[Federal Register Volume 85, Number 231 (Tuesday, December 1, 2020)]
[Rules and Regulations]
[Pages 76979-77007]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2020-26338]
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
45 CFR Part 153
[CMS-9913-F]
RIN 0938-AU23
Amendments to the HHS-Operated Risk Adjustment Data Validation
(HHS-RADV) Under the Patient Protection and Affordable Care Act's HHS-
Operated Risk Adjustment Program
AGENCY: Centers for Medicare & Medicaid Services (CMS), Department of
Health and Human Services (HHS).
ACTION: Final rule.
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SUMMARY: This final rule adopts certain changes to the risk adjustment
data validation error estimation methodology beginning with the 2019
benefit year for states where the Department of Health and Human
Services (HHS) operates the risk adjustment program. This rule is
finalizing changes to the HHS-RADV error estimation methodology, which
is used to calculate adjusted risk scores and risk adjustment
transfers, beginning with the 2019 benefit year of HHS-RADV. This rule
also finalizes a change to the benefit year to which HHS-RADV
adjustments to risk scores and risk adjustment transfers would be
applied beginning with the 2020 benefit year of HHS-RADV. These
policies seek to further the integrity of HHS-RADV, address stakeholder
feedback, promote fairness, and improve the predictability of HHS-RADV
adjustments.
DATES: These regulations are effective on December 31, 2020.
FOR FURTHER INFORMATION CONTACT: Allison Yadsko, (410) 786-1740; Joshua
Paul, (301) 492-4347; Adrianne Patterson, (410) 786-0686; and Jaya
Ghildiyal, (301) 492-5149.
SUPPLEMENTARY INFORMATION:
I. Background
A. Legislative and Regulatory Overview
The Patient Protection and Affordable Care Act (Pub. L. 111-148)
was enacted on March 23, 2010; the Health Care and Education
Reconciliation Act of 2010 (Pub. L. 111-152) was enacted on March 30,
2010. These statutes are collectively referred to as ``PPACA'' in this
final rule. Section 1343 of the PPACA \1\ established a permanent risk
adjustment program to provide payments to health insurance issuers that
attract higher-than-average risk populations, such as those with
chronic conditions, funded by payments from those that attract lower-
than-average risk populations, thereby reducing incentives for issuers
to avoid higher-risk enrollees. The PPACA directs the Secretary of the
Department of Health and Human Services (Secretary), in consultation
with the states, to establish criteria and methods to be used in
carrying out risk adjustment activities, such as determining the
actuarial risk of enrollees in risk adjustment covered plans within a
state market risk pool.\2\ The statute also provides that the Secretary
may utilize criteria and methods similar to the ones utilized under
Medicare Parts C or D.\3\ Consistent with section 1321(c)(1) of the
PPACA, the Secretary is responsible for operating the risk adjustment
program on behalf of any state that elected not to do so. For the 2014
through 2016 benefit years, all states and the District of Columbia,
except Massachusetts, participated in the HHS-operated risk adjustment
program. Since the 2017 benefit year, all states and the District of
Columbia have participated in the HHS-operated risk adjustment program.
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\1\ 42 U.S.C. 18063.
\2\ 42 U.S.C. 18063(a) and (b).
\3\ 42 U.S.C. 18063(b).
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Data submission requirements for the HHS-operated risk adjustment
program are set forth at 45 CFR 153.700 through 153.740. Each issuer is
required to establish and maintain an External Data Gathering
Environment (EDGE) server on which the issuer submits masked enrollee
demographics, claims, and encounter diagnosis-level data in a format
specified by the Department of Health and Human Services (HHS). Issuers
must also execute software provided by HHS on their respective EDGE
servers to generate summary reports, which HHS uses to calculate the
enrollee-level risk scores to determine the average plan liability risk
scores for each state market risk pool, the individual issuers' plan
liability risk scores, and the transfer amounts by state market risk
pool for the applicable benefit year.\4\
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\4\ HHS also uses the data issuers submit to their EDGE servers
for the calculation of the high-cost risk pool payments and charges
added to the HHS risk adjustment methodology beginning with the 2018
benefit year.
