DS, Olfson M. Outpatient
follow-up care and risk of hospital readmission in schizophrenia and
bipolar disorder. Psychiatr Serv. 2017;68(12):1239-1246. doi:
10.1176/appi.ps.201600498.
\123\ Batscha C, McDevitt J, Weiden P, Dancy B. The effect of an
inpatient transition intervention on attendance at the first
appointment post discharge from a psychiatric hospitalization. J Am
Psychiatr Nurses Assoc. 2011;17(5):330-338. doi: 10.1177/
1078390311417307.
\124\ Agarin T, Okorafor E, Kailasam V, et al. Comparing kept
appointment rates when calls are made by physicians versus behavior
health technicians in inner city hospital: Literature review and
cost considerations. Community Ment Health J. 2015;51(3):300-304.
doi: 10.1007/s10597-014-9812-x.
\125\ Olfson M, Mechanic D, Boyer CA, Hansell S. Linking
inpatients with schizophrenia to outpatient care. Psychiatr Serv.
1998;49(7):911-917. doi: 10.1176/ps.49.7.911. Quality AFHRA. 2017
National Healthcare Quality and Disparities Report. Rockville, MD:
Services USDoHaH; 2018.
\126\ https://data.cms.gov/provider-data/archived-data/
hospitals''.
\127\ https://nhqrnet.ahrq.gov/inhqrdr/resources/methods#Benchmarks.
\128\ Quality AfHRa. 2017 National Healthcare Quality and
Disparities Report. Rockville, MD: Services USDoHaH; 2018.
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During the development process, we used the CMS Quality Measures
Public Comment Page to ask for public comments on the measure.\129\ We
accepted public comments from Friday, January 25, 2019, to Wednesday,
February 13, 2019. During this period, we received comments from 29
organizations or individuals. Many commenters acknowledged the
importance of developing a measure that assesses acute care providers
for follow-up post-hospitalization. Some commenters expressed
skepticism about the measure's appropriateness as a tool for evaluating
the performance of discharging IPFs due to factors beyond the IPFs'
control that can affect whether a patient receives timely post-
discharge follow-up care. Ten stakeholders expressed support for the
measure based on the expanded list of qualifying diagnoses in the
denominator and the inclusion of more patients who could benefit from
post-discharge follow-up visits.\130\
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\129\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Downloads/IPF_-Follow-Up-After-Psychiatric-Hospitalization_Public-Comment-Summary.pdf.
\130\ Mathematica. FAPH public comment summary. April 2019.
---------------------------------------------------------------------------
We reviewed the comments we received with the TEP, whose members
shared similar feedback regarding the importance of follow-up for
patients with both mental health diagnoses and substance use disorders,
as well as concerns about the ability of IPFs to influence follow-up
care. We agree with commenters that some factors that influence follow-
up are outside of a facility's control. However, as described, in
section IV.E.3.a, we believe that there are interventions (such as pre-
discharge transition interviews, appointment reminder letters or
reminder phone calls, meetings with outpatient
[[Page 19506]]
clinicians before discharge, and meetings with inpatient staff familiar
to patients at the first post-discharge appointment) that allow
facilities to improve their follow-up adherence. We remain committed to
monitoring follow-up to improve health outcomes and view this measure
as an expansion of our ability to measure appropriate follow-up care
established by FUH.
b. Overview of Measure
(1). Measure Calculation
The FAPH measure would be calculated by dividing the number of
discharges that meet the numerator criteria by the number that meet the
denominator criteria. Two rates are reported for this measure: The 7-
day rate and the 30-day rate.
(a) Numerator
The first rate that would be reported for this measure includes
discharges from a psychiatric facility that are followed by an
outpatient visit for treatment of mental illness or SUD within 7 days.
The second rate reported for this measure would include discharges from
a psychiatric facility that are followed by an outpatient visit for
treatment of mental illness or SUD within 30 days. Outpatient visits
are defined as outpatient visits, intensive outpatient encounters, or
partial hospitalization and are defined by the Current Procedural
Terminology (CPT), Healthcare Common Procedure Coding System (HCPCS),
and Uniform Billing (UB) Revenue codes. Claims with codes for emergency
room visits do not count toward the numerator.
(b) Denominator
The denominator includes discharges paid under the IPF prospective
payment system during the performance period for Medicare FFS patients
with a principal diagnosis of mental illness or SUD. Specifically, the
measure includes IPF discharges for which the patient was:
Discharged with a principal diagnosis of mental illness or
SUD that would necessitate outpatient follow-up care,
Alive at the time of discharge,
Enrolled in Medicare Parts A and B during the month of the
discharge date and at least one month after the discharge date to
ensure that data are available to capture the index admission and
follow-up visits, and
Age 6 or older on the date of discharge, because follow-up
treatment for mental illness or SUD might not always be recommended for
younger children.
The denominator excludes IPF discharges for patients who:
Were admitted or transferred to acute and non-acute
inpatient facilities within the 30-day follow-up period, because
admission or transfer to other institutions could prevent an outpatient
follow-up visit from taking place,
Were discharged against medical advice, because the IPF
could have limited opportunity to complete treatment and prepare for
discharge,
Died during the 30-day follow-up period, or
Use hospice services or elect to use a hospice benefit at
any time during the measurement year regardless of when the services
began, because hospice patients could require different follow-up
services.
The FAPH measure differs from FUH mostly in the expansion of the
measure population to include SUD and other mental health diagnoses in
the measure's denominator, but it includes some additional differences:
The FAPH measure simplifies the exclusion of admission or
transfer to acute or non-acute inpatient facilities within 30 days
after discharge by aligning with the HEDIS[supreg] Inpatient Stay Value
Set used in both the HEDIS[supreg] FUH and the HEDIS[supreg] Follow-Up
After Emergency Department Visit for Alcohol and Other Drug Abuse or
Dependence (FUA) measures to identify acute and non-acute inpatient
stays. A discharge is excluded from the FAPH measure if it is followed
by an admission or a transfer with one of the codes in the value set.
The FAPH measure uses Medicare UB Revenue codes (rather
than inpatient discharge status code, which the FUH measure uses) to
identify discharge or transfer to other health care institutions. This
is to align better with the intent of the HEDIS[supreg] FUH and
HEDIS[supreg] FUA measures.
The FAPH measure allows mental illness or SUD diagnoses in
any position on the follow-up visit claim to count toward the numerator
and does not require that it be in the primary position as the FUH
measure does.
(2) Measure Reliability and Validity
In 2019, CMS used the final measure specifications to complete
reliability and validity testing, which revealed that the FAPH measure
provides reliable and valid facility-level rates of follow-up after
psychiatric hospitalization. We evaluated measure reliability based on
a signal-to-noise analysis,\131\ in which a score of 0.0 implies that
all variation is attributed to measurement error (noise), and a score
of 1.0 implies that all measure score variation is caused by a real
difference in performance across IPFs. Using that approach, we
established a minimum denominator size of 40 discharges to attain an
overall reliability score of 0.7 for both the 7-day and the 30-day
rate. These analyses revealed that the measure can reliably distinguish
differences in performance between IPFs with adequate denominator size.
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\131\ For additional information on reliability tests see http://www.qualityforum.org/Measuring_Performance/Improving_NQF_Process/Measure_Testing_Task_Force_Final_Report.aspx.
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We evaluated the validity of the measure based on its correlation
to two conceptually related measures in the IPFQR Program: The 30-Day
All-Cause Unplanned Readmission After Psychiatric Discharge from an IPF
(IPF Readmission) measure, and the Medication Continuation Following
Inpatient Psychiatric Discharge (Medication Continuation) measure. We
observed a weak negative correlation between FAPH and the IPF
Readmission measure for both 7-day (-0.11) and 30-day (-0.18) measure
rates. This negative correlation is expected because a higher score is
indicative of better quality of care for the FAPH, while a lower score
is indicative of better quality of care for the IPF readmission measure
(that is, a lower rate of unplanned readmissions). High rates of
follow-up after visits after discharge and low rates of unplanned
readmissions both indicate good care coordination during the discharge
process. We observed a weak positive correlation between the 7-day FAPH
measure rate and the Medication Continuation measure (0.32), and
between the 30-day FAPH measure rate and the Medication Continuation
measure (0.42). This result is expected because for both the FAPH and
the Medication Continuation measures higher scores are indicative of
better-quality care. Follow-up visits after discharge and continuation
of medication after discharge both indicate good care coordination
during the discharge process. After reviewing these results and the
proposed measure specifications, all 13 TEP members who were present
agreed that the measure had face validity.\132\
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\132\ Face validity is defined as a subjective determination by
experts that the measure appears to reflect quality of care, done
through a systematic and transparent process, that explicitly
addresses whether performance scores resulting from the measure as
specified can be used to distinguish good from poor quality, with
degree of consensus and any areas of disagreement provided/
discussed: https://www.qualityforum.org/Measuring_Performance/Scientific_Methods_Panel/Docs/Evaluation_Guidance.aspx.
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[[Page 19507]]
(3) Review by the Measure Applications Partnership and NQF
Under section 1890A(a)(2) of the Act, this measure was included in
a publicly available document: ``List of Measures Under Consideration
for December 1, 2019,'' available at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/Downloads/Measures-under-Consideration-List-for-2018.pdf.
On January 15, 2020, the MAP Coordinating Committee rated the
measure as ``Conditional Support for Rulemaking'' contingent upon NQF
endorsement. We submitted the measure to the NQF for endorsement in the
spring 2020 cycle. However, some members of the NQF Behavioral Health
and Substance Use Standing Committee were concerned about the measure's
exclusions for patients who died during the 30-day follow-up period or
who were transferred. In addition, some members objected to combining
persons with a diagnosis of SUD and those with a diagnosis for a mental
health disorder into a single measure of follow-up care. Therefore, the
NQF declined to endorse this measure. We noted that the exclusions for
patients who died or who were admitted or transferred to an acute or
non-acute inpatient facility during the 30-day follow up period align
with the FUH measure currently in the IPFQR Program.
Section 1886(s)(4)(D)(ii) of the Act authorizes the Secretary to
specify a measure for the IPFQR Program that is not endorsed by NQF.
The exception to the requirement to specify an endorsed measure states
that in the case of a specified area or medical topic determined
appropriate by the Secretary for which a feasible and practical measure
has not been endorsed by the entity with a contract under section
1890(a) of the Act, the Secretary may specify a measure that is not so
endorsed as long as due consideration is given to measures that have
been endorsed or adopted by a consensus organization.
The FAPH measure is not NQF endorsed. We have reviewed NQF-endorsed
and other consensus-endorsed measures related to follow-up care and
identified the FUH measure (NQF #0576) currently in the IPFQR Program
and Continuity of Care after Inpatient or [bond]Residential Treatment
for SUD (NQF #3453). We believe that the FAPH measure is an improvement
over the current FUH measure and over the Continuity of Care after
Inpatient or Residential Treatment of Substance Use Disorder because we
believe that it is important to ensure appropriate access to follow-up
treatment for the largest patient population possible and the FAPH
measure applies to a larger patient population than either of the
measures we considered. Therefore, we propose to adopt the FAPH measure
described in this section for the FY 2024 payment determination and
subsequent years.
c. Data Collection, Submission and Reporting
FAPH uses Medicare FFS Part A and Part B claims that are received
by Medicare for payment purposes. The measure links Medicare FFS claims
submitted by IPFs and subsequent outpatient providers for Medicare FFS
IPF discharges. Therefore, no additional data collection would be
required from IPFs. For additional information on data submission for
this measure, see section IV.J.2.b of this proposed rule. The
performance period used to identify cases in the denominator is 12
months. Data from this period and 30 days afterward are used to
identify follow-up visits in the numerator. Consistent with other
claims-based measures in the IPFQR Program, the performance period for
this measure is July 1 through June 30. For example, for the FY 2024
payment determination, the performance period will include discharges
between July 1, 2021 and June 30, 2022.\133\
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\133\ If data availability or operational issues prevent use of
this performance period, we would announce the updated performance
period through subregulatory communications including announcement
on a CMS website and/or on our applicable listservs.
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We invite public comment on our proposal to add a new measure,
Follow-Up After Psychiatric Hospitalization, to the IPFQR Program,
beginning with the FY 2024 payment determination and subsequent years.
F. Removal or Retention of IPFQR Program Measures
1. Background
In the FY 2018 IPPS/LTCH PPS final rule (82 FR 38463 through
38465), we adopted considerations for removing or retaining measures
within the IPFQR Program and criteria for determining when a measure is
``topped out.'' In the FY 2019 IPF PPS final rule (83 FR 38591 through
38593), we adopted one additional measure removal factor. We are not
proposing any changes to these removal factors, topped-out criteria, or
retention factors and refer readers to the FY 2018 IPPS/LTCH PPS final
rule (82 FR 38463 through 38465) and the FY 2019 IPF PPS final rule (83
FR 38591 through 38593) for more information. We will continue to
retain measures from each previous year's IPFQR Program measure set for
subsequent years' measure sets, except when we specifically propose to
remove or replace a measure. We will continue to use the notice-and-
comment rulemaking process to propose measures for removal or
replacement, as we described upon adopting these factors in the FY 2018
IPPS/LTCH PPS final rule (82 FR 38464 through 38465).
In our continual evaluation of the IPFQR Program measure set under
our Meaningful Measures Framework and according to our measure removal
and retention factors, we identified four measures that we believe are
appropriate to propose removing from the IPFQR Program for the FY 2024
payment determination and subsequent years. Our discussion of these
measures follows.
2. Measures for Removal
a. Proposal To Remove Alcohol Use Brief Intervention Provided or
Offered and Alcohol Use Brief Intervention (SUB-2/2a) Beginning With FY
2024 Payment Determination
We are proposing to remove the Alcohol Use Brief Intervention
Provided or Offered and Alcohol Use Brief Intervention (SUB-2/2a)
measure from the IPFQR Program beginning with the FY 2024 payment
determination under our measure removal Factor 8, ``The costs
associated with a measure outweigh the benefit of its continued use in
the program.'' We adopted the Alcohol Use Brief Intervention Provided
or Offered and Alcohol Use Brief Intervention (SUB-2/2a) measure in the
FY 2016 IPF PPS final rule (80 FR 46699 through 46701) because we
believe it is important to address the common comorbidity of alcohol
use among IPF patients. This measure requires facilities to chart-
abstract measure data on a sample of IPF patient records, in accordance
with established sampling policies (80 FR 46717 through 46719).
We have previously stated our intent to move away from chart-
abstracted measures to reduce information collection burden in this and
other CMS quality programs (78 FR 50808; 79 FR 50242; 80 FR 49693).
When we adopted the SUB-2/2a measure to the IPFQR Program, the benefits
of this measure were high because facility performance was not
consistent. Therefore, the measure provided a means of distinguishing
facility performance and incentivized facilities to improve rates of
treatment for this common comorbidity. Between the FY 2018
[[Page 19508]]
payment determination (the first year that SUB-2/2a was included in the
IPFQR Program measure set) and the FY 2019 payment determination, we
saw substantial performance improvement on the SUB-2 measure (which is
the portion of the SUB-2/2a measure that assess whether the facility
provided or offered a brief intervention for alcohol use). However, for
the FY 2019 and FY 2020 payment determinations, that improvement has
leveled off to consistently high performance, as indicated in Table 2.
These data further show that at this time there is little room for
improvement in the SUB 2 measure, and that the quality improvement
benefits from the measure have greatly diminished. We continue to
believe that alcohol use is an important comorbidity to address in the
IPF setting, and that brief interventions are a key component of
addressing this comorbidity. However, based on these data, we believe
that most IPFs routinely offer alcohol use brief interventions, and
that IPFs will continue to offer these interventions to patients,
regardless of whether the SUB-2/2a measure is in the IPFQR Program
measure set, because it has become an embedded part of their clinical
workflows.
Table 2--Performance Analysis for Alcohol Use Brief Intervention Provided or Offered (SUB-2)
----------------------------------------------------------------------------------------------------------------
Truncated
75th 90th coefficient of
Year Mean Median percentile percentile variation
(TCV)
----------------------------------------------------------------------------------------------------------------
2016 (2018 Payment 66.96 77 96 100 0.49
Determination).................
2017 (2019 Payment 77.11 88 99 100 0.28
Determination).................
2018 (2020 Payment 79.49 91 100 100 0.25
Determination).................
----------------------------------------------------------------------------------------------------------------
While the measure does not meet our criteria for ``topped-out''
status because of the TCV higher than 0.1, we believe that this measure
no longer meaningfully supports the program objectives of informing
beneficiary choice and driving improvement in IPF interventions for
alcohol use because it is no longer showing significant improvement in
facility performance (that is, in providing or offering alcohol use
brief interventions). Furthermore, as we stated in the FY 2019 IPF PPS
final rule, costs are multi-faceted and include not only the burden
associated with reporting, but also the costs associated with
implementing and maintaining the program (83 FR 38592). For example, it
may be costly for health care providers to maintain general
administrative knowledge to report this measure. Additionally, CMS must
expend resources in maintaining information collection systems,
analyzing reported data, and providing public reporting of the
collected information.
Here, IPF information collection burden and related costs
associated with reporting the SUB 2/2a measure to CMS are high because
it is a chart-abstracted measure. Furthermore, CMS incurs costs
associated with the program oversight of the measure for public
display. As a result, we believe that the costs and burdens associated
with this chart-abstracted measure outweigh the benefit of its
continued use in the program.
Therefore, we are proposing to remove the Alcohol Use Brief
Intervention Provided or Offered and Alcohol Use Brief Intervention
(SUB-2/2a) measure from the IPFQR Program beginning with the FY 2024
payment determination. We welcome public comments on our proposal to
remove the SUB-2/2a measure from the IPFQR Program.
b. Proposal To Remove Tobacco Use Brief Intervention Provided or
Offered and Tobacco Use Brief Intervention (TOB-2/2a) Beginning With FY
2024 Payment Determination
We are proposing to remove the Tobacco Use Brief Intervention
Provided or Offered and Tobacco Use Brief Intervention (TOB-2/2a)
measure from the IPFQR Program beginning with the FY 2024 payment
determination under our measure removal Factor 8, ``The costs
associated with a measure outweigh the benefit of its continued use in
the program.'' We adopted the Tobacco Use Brief Intervention Provided
or Offered and Tobacco Use Brief Intervention (TOB-2/2a) measure in the
FY 2015 IPF PPS final rule (79 FR 45971 through 45972) because we
believe it is important to address the common comorbidity of tobacco
use among IPF patients. Like SUB-2/2a described in the previous
subsection, this measure requires facilities to chart-abstract measure
data on a sample of IPF patient records, in accordance with established
sampling policies (80 FR 46717 through 46719). When we introduced the
TOB-2/2a measure to the IPFQR Program, the benefits of this measure
were high, because facility performance was not consistent and
therefore the measure provided a means of distinguishing facility
performance and incentivized facilities to improve rates of treatment
for this common comorbidity. Between the FY 2017 payment determination
(the first year that TOB-2/2a was included in the IPFQR Program's
measure set) and the FY 2019 payment determination we saw substantial
performance improvement on TOB-2. However, between the FY 2019 and FY
2020 payment determinations, that improvement has leveled off to
consistently high performance, as indicated in Table 3. These data
further show that currently there is little room for improvement in the
TOB-2 measure, and that the quality improvement benefits from the
measure have greatly diminished. We continue to believe that tobacco
use is an important comorbidity to address in the IPF setting, and that
brief interventions are a key component of addressing this comorbidity.
However, based on these data, we believe that most IPFs routinely offer
tobacco use brief interventions, and that IPFs will continue to offer
these interventions to patients, regardless of whether the TOB-2/2a
measure is in the IPFQR Program measure set, because it has become an
embedded part of their clinical workflows.
[[Page 19509]]
Table 3--Performance Analysis for Tobacco Use Brief Intervention Provided or Offered (TOB-2)
----------------------------------------------------------------------------------------------------------------
Truncated
75th 90th coefficient of
Year Mean Median percentile percentile variation
(TCV)
----------------------------------------------------------------------------------------------------------------
2015 (2017 Payment 63.83 71.5 91 99 0.49
Determination).................
2016 (2018 Payment 74.72 84 95 100 0.28
Determination).................
2017 (2019 Payment 79.04 88 97 100 0.22
Determination).................
2018 (2020 Payment 79.08 88 98 100 0.22
Determination).................
----------------------------------------------------------------------------------------------------------------
While the measure does not meet our criteria for ``topped-out''
status because of the TCV higher than 0.1, we believe that this measure
no longer meaningfully supports the program objectives of informing
beneficiary choice and driving improvement in IPF interventions for
tobacco use because it is no longer showing significant improvement in
facility performance (that is, in providing or offering tobacco use
brief interventions). Furthermore, as we stated in the FY 2019 IPF PPS
final rule, costs are multi-faceted and include not only the burden
associated with reporting, but also the costs associated with
implementing and maintaining the program (83 FR 38592). For example, it
may be costly for health care providers to maintain general
administrative knowledge to report this measure. Additionally, CMS must
expend resources in maintaining information collection systems,
analyzing reported data, and providing public reporting of the
collected information. Here, IPF information collection burden and
related costs associated with reporting this measure to CMS are high
because the measure is a chart-abstracted measure. Furthermore, CMS
incurs costs associated with the program oversight of the measure for
public display. As a result, we believe that the costs and burdens
associated with this chart-abstracted measure outweigh the benefit of
its continued use in the program.
Therefore, we are proposing to remove the Tobacco Use Brief
Intervention Provided or Offered and Tobacco Use Brief Intervention
(TOB-2/2a) measure from the IPFQR Program beginning with the FY 2024
payment determination. We welcome public comments on our proposal to
remove the TOB-2/2a measure from the IPFQR Program.
c. Proposal To Remove Timely Transmission of Transition Record
(Discharges From an Inpatient Facility to Home/Self Care or Any Other
Site of Care) Beginning With FY 2024 Payment Determination
We are proposing to remove the Timely Transmission of Transition
Record (Discharges from an Inpatient Facility to Home/Self Care or Any
Other Site of Care) measure from the IPFQR Program beginning with the
FY 2024 payment determination under our measure removal Factor 8, ``The
costs associated with a measure outweigh the benefit of its continued
use in the program.''
We adopted the Timely Transmission of Transition Record (Discharges
from an Inpatient Facility to Home/Self Care or Any Other Site of Care)
measure in the FY 2016 IPF PPS final rule (80 FR 46706 through 46709)
because more timely communication of vital information regarding the
inpatient hospitalization results in better care, reduction of systemic
medical errors, and improved patient outcomes. The Timely Transmission
of Transition Record (Discharges from an Inpatient Facility to Home/
Self Care or Any Other Site of Care) measure builds on the Transition
Record with Specified Elements Received by Discharged Patients
(Discharges from an Inpatient Facility to Home/Self Care or Any Other
Site of Care) measure, which requires facilities to provide a discharge
record with 11 specified elements to patients at discharge.
We continue to believe that the 11 elements required by the
Transition Record with Specified Elements measure provide meaningful
information about the quality of care provided by IPFs, and we are
therefore not proposing to remove that measure from the IPFQR Program.
However, we believe that the benefits of requiring facilities to
transmit the discharge record with 11 specified elements to the next
level care provided within 24 hours, as required by the Timely
Transmission of Transition Record (Discharges from an Inpatient
Facility to Home/Self Care or Any Other Site of Care) measure, have
been reduced. Reporting this measure requires facilities to chart-
abstract measure data on a sample of IPF patient records, in accordance
with established sampling policies (80 FR 46717 through 46719). On May
1, 2020, we updated the Conditions of Participation (CoPs) for IPFs
participating in the Medicare program in the Medicare and Medicaid
Programs; Patient Protection and Affordable Care Act; Interoperability
and Patient Access for Medicare Advantage Organization and Medicaid
Managed Care Plans, State Medicaid Agencies, CHIP Agencies and CHIP
Managed Care Entities, Issuers of Qualified Health Plans on the
Federally Facilitated Exchanges, and Health Care Providers final rule
(85 FR 25588).
In the May 1, 2020 update to the CoPs, we adopted a requirement for
psychiatric hospitals that possess EHR systems with the technical
capacity to generate information for electronic patient event
notifications to send electronic patient event notifications of a
patient's admission, discharge, and/or transfer to another health care
facility or to another community provider at the time of a patient's
discharge or transfer. Because these updated CoP requirements overlap
with, but are not the same as, the requirements for the Timely
Transmission of Transition Record (Discharges from an Inpatient
Facility to Home/Self Care or Any Other Site of Care) measure (which
requires transmission of a discharge record with 11 specified elements
to the next level care provider within 24 hours of the patient's
discharge rather that requiring notification regarding the patient's
inpatient stay to be transmitted at discharge), we believe that the
adoption of these updated CoPs increases the costs of the Timely
Transmission of Transition Record (Discharges from an Inpatient
Facility to Home/Self Care or Any Other Site of Care) measure while
decreasing its benefit. Specifically, we believe that the costs of this
measure are increased because facilities to which the new CoPs apply
(that is, facilities that possess EHR or other administrative systems
with the technical capacity to generate information for electronic
patient event notifications) could bear increased cost if they
separately implement the patient event notifications meeting both the
criteria for the updated CoPs and the capacity to share a transition
record that meets the requirements of our measure. We
[[Page 19510]]
note that the updated CoPs do not include the level of detail regarding
data to be transferred at discharge that our Timely Transmission of
Transition Record (Discharges from an Inpatient Facility to Home/Self
Care or Any Other Site of Care) measure requires. While the set of
information in the CoP notification policy is a minimal set of
information, we believe that it would continue to be appropriate for
providers to transmit the transition record that they will continue to
be providing to patients under our Transition Record Received by
Discharged Patients (Discharges from an Inpatient Facility to Home/Self
Care or Any Other Site of Care) measure.
We believe the different requirements regarding both timeliness of
notification and contents of notification could lead some providers to
send two separate discharge notifications to meet the separate
requirements. Further, we believe that the benefits of the measure are
reduced because all facilities to which the new CoPs apply will be
sending patient discharge information to the next level of care
provider as required by the CoPs. Therefore, the benefits of this
measure are reduced because it is less likely to ensure that these
facilities provide patient discharge information to the next level care
provider, and it is less likely to provide information to help
consumers differentiate quality between facilities. While these updated
CoPs do not directly address transmission of patient event
notifications for facilities that do not possess EHR systems with
patient event notification capabilities, such facilities should
continue to transmit data using their existing infrastructure and
timelines.
Because we believe that the costs are now increased and the
benefits are now reduced, we believe that the costs and burdens
associated with this chart-abstracted measure outweigh the benefit of
its continued use in the IPFQR Program.
Therefore, we are proposing to remove the Timely Transmission of
Transition Record (Discharges from an Inpatient Facility to Home/Self
Care or Any Other Site of Care) measure from the IPFQR Program
beginning with the FY 2024 payment determination. We welcome public
comments on our proposal to remove the Timely Transmission of
Transition Record measure from the IPFQR Program.
d. Proposal To Remove Follow-Up After Hospitalization for Mental
Illness (FUH, NQF #0576) Beginning With FY 2024 Payment Determination
If we finalize adoption of the Follow-Up After Psychiatric
Hospitalization measure described in Section IV.E.3, we believe that
our current measure removal Factor 3 would apply to the existing
Follow-Up After Hospitalization for Mental Illness (FUH, NQF #0576)
measure. Measure removal Factor 3 applies when a ``measure can be
replaced by a more broadly applicable measure (across settings or
populations) or a measure that is more proximal in time to desired
patient outcomes for the particular topics.'' We adopted removal factor
3 in the FY 2017 IPPS/LTCH PPS final rule (82 FR 38463 through 38465).
The proposed FAPH measure expands the patient population from patients
with mental illness to also include patients with primary SUD diagnoses
while addressing the same important aspect of care transitions. Because
this FAPH measure uses the same methodology to address the same element
of care for a broader patient population than the FUH measure, we
believe that it is more broadly applicable across populations.
Therefore, we are proposing to remove the FUH measure under measure
removal Factor 3 only if we finalize our proposal to adopt of the FAPH
measure. We note that if we do not adopt the FAPH measure, we will
retain the FUH measure because we believe this measure addresses an
important clinical topic. We welcome public comments on our proposal to
remove FUH if we adopt FAPH.
G. Summary of Previously Finalized and Newly Proposed Measures
1. Previously Finalized and Newly Proposed Measures for the FY 2023
Payment Determination and Subsequent Years
There are 14 previously finalized measures for the FY 2023 payment
determination and subsequent years. In this proposed rule, we are
proposing to adopt one measure for the FY 2023 payment determination
and subsequent years. The 15 measures which would be in the program if
this proposal is finalized are shown in Table 4.
Table 4--IPFQR Program Measure Set for the FY 2023 Payment Determination
and Subsequent Years if Measure Adoption Is Finalized as Proposed
------------------------------------------------------------------------
NQF # Measure ID Measure
------------------------------------------------------------------------
0640............... HBIPS-2............... Hours of Physical Restraint
Use.
0641............... HBIPS-3............... Hours of Seclusion Use.
0560............... HBIPS-5............... Patients Discharged on
Multiple Antipsychotic
Medications with
Appropriate Justification.
0576............... FUH................... Follow-Up After
Hospitalization for Mental
Illness.
N/A *.............. SUB-2 and SUB-2a...... Alcohol Use Brief
Intervention Provided or
Offered and SUB-2a Alcohol
Use Brief Intervention.
N/A *.............. SUB-3 and SUB-3a...... Alcohol and Other Drug Use
Disorder Treatment
Provided or Offered at
Discharge and SUB-3a
Alcohol and Other Drug Use
Disorder Treatment at
Discharge.
N/A *.............. TOB-2 and TOB-2a...... Tobacco Use Treatment
Provided or Offered and
TOB-2a Tobacco Use
Treatment.
N/A *.............. TOB-3 and TOB-3a...... Tobacco Use Treatment
Provided or Offered at
Discharge and TOB-3a
Tobacco Use Treatment at
Discharge.
1659............... IMM-2................. Influenza Immunization.
N/A *.............. N/A................... Transition Record with
Specified Elements
Received by Discharged
Patients (Discharges from
an Inpatient Facility to
Home/Self Care or Any
Other Site of Care).
N/A *.............. N/A................... Timely Transmission of
Transition Record
(Discharges from an
Inpatient Facility to Home/
Self Care or any Other
Site of Care).
N/A................ N/A................... Screening for Metabolic
Disorders.
2860............... N/A................... Thirty-Day All-Cause
Unplanned Readmission
Following Psychiatric
Hospitalization in an
Inpatient Psychiatric
Facility.
3205............... Med Cont.............. Medication Continuation
Following Inpatient
Psychiatric Discharge.
[[Page 19511]]
TBD................ COVID HCP............. COVID-19 Healthcare
Personnel (HCP)
Vaccination Measure.
------------------------------------------------------------------------
* Measure is no longer endorsed by the NQF but was endorsed at time of
adoption. Section 1886(s)(4)(D)(ii) of the Act authorizes the
Secretary to specify a measure that is not endorsed by the NQF as long
as due consideration is given to measures that have been endorsed or
adopted by a consensus organization identified by the Secretary. We
attempted to find available measures for each of these clinical topics
that have been endorsed or adopted by a consensus organization and
found no other feasible and practical measures on the topics for the
IPF setting.
2. Previously Finalized and Newly Proposed Measures for the FY 2024
Payment Determination and Subsequent Years
There are 14 previously finalized measures for the FY 2024 payment
determination and subsequent years. In this proposed rule, we are
proposing to adopt one measure for the FY 2023 payment determination
and subsequent years. Additionally, we are proposing to remove three
measures and replace one measure for the FY 2024 payment determination
and subsequent years. The 12 measures which would be in the program for
FY 2024 payment determination and subsequent years if these proposals
are finalized are shown in Table 5.
Table 5--IPFQR Program Measure Set for the FY 2024 Payment Determination
and Subsequent Years if Adoptions and Removals Are Finalized as Proposed
------------------------------------------------------------------------
NQF # Measure ID Measure
------------------------------------------------------------------------
0640............... HBIPS-2............... Hours of Physical Restraint
Use.
0641............... HBIPS-3............... Hours of Seclusion Use.
0560............... HBIPS-5............... Patients Discharged on
Multiple Antipsychotic
Medications with
Appropriate Justification.
N/A................ FAPH.................. Follow-Up After Psychiatric
Hospitalization.
1659............... IMM-2................. Influenza Immunization.
N/A *.............. SUB-3 and SUB-3a...... Alcohol and Other Drug Use
Disorder Treatment
Provided or Offered at
Discharge and SUB-3a
Alcohol and Other Drug Use
Disorder Treatment at
Discharge.
N/A *.............. TOB-3 and TOB-3a...... Tobacco Use Treatment
Provided or Offered at
Discharge and TOB-3a
Tobacco Use Treatment at
Discharge.
N/A *.............. N/A................... Transition Record with
Specified Elements
Received by Discharged
Patients (Discharges from
an Inpatient Facility to
Home/Self Care or Any
Other Site of Care).
N/A................ N/A................... Screening for Metabolic
Disorders.
2860............... N/A................... Thirty-Day All-Cause
Unplanned Readmission
Following Psychiatric
Hospitalization in an
Inpatient Psychiatric
Facility.