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Pursuant to 45 CFR 153.350, HHS performs HHS-RADV to validate the
accuracy of data submitted by issuers
[[Page 76980]]
for the purposes of risk adjustment transfer calculations for states
where HHS operates the risk adjustment program. The purpose of HHS-RADV
is to ensure issuers are providing accurate and complete risk
adjustment data to HHS, which is crucial to the purpose and proper
functioning of the HHS-operated risk adjustment program. This process
establishes uniform audit standards to ensure that actuarial risk is
accurately and consistently measured, thereby strengthening the
integrity of the HHS-operated risk adjustment program.\5\ HHS-RADV also
ensures that issuers' actual actuarial risk is reflected in risk
adjustment transfers and that the HHS-operated program assesses charges
to issuers with plans with lower-than-average actuarial risk while
making payments to issuers with plans with higher-than-average
actuarial risk. Pursuant to 45 CFR 153.350(a), HHS, in states where it
operates the program, must ensure proper validation of a statistically
valid sample of risk adjustment data from each issuer that offers at
least one risk adjustment covered plan \6\ in that state. Under 45 CFR
153.350, HHS, in states where it operates the program, may adjust the
plan average actuarial risk for a risk adjustment covered plan based on
errors discovered as a result of HHS-RADV and use those adjusted risk
scores to modify charges and payments to all risk adjustment covered
plan issuers in the same state market risk pool.
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\5\ HHS also has general authority to audit issuers of risk
adjustment covered plans pursuant to 45 CFR 153.620(c).
\6\ See 45 CFR 153.20 for the definition of ``risk adjustment
covered plan.''
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For the HHS-operated risk adjustment program, 45 CFR 153.630
requires an issuer of a risk adjustment covered plan to have an initial
and second validation audit performed on its risk adjustment data for
the applicable benefit year. Each issuer must engage one or more
independent auditors to perform the initial validation audit (IVA) of a
sample of risk adjustment data selected by HHS.\7\ The issuer provides
demographic, enrollment, and claims data and medical record
documentation for a sample of enrollees selected by HHS to its IVA
entity for data validation. After the IVA entity has validated the HHS-
selected sample, a subsample is validated in a second validation audit
(SVA).\8\ The SVA is conducted by an entity HHS retains to verify the
accuracy of the findings of the IVA.
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\7\ 45 CFR 153.630(b).
\8\ 45 CFR 153.630(c).
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HHS conducted two pilot years of HHS-RADV for the 2015 and 2016
benefit years \9\ to give HHS and issuers experience with HHS-RADV
prior to applying HHS-RADV findings to adjust issuers' risk scores, as
well as the risk adjustment transfers in the applicable state market
risk pools. The 2017 benefit year HHS-RADV was the first payment year
that resulted in adjustments to issuers' risk scores and the risk
adjustment transfers in the applicable state market risk pools as a
result of HHS-RADV findings.10 11
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\9\ HHS-RADV was not conducted for the 2014 benefit year. See
FAQ ID 11290a (March 7, 2016), available at: https://www.regtap.info/faq_viewu.php?id=11290.
\10\ The Summary Report of 2017 Benefit Year HHS-RADV
Adjustments to Risk Adjustment Transfers released on August 1, 2019
is available at: https://www.cms.gov/CCIIO/Programs-and-Initiatives/Premium-Stabilization-Programs/Downloads/BY2017-HHSRADV-Adjustments-to-RA-Transfers-Summary-Report.pdf.
\11\ The one exception is for Massachusetts issuers, who were
not able to participate in prior HHS-RADV pilot years because the
state operated risk adjustment for the 2014-2016 benefit years.
Therefore, HHS made the 2017 benefit year HHS-RADV a pilot year for
Massachusetts issuers. See 84 FR 17454 at 17508.