3205............... Med Cont.............. Medication Continuation
Following Inpatient
Psychiatric Discharge.
TBD................ COVID HCP............. COVID-19 Healthcare
Personnel (HCP)
Vaccination Measure.
------------------------------------------------------------------------
* Measure is no longer endorsed by the NQF but was endorsed at time of
adoption. Section 1886(s)(4)(D)(ii) of the Act authorizes the
Secretary to specify a measure that is not endorsed by the NQF as long
as due consideration is given to measures that have been endorsed or
adopted by a consensus organization identified by the Secretary. We
attempted to find available measures for each of these clinical topics
that have been endorsed or adopted by a consensus organization and
found no other feasible and practical measures on the topics for the
IPF setting.
H. Considerations for Future Measure Topics
As we have previously indicated, we seek to develop a comprehensive
set of quality measures to be available for widespread use for informed
decision-making and quality improvement in the IPF setting (79 FR 45974
through 45975). Therefore, through future rulemaking, we intend to
propose new measures for development or adoption that will help further
our goals of achieving better healthcare and improved health for
individuals who obtain inpatient psychiatric services through the
widespread dissemination and use of quality information. In 2017, we
introduced the Meaningful Measures Framework as a tool to foster
operational efficiencies and reduce costs including collection and
reporting burden while producing quality measurement that is more
focused on meaningful outcomes (83 FR 38591). As we continue to evolve
the Meaningful Measures Framework, we have stated that we intend to
better address health care priorities and gaps, emphasize digital
quality measurement, and promote patient perspectives.\134\ As we work
to align the IPFQR Program's measure set with these priorities, we have
identified the following areas that we believe are important to
stakeholders, but which are not covered in the current IPFQR Program
measure set: Patient Experience of Care, Functional Outcomes
Measurement, and digital measures. As described in the following
subsections, we are seeking public comment on each of these topics and
other future measure considerations which stakeholders believe are
important.
---------------------------------------------------------------------------
\134\ https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization.
---------------------------------------------------------------------------
1. Patient Experience of Care Data Collection Instrument
When we finalized removal of the Assessment of Patient Experience
of Care attestation measure in the FY 2019 IPF PPS final rule (83 FR
38596) we stated that we believed we had collected sufficient
information to inform development of a patient experience of care
measure that would capture data on the results of such a survey. In the
FY 2020 IPF PPS proposed rule (84 FR 16986 through 16987), we solicited
input on how providers had implemented the Hospital Consumer Assessment
of Healthcare Providers and Systems (HCAHPS) survey in their
[[Page 19512]]
facilities. We also sought public comment on other potential surveys
that commenters believed would be appropriate to adopt for the IPFQR
Program. We received many comments on this subject, and many of these
comments expressed that there is not one survey used predominantly
across IPFs (84 FR 38467). Additional commenters expressed concerns
that the HCAHPS survey may not be appropriate for the IPF setting
because it does not include some of the unique aspects of inpatient
psychiatric care including, group therapy, non-physician providers, and
involuntary admissions. While we did not solicit public comment on this
issue in the FY 2021 IPF PPS proposed rule, we received many comments
addressing this issue (85 FR 47043). We continue to seek to identify a
minimally burdensome patient experience of care instrument that would
be appropriate for the IPF setting. Therefore, we are seeking public
comment on instruments currently in use in the IPF setting, input on
whether the HCAHPS survey may be appropriate for this setting, and
information on how facilities that currently use the HCAHPS survey have
addressed challenges with using this survey within this setting (that
is, concerns regarding unique aspects of inpatient psychiatric care).
2. Functional Outcomes Instrument for Use in a Patient Reported
Outcomes Measure
When we introduced the Meaningful Measures Framework, we stated
that we wanted to focus on meaningful outcomes (83 FR 38591). As we
have assessed the IPFQR Program measure set against the Meaningful
Measures Framework, we have identified functional outcomes as a
potential gap area in the IPFQR Program's measure set. Therefore, we
are evaluating whether a patient reported outcomes measure that
assesses functional outcomes, such as global functioning, interpersonal
problems, psychotic symptoms, alcohol or drug use, emotional lability,
and self-harm, would be an appropriate measure to include in the IPFQR
program measure set. If we were to develop such a measure, we would
develop a measure that compares a patient's responses to a standardized
functional outcomes assessment instrument at admission with the
patient's results on the same assessment instrument at discharge. We
are seeking public comment on the value of such a measure in the IPFQR
program measure set, what would be an appropriate functional outcome
assessment instrument to use in the potential development of such a
measure, and any additional topics or concepts stakeholders believe
would be appropriate for patient reported outcomes measures.
3. Measures for Electronic Data Reporting
As we seek to improve digital measurement across our quality
reporting and value-based payment programs, we are considering measures
both within and appropriate to adopt for the IPFQR Program measure set
that would be appropriate for digital data collection. In our
assessment of the current measure set, we identified the Transition
Record with Specified Elements Received by Discharged Patients
(Discharges from an Inpatient Facility to Home/Self Care or Any Other
Site of Care) measure as a potential option for digital data
collection. We are seeking stakeholder input on the current data
collection burden associated with this measure, concerns regarding
potential electronic specification and data collection for this
measure, and other measures that may be appropriate for electronic data
collection, either those currently in the IPFQR Program measure set, or
those that we could adopt in the future.
I. Public Display and Review Requirements
We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR
53653 through 53654), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50897
through 50898), and the FY 2017 IPPS/LTCH PPS final rule (81 FR 57248
through 57249) for discussion of our previously finalized public
display and review requirements. In this proposed rule, we are not
proposing any changes to these requirements.
J. Form, Manner, and Timing of Quality Data Submission for the FY 2022
Payment Determination and Subsequent Years
1. Procedural Requirements for the FY 2023 Payment Determination and
Subsequent Years
We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR
53654 through 53655), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50898
through 50899), and the FY 2018 IPPS/LTCH PPS final rule (82 FR 38471
through 38472) for our previously finalized procedural requirements. In
this proposed rule, we are proposing to use the term ``QualityNet
security official'' instead of ``QualityNet system administrator,''
proposing to revise Sec. 412.434(b)(3) by replacing the term
``QualityNet system administrator'' with the term ``QualityNet security
official,'' and clarifying our policy under the previously finalized
requirement that hospitals ``[i]dentify a QualityNet Administrator who
follows the registration process located on the QualityNet website''
(77 FR 53654).
a. Proposal To Update Reference to QualityNet System Administrator and
to No Longer Require Active Account To Qualify for Payment
The previously finalized QualityNet security administrator
requirements, including those for setting up a QualityNet account and
the associated timelines, are described in the FY 2013 IPPS/LTCH final
rule (77 FR 53654).
In this proposed rule, we propose to use the term ``QualityNet
security official'' instead of ``QualityNet system administrator'' to
denote the exercise of authority invested in the role and align with
the Hospital Outpatient Quality Reporting Program and other programs.
The term ``security official'' would refer to ``the individual(s)'' who
have responsibilities for security and account management requirements
for a facility's QualityNet account. To clarify, this proposed update
in terminology would not change the individual's responsibilities or
add burden.
We invite public comment on our proposal to replace the term
``QualityNet system administrator'' with ``QualityNet security
official.''
Additionally, we are proposing to no longer require IPFs to
maintain an active QualityNet security official account to qualify for
payment. As we reviewed the requirements for the security official role
and the basic user \135\ role to identify the most appropriate language
to describe the distinguishing authority invested in the security
official role, we recognized that the QualityNet security official is
not required for submitting data--a basic user can serve in this role--
but remains necessary to set up QualityNet basic user accounts and for
security purposes. Therefore, consistent with our proposal to adopt the
security official term to differentiate the unique security authority
and responsibilities of the role from the data submission
responsibilities of the basic user role, we would continue to require a
QualityNet basic user account to meet
[[Page 19513]]
IPFQR Program requirements, including data submission and
administrative requirements, while recommending, but not requiring,
that hospitals maintain an active QualityNet security official account.
---------------------------------------------------------------------------
\135\ We also noted that a basic user is a QualityNet user who
(1) does not have the registration access described for security
officials, (2) has the appropriate data entry roles and permissions
for program participation, (3) can submit and review measures and
non-measure data, (4) signs and submits the Data Accuracy
Completeness Acknowledgement (DACA) form, and (5) refreshes their
QualityNet account password every 180 days to ensure that the
facility's IPFQR Program Notice of Participation status is
``Participating.''
---------------------------------------------------------------------------
We welcome public comments on our proposal to no longer require
facilities to maintain an active QualityNet security official account
to qualify for payment.
b. Proposal To Update Reference to QualityNet Administrator in Code of
Federal Regulations
In this proposed rule, we propose to revise our regulation at Sec.
412.434(b)(3) by replacing ``QualityNet system administrator'' with
``QualityNet security official.'' The term ``QualityNet security
official'' refers to the individual(s) who have responsibilities for
security and account management requirements for a hospital's
QualityNet account. To clarify, this proposed update in terminology
would not change the individual's responsibilities or add burden. If
finalized, the revised paragraph (b)(3) would read: ``Contact
information for the inpatient psychiatric facility's chief executive
officer and QualityNet security official, including each individual's
name, email address, telephone number, and physical mailing address.''
We invite public comment on our proposal to replace the term
``QualityNet system administrator'' with ``QualityNet security
official'' at Sec. 412.434(b)(3).
2. Data Submission Requirements
We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR
53655 through 53657), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50899
through 50900), and the FY 2018 IPPS/LTCH PPS final rule (82 FR 38472
through 38473) for our previously finalized data submission
requirements. In this proposed rule, we are proposing to adopt one
measure for the FY 2023 payment determination and subsequent years and
one measure for the FY 2024 payment determination and subsequent years.
Data submission requirements for each of these measures are described
in the following subsections. Additionally, we are proposing to adopt
patient level data submission for certain chart abstracted measures
beginning with data submitted for the FY 2023 payment determination and
subsequent years; details of this proposal are in subsection c. of this
section.
a. Data Submission Requirements for FY 2023 Payment Determination and
Subsequent Years
The measure we are proposing for FY 2023 payment determination and
subsequent years (the COVID-19 HCP--Vaccination measure) requires
facilities to report data on the number of HCP who have received
completed vaccination course of a COVID-19 vaccine through the CDC's
National Healthcare Safety Network (NHSN). Specific details on data
submission for this measure can be found in the CDC's Overview of the
Healthcare Safety Component, available at https://www.cdc.gov/nhsn/PDFs/slides/NHSN-Overview-HPS_Aug2012.pdf. For each CMS Certification
Number (CCN), a percentage of the HCP who received a completed vaccine
course of the COVID-19 vaccination will be calculated and publicly
reported, so that the public will know what percentage of the HCP have
been vaccinated in each IPF.
For the COVID-19 HCP Vaccination measure, we are proposing that
facilities would report the numerator and denominator for the COVID-19
HCP vaccination measure to the NHSN for at least one week each month,
beginning in October 2021 for the October 1, 2021 through December 31,
2021 reporting period affecting the FY 2023 payment determination. If
facilities report more than one week of data in a month, the most
recent week's data would be used to calculate the measure. Each
quarter, the CDC would calculate a single quarterly result of COVID-19
vaccination coverage which would summarize the data submitted by IPFs
for each of the three weeks of data submitted over the three month
period. If finalized, CMS would publicly report the CDC's quarterly
summary of COVID-19 vaccination coverage for IPFs.
We invite public comment on our proposal to require facilities to
report the COVID-19 HCP vaccination measure.
b. Data Submission Requirements for FY 2024 Payment Determination and
Subsequent Years
Because the Follow-Up After Psychiatric Hospitalization (FAPH)
measure would be calculated by CMS using Medicare Fee-for-Service
claims, there will be no additional data submission requirements for
the FY 2024 payment determination and subsequent years. Therefore, we
are not proposing any changes to our data submission policies
associated with the proposal to adopt this measure.
c. Proposal To Adopt Patient-Level Reporting for Certain Chart-
Abstracted Measures Beginning With FY 2024 Payment Determination and
Subsequent Years
In the FY 2013 IPPS/LTCH PPS final rule (77 FR 53655 through
53657), we finalized that IPFs participating in the IPFQR Program must
submit data to the Web-Based Measures Tool found in the Inpatient
Psychiatric Facility section of the QualityNet website's secure portal
between July 1 and August 15 of each year. We noted that the data input
forms within the Quality Net secure portal require submission of
aggregate data for each separate quarter. In the FY 2014 IPPS/LTCH PPS
final rule, we clarified our intent to require that IPFs submit
aggregate data on measures on an annual basis via the Web-Based
Measures Tool found in the IPF section of the Quality Net website's
secure portal and that the forms available require aggregate data for
each separate quarter (78 FR 50899 through 50900). In the FY 2016 IPF
PPS final rule (80 FR 46716), we updated our data submission
requirements to require facilities to report data for chart-abstracted
measures to the Web-Based Measures Tool on an aggregate basis by year,
rather than by quarter. Additionally, we discontinued the requirement
for reporting by age group. We updated these policies in the FY 2018
IPPS/LTCH PPS final rule (82 FR 38472 through 38473) to change the
specification of the submission deadline from exact dates to a 45-day
submission period beginning at least 30 days following the end of the
data collection period.
In the FY 2019 IPF PPS final rule (83 FR 38607), we observed that
reporting aggregate measure data increases the possibility of human
error, such as making typographical errors while entering data, which
cannot be detected by CMS or by data submission systems. We noted that
unlike patient-level data reporting, aggregate measure data reporting
does not allow for data accuracy validation, thereby lowering the
ability to detect error. We stated that we were considering requiring
patient-level data reporting (data regarding each patient included in a
measure and whether the patient was included in each numerator and
denominator of the measure) of IPFQR measure data in the future. We
sought public comment on including patient-level data collection in the
IPFQR program. Several commenters expressed support for patient-level
data collection, observing that it provides greater confidence in the
data's validity and reliability. Other
[[Page 19514]]
commenters recommended that CMS use a system that has already been
tested and used for IPF data reporting or work with IPFs in selecting a
system so that any selected system would avoid additional burden.
We believe that patient-level data reporting would improve the
accuracy of the submitted and publicly reported data without increasing
burden. As we considered the current IPFQR measure set, we determined
that patient-level reporting of the Hours of Physical Restraint Use
(HBIPS-2, NQF #0640) measure and Hours of Seclusion Use (HBIPS-2, NQF
#0641) measure would be appropriate for the numerators of these
measures only, because these measures are calculated with a denominator
of 1,000 hours rather than a denominator of patients who meet specific
criteria for inclusion in the measure. Therefore, we are proposing to
require reporting patient-level information for the numerators of these
measures only. For the remainder of the chart-abstracted measures in
the IPFQR Program we are proposing to require patient-level reporting
of the both the numerator and the denominator. Table 6 lists the
proposed FY 2023 IPFQR measure set categorized by whether we would
require patient-level data submission through the QualityNet secure
portal.
Table 6--Patient-Level Data Submission Requirements for FY 2024 IPFQR
Program Measure Set
------------------------------------------------------------------------
Patient-level
NQF # Measure ID Measure data submission
------------------------------------------------------------------------
0640......... HBIPS-2......... Hours of Physical Yes, numerator
Restraint Use. only.
0641......... HBIPS-3......... Hours of Seclusion Yes, numerator
Use. only.
0560......... HBIPS-5......... Patients Discharged Yes.
on Multiple
Antipsychotic
Medications with
Appropriate
Justification.
0576......... FUH............. Follow-Up After No (claims-
Hospitalization for based).
Mental Illness.
N/A *........ SUB-2 and SUB-2a Alcohol Use Brief Yes.
Intervention
Provided or Offered
and SUB-2a Alcohol
Use Brief
Intervention.
N/A *........ SUB-3 and SUB-3a Alcohol and Other Yes.
Drug Use Disorder
Treatment Provided
or Offered at
Discharge and SUB-3a
Alcohol and Other
Drug Use Disorder
Treatment at
Discharge.
N/A *........ TOB-2 and TOB-2a Tobacco Use Treatment Yes.
Provided or Offered
and TOB-2a Tobacco
Use Treatment.
N/A *........ TOB-3 and TOB-3a Tobacco Use Treatment Yes.
Provided or Offered
at Discharge and TOB-
3a Tobacco Use
Treatment at
Discharge.
1659......... IMM-2........... Influenza Yes.
Immunization.
N/A *........ N/A............. Transition Record Yes.
with Specified
Elements Received by
Discharged Patients
(Discharges from an
Inpatient Facility
to Home/Self Care or
Any Other Site of
Care).
N/A *........ N/A............. Timely Transmission Yes.
of Transition Record
(Discharges from an
Inpatient Facility
to Home/Self Care or
any Other Site of
Care).