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When initially developing the HHS-RADV process, HHS sought the
input of issuers, consumer advocates, providers, and other
stakeholders, and issued the ``Affordable Care Act HHS-Operated Risk
Adjustment Data Validation Process White Paper'' on June 22, 2013 (the
2013 RADV White Paper).\12\ The 2013 RADV White Paper discussed and
sought comment on a number of potential considerations for the
development and operation of HHS-RADV. Based on the feedback received,
HHS promulgated regulations to implement HHS-RADV that we have modified
in certain respects based on experience and public input, as follows.
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\12\ A copy of the Affordable Care Act HHS-Operated Risk
Adjustment Data Validation Process White Paper (June 22, 2013) is
available at: https://www.regtap.info/uploads/library/ACA_HHS_OperatedRADVWhitePaper_062213_5CR_050718.pdf.
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In the July 15, 2011 Federal Register (76 FR 41929), we published a
proposed rule outlining the framework for the risk adjustment program,
including standards related to HHS-RADV. We implemented the risk
adjustment program and adopted standards related to HHS-RADV in a final
rule, published in the March 23, 2012 Federal Register (77 FR 17219)
(Premium Stabilization Rule). The HHS-RADV regulations adopted in the
Premium Stabilization Rule provide for adjustments to risk scores and
risk adjustment transfers to reflect HHS-RADV errors, including the
two-sided nature of such adjustments.
In the December 7, 2012 Federal Register (77 FR 73117), we
published a proposed rule outlining benefit and payment parameters
related to the risk adjustment program, including six steps for error
estimation for HHS-RADV in 45 CFR 153.630 (proposed 2014 Payment
Notice). We published the 2014 Payment Notice final rule in the March
11, 2013 Federal Register (78 FR 15436). In addition to finalizing 45
CFR 153.630, this final rule further clarified HHS-RADV policies,
including that adjustments would occur when an issuer under-reported
its risk scores.
In the December 2, 2013 Federal Register (78 FR 72321), we
published a proposed rule outlining the benefit and payment parameters
related to the risk adjustment program (proposed 2015 Payment Notice).
This rule also included several HHS-RADV proposals. In the March 11,
2014 Federal Register (79 FR 13743), we published the 2015 Payment
Notice final rule, which finalized HHS-RADV requirements related to
sampling; IVA standards, SVA processes, and medical record review as
the basis of enrollee risk score validation; the error estimation
process and original methodology; and HHS-RADV appeals, oversight, and
data security standards. Under the original methodology adopted in that
final rule, almost every failure to validate an Hierarchical Condition
Category (HCC) during HHS-RADV would have resulted in an adjustment to
the issuer's risk score and an accompanying adjustment to all transfers
in the applicable state market risk pool.
In the September 6, 2016 Federal Register (81 FR 61455), we
published a proposed rule outlining benefit and payment parameters
related to the risk adjustment program (proposed 2018 Payment Notice)
that included proposals related to HHS-RADV. We published the 2018
Payment Notice final rule in the December 22, 2016 Federal Register (81
FR 94058), which included finalizing proposals related to HHS-RADV
discrepancy reporting, clarifications related to certain aspects of the
HHS-RADV appeals process, and a materiality threshold for HHS-RADV to
ease the burden of the annual audit requirements for smaller issuers.
Under the materiality threshold, issuers with total annual premiums at
or below $15 million are not subject to annual IVA requirements, but
would be subject to such audits approximately every 3 years (barring
risk-based triggers that would warrant more frequent audits).