N/A.......... N/A............. Screening for Yes.
Metabolic Disorders.
2860......... N/A............. Thirty-Day All-Cause No (claims-
Unplanned based).
Readmission
Following
Psychiatric
Hospitalization in
an Inpatient
Psychiatric Facility.
3205......... Med Cont........ Medication No (claims-
Continuation based).
Following Inpatient
Psychiatric
Discharge.
TBD.......... COVID HCP....... COVID-19 Healthcare No (calculated
Personnel (HCP) for HCP).
Vaccination Measure.
------------------------------------------------------------------------
* Measure is no longer endorsed by the NQF but was endorsed at time of
adoption. Section 1886(s)(4)(D)(ii) of the Act authorizes the
Secretary to specify a measure that is not endorsed by the NQF as long
as due consideration is given to measures that have been endorsed or
adopted by a consensus organization identified by the Secretary. We
attempted to find available measures for each of these clinical topics
that have been endorsed or adopted by a consensus organization and
found no other feasible and practical measures on the topics for the
IPF setting.
Submission of aggregate data requires facilities to abstract
patient-level data, then calculate measure performance prior to
submitting data through the QualityNet website's secure portal. For
measures for which we would require patient-level data submission, we
would allow facilities to submit data using a tool such as the CMS
Abstraction & Reporting Tool (CART). This is the tool we use in our
other quality reporting and value-based purchasing programs, and
therefore, we believe that many facilities may already have familiarity
with using this tool to abstract and report data. Additionally, the
tool has been specifically designed to facilitate data reporting and
minimize provider burden.
We note that under aggregate data reporting, facilities submit
aggregate numerators and aggregate denominators for all measures to CMS
in the Hospital Quality Reporting (HQR) system. These aggregate
numerators and denominators are generally calculated by manually
abstracting the medical record of each included patient using the
algorithm, a paper tool, or a vendor abstraction tool. After each
required medical record has been abstracted, the numerator and
denominator results are added up and submitted as aggregate values in
the HQR system. Under our patient level data reporting proposal,
facilities would still manually abstract the medical record using
either a vendor abstraction tool or an abstraction tool provided by
CMS. The vendor abstraction tool or the CMS tool would then produce an
individual XML file for each of the cases abstracted. Instead of
submitting the aggregate data, the facility will log into HQR and
upload batches of XML files that contain patient level data for each
measure with data from all patients whose records were abstracted, and
CMS would calculate the aggregate numerators, aggregate denominators,
and measure rates from those XML file submissions. Because facilities
must abstract patient-level data as one step in calculating measure
results, we do not believe that requiring patient-level data submission
will increase provider costs or burden associated with measure
submission.
Because we believe that patient-level data will improve the data
accuracy without increasing provider burden, we are now proposing to
adopt patient-level data reporting for numerators only for the Hours of
Physical Restraint Use
[[Page 19515]]
(HBIPS-2, NQF #0640) and the Hours of Seclusion Use (HBIPS-3, NQF
#0631) for numerators and denominators for the following 9 chart-
abstracted IPFQR Program measures as detailed in Table 6: Patients
Discharged on Multiple Antipsychotic Medications with Appropriate
Justification (NQF #0560); Alcohol Use Brief Intervention Provided or
Offered and SUB-2a Alcohol Use Brief Intervention; Alcohol and Other
Drug Use Disorder Treatment Provided or Offered at Discharge and SUB-3a
Alcohol and Other Drug Use Disorder Treatment at Discharge, Tobacco Use
Treatment Provided or Offered and TOB-2a Tobacco Use Treatment, Tobacco
Use Treatment Provided or Offered at Discharge and TOB-3a Tobacco Use
Treatment at Discharge, Influenza Immunization (NQF #1659), Transition
Record with Specified Elements Received by Discharged Patients
(discharges from an Inpatient Facility to Home/Self Care or Any Other
Site of Care), Timely Transmission of Transition Record (Discharges
from an Inpatient Facility to Home/Self Care or any Other Site of
Care), and Screening for Metabolic Disorders.
We believe that it is appropriate to transition to patient-level
reporting incrementally. This would allow facilities to become familiar
with the data submission systems and to provide feedback on any
challenges they face in reporting data to us. Therefore, we are
proposing to allow voluntary patient-level data submission for the FY
2023 payment determination (that is, data submitted during CY 2022). We
note that because participation in patient-level reporting for these
chart-abstracted measures would be voluntary for this one-year period,
facilities would be able to choose whether to submit measure data in
aggregate or at the patient level, and would not face a payment
reduction as long as they submit all measure data either at the patient
level or in aggregate for each measure for which reporting is required,
and as long as they met all other IPFQR Program requirements.
Therefore, we are proposing to allow voluntary patient-level reporting
prior to requiring such data submission for one year prior to the FY
2024 payment determination. If we transition to patient-level
reporting, we will ensure that facilities have guidance available
through our standard communications channels (that is, listserv
announcements, educational webinars, and training material on the
QualityNet website).
We are also proposing to require patient-level data submission for
these chart-abstracted measures for the FY 2024 payment determination
(that is, data submitted during CY 2023) and subsequent years.
We welcome comment on our proposals to allow voluntary patient-
level data reporting for these chart-abstracted measures for the FY
2023 payment determination and then to require patient-level data
reporting for the FY 2024 payment determination and subsequent years.
3. Considerations for Data Validation Pilot
As discussed in section IV.J.4 and in the FY 2019 IPF PPS final
rule, we are concerned about the limitations of aggregate data
submission (83 FR 28607). One such concern was that the ability to
detect error is lower for aggregate measure data reporting than for
patient-level data reporting (that is, data regarding each patient
included in a measure and whether the patient was included in the
numerator and denominator of the measure). We note that if we finalize
our proposal to adopt patient-level data requirements, we would be able
to adopt a data validation policy for the IPFQR Program in the future.
We believe that it would be appropriate to develop such a policy
incrementally through adoption of a data validation pilot prior to
national implementation of data validation within the IPFQR Program. We
seek public input on elements of a potential data validation pilot, for
example, the number of measures to validate, number of participating
facilities, whether the pilot should be mandatory or voluntary,
potential thresholds for determining measure accuracy, or any other
policies that commenters believe would be appropriate to include in a
data validation pilot or eventual data validation policy.
4. Reporting Requirements for the FY 2022 Payment Determination and
Subsequent Years
We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR
53656 through 53657), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50900
through 50901), and the FY 2015 IPF PPS final rule (79 FR 45976 through
45977) for our previously finalized reporting requirements. In this
proposed rule, we are not proposing any changes to these policies.
5. Quality Measure Sampling Requirements
We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR
53657 through 53658), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50901
through 50902), the FY 2016 IPF PPS final rule (80 FR 46717 through
46719), and the FY 2019 IPF PPS final rule (83 FR 38607 through 38608)
for discussions of our previously finalized sampling policies. We note
that neither the measure we are proposing to remove (FUH-NQF #0576) nor
the measure we are proposing to adopt (FAPH) if we remove the FUH-NQF
#0576 are affected by our sampling policies because these are both
calculated by CMS using Medicare Fee-for-Service claims and, therefore,
apply to all Medicare patients in the denominator. Furthermore, the
denominator of the COVID-19 Healthcare Personnel Vaccination measure we
are proposing to adopt in this proposed rule is all healthcare
personnel, and therefore, this measure is not eligible for sampling. In
this proposed rule, we are not proposing any changes to our previously
finalized sampling policies.
6. Non-Measure Data Collection
We refer readers to the FY 2015 IPF PPS final rule (79 FR 45973),
the FY 2016 IPF PPS final rule (80 FR 46717), and the FY 2019 IPF PPS
final rule (83 FR 38608) for our previously finalized non-measure data
collection policies. In this proposed rule, we are not proposing any
changes to these policies.
7. Data Accuracy and Completeness Acknowledgement (DACA) Requirements
We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR
53658) for our previously finalized DACA requirements. In this proposed
rule, we are not proposing any changes to these policies.
K. Reconsideration and Appeals Procedures
We refer readers to 42 CFR 412.434 for the IPFQR Program's
reconsideration and appeals procedures. In this proposed rule, we are
not proposing any changes to these policies.
L. Extraordinary Circumstances Exceptions (ECE) Policy
We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR
53659 through 53660), the FY 2014 IPPS/LTCH PPS final rule (78 FR
50903), the FY 2015 IPF PPS final rule (79 FR 45978), and the FY 2018
IPPS/LTCH PPS final rule (82 FR 38473 through 38474) for our previously
finalized ECE policies. In this proposed rule, we are not proposing any
changes to these policies.
V. Collection of Information Requirements
Under the Paperwork Reduction Act of 1995 (PRA) (44 U.S.C. 3501 et
seq.), we are required to provide 60-day notice
[[Page 19516]]
in the Federal Register and solicit public comment before a
``collection of information'' (as defined under 5 CFR 1320.3(c) of the
PRA's implementing regulations) requirement is submitted to the Office
of Management and Budget (OMB) for review and approval. In order to
fairly evaluate whether an information collection should be approved by
OMB, section 3506(c)(2)(A) of the PRA requires that we solicit comment
on the following issues:
The need for the information collection and its usefulness
in carrying out the proper functions of our agency.
The accuracy of our estimate of the information collection
burden.
The quality, utility, and clarity of the information to be
collected.
Recommendations to minimize the information collection
burden on the affected public, including automated collection
techniques.
We are soliciting public comment on each of the section
3506(c)(2)(A)--required issues for the following information collection
requirements (ICRs).
A. Proposed ICRs for the (IPFQR) Program
The following proposed requirement and burden changes will be
submitted to OMB for approval under control number 0938-1171 (CMS-
10432).
1. Wage Estimates
In the FY 2020 IPF PPS final rule (84 FR 38468), which was the most
recent rule in which we adopted updates to the IPFQR Program, we
estimated that reporting measures for the IPFQR Program could be
accomplished by a Medical Records and Health Information Technician
(BLS Occupation Code: 29-2071) with a median hourly wage of $18.83/hr.
(May 2017). Since then, BLS (the Bureau of Labor Statistics) has
revised their wage data (May 2019) to $20.50/hr.\136\ In response, we
are proposing to adjust our cost estimates using the updated median
wage rate figure of $20.50/hr., an increase of $1.67/hr.
---------------------------------------------------------------------------
\136\ https://www.bls.gov/oes/current/oes292098.htm (Accessed on
March 30, 2021).
---------------------------------------------------------------------------
Under OMB Circular A-76, in calculating direct labor, agencies
should not only include salaries and wages, but also ``other
entitlements'' such as fringe benefits and overhead.\137\ Consistent
with our past approach, we continue to calculate the cost of fringe
benefits and overhead at 100 percent of the median hourly wage (81 FR
57266). This is necessarily a rough adjustment, both because fringe
benefits and overhead costs vary significantly from employer to
employer, and methods of estimating these costs vary widely from study
to study. Therefore, using these assumptions, we estimate an hourly
labor cost increase from $37.66/hr ($18.83/hr base salary + $18.83/hr
fringe benefits and overhead) to $41.00/hr ($20.50/hr base salary +
$20.50/hr fringe benefits and overhead). Table 7 presents these
assumptions.
---------------------------------------------------------------------------
\137\ http://www.whitehouse.gov/omb/circulars_a076_a76_incl_tech_correction.
Table 7--Wage Assumptions for the IPFQR Program
----------------------------------------------------------------------------------------------------------------
Fringe benefits
Occupation title Occupation code Median hourly and overhead ($/ Adjustedhourly
wage ($/hr) hr) wage ($/hr)
----------------------------------------------------------------------------------------------------------------
Medical Records and Health Information 29-2071 20.50 20.50 41.00
Technician.................................
----------------------------------------------------------------------------------------------------------------
2. ICRs Regarding the Inpatient Psychiatric Facility Quality Reporting
(IPFQR) Program
In subsection 2.a., we restate our currently approved burden
estimates. In subsection 2.b., we estimate the proposed adjustments in
burden associated with the updated BLS wage rate, our facility
estimates, and our case estimates. In subsection 2.c., we estimate the
changes in burden associated with the proposals in this rule. Finally,
in subsection 2.d., we provide an overview of the total estimated
burden.
a. Currently Approved Burden
For a detailed discussion of the burden for the IPFQR Program
requirements that we have previously adopted, we refer readers to the
following rules:
The FY 2013 IPPS/LTCH PPS final rule (77 FR 53673);
The FY 2014 IPPS/LTCH PPS final rule (78 FR 50964);
The FY 2015 IPF PPS final rule (79 FR 45978 through
45980);
The FY 2016 IPF PPS final rule (80 FR 46720 through
46721);
The FY 2017 IPPS/LTCH PPS final rule (81 FR 57265 through
57266);
The FY 2018 IPPS/LTCH PPS final rule (82 FR 38507 through
38508);
The FY 2019 IPF PPS final rule (83 FR 38609 through
38612); and
The FY 2020 IPF PPS final rule (84 FR 38468 through
38476).
Tables 8, 9, and 10 provide an overview of our currently approved
burden. These tables use our previous estimate of $37.66 ($18.83 base
salary plus $18.83 fringe benefits and overhead) hourly labor cost. For
more information on our currently approved burden estimates, please see
PRA Supporting Statement A on the Office of Information and Regulatory
Affairs website.\138\
---------------------------------------------------------------------------
\138\ https://www.reginfo.gov/public/do/PRAViewDocument?ref_nbr=201908-0938-011.
Table 8--Currently Approved Measure Collection and Reporting Burden
--------------------------------------------------------------------------------------------------------------------------------------------------------
Annual time
Estimated Time per per Total annual Total annual
NQF # Measure ID Measure description cases (per case facility Number IPFs time (hours) cost ($)
facility) (hours) (hours)
--------------------------------------------------------------------------------------------------------------------------------------------------------
0640................ HBIPS-2............... Hours of Physical 1,283 0.25 320.75 1,679 538,539.25 20,281,388
Restraint Use.
0641................ HBIPS-3............... Hours of Seclusion Use 1,283 0.25 320.75 1,679 538,539.25 20,281,388
0560................ HBIPS-5............... Patients Discharged on 609 0.25 152.25 1,679 255,627.75 9,626,941
Multiple
Antipsychotic
Medications with
Appropriate
Justification.
N/A................. SUB-2 and SUB-2a...... Alcohol Use Brief 609 0.25 152.25 1,679 255,627.75 9,626,941
Intervention Provided
or Offered.
[[Page 19517]]
N/A................. SUB-3 and SUB-3a...... Alcohol and Other Drug 609 0.25 152.25 1,679 255,627.75 9,626,941
Use Disorder
Treatment Provided or
Offered at Discharge
and Alcohol and Other
Drug Use Disorder
Treatment at
Discharge.
0576................ FUH................... Follow-Up After 0 0 0 0 0 0
Hospitalization for
Mental Illness *.
N/A................. TOB-2 and TOB-2a...... Tobacco Use Treatment 609 0.25 152.25 1,679 255,627.75 9,626,941
Provided or Offered
and Tobacco Use
Treatment.
N/A................. TOB-3 and TOB-3a...... Tobacco Use Treatment 609 0.25 152.25 1,679 255,627.75 9,626,941
Provided or Offered
at Discharge and
Tobacco Use Treatment
at Discharge.
1659................ IMM-2................. Influenza Immunization 609 0.25 152.25 1,679 255,627.75 9,626,941
0647................ N/A................... Transition Record with 609 0.25 152.25 1,679 255,627.75 9,626,941
Specified Elements
Received by
Discharged Patients
(Discharges from an
Inpatient Facility to
Home/Self Care or Any
Other Site of Care).
0648................ N/A................... Timely Transmission of 609 0.25 152.25 1,679 255,627.75 9,626,941
Transition Record
(Discharges from an
Inpatient Facility to
Home/Self Care or Any
Other Site of Care).
N/A................. N/A................... Screening for 609 0.25 152.25 1,679 255,627.75 9,626,941
Metabolic Disorders.
2860................ N/A................... Thirty-day all-cause 0 0 0 0 0 0
unplanned readmission
following psychiatric
hospitalization in an
IPF *.
3205................ Med Cont.............. Medication 0 0 0 0 0 0
Continuation
Following Inpatient
Psychiatric Discharge
*.
-----------------------------------------------------------------------------------
Total........... ...................... ...................... 8,047 Varies 2,011.75 1,679 3,377,728 127,205,245
--------------------------------------------------------------------------------------------------------------------------------------------------------
* CMS will collect these data using Medicare Part A and Part B claims; therefore, these measures will not require facilities to submit data on any
cases.