In the November 2, 2017 Federal Register (82 FR 51042), we
published a proposed rule outlining benefit and payment parameters
related to the risk adjustment program (proposed 2019 Payment Notice)
that included proposed provisions related to HHS-RADV. We
[[Page 76981]]
published the 2019 Payment Notice final rule in the April 17, 2018
Federal Register (83 FR 16930), which included finalizing for 2017
benefit year HHS-RADV and beyond, an amended error estimation
methodology to only adjust issuers' risk scores when an issuer's
failure rate is materially different from other issuers based on three
HCC groupings (low, medium, and high), that is, when an issuer is
identified as an outlier. We also finalized an exemption for issuers
with 500 or fewer billable member months from HHS-RADV; a requirement
that IVA samples only include enrollees from state market risk pools
with more than one issuer; clarifications regarding civil money
penalties for non-compliance with HHS-RADV; and a process to handle
demographic or enrollment errors discovered during HHS-RADV. We
finalized an exception to the prospective application of HHS-RADV
results for exiting issuers,\13\ such that exiting outlier issuers'
results are used to adjust the benefit year being audited (rather than
the following transfer year).
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\13\ To be an exiting issuer, the issuer has to exit all of the
market risk pools in the state (that is, not sell or offer any new
plans in the state). If an issuer only exits some market risk pools
in the state, but continues to sell or offer plans in others, it is
not an exiting issuer. A small group issuer with off-calendar year
coverage, who exits the small group market risk pool in a state and
only has small group carry-over coverage that ends in the next
benefit year, and is not otherwise selling or offering new plans in
any market risk pools in the state, would be an exiting issuer. See
83 FR 16965 through 16966 and 84 FR 17503 through 17504.
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In the July 30, 2018 Federal Register (83 FR 36456), we published a
final rule that adopted the 2017 benefit year HHS-operated risk
adjustment methodology set forth in the final rules published in the
March 23, 2012 and March 8, 2016 editions of the Federal Register (77
FR 17220 through 17252 and 81 FR 12204 through 12352, respectively).
This final rule set forth additional explanation of the rationale
supporting the use of statewide average premium in the HHS-operated
risk adjustment state payment transfer formula for the 2017 benefit
year, including why the program is operated in a budget-neutral manner.
This final rule permitted HHS to resume 2017 benefit year program
operations, including collection of risk adjustment charges and
distribution of risk adjustment payments. HHS also provided guidance as
to the operation of the HHS-operated risk adjustment program for the
2017 benefit year in light of publication of this final rule.\14\
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\14\ ``Update on the HHS-operated Risk Adjustment Program for
the 2017 Benefit Year.'' July 27, 2018. Available at https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/2017-RA-Final-Rule-Resumption-RAOps.pdf.
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In the August 10, 2018 Federal Register (83 FR 39644), we published
a proposed rule concerning the adoption of the 2018 benefit year HHS-
operated risk adjustment methodology set forth in the final rules
published in the March 23, 2012 and December 22, 2016 editions of the
Federal Register (77 FR 17220 through 17252 and 81 FR 94058 through
94183, respectively). The proposed rule set forth additional
explanation of the rationale supporting use of statewide average
premium in the HHS-operated risk adjustment state payment transfer
formula for the 2018 benefit year, including why the program is
operated in a budget-neutral manner. In the December 10, 2018 Federal
Register (83 FR 63419), we issued a final rule adopting the 2018
benefit year HHS-operated risk adjustment methodology as established in
the final rules published in the March 23, 2012 and the December 22,
2016 (77 FR 17220 through 1752 and 81 FR 94058 through 94183,
respectively) editions of the Federal Register. This final rule
permitted HHS to resume 2018 benefit year program operations, including
collection of risk adjustment charges and distribution of risk
adjustment payments.