Table 9--Currently Approved Non-Measure Data Collection and Reporting Burden
--------------------------------------------------------------------------------------------------------------------------------------------------------
Annual time per Total annual
Tasks Number IPFs facility Total annual Wage rate ($/ Cost per IPF cost for all
(hours) time (hours) hr) ($) IPFs ($)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Non-measure Data Collection and Submission........ 1,679 2.0 3,358 37.66 75.32 126,462
--------------------------------------------------------------------------------------------------------------------------------------------------------
Table 10--Currently Approved Total Burden
----------------------------------------------------------------------------------------------------------------
Requirement Respondents Responses Time (hours) Cost ($)
----------------------------------------------------------------------------------------------------------------
Measure Data Collection and Reporting. 1,679 13,510,913 (8,047 3,377,728 127,205,245
responses or cases per
facility * 1,679
facilities).
Non-Measure Data Collection and 1,679 6,716 (4 * responses per 3,358 126,462
Reporting. facility * 1,679
facilities) 4.
Notice of Participation, Data Accuracy N/A N/A..................... N/A N/A
Acknowledgment, and Vendor
Authorization Form *.
-------------------------------------------------------------------------
Total............................. 1,679 13,517,629.............. 3,381,086 127,331,707
----------------------------------------------------------------------------------------------------------------
* The 15 minutes per measure for chart abstraction under Measure Data Collection and Reporting also includes the
time for completing and submitting any forms.
b. Proposed Adjustments in Burden Due to Updated Wage, Facility Count,
and Case Count Estimates
In the FY 2020 IPF PPS final rule (84 FR 38468), which is the most
recent rule, that updated the IPFQR Program policies, we estimated that
there were 1,679 participating IPFs and that (for measures that require
reporting on the entire patient population) these facilities will
report on an average of 1,283 cases per facility. In this FY 2022 rule,
we are proposing to update our facility count and case estimates by
using the most recent data available. Specifically, we estimate that
there are now approximately 1,634 facilities (a decrease of 45
facilities) and an average of 1,346 cases per facility (an increase of
63 cases per facility). Tables 11, 12,
[[Page 19518]]
and 13, depict the effects of these updates, as well as the wage rate
update to $41.00/hr described in section V.A.1 of the preamble of this
proposed rule, on our previously estimated burden.
Table 11--Measure Collection and Reporting Burden Based on Updated Cases per Facility, Facility Counts, and Wage Rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
Annual time
Estimated Time per per Total annual Total annual
NQF # Measure ID Measure description cases (per case facility Number IPFs time (hours) cost ($)
facility) (hours) (hours)
--------------------------------------------------------------------------------------------------------------------------------------------------------
0640................ HBIPS-2.............. Hours of Physical 1,346 0.25 336.50 1,634 549,841 22,543,481
Restraint Use.
0641................ HBIPS-3.............. Hours of Seclusion 1,346 0.25 336.50 1,634 549,841 22,543,481
Use.
0560................ HBIPS-5.............. Patients Discharged * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
on Multiple
Antipsychotic
Medications with
Appropriate
Justification.
N/A................. SUB-2 and SUB-2a..... Alcohol Use Brief * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
Intervention
Provided or Offered
and Alcohol Use
Brief Intervention
Provided.
N/A................. SUB-3 and SUB-3a..... Alcohol and Other * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
Drug Use Disorder
Treatment Provided
or Offered at
Discharge and
Alcohol and Other
Drug Use Disorder
Treatment at
Discharge.
0576................ FUH.................. Follow-Up After 0 0 0 0 0 0
Hospitalization for
Mental Illness *.
N/A................. TOB-2 and TOB-2a..... Tobacco Use Treatment * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
Provided or Offered
and Tobacco Use
Treatment.
N/A................. TOB-3 and TOB-3a..... Tobacco Use Treatment * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
Provided or Offered
at Discharge and
Tobacco Use
Treatment at
Discharge.
1659................ IMM-2................ Influenza * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
Immunization.
0647................ N/A.................. Transition Record * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
with Specified
Elements Received by
Discharged Patients
(Discharges from an
Inpatient Facility
to Home/Self Care or
Any Other Site of
Care).
0648................ N/A.................. Timely Transmission * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
of Transition Record
(Discharges from an
Inpatient Facility
to Home/Self Care or
Any Other Site of
Care).
N/A................. N/A.................. Screening for * 609 0.25 152.25 1,634 248,776.5 10,199,836.50
Metabolic Disorders.
2860................ N/A.................. Thirty-day all-cause 0 0 0 0 0 0
unplanned
readmission
following
psychiatric
hospitalization in
an IPF*.
3205................ Med Cont............. Medication 0 0 0 0 0 0
Continuation
Following Inpatient
Psychiatric
Discharge*.
N/A................. COVID-19 HCP......... COVID-19 Vaccination ** 0 0 0 0 0 0
Rate Among
Healthcare Personnel.
N/A................. FAPH................. Follow-Up After 0 0 0 0 0 0
Psychiatric
Hospitalization.
-------------------------------------------------------------------------------------
Total........... ..................... ..................... 8,173 Varies 2,043.25 1,634 3,338,671 136,885,491
--------------------------------------------------------------------------------------------------------------------------------------------------------
* Under our previously finalized ``global sample'' (80 FR 46717 through 46718) we allow facilities to apply the same sampling methodology to all
measures eligible for sampling. In the FY 2016 IPF PPS final rule (80 FR 46718), we finalized that facilities with between 609 and 3,056 cases that
choose to participate in the global sample would be required to report data for 609 cases. Because facilities are only required to submit data on a
number specified by the global sampling methodology, rather than abstracting data for all patients or applying measure specific sampling
methodologies, we believe that the number of cases under the global sample is a good approximation of facility burden associated with these measures.
Therefore, for the average IPF discharge rate of 1,346 discharges versus the previously estimated 1,283, the global sample continues to require
abstraction of 609 records.
** The COVID-19 HCP measure will be calculated using data submitted to the CDC under a separate OMB Control Number (0920-1317).
Table 12--Non-Measure Data Collection and Reporting Burden Based on Updated Cases per Facility, Facility Counts, and Wage Rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
Annual time per Total annual
Tasks Number IPFs facility Total annual Wage rate ($/ Cost per IPF cost for all
(hours) time (hours) hr) ($) IPFs ($)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Non-measure Data Collection and Submission........ 1,634 2.0 3,268 41.00 82.00 133,988
--------------------------------------------------------------------------------------------------------------------------------------------------------
[[Page 19519]]
Table 13--Total Burden Based on Updated Cases per Facility, Facility Counts, and Wage Rate
----------------------------------------------------------------------------------------------------------------
Requirement Respondents Responses Time (hours) Cost ($)
----------------------------------------------------------------------------------------------------------------
Measure Data Collection and Reporting. 1,634 13,354,682 (8,173 3,338,671 136,885,491
responses per facility
* 1,634 facilities).
Non-Measure Data Collection and 1,634 6,536 (4 responses per 3,268 133,988
Reporting. facility * 1,634
facilities).
-------------------------------------------------------------------------
Total............................. 1,634 13,361,218.............. 3,341,939 137,019,479
----------------------------------------------------------------------------------------------------------------
c. Changes in Burden Due to This Proposed Rule
(1). Updates Due to Proposed Measure Adoptions
In section IV.E of this preamble, we are proposing to adopt the
following two measures:
COVID-19 HCP Vaccination for FY 2023 Payment Determination
and Subsequent Years; and
Follow-Up After Psychiatric Hospitalization (FAPH) for FY
2024 Payment Determination and Subsequent Years.
We are proposing to adopt the COVID-19 HCP Vaccination measure
beginning with an initial reporting period from October 1 to December
31, 2021 affecting the FY 2023 payment determination followed by annual
reporting beginning with the FY 2024 payment determination and
subsequent years. IPFs would submit data through the CDC NHSN. The NHSN
is a secure, internet-based system maintained by the CDC and provided
free. Currently the CDC does not estimate burden for COVID-19
vaccination reporting under the CDC PRA package currently approved
under OMB control number 0920-1317 because the agency has been granted
a waiver under Section 321 of the National Childhood Vaccine Injury Act
(NCVIA).\139\
---------------------------------------------------------------------------
\139\ Section 321 of the National Childhood Vaccine Injury Act
(NCVIA) provides the PRA waiver for activities that come under the
NCVIA, including those in the NCVIA at section 2102 of the Public
Health Service Act (42 U.S.C. 300aa-2). Section 321 is not codified
in the U.S. Code, but can be found in a note at 42 U.S.C. 300aa-1.
---------------------------------------------------------------------------
Although the burden as associated with the COVID-19 HCP Vaccination
measure is not accounted for under the CDC PRA package currently
approved under OMB control number 0920-1317 due to the NCVIA waiver,
the cost and burden information is discussed here and will be included
in a revised information collection request for 0920-1317. Consistent
with the CDC's experience of collecting data using the NHSN, we
estimate that it would take each IPF on average approximately 1 hour
per month to collect data for the COVID-19 Vaccination Coverage among
HCP measure and enter it into NHSN. We have estimated the time to
complete this entire activity, since it could vary based on provider
systems and staff availability. This burden is comprised of
administrative hours and wages. We believe it would take an
Administrative Assistant \140\ between 45 minutes and 1 hour and 15
minutes to enter this data into NHSN. For the CY 2021 reporting period
(consisting of October 1, 2021 through December 31, 2021) 3 months are
required. For the CY 2021 reporting period/FY 2023 payment
determination, IPFs would incur an additional burden between 2.25 hours
(0.75 hours * 3 months) and 3.75 hours (1.25 hours * 3 months) per IPF.
For all 1,634 IPFs, the total burden would range from 3,676.5 (2.25
hours * 1,634 IPFs) and 6,127.5 hours (3.75 hours * 1,634 IPFs). Each
IPF would incur an estimated cost of between $27.47 (0.75 hour *
$36.62/hr) and $45.78 (1.25 hours * 36.63/hr) monthly and between
$82.40 (2.25 hours * $36.62/hr) and $137.33 (3.75 hours * $36.62/hr) in
total over the CY 2021 reporting period to complete this task.
Thereafter, 12 months of data are required annually. Therefore, IPFs
would incur an additional annual burden between 9 hours (0.75 hours/
month * 12 months) and 15 hours (1.25 hours/month * 12 months) per IPF
and between 14,706 hours (9 hours/IPF * 1,634 IPFs) and 24,510 hours
(15 hours/IPF * 1,634 IPFs) for all IPFs. Each IPF would incur an
estimated cost of between $329.58 (9 hours x $36.62/hr) and $549.30
annually (15 hours x $36.62/hr). The estimated cost across all 1,634
IPFs would be between $134,641.6 ($82.40/IPF * 1,634 IPFs) and
$224,397.22 ($137.33/IPF * 1,634 IPFs) for the CY 2021 reporting
period. The estimated cost across all 1,634 IPFs would be between
$538,533.72 ($329.58/IPF * 1,634 IPFs) and $897,556.2 ($549.30/IPF *
1,634 IPFs) annually thereafter.
---------------------------------------------------------------------------
\140\ https://www.bls.gov/oes/current/oes436013.htm (accessed on
March 30, 2021). The hourly rate of $36.62 includes an adjustment of
100 percent of the median hourly wage to account for the cost of
overhead, including fringe benefits.
---------------------------------------------------------------------------
We recognize that many healthcare facilities are also reporting
other COVID-19 data to HHS. We believe the benefits of requiring IPFs
to report data on the COVID-19 HCP Vaccination measure to assess
whether they are taking steps to limit the spread of COVID-19 among
their healthcare workers and to help sustain the ability of IPFs to
continue serving their communities throughout the PHE and beyond
outweigh the costs of reporting. We welcome comments on the estimated
time to collect data and enter it into the NHSN.
We further note that as described in section IV.E.C of this
preamble, we will calculate performance on the FAPH measure using
Medicare Part A and Part B claims that facilities and other providers
submit for payment. Since this is a claims-based measure, there is no
additional burden outside of submitting the claim. The claim submission
is approved by OMB under control number 0938-0050 (CMS-2552-10). This
rule does not propose any changes under that control number.
(2). Updates Due to Proposed Measure Removals
In section IV.F. of this preamble, we are proposing to remove the
following four measures for the FY 2024 payment determination and
subsequent years:
SUB-2--Alcohol Use Brief Intervention Provided or Offered
and the subset measure SUB-2a Alcohol Use Brief Intervention Provided;
TOB-2--Tobacco Use Brief Intervention Provided or Offered
and the subset measure TOB-2a Tobacco Use Brief Intervention;
Timely Transmission of Transition Record (Discharges from
an Inpatient Facility to Home/Self Care or Any Other Site of Care); and
FUH--Follow-Up After Hospitalization for Mental Illness
(NQF #0576).
For the FY 2024 payment determination, data on CY 2022 performance
would be reported during the summer of 2023. Therefore, we are applying
the burden reduction that would occur to the FY 2023 burden
calculation. Three of these measures
[[Page 19520]]
(SUB-2/2a, TOB-2/2a, and the Timely Transmission measure) fall under
our previously finalized ``global sample'' (80 FR 46717 through 46718)
and, therefore, would require abstraction of 609 records. We estimate
that removing each of these three measures would result in a decrease
in burden of 152.25 hours per facility, or 248,776.5 hours (152.25
hours x 1,634 facilities) across all IPFs. Therefore, the decrease in
costs for each measure is approximately $6,242.25 per IPF ($41.00hr *
152.25 hours), or $10,199,836.50 across all IPFs ($6,242.25/facility *
1,634 facilities). For all three of these chart-abstracted measures the
total decrease in burden is approximately 456.75 hours per IPF (3
measures * 152.25 hours per measure) or 746,329.5 hours across all IPFs
(3 measures * 248,776.5 hours per measure). This equates to $18,726.75
per IPF (3 measures * $6,242.25 per measure), or $30,599,509.50 across
all IPFs (3 measures * $10,199,836.50 per measure).
We have previously estimated that the FUH (NQF #0576) measure does
not have any reporting burden because it is calculated from Medicare
FFS claims. Therefore, we do not anticipate a reduction in facility
burden associated with the removal of this measure. Table 14 describes
our estimated reduction in burden associated with removing these four
measures.
Table 14--Burden Updates Due to Proposed Measure Removals
--------------------------------------------------------------------------------------------------------------------------------------------------------
Annual time
Estimated Time per per Total annual Total annual
NQF # Measure ID Measure description cases (per case facility Number IPFs time (hours) cost ($)
facility) (hours) (hours)
--------------------------------------------------------------------------------------------------------------------------------------------------------
N/A................. SUB-2 and SUB-2a..... Alcohol Use Brief (609) 0.25 152.25 1,634 (248,776.5) (10,199,836.5)
Intervention
Provided or Offered.
0576................ FUH.................. Follow-Up After 0 0 0 1,634 0 0
Hospitalization for
Mental Illness *.
N/A................. TOB-2 and TOB-2a..... Tobacco Use Treatment (609) 0.25 152.25 1,634 (248,776.5) (10,199,836.5)
Provided or Offered
and Tobacco Use
Treatment.
0648................ N/A.................. Timely Transmission (609) 0.25 152.25 1,634 (248,776.5) (10,199,836.5)
of Transition Record
(Discharges from an
Inpatient Facility
to Home/Self Care or
Any Other Site of
Care).
-------------------------------------------------------------------------------------
Total........... ..................... ..................... (1,827) Varies (456.75) 1,634 (746,329.5) (30,599,509.50)
--------------------------------------------------------------------------------------------------------------------------------------------------------
* CMS will collect these data using Medicare Part A and Part B claims; therefore, these measures will not require facilities to submit data on any
cases.
(3). Updates Due to Proposed Administrative Policies
(a). Updates Associated With Proposed Updated Reference to QualityNet
System Administrator
In section IV.J.1.a of this preamble, we proposed to use the term
``QualityNet security official'' instead of ``QualityNet system
administrator.'' Because this proposed update would not change the
individual's responsibilities, we do not believe there would be any
changes to the information collection burden as a result of this
update. We also do not believe that removing the requirement for
facilities to have an active QualityNet security official account to
qualify for payment updates will affect burden because we continue to
recommend that facilities maintain an active QualityNet security
official account.
(b). Updates Associated With Proposed Adoption of Patient-Level
Reporting for Certain Chart Abstracted Measures
In section IV.J.2.c of this preamble, we propose to adopt patient-
level data submission for the eleven chart-abstracted measures
currently in the IPFQR Program measure set (for more details on these
measures we refer readers to Table 6). Because submission of aggregate
data requires facilities to abstract patient-level data, then calculate
measure performance prior to submitting data through the QualityNet
website's secure portal, facilities must already abstract patient-level
data. Therefore, we do not believe that submitting data that facilities
must already calculate through a tool that facilities already have
experience using will change provider burden.
d. Overall Burden Summary
Table 15 summarizes the estimated burden associated with the IPFQR
Program if the proposals in this rule are finalized.