In the January 24, 2019 Federal Register (84 FR 227), we published
a proposed rule outlining the benefit and payment parameters related to
the risk adjustment program, including updates to HHS-RADV requirements
(proposed 2020 Payment Notice). We published the 2020 Payment Notice
final rule in the April 25, 2019 Federal Register (84 FR 17454) (2020
Payment Notice). The final rule included policies related to
incorporating risk adjustment prescription drug categories (RXCs) \15\
into HHS-RADV beginning with the 2018 benefit year and extending the
Neyman allocation to the 10th stratum for HHS-RADV sampling. We also
finalized using precision analysis to determine whether the SVA results
of the full sample or the subsample (of up to 100 enrollees) results
should be used in place of IVA results when an issuer's IVA results
have insufficient agreement with SVA results following a pairwise means
test. We clarified the application and distribution of default data
validation charges under 45 CFR 153.630(b)(10) and how HHS will apply
error rates for exiting issuers and sole issuer markets. We codified
the previously established materiality threshold and exemption for
issuers with 500 or fewer billable member months and established a new
exemption from HHS-RADV for issuers in liquidation who met certain
conditions. In response to comments, in the final rule, we updated the
timeline for collection, distribution, and reporting of HHS-RADV
adjustments to transfers; provided that the 2017 benefit year would be
a pilot year for HHS-RADV for Massachusetts; and established that the
2018 benefit year would be a pilot year for incorporating RXCs into
HHS-RADV.
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\15\ An RXC uses a drug to impute a diagnosis (or indicate the
severity of diagnosis) otherwise indicated through medical coding in
a hybrid diagnoses-and-drugs risk adjustment model.
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In the February 6, 2020 Federal Register (85 FR 7088), we published
a proposed rule outlining the benefit and payment parameters related to
the risk adjustment program (proposed 2021 Payment Notice), including
several HHS-RADV proposals. Among other things, in this rule, we
proposed updates to the diagnostic classifications and risk factors in
the HHS risk adjustment models beginning with the 2021 benefit year to
reflect more recent claims data, as well as proposed amendments to the
outlier identification process for HHS-RADV in cases where an issuer's
HCC count is low. We proposed that beginning with 2019 benefit year
HHS-RADV, any issuer with fewer than 30 EDGE HCCs (hierarchical
condition categories) within an HCC failure rate group would not be
determined to be an outlier. We also proposed to make 2019 benefit year
HHS-RADV another pilot year for the incorporation of RXCs to allow
additional time for HHS, issuers, and auditors to gain experience with
validating RXCs. On May 14, 2020, we published the HHS Notice of
Benefit and Payment Parameters for 2021 final rule (85 FR 29164) (2021
Payment Notice) that finalized these HHS-RADV changes as proposed. The
proposed updates to the diagnostic classifications and risk factors in
the HHS risk adjustment models were also finalized with some
modifications.
As explained in prior notice-and-comment rulemaking,\16\ while the
PPACA did not include an explicit requirement that the risk adjustment
program operate in a budget-neutral manner, HHS is constrained by
appropriations law to devise and implement its risk adjustment program
in a budget-neutral fashion.\17\ Although the statutory provisions for
many other PPACA programs appropriated funding, authorized amounts to
be appropriated, or provided budget authority in advance
[[Page 76982]]
of appropriations,\18\ the PPACA neither authorized nor appropriated
additional funding for risk adjustment payments beyond the amount of
charges paid in, and did not authorize HHS to obligate itself for risk
adjustment payments in excess of charges collected.\19\ Indeed, unlike
the Medicare Prescription Drug, Improvement and Modernization Act of
2003, which expressly authorized the appropriation of funds and
provided budget authority in advance of appropriations to make Part D
risk-adjusted payments, the PPACA's risk adjustment statute made no
reference to additional appropriations.\20\ Congress did not give HHS
discretion to implement a risk adjustment program that was not budget
neutral. Because Congress omitted from the PPACA any provision
appropriating independent funding or creating budget authority in
advance of an appropriation for the risk adjustment program, we
explained that HHS could not--absent another source of appropriations--
have designed the program in a way that required payments in excess of
collections consistent with binding appropriations law.
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\16\ See, e.g., 78 FR 15441 and 83 FR 16930.
\17\ Also see New Mexico Health Connections v. United States
Department of Health and Human Services, 946 F.3d 1138 (10th Cir.
2019).