Table 15--Total Estimated IPFQR Program Burden
----------------------------------------------------------------------------------------------------------------
Estimated Annual time
responses Time per per Total annual Total annual
Measure/response description per response facility time (hours) cost ($)
facility (hours) (hours)
----------------------------------------------------------------------------------------------------------------
Hours of Physical Restraint Use........ 1,346 0.25 336.50 549,841 $22,543,481
Hours of Seclusion Use................. 1,346 0.25 336.50 549,841 22,543,481
Patients Discharged on Multiple * 609 0.25 152.25 248,776.5 10,199,836.50
Antipsychotic Medications with
Appropriate Justification.............
Alcohol and Other Drug Use Disorder * 609 0.25 152.25 248,776.5 10,199,836.50
Treatment Provided or Offered at
Discharge and Alcohol and Other Drug
Use Disorder Treatment at Discharge...
Tobacco Use Treatment Provided or * 609 0.25 152.25 248,776.5 10,199,836.50
Offered at Discharge and Tobacco Use
Treatment at Discharge................
Influenza Immunization................. * 609 0.25 152.25 248,776.5 10,199,836.50
Transition Record with Specified * 609 0.25 152.25 248,776.5 10,199,836.50
Elements Received by Discharged
Patients (Discharges from an Inpatient
Facility to Home/Self Care or Any
Other Site of Care)...................
Screening for Metabolic Disorders...... * 609 0.25 152.25 248,776.5 10,199,836.50
Thirty-day all-cause unplanned ** 0 0 0 0 0
readmission following psychiatric
hospitalization in an IPF.............
Medication Continuation Following ** 0 0 0 0 0
Inpatient Psychiatric Discharge.......
COVID-19 Vaccination Rate Among *** 0 0 0 0 0
Healthcare Personnel..................
Follow-Up After Psychiatric ** 0 0 0 0 0
Hospitalization.......................
[[Page 19521]]
Non-Measure Data Collection and 4 0.5 2.0 3,268 133,988
Reporting.............................
------------------------------------------------------------------------
Total.............................. 6,346 N/A 1,588.5 2,595,609 106,419,969
----------------------------------------------------------------------------------------------------------------
* Under our previously finalized ``global sample'' (80 FR 46717 through 46718) we allow facilities to apply the
same sampling methodology to all measures eligible for sampling. In the FY 2016 IPF PPS final rule (80 FR
46718), we finalized that facilities with between 609 and 3,056 cases that choose to participate in the global
sample would be required to report data for 609 cases. Because facilities are only required to submit data on
a number specified by the global sampling methodology, rather than abstracting data for all patients or
applying measure specific sampling methodologies, we believe that the number of cases under the global sample
is a good approximation of facility burden associated with these measures. Therefore, for the average IPF
discharge rate of 1,346 discharges versus the previously estimated 1,283, the global sample continues to
require abstraction of 609 records.
** CMS will collect these data using Medicare Part A and Part B claims; therefore, these measures will not
require facilities to submit data on any cases.
*** The COVID-19 HCP measure will be calculated using data submitted to the CDC under a separate OMB Control
Number (0920-1317).
The total change in burden associated with this proposed rule
(including all updates to wage rate, case counts, facility numbers, and
the measures and administrative policies) is a reduction of 785,477
hours and $20,911,738 from our currently approved burden of 3,381,086
hours and $127,331,707. We refer readers to Table 16 for details.
Table 16--Summary of Proposed Requirements and Annual Burden Estimates Under OMB Control Number 0938-1171 (CMS-10432)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Time per
Program changes Number Total response Total time Labor cost per Total cost ($)
respondents responses (hr) (hr) hour ($/hr)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Active Burden.............................................. 1,679 13,517,629 Varies 3,381,086 37.66 127,331,707
Total Burden Under CMS-1750-P.............................. 1,634 10,375,900 Varies 2,595,609 41.00 106,419,969
PROPOSED CHANGES........................................... (45) (3,141,729) Varies (785,477) +3.34 (20,911,738)
--------------------------------------------------------------------------------------------------------------------------------------------------------
B. Submission of PRA-Related Comments
We have submitted a copy of this proposed rule to OMB for its
review of the rule's information collection and recordkeeping
requirements. The requirements are not effective until they have been
approved by OMB.
To obtain copies of the supporting statement and any related forms
for the proposed collections previously discussed, visit CMS's website
at: https://www.cms.gov/Regulations-and-Guidance/Legislation/PaperworkReductionActof1995/PRA-Listing.html or call the Reports
Clearance Office at (410) 786-1326.
We invite public comments on these information collection
requirements. If you wish to comment, identify the rule (CMS-1750-P)
and, where applicable, the preamble section, and the ICR section. See
this rule's DATES and ADDRESSES sections for the comment due date and
for additional instructions.
VI. Regulatory Impact Analysis
A. Statement of Need
This rule proposes updates to the prospective payment rates for
Medicare inpatient hospital services provided by IPFs for discharges
occurring during FY 2022 (October 1, 2021 through September 30, 2022).
We are proposing to apply the 2016-based IPF market basket increase of
2.3 percent, less the productivity adjustment of 0.2 percentage point
as required by 1886(s)(2)(A)(i) of the Act for a proposed total FY 2022
payment rate update of 2.1 percent. In this proposed rule, we are
proposing to update the IPF labor-related share and update the IPF wage
index to reflect the FY 2022 hospital inpatient wage index.
B. Overall Impact
We have examined the impacts of this proposed rule as required by
Executive Order 12866 on Regulatory Planning and Review (September 30,
1993), Executive Order 13563 on Improving Regulation and Regulatory
Review (January 18, 2011), the Regulatory Flexibility Act (RFA)
(September 19, 1980, Pub. L. 96-354), section 1102(b) of the Social
Security Act (the Act), section 202 of the Unfunded Mandates Reform Act
of 1995 (March 22, 1995; Pub. L. 104-4), and Executive Order 13132 on
Federalism (August 4, 1999).
Executive Orders 12866 and 13563 direct agencies to assess all
costs and benefits of available regulatory alternatives and, if
regulation is necessary, to select regulatory approaches that maximize
net benefits (including potential economic, environmental, public
health and safety effects, distributive impacts, and equity). Section
3(f) of Executive Order 12866 defines a ``significant regulatory
action'' as an action that is likely to result in a rule: (1) Having an
annual effect on the economy of $100 million or more in any 1 year, or
adversely and materially affecting a sector of the economy,
productivity, competition, jobs, the environment, public health or
safety, or state, local or tribal governments or communities (also
referred to as ``economically significant''); (2) creating a serious
inconsistency or otherwise interfering with an action taken or planned
by another agency; (3) materially altering the budgetary impacts of
entitlement grants, user fees, or loan programs or the rights and
obligations of recipients thereof; or (4) raising novel legal or policy
issues arising out of legal mandates, the President's priorities, or
the principles set forth in the Executive Order. In accordance with the
provisions of Executive Order 12866, this regulation was reviewed by
the Office of Management and Budget.
We estimate that this rulemaking is likely to be economically
significant as measured by the $100 million threshold, and hence, if
finalized as proposed, a major rule under the Congressional Review Act.
Accordingly, we have prepared a Regulatory Impact Analysis that to the
best of our ability presents the costs and benefits of the rulemaking.
We estimate that the total impact of these changes for FY 2022
payments compared to FY 2021 payments will be a net increase of
approximately $90 million. This reflects an $80 million increase from
the update to the payment rates (+$90 million from the 4th quarter 2020
IGI forecast of the 2016-based IPF market basket of 2.3 percent, and -
$10 million for the productivity adjustment
[[Page 19522]]
of 0.2 percentage point), as well as a $10 million increase as a result
of the update to the outlier threshold amount. Outlier payments are
estimated to change from 1.8 percent in FY 2021 to 2.0 percent of total
estimated IPF payments in FY 2022.
C. Detailed Economic Analysis
In this section, we discuss the historical background of the IPF
PPS and the impact of this proposed rule on the Federal Medicare budget
and on IPFs.
1. Budgetary Impact
As discussed in the November 2004 and RY 2007 IPF PPS final rules,
we applied a budget neutrality factor to the Federal per diem base rate
and ECT payment per treatment to ensure that total estimated payments
under the IPF PPS in the implementation period would equal the amount
that would have been paid if the IPF PPS had not been implemented. The
budget neutrality factor includes the following components: Outlier
adjustment, stop-loss adjustment, and the behavioral offset. As
discussed in the RY 2009 IPF PPS notice (73 FR 25711), the stop-loss
adjustment is no longer applicable under the IPF PPS.
As discussed in section III.D.1 of this proposed rule, we are
updating the wage index and labor-related share in a budget neutral
manner by applying a wage index budget neutrality factor to the Federal
per diem base rate and ECT payment per treatment. Therefore, the
budgetary impact to the Medicare program of this proposed rule will be
due to the market basket update for FY 2022 of 2.3 percent (see section
III.A.4 of this proposed rule) less the productivity adjustment of 0.2
percentage point required by section 1886(s)(2)(A)(i) of the Act and
the update to the outlier fixed dollar loss threshold amount.
We estimate that the FY 2022 impact will be a net increase of $90
million in payments to IPF providers. This reflects an estimated $80
million increase from the update to the payment rates and a $10 million
increase due to the update to the outlier threshold amount to set total
estimated outlier payments at 2.0 percent of total estimated payments
in FY 2022. This estimate does not include the implementation of the
required 2.0 percentage point reduction of the market basket update
factor for any IPF that fails to meet the IPF quality reporting
requirements (as discussed in section V.A. of this proposed rule).
2. Impact on Providers
To show the impact on providers of the changes to the IPF PPS
discussed in this proposed rule, we compare estimated payments under
the IPF PPS rates and factors for FY 2022 versus those under FY 2021.
We determined the percent change in the estimated FY 2022 IPF PPS
payments compared to the estimated FY 2021 IPF PPS payments for each
category of IPFs. In addition, for each category of IPFs, we have
included the estimated percent change in payments resulting from the
update to the outlier fixed dollar loss threshold amount; the updated
wage index data including the updated labor-related share; and the
market basket update for FY 2022, as adjusted by the productivity
adjustment according to section 1886(s)(2)(A)(i) of the Act.
Our longstanding methodology uses the best available data as the
basis for our estimates of payments. Typically, this is the most recent
update of the latest available fiscal year of IPF PPS claims, and for
this proposed rulemaking, that would be the FY 2020 claims. However, as
discussed in section III.F.2 of this proposed rule, the U.S. healthcare
system undertook an unprecedented response to the COVID-19 PHE during
FY 2020. Therefore, 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.
To illustrate the impacts of the FY 2022 changes in this proposed
rule, our analysis presents a side-by-side comparison of payments
estimated using FY 2019 claims versus payments estimated using FY 2020
claims. We begin with FY 2019 IPF PPS claims (based on the 2019 MedPAR
claims, June 2020 update) and FY 2020 IPF PPS claims (based on the 2020
MedPAR claims, December 2020 update). We estimate FY 2021 IPF PPS
payments using these 2019 and 2020 claims, the finalized FY 2021 IPF
PPS Federal per diem base rates, and the finalized FY 2021 IPF PPS
patient and facility level adjustment factors (as published in the FY
2021 IPF PPS final rule (85 FR 47042 through 47070)). We then estimate
the FY 2021 outlier payments based on these simulated FY 2021 IPF PPS
payments using the same methodology as finalized in the FY 2021 IPF PPS
final rule (85 FR 47061 through 47062) where total outlier payments are
maintained at 2 percent of total estimated FY 2021 IPF PPS payments.
Each of the following changes is added incrementally to this
baseline model in order for us to isolate the effects of each change:
The proposed update to the outlier fixed dollar loss
threshold amount.
The proposed FY 2022 IPF wage index, the proposed FY 2022
labor-related share, and the proposed updated COLA factors.
The proposed market basket update for FY 2022 of 2.3
percent less the productivity adjustment of 0.2 percentage point in
accordance with section 1886(s)(2)(A)(i) of the Act for a payment rate
update of 2.1 percent.
Our proposed column comparison in Table 17 illustrates the percent
change in payments from FY 2021 (that is, October 1, 2020, to September
30, 2021) to FY 2022 (that is, October 1, 2021, to September 30, 2022)
including all the payment policy changes in this proposed rule. For
each column, Table 17 presents a side-by-side comparison of the results
using FY 2019 and FY 2020 IPF PPS claims.
Table 17--FY 2022 IPF PPS Proposed Payment Impacts
[Percent change in columns 3 through 5]
--------------------------------------------------------------------------------------------------------------------------------------------------------
Number of facilities Outlier Wage index FY22, LRS, Total percent change \1\
---------------------------------------------------- and COLA -------------------------
Facility by type --------------------------
FY 2019 FY 2020 FY 2019 FY 2020 FY 2019 FY 2020 FY 2019 FY 2020
Claims Claims Claims Claims Claims Claims Claims Claims
--------------------------------------------------------------------------------------------------------------------------------------------------------
(1) (2)
(3)
(4)
(5)
--------------------------------------------------------------------------------------------------------------------------------------------------------
All Facilities.................................. 1,526 1,536 0.2 -0.7 0.0 0.0 2.3 1.4
Total Urban................................. 1,226 1,238 0.2 -0.7 0.0 0.0 2.3 1.3
Urban unit.............................. 742 738 0.3 -1.1 -0.1 -0.1 2.3 0.9
Urban hospital.......................... 484 500 0.1 -0.2 0.0 0.0 2.2 1.9
Total Rural................................. 300 298 0.1 -0.5 0.1 0.1 2.4 1.8
Rural unit.............................. 240 237 0.1 -0.6 0.0 0.0 2.2 1.5
Rural hospital.......................... 60 61 0.1 -0.2 0.5 0.5 2.7 2.4
[[Page 19523]]
By Type of Ownership:
Freestanding IPFs:
Urban Psychiatric Hospitals:
Government.............................. 117 123 0.3 -1.1 -0.2 -0.2 2.2 0.7
Non-Profit.............................. 93 95 0.1 -0.3 -0.3 -0.2 1.9 1.6
For-Profit.............................. 274 282 0.0 -0.1 0.1 0.2 2.3 2.2
Rural Psychiatric Hospitals:
Government.............................. 31 32 0.1 -0.4 0.5 0.6 2.8 2.2
Non-Profit.............................. 12 12 0.2 -0.7 0.0 0.1 2.3 1.5
For-Profit.............................. 17 17 0.0 0.0 0.6 0.6 2.7 2.7
IPF Units:
Urban:
Government.............................. 109 108 0.4 -2.1 0.1 0.1 2.7 0.0
Non-Profit.............................. 482 480 0.3 -1.1 -0.1 -0.1 2.3 0.9
For-Profit.............................. 151 150 0.1 -0.5 -0.1 -0.1 2.2 1.5
Rural:
Government.............................. 58 57 0.1 -0.2 0.3 0.2 2.5 2.1
Non-Profit.............................. 133 130 0.2 -0.8 0.0 0.0 2.2 1.2
For-Profit.............................. 49 50 0.1 -0.4 -0.2 -0.2 2.0 1.4
By Teaching Status:
Non-teaching................................ 1,329 1,339 0.1 -0.6 0.0 0.0 2.2 1.5
Less than 10% interns and residents to beds. 106 106 0.3 -1.2 0.0 0.0 2.4 0.9
10% to 30% interns and residents to beds.... 70 70 0.4 -1.6 0.0 0.0 2.4 0.5
More than 30% interns and residents to beds..... 21 21 0.4 -1.9 -0.1 -0.1 2.4 0.1
By Region:
New England................................. 106 106 0.2 -0.8 -0.3 -0.4 2.0 1.0
Mid-Atlantic................................ 215 217 0.3 -1.3 -0.2 -0.2 2.1 0.5
South Atlantic.............................. 241 243 0.1 -0.5 0.7 0.7 2.9 2.3
East North Central.......................... 245 245 0.1 -0.4 -0.1 -0.1 2.2 1.5
East South Central.......................... 152 155 0.1 -0.5 -0.7 -0.7 1.5 0.8
West North Central.......................... 110 110 0.2 -0.9 0.2 0.2 2.6 1.4
West South Central.......................... 225 227 0.1 -0.4 -0.3 -0.3 1.9 1.4
Mountain.................................... 103 102 0.1 -0.4 0.1 0.1 2.3 1.8
Pacific..................................... 129 131 0.2 -0.9 0.4 0.5 2.8 1.6
By Bed Size:
Psychiatric Hospitals:
Beds: 0-24.............................. 85 90 0.1 -0.3 0.1 0.1 2.3 1.9
Beds: 25-49............................. 79 83 0.1 -0.2 -0.5 -0.4 1.7 1.4
Beds: 50-75............................. 84 87 0.0 -0.1 0.1 0.3 2.3 2.3
Beds: 76 +.............................. 296 301 0.1 -0.3 0.1 0.1 2.3 2.0
Psychiatric Units:
Beds: 0-24.............................. 540 531 0.2 -0.8 0.0 -0.1 2.3 1.2
Beds: 25-49............................. 258 259 0.2 -0.9 0.0 0.0 2.4 1.2
Beds: 50-75............................. 115 115 0.3 -1.1 -0.2 -0.3 2.2 0.7
Beds: 76 +.............................. 69 70 0.3 -1.6 0.0 0.0 2.4 0.4
--------------------------------------------------------------------------------------------------------------------------------------------------------
\1\ This column includes the impact of the updates in column (3) and (4) above, and of the proposed IPF market basket increase factor for FY 2022 (2.3
percent), reduced by 0.2 percentage point for the productivity adjustment as required by section 1886(s)(2)(A)(i) of the Act. Note, the products of
these impacts may be different from the percentage changes shown here due to rounding effects.