\18\ For examples of PPACA provisions appropriating funds, see
PPACA secs. 1101(g)(1), 1311(a)(1), 1322(g), and 1323(c). For
examples of PPACA provisions authorizing the appropriation of funds,
see PPACA secs. 1002, 2705(f), 2706(e), 3013(c), 3015, 3504(b),
3505(a)(5), 3505(b), 3506, 3509(a)(1), 3509(b), 3509(e), 3509(f),
3509(g), 3511, 4003(a), 4003(b), 4004(j), 4101(b), 4102(a), 4102(c),
4102(d)(1)(C), 4102(d)(4), 4201(f), 4202(a)(5), 4204(b), 4206,
4302(a), 4304, 4305(a), 4305(c), 5101(h), 5102(e), 5103(a)(3), 5203,
5204, 5206(b), 5207, 5208(b), 5210, 5301, 5302, 5303, 5304, 5305(a),
5306(a), 5307(a), and 5309(b).
\19\ See 42 U.S.C. 18063.
\20\ Compare 42 U.S.C. 18063 (failing to specify source of
funding other than risk adjustment charges), with 42 U.S.C. 1395w-
116(c)(3) (authorizing appropriations for Medicare Part D risk
adjusted payments); 42 U.S.C. 1395w-115(a) (establishing ``budget
authority in advance of appropriations Acts'' for Medicare Part D
risk adjusted payments).
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B. Stakeholder Consultation and Input
HHS has consulted with stakeholders on policies related to the HHS-
operated risk adjustment program and HHS-RADV. We held a series of
stakeholder listening sessions to gather input, and received input from
numerous interested groups, including states, health insurance issuers,
and trade groups. Prior to the proposed rule, we also issued a white
paper for public comment on December 6, 2019 entitled the HHS Risk
Adjustment Data Validation (HHS-RADV) White Paper (2019 RADV White
Paper).\21\ We considered comments received on the 2019 RADV White
Paper and in connection with previous rules as we developed the
policies in the proposed rule. For this final rule, we considered all
public input we received on the topics addressed in the proposed rule
as we developed the finalized policies.
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\21\ The 2019 RADV White Paper is available at: https://www.cms.gov/files/document/2019-hhs-risk-adjustment-data-validation-hhs-radv-white-paper.
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II. Provisions of the Final Regulations and Analyses and Responses to
Public Comments
In the June 2, 2020 Federal Register (85 FR 33595), we published
the ``Amendments to the HHS-Operated Risk Adjustment Data Validation
Under the Patient Protection and Affordable Care Act's HHS-Operated
Risk Adjustment Program'' proposed rule. The proposed rule proposed
several refinements to the HHS-RADV error rate calculation, and
proposed to transition away from the current prospective application of
HHS-RADV results.\22\ The proposals were designed to specifically
address stakeholder feedback received after the first payment year of
HHS-RADV. In addition to soliciting comments on the specific policy
proposals in the proposed rule, we requested feedback on the potential
impact of the COVID-19 public health emergency on the proposed
effective dates for implementation of the proposals. We received 25
comments from health insurance issuers, industry trade associations,
and other stakeholders. These comments ranged from general support of
or opposition to the proposed changes to specific questions or comments
regarding proposed changes. We also received a number of comments and
suggestions that were outside the scope of the proposed rule that are
not addressed in this final rule. In this final rule, we provide a
summary of the proposed changes, a summary of the public comments
received that directly relate to these proposals, our responses to
these comments, and a description of the provisions we are finalizing.