3. Impact Results
Table 17 displays the results of our analysis. The table groups
IPFs into the categories listed here based on characteristics provided
in the Provider of Services file, the IPF PSF, and cost report data
from the Healthcare Cost Report Information System:
Facility Type.
Location.
Teaching Status Adjustment.
Census Region.
Size.
The top row of the table shows the overall impact on the 1,526 IPFs
included in the analysis for FY 2019 claims or the 1,536 IPFs included
in the analysis for FY 2020 claims. In column 2, we present the number
of facilities of each type that had information available in the PSF
and also had claims in the MedPAR dataset for FY 2019 or FY 2020. The
number of providers in each category therefore differs slightly between
the two years.
In column 3, we present the effects of the update to the outlier
fixed dollar loss threshold amount. Based on the FY 2019 claims, we
would estimate that IPF outlier payments as a percentage of total IPF
payments are 1.8 percent in FY 2021. Alternatively, based on the FY
2020 claims, we would estimate that IPF outlier payments as a
percentage of total IPF payments are 2.7 percent in FY 2021.
Thus, we are proposing to adjust the outlier threshold amount in
this proposed rule to set total estimated outlier payments equal to 2.0
percent of total payments in FY 2022. Based on the FY 2019 claims, the
estimated change in total IPF payments for FY 2022 would include an
approximate 0.2 percent increase in payments because we would expect
the outlier portion of total payments to increase from approximately
1.8 percent to 2.0 percent. Alternatively, based on the FY 2020 claims,
the estimated change in total IPF payments for FY 2022 would include an
approximate 0.7 percent decrease in payments because we would expect
the outlier portion of total
[[Page 19524]]
payments to decrease from approximately 2.7 percent to 2.0 percent.
The overall impact of the estimated increase or decrease to
payments due to updating the outlier fixed dollar loss threshold (as
shown in column 3 of Table 17), across all hospital groups, is 0.2
percent based on the FY 2019 claims, or -0.7 percent based on the FY
2020 claims. If we decrease the outlier fixed dollar loss threshold
based on the FY 2019 claims, the largest increase in payments due to
this change is estimated to be 0.4 percent for urban, government-owned
IPF units and also 0.4 percent for teaching IPFs with 10 percent or
more interns and residents to beds. These same provider types, along
with IPF units having more than 75 beds, would experience the largest
estimated decrease in payments if we instead increase the outlier fixed
dollar loss threshold based on the FY 2020 claims distribution.
In column 4, we present the effects of the budget-neutral update to
the IPF wage index, the Labor-Related Share (LRS), and the proposed
updated COLA factors discussed in section III.D.3. This represents the
effect of using the concurrent hospital wage data as discussed in
section III.D.1.a of this proposed rule. That is, the impact
represented in this column reflects the proposed updated COLA factors
and the update from the FY 2021 IPF wage index to the proposed FY 2022
IPF wage index, which includes basing the FY 2022 IPF wage index on the
FY 2022 pre-floor, pre-reclassified IPPS hospital wage index data and
updating the LRS from 77.3 percent in FY 2021 to 77.1 percent in FY
2022. We note that there is no projected change in aggregate payments
to IPFs, as indicated in the first row of column 4; however, there will
be distributional effects among different categories of IPFs. We also
note that when comparing the results using FY 2019 and FY 2020 claims,
the distributional effects are very similar. For example, we estimate
the largest increase in payments to be 0.7 percent for IPFs in the
South Atlantic region, and the largest decrease in payments to be -0.7
percent for IPFs in the East South Central region, based on either the
FY 2019 or FY 2020 claims.
Finally, column 5 compares the total proposed changes reflected in
this proposed rule for FY 2022 to the estimates for FY 2021 (without
these changes). The average estimated increase for all IPFs is
approximately 2.3 percent based on the FY 2019 claims, or 1.4 percent
based on the FY 2020 claims. These estimated net increases include the
effects of the 2016-based market basket update of 2.3 percent reduced
by the productivity adjustment of 0.2 percentage point, as required by
section 1886(s)(2)(A)(i) of the Act. They also include the overall
estimated 0.2 percent increase or 0.7 percent decrease in estimated IPF
outlier payments as a percent of total payments from updating the
outlier fixed dollar loss threshold amount. In addition, column 5
includes the distributional effects of the proposed updates to the IPF
wage index, the labor-related share, and the proposed updated COLA
factors, whose impacts are displayed in column 4. Based on the FY 2020
claims distribution, the increase to estimated payments due to the
market basket update factor are offset in large part for some provider
types by the increase to the outlier fixed dollar loss threshold.
In summary, comparing the impact results for the FY 2019 and FY
2020 claims, the largest difference in the results is due to the update
to the outlier fixed dollar loss threshold. Therefore, we examined the
differences between the FY 2019 and FY 2020 claims distributions to
better understand the disparity in the estimate of outlier payments as
a percentage of total PPS payments between the two years, which is
driving the divergent results in column 3 of Table 17.
The calculation of the estimated outlier percentage has two
components: Estimated outlier payments and estimated total PPS
payments. As discussed in section III.F.1 of this proposed rule, 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. Therefore, estimated outlier
payments are a function of both estimated IPF costs and estimated IPF
Federal per diem payment amounts per case. As such, we looked at
changes in estimated costs, estimated Federal per diem payment amounts,
estimated outlier payments, and estimated total PPS payments in order
to understand the differences in the estimated outlier percentage when
using the FY 2019 and FY 2020 claims data. To facilitate the comparison
between our FY 2019 and FY 2020 datasets, we inflated all estimated
costs to the midpoint of FY 2021 and estimated all payments based on
the finalized FY 2021 IPF PPS patient and facility level adjustment
factors (as published in the FY 2021 IPF PPS final rule (85 FR 47042
through 47070)). In summary, we found that estimated outlier payments
using the FY 2020 claims dataset are 26 percent higher than the
estimated outlier payments using the FY 2019 claims dataset, due to
estimated costs per stay that were relatively higher than estimated
Federal per diem payment amounts per stay. Estimated total payments
using the FY 2020 claims dataset are 14 percent lower than the
estimated total payments using the FY 2019 claims dataset. Therefore,
both the estimated outlier payments and estimated total payments are
contributing to the differences in the estimated outlier payment
percentage of 2.7 percent using the FY 2020 claims dataset and 1.8
percent using the FY 2019 claims dataset. We discuss estimated total
payments and estimated outlier payments in more detail below.
As stated above, we observed a reduction of estimated total PPS
payments of approximately 14 percent using the FY 2020 claims dataset
relative to estimated total PPS payments in our FY 2019 claims dataset.
The reduction in estimated total PPS payments corresponds with a
roughly 15 percent decline in covered IPF days and a roughly 17 percent
decline in covered IPF stays. The consistency between the decline in
IPF stays and IPF days indicates the overall length of stay is fairly
consistent in the FY 2019 claims dataset and FY 2020 claims dataset.
An important consideration for how we estimate the percentage of
estimated outlier payments in FY 2022 is whether we expect this lower
level of total payments to persist in future years. We note that
although there has been a downward trend in IPF stays and total
payments in recent years, the decrease from FY 2019 to FY 2020 is 2 to
3 times greater than the decreases in recent prior years. Looking on a
monthly basis at the claims in our FY 2020 claims dataset, we observed
that estimated total PPS payments per month declined sharply, nearly 28
percent, from January 2020 to April 2020. Estimated total PPS payments
per month decreased overall by approximately 21 percent from January
2020 to September 2020. The lower estimated total PPS payments per
month were a result of both lower covered IPF days and covered IPF
stays. The COVID-19 PHE was declared on January 31, 2020, and continued
through the end of FY 2020, with an initial surge in cases occurring in
many places in the early months of the PHE. Based on the timing of the
declines in covered IPF stays and covered IPF days, we believe they are
related to the response to the COVID-19 PHE. Therefore, we do not
anticipate that decreases in total PPS payments,
[[Page 19525]]
covered IPF days, and covered IPF stays of the same magnitude as
observed in FY 2020 are likely to occur in FY 2022. We are seeking
comments on this analysis. Specifically, we are requesting comments
from stakeholders about likely explanations for the declines in total
PPS payments, covered IPF days, and covered IPF stays in FY 2020.
Next, we looked at estimated outlier payments. Estimated outlier
payments were approximately 26 percent higher using the FY 2020 claims
data compared to estimated outlier payments using the FY 2019 claims
data despite overall covered IPF stays being approximately 17 percent
lower using the FY 2020 claims data. As stated above, 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. We examined estimated IPF costs and estimated IPF
Federal per diem payment amounts in order to understand the increase in
estimated outlier payments. Overall, estimated costs were approximately
12 percent lower when using the FY 2020 claims dataset. However,
estimated Federal per diem payment amounts were approximately 15
percent lower. In other words, both estimated costs and estimated
Federal per diem payments declined along with the number of stays, but,
importantly, estimated Federal per diem payment amounts decreased by a
greater amount. When we account for the number of stays, we can see
that estimated costs and Federal per diem payment amounts per stay were
greater in FY 2020 than in FY 2019, but the increase in estimated cost
per stay was greater. Estimated Federal per diem payment amounts per
stay were approximately 2.5 percent higher using the FY 2020 claims
dataset than estimated Federal per diem payment amounts per stay using
the FY 2019 claims dataset. However, estimated costs per stay were
about 6.0 percent higher than estimated Federal per diem payments per
stay using the FY 2019 claims dataset. In other words, we observed that
estimated costs per stay increased by more than estimated IPF Federal
per diem payment amounts per stay when the FY 2020 claims dataset was
used. As a result, total estimated costs were approximately 12 percent
lower but total estimated Federal per diem payments were approximately
15 percent lower. This difference between estimated costs and estimated
Federal per diem payments contributed to the 26 percent greater
estimated outlier payments using the FY 2020 claims dataset.
We wanted to understand whether there were monthly trends in
estimated costs and estimated Federal per diem payment amounts that
would explain why estimated costs increased more than estimated Federal
per diem payment amounts from FY 2019 to FY 2020, and if any of these
monthly trends might be related to the COVID-19 PHE. Looking on a
monthly basis, we observed that estimated cost per stay and estimated
IPF Federal per diem payment per stay generally moved in line with
average length of stay until July 2020, however estimated costs
remained relatively higher than estimated payments from July 2020 until
September 2020. Discharges in our dataset occurring in February and
March 2020 had an average length of stay that was roughly 6 percent
shorter than for discharges occurring in April 2020, and for May 2020,
average length of stay was approximately 4 percent shorter than in the
preceding month. We observed comparable peaks and valleys in average
cost per stay and average estimated IPF Federal per diem payment per
stay. However, the changes in average cost per stay were smaller,
around a 3 percent increase from March 2020 to April 2020 and a 3.4
decrease percent from April 2020 to May 2020. Additionally, we observed
that estimated cost per stay declined less than average length of stay
and estimated IPF Federal per diem payment per stay from July 2020 to
September 2020, declining approximately 0.6 percent compared to 1.4
percent for length of stay and 1.5 percent for estimated IPF Federal
per diem payment per stay. In other words, we observed that from July
2020 to September 2020, the declines in estimated payments were greater
than the declines in estimated costs, and therefore the gap between
costs and payments increased during this period.
Looking specifically at estimated outlier cases on a monthly basis,
we observed a similar trend from March 2020 to May 2020 in average
length of stay, estimated IPF Federal per diem payment per stay, and
estimated cost per stay to those we observed in all FY 2020 claims in
our dataset. However, from July 2020 to September 2020, estimated cost
per stay, estimated IPF Federal per diem payment per stay, and average
length of stay all increased. Estimated cost per stay and estimated
length of stay increased approximately 3.9 percent and 2.0 percent,
whereas estimated IPF Federal per diem payment per stay increased by a
lower amount, approximately 2.4 percent. Additionally, we observed that
estimated outlier payment per outlier stay was approximately 50 percent
higher in July 2020 than it was in May 2020. In September 2020
estimated outlier payment per outlier stay was approximately 62 percent
higher than May 2020. In other words, we observed that the divergence
in estimated costs and estimated payments in our FY 2020 dataset
corresponded with the increase in estimated outlier payment per stay.
Because the IPF PPS is a per diem payment system, we also looked at
whether increased length of stay was contributing to the increased
estimated outlier payment per case. Among estimated outlier cases, we
calculated the estimated outlier payment per covered IPF day. We
observed that estimated outlier payment per covered day was nearly 69
percent greater in July 2020 than it was in May 2020, and remained at a
higher level through the end of the year than at the start of the year.
Compared to January 2020, average length of stay for estimated outlier
cases in September 2020 was approximately 10 percent lower, whereas
estimated outlier payment per outlier stay was approximately 52 percent
higher. Therefore, we concluded that increased length of stay among
estimated outlier cases does not appear to be driving the increase in
estimated outlier payments.
We examined the distribution of DRGs throughout the FY 2020 claims
in our dataset but did not observe variation that would explain the
substantial increases in estimated outlier payments. In general, the
majority of IPF cases have a DRG of 885 (Psychoses). The percentage of
claims with this DRG remained very similar from FY 2019 (74.5 percent)
to FY 2020 (75.2 percent), and this percentage did not appear to
diverge or fluctuate meaningfully during FY 2020. We also looked at
comorbidities and observed that the percentage of cases with a
comorbidity increased slightly, from approximately 3.6 percent in our
FY 2019 dataset to 3.8 percent in our FY 2020 dataset. In general, most
IPF cases in both FY 2019 and FY 2020 did not have any IPF
comorbidities. Among cases with at least one comorbidity, the number of
cases for each comorbidity category declined in FY 2020, with the
exception of Chronic Obstructive Pulmonary Disorder. We note that this
is the IPF comorbidity category in which the COVID-19 diagnosis code,
U07.1, falls. However, cases with this comorbidity category remained a
relatively small percentage of all IPF cases, approximately 0.8 percent
in FY 2019 and approximately 1.3 percent in FY 2020. Additionally,
among estimated
[[Page 19526]]
outlier cases, those with at least one comorbidity received
approximately 58 percent less estimated outlier pay per covered day
than those without any comorbidities. This makes intuitive sense,
because cases with an IPF comorbidity would receive a payment
adjustment corresponding to the appropriate IPF comorbidity category,
therefore reducing the difference between estimated IPF Federal per
diem payments and costs for those cases. Therefore, it does not seem
likely that cases with IPF comorbidities were the main driver of the
increases in estimated outlier payments.
Observing that changes in DRGs and comorbidities did not appear to
be driving the increased estimated outlier payments in FY 2020, we
wanted to understand what was causing the higher estimated costs
relative to estimated IPF Federal per diem payments that we observed in
FY 2020. Following our longstanding methodology, we estimate the costs
per case based on the covered charges on each IPF claim and the IPF's
most recent CCR. Therefore, in order to better understand estimated
costs, we looked at covered charges in FY 2019 and FY 2020. For this
analysis, we used a different source for claims which enabled us to
calculate covered charge by categories corresponding to the MedPAR
ancillary departments. We analyzed FY 2019 and FY 2020 IPF claims data
from the Common Working File (CWF).
In general, laboratory charges make up roughly one third of the
covered charges per IPF claim. Comparing FY 2019 to FY 2020, we
observed that covered lab charges per claim in our CWF dataset
increased approximately 6.8 percent. Looking on a monthly basis, we
observed fluctuation in covered lab charges per claim and per day
during the COVID-19 PHE. We looked specifically at the period January
2020 (the month in which the COVID-19 PHE was declared) to September
2020 (the end of FY 2020), and observed peaks and valleys in covered
lab charges that we believe may be related to the response to the
COVID-19 PHE. Covered lab charges per day increased approximately 6.3
percent (2.4 percent per claim) from January 2020 to March 2020,
decreased approximately 7.1 percent (1.1 percent per claim) from March
2020 to April 2020, and then increased approximately 6.2 percent (0.9
percent per claim) from April 2020 to September 2020. Overall, covered
lab charges per day increased approximately 4.9 percent (2.2 percent
per claim) from January 2020 to September 2020. In other words, most of
the 6.8 percent increase in covered lab charges from FY 2019 to FY 2020
occurred during the period January 2020 to September 2020, with the
highest levels of lab charges occurring during February/March and June
through September. Based on the data available, we are not able to
determine the root cause of these increases in covered lab charges
during the COVID-19 PHE, however we acknowledge that these increased
charges may be related to services in response to the COVID-19 PHE,
such as COVID-19 testing. We are requesting comments on this analysis.
Specifically, we are requesting comments from stakeholders about likely
explanations for the observed fluctuations and overall increases in
covered lab charges per claim and per day. We are also requesting
comments regarding likely explanations for the increases in estimated
cost per stay relative to estimated IPF Federal per diem payment
amounts per stay.
As discussed in this section, estimated outlier payments increased
and estimated total PPS payments decreased, when comparing FY 2020 to
FY 2019. Based on our analysis, we believe it is likely that the
response to the COVID-19 PHE in FY 2020 has contributed to both of
these trends. As a result, in contrast to our usual methodology, we are
not confident that FY 2020 claims are the best available data for
setting the FY 2022 proposed outlier fixed dollar loss threshold.
Furthermore, the distributional effects of the updates presented in
column 4 of Table 17 (the budget-neutral update to the IPF wage index,
the LRS, and the proposed updated COLA factors) are very similar when
using the FY 2019 or FY 2020 claims data. Therefore, we believe the FY
2019 claims would be the best available data for estimating payments in
this FY 2022 proposed rulemaking, and we are proposing to use the FY
2019 claims to calculate the outlier fixed dollar loss threshold and
wage index budget neutrality factor.
IPF payments are therefore estimated to increase by 2.3 percent in
urban areas and 2.4 percent in rural areas based on this proposal.
Overall, IPFs are estimated to experience a net increase in payments as
a result of the updates in this proposed rule. The largest payment
increase is estimated at 2.9 percent for IPFs in the South Atlantic
region.
4. Effect on Beneficiaries
Under the FY 2022 IPF PPS, IPFs will continue to receive payment
based on the average resources consumed by patients for each day. Our
longstanding payment methodology reflects the differences in patient
resource use and costs among IPFs, as required under section 124 of the
BBRA. We expect that updating IPF PPS rates as proposed in this rule
will improve or maintain beneficiary access to high quality care by
ensuring that payment rates reflect the best available data on the
resources involved in inpatient psychiatric care and the costs of these
resources. We continue to expect that paying prospectively for IPF
services under the FY 2022 IPF PPS will enhance the efficiency of the
Medicare program.
As discussed in sections IV.E.2, IV.E.3, and V.A.2.d of this
proposed rule, we expect that additional program measures will improve
follow-up for patients with both mental health and substance use
disorders and ensure health-care personnel COVID-19 vaccinations. We
also estimate a $20,911,738 reduction in information collection burden
as a result of our measure removal proposals. Therefore, we expect that
the proposed updates to the IPFQR program will improve quality for
beneficiaries.
5. Effects of Updates to the IPFQR Program
As discussed in section V. of this proposed rule and in accordance
with section 1886(s)(4)(A)(i) of the Act, we will apply a 2 percentage
point reduction in the FY 2022 market basket update for IPFs that have
failed to comply with the IPFQR Program requirements for FY 2022,
including reporting on the required measures. In section V. of this
proposed rule, we discuss how the 2 percentage point reduction will be
applied. For FY 2021, of the 1,634 IPFs eligible for the IPFQR Program,
43 IPFs (2.6 percent) did not receive the full market basket update
because of the IPFQR Program; 31 of these IPFs chose not to participate
and 12 did not meet the requirements of the program. We anticipate that
even fewer IPFs would receive the reduction for FY 2022 as IPFs become
more familiar with the requirements. Thus, we estimate that the IPFQR
Program will have a negligible impact on overall IPF payments for FY
2022.
Based on the IPFQR Program proposals made in this proposed rule, we
estimate a total decrease in burden of 785,477 hours across all IPFs,
resulting in a total decrease in information collection burden of
$20,911,738 across all IPFs. As discussed in section VI. of this
proposed rule, we will attribute the cost savings associated with the
proposals to the year in which these savings begin; for the purposes of
all the proposals in this proposed rule, that year is FY 2023. Further
information on these estimates
[[Page 19527]]
can be found in section VI. of this proposed rule.
We intend to closely monitor the effects of the IPFQR Program on
IPFs and help facilitate successful reporting outcomes through ongoing
stakeholder education, national trainings, and a technical help desk.
6. Regulatory Review Costs
If regulations impose administrative costs on private entities,
such as the time needed to read and interpret this proposed rule, we
should estimate the cost associated with regulatory review. Due to the
uncertainty involved with accurately quantifying the number of entities
that will be directly impacted and will review this proposed rule, we
assume that the total number of unique commenters on the most recent
IPF proposed rule from FY 2021 (85 FR 20625) will be the number of
reviewers of this proposed rule. We acknowledge that this assumption
may understate or overstate the costs of reviewing this proposed rule.
It is possible that not all commenters reviewed the FY 2021 IPF
proposed rule in detail, and it is also possible that some reviewers
chose not to comment on that proposed rule. For these reasons, we
thought that the number of commenters would be a fair estimate of the
number of reviewers who are directly impacted by this proposed rule. We
solicited comments on this assumption.
We also recognize that different types of entities are in many
cases affected by mutually exclusive sections of this proposed rule;
therefore, for the purposes of our estimate, we assume that each
reviewer reads approximately 50 percent of this proposed rule.
Using the May, 2019 mean (average) wage information from the BLS
for medical and health service managers (Code 11-9111), we estimate
that the cost of reviewing this proposed rule is $110.74 per hour,
including overhead and fringe benefits (https://www.bls.gov/oes/current/oes119111.htm). Assuming an average reading speed of 250 words
per minute, we estimate that it would take approximately 93.5 minutes
(1.56 hours) for the staff to review half of this proposed rule, which
is approximately 23,365 words. For each IPF that reviews the proposed
rule, the estimated cost is (1.56 x $110.74) or $172.75. Therefore, we
estimate that the total cost of reviewing this proposed rule is
$79,810.50 ($172.75 x 462 reviewers).
D. Alternatives Considered
The statute does not specify an update strategy for the IPF PPS and
is broadly written to give the Secretary discretion in establishing an
update methodology. We continue to believe it is appropriate to
routinely update the IPF PPS so that it reflects the best available
data about differences in patient resource use and costs among IPFs as
required by the statute. Therefore, we are proposing to update the IPF
PPS using the methodology published in the November 2004 IPF PPS final
rule; applying the 2016-based IPF PPS market basket update for FY 2022
of 2.3 percent, reduced by the statutorily required multifactor
productivity adjustment of 0.2 percentage point along with the wage
index budget neutrality adjustment to update the payment rates; and
proposing a FY 2022 IPF wage index which uses the FY 2022 pre-floor,
pre-reclassified IPPS hospital wage index as its basis.
As discussed in section VI.C.3 of this proposed rule, we also
considered using FY 2020 claims data to determine the proposed FY 2022
outlier fixed dollar loss threshold, wage index budget neutrality
factor, per diem base rate, and ECT rate. For the reasons discussed in
that section, we are proposing to use FY 2019 claims data.
E. Accounting Statement
As required by OMB Circular A-4 (available at www.whitehouse.gov/sites/whitehouse.gov/files/omb/circulars/A4/a-4.pdf), in Table 18, we
have prepared an accounting statement showing the classification of the
expenditures associated with the updates to the IPF wage index and
payment rates in this proposed rule. Table 18 provides our best
estimate of the increase in Medicare payments under the IPF PPS as a
result of the changes presented in this proposed rule and based on the
data for 1,526 IPFs with data available in the PSF and with claims in
our FY 2019 MedPAR claims dataset. Table 18 also includes our best
estimate of the cost savings for the 1,634 IPFs eligible for the IPFQR
Program. Lastly, Table 18 also includes our best estimate of the costs
of reviewing and understanding this proposed rule.
Table 18--Accounting Statement: Classification of Estimated Costs, Savings, and Transfers
----------------------------------------------------------------------------------------------------------------
Primary Units
estimate Low High --------------------------------------
Category ($million/ estimate estimate Year Discount Period
year) dollars rate (%) covered
----------------------------------------------------------------------------------------------------------------
Regulatory Review Costs........... 0.08 ........... ........... 2020 ........... * 2021-2022
Annualized Monetized Costs Savings -20.91 -15.68 -26.14 2020 7 * 2023-2031
-17.79 -13.34 -22.24 2020 3 2023-2031
Annualized Monetized Transfers 90 ........... ........... 2020 ........... 2021-2022
from Federal Government to IPF
Medicare Providers...............
----------------------------------------------------------------------------------------------------------------
F. Regulatory Flexibility Act
The RFA requires agencies to analyze options for regulatory relief
of small entities if a rule has a significant impact on a substantial
number of small entities. For purposes of the RFA, small entities
include small businesses, nonprofit organizations, and small
governmental jurisdictions. Most IPFs and most other providers and
suppliers are small entities, either by nonprofit status or having
revenues of $8 million to $41.5 million or less in any 1 year.
Individuals and states are not included in the definition of a small
entity.
Because we lack data on individual hospital receipts, we cannot
determine the number of small proprietary IPFs or the proportion of
IPFs' revenue derived from Medicare payments. Therefore, we assume that
all IPFs are considered small entities.
The Department of Health and Human Services generally uses a
revenue impact of 3 to 5 percent as a significance threshold under the
RFA. As shown in Table 17, we estimate that the overall revenue impact
of this proposed rule on all IPFs is to increase estimated Medicare
payments by approximately 2.3 percent. As a result, since the estimated
impact of this proposed rule is a net increase in revenue across almost
all categories of IPFs, the Secretary has determined that this proposed
rule will have a positive revenue impact on a substantial number of
small entities.
In addition, section 1102(b) of the Act requires us to prepare a
regulatory impact analysis if a rule may have a
[[Page 19528]]
significant impact on the operations of a substantial number of small
rural hospitals. This analysis must conform to the provisions of
section 603 of the RFA. For purposes of section 1102(b) of the Act, we
define a small rural hospital as a hospital that is located outside of
a metropolitan statistical area and has fewer than 100 beds. As
discussed in section V.C.1 of this proposed rule, the rates and
policies set forth in this proposed rule will not have an adverse
impact on the rural hospitals based on the data of the 240 rural
excluded psychiatric units and 60 rural psychiatric hospitals in our
database of 1,526 IPFs for which data were available. Therefore, the
Secretary has determined that this proposed rule will not have a
significant impact on the operations of a substantial number of small
rural hospitals.
G. Unfunded Mandate Reform Act (UMRA)
Section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA) also
requires that agencies assess anticipated costs and benefits before
issuing any rule whose mandates require spending in any 1 year of $100
million in 1995 dollars, updated annually for inflation. In 2021, that
threshold is approximately $158 million. This proposed rule does not
mandate any requirements for state, local, or tribal governments, or
for the private sector. This proposed rule would not impose a mandate
that will result in the expenditure by state, local, and Tribal
Governments, in the aggregate, or by the private sector, of more than
$158 million in any one year.
H. Federalism
Executive Order 13132 establishes certain requirements that an
agency must meet when it promulgates a proposed rule that imposes
substantial direct requirement costs on state and local governments,
preempts state law, or otherwise has Federalism implications. This
proposed rule does not impose substantial direct costs on state or
local governments or preempt state law.
List of Subjects in 42 CFR Part 412
Administrative practice and procedure, Health facilities, Medicare,
Puerto Rico, Reporting and recordkeeping requirements.
For the reasons set forth in the preamble, the Centers for Medicare
& Medicaid Services proposes to amend 42 CFR chapter IV as set forth
below:
PART 412--PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL
SERVICES
0
1. The authority citation for part 412 continues to read as follows:
Authority: 42 U.S.C. 1302 and 1395hh.
0
2. Section 412.402 is amended by adding definitions for ``Closure of an
IPF'', ``Closure of an IPF's residency training program'', and
``Displaced resident'' in alphabetical order to read as follows:
Sec. 412.402 Definitions.
* * * * *
Closure of an IPF means closure of a hospital as defined in Sec.
413.79(h)(1)(i) by an IPF meeting the requirements of Sec. 412.404(b)
for the purposes of accounting for indirect teaching costs.
Closure of an IPF's residency training program means closure of a
hospital residency training program as defined in Sec.
413.79(h)(1)(ii) by an IPF meeting the requirements of Sec. 412.404(b)
for the purposes of accounting for indirect teaching costs.
* * * * *
Displaced resident means a displaced resident as defined in Sec.
413.79(h)(1)(iii) for the purposes of accounting for indirect teaching
costs.
* * * * *
0
3. Section 412.424 is amended by revising paragraph (d)(1)(iii)(F) to
read as follows:
Sec. 412.424 Methodology for calculating the Federal per diem
payment amount.
* * * * *
(d) * * *
(1) * * *
(iii) * * *
(F) Closure of an IPF. (1) For cost reporting periods beginning on
or after July 1, 2011, an IPF may receive a temporary adjustment to its
FTE cap to reflect displaced residents added because of another IPF's
closure if the IPF meets the following criteria:
(i) The IPF is training additional displaced residents from an IPF
that closed on or after July 1, 2011.
(ii) No later than 60 days after the IPF begins to train the
displaced residents, the IPF submits a request to its Medicare
contractor for a temporary adjustment to its cap, documents that the
IPF is eligible for this temporary adjustment by identifying the
displaced residents who have come from the closed IPF and have caused
the IPF to exceed its cap, and specifies the length of time the
adjustment is needed.
(2) Closure of an IPF's residency training program. If an IPF that
closes its residency training program on or after July 1, 2011, agrees
to temporarily reduce its FTE cap according to the criteria specified
in paragraph (d)(1)(iii)(F)(2)(ii) of this section, another IPF(s) may
receive a temporary adjustment to its FTE cap to reflect displaced
residents added because of the closure of the residency training
program if the criteria specified in paragraph (d)(1)(iii)(F)(2)(i) of
this section are met.
(i) Receiving IPF(s). For cost reporting periods beginning on or
after July 1, 2011, an IPF may receive a temporary adjustment to its
FTE cap to reflect displaced residents added because of the closure of
another IPF's residency training program if the IPF is training
additional displaced residents from the residency training program of
an IPF that closed a program; and if no later than 60 days after the
IPF begins to train the displaced residents, the IPF submits to its
Medicare Contractor a request for a temporary adjustment to its FTE
cap, documents that it is eligible for this temporary adjustment by
identifying the displaced residents who have come from another IPF's
closed program and have caused the IPF to exceed its cap, specifies the
length of time the adjustment is needed, and submits to its Medicare
contractor a copy of the FTE reduction statement by the hospital that
closed its program, as specified in paragraph (d)(1)(iii)(F)(2)(ii) of
this section.
(ii) IPF that closed its program. An IPF that agrees to train
displaced residents who have been displaced by the closure of another
IPF's program may receive a temporary FTE cap adjustment only if the
hospital with the closed program temporarily reduces its FTE cap based
on the FTE of displaced residents in each program year training in the
program at the time of the program's closure. This yearly reduction in
the FTE cap will be determined based on the number of those displaced
residents who would have been training in the program during that year
had the program not closed. No later than 60 days after the displaced
residents who were in the closed program begin training at another
hospital, the hospital with the closed program must submit to its
Medicare contractor a statement signed and dated by its representative
that specifies that it agrees to the temporary reduction in its FTE cap
to allow the IPF training the displaced residents to obtain a temporary
adjustment to its cap; identifies the displaced residents who were in
training at the time of the program's closure; identifies the IPFs to
which the displaced residents are transferring once the program closes;
and specifies the
[[Page 19529]]
reduction for the applicable program years.
* * * * *
0
4. Section 412.434 is amended by revising paragraph (b)(3) to read as
follows:
Sec. 412.434 Reconsideration and appeals procedures of Inpatient
Psychiatric Facilities Quality Reporting (IPFQR) Program decisions.
* * * * *
(b) * * *
(3) Contact information for the inpatient psychiatric facility's
chief executive officer and QualityNet security official, including
each individual's name, email address, telephone number, and physical
mailing address;
* * * * *
Dated: March 29, 2021.
Elizabeth Richter,
Acting Administrator, Centers for Medicare & Medicaid Services.
Dated: April 6, 2021.
Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2021-07433 Filed 4-7-21; 4:15 pm]
BILLING CODE 4120-01-P