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\22\ The exception to the current prospective application of
HHS-RADV results is for exiting issuers identified as positive error
rate outliers, whose HHS-RADV results are applied to the risk scores
and transfer amounts for the benefit year being audited. See the
2020 Payment Notice, 84 FR at 17503-17504.
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This rule finalizes the proposed changes to two aspects of HHS-
RADV: (A) The error rate calculation, and (B) the application of HHS-
RADV results, with the modifications described below. Beginning with
the 2019 benefit year of HHS-RADV,\23\ we are finalizing as proposed
the following refinements to the error rate calculation: (1) An
adjustment to the HCC grouping methodology to address the influence of
the HCC hierarchies and coefficient estimation groups; (2) a sliding
scale adjustment for calculating an issuer's adjustment factor that
changes the confidence intervals for determining outliers and applies a
sliding scale adjustment in cases where an outlier issuer is close to
the edges of the confidence interval for one or more HCC failure rate
groups; and (3) a modification to the error rate calculation in cases
where a negative error rate outlier issuer also has a negative failure
rate. We are also finalizing the transition from the current
prospective application of HHS-RADV results \24\ to an approach that
would apply HHS-RADV results to the benefit year being audited. After
consideration of comments, we will switch to the concurrent application
of HHS-RADV results beginning with the 2020 benefit year.\25\ We
believe these policies address stakeholder feedback received and our
experience with the first payment year of HHS-RADV on these issues.
These finalized policies seek to further the integrity of HHS-RADV
while maintaining stability, promoting fairness and improving the
predictability of HHS-RADV. The following is a summary of the comments
received on the proposed rule's timeline for implementing these
policies: \26\
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\23\ As part of the Administration's efforts to combat the
Coronavirus Disease 2019 (COVID-19), we announced the postponement
of the 2019 benefit year HHS-RADV process. See https://www.cms.gov/files/document/2019-HHS-RADV-Postponement-Memo.pdf. Also, we have
provided further guidance on the updated schedule for the 2019
benefit year HHS-RADV, which is outlined in the 2019 Benefit Year
Timeline of Activities: https://www.regtap.info/uploads/library/HRADV_Timeline_091020_5CR_091020.pdf.
\24\ The exception to the current prospective application of
HHS-RADV results is for exiting issuers identified as positive error
rate outliers, whose HHS-RADV results are applied to the risk scores
and transfer amounts for the benefit year being audited.
\25\ As detailed in section II.B, to effectuate the transition
beginning with the 2020 benefit year, we will aggregate results from
the 2019 and 2020 benefit years of HHS-RADV for non-exiting issuers
using the average error rate approach and apply the aggregated
results to 2020 risk scores and transfers.
\26\ We note that a correction notice was issued for the
proposed rule to address the misalignment of certain text between
the final draft version of the proposed rule approved for
publication and the published version in the Federal Register. See
85 FR 38107 (June 25, 2020). Since publishing the correction notice,
an additional error between the two versions was identified. When
describing the current HHS-RADV error methodology in the proposed
rule at 85 FR 33599, the upper bound of the confidence interval was
incorrectly published as U BG = [mu]{GF RG{time} -sigma_cutoff *
Sd{GF RG{time} . This formula should have instead been published as
U BG = [mu]{GF RG{time} + sigma_cutoff * Sd{GF RG{time} .
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Comments: One commenter was concerned that the COVID-19 public
health emergency would impact the completeness of 2019 (and possibly
2020) data while another commenter
[[Page 76983]]
expected COVID-19 to affect chart retrieval and provider documentation
within the chart. One commenter did not see a need to further delay the
stabilizing measures in the proposed rule due to COVID-19.
Response: Recognizing the need for providers and provider
organizations to focus exclusively on caring for patients during the
COVID-19 public health emergency, we postponed the start of 2019
benefit year HHS-RADV activities.\27\ As recently announced, IVA
samples for 2019 benefit year HHS-RADV will be released in January 2021
and we anticipate 2020 benefit year HHS-RADV will commence as usual
with the release of IVA samples in May 2021.\28\ We continue to monitor
the COVID-19 pandemic, including potential medical record retrieval
issues and will consider whether additional flexibilities for HHS-RADV
are appropriate. However, we are not codifying or finalizing any
specific COVID-19 policies in this rulemaking.
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\27\ https://www.cms.gov/files/document/2019-HHS-RADV-Postponement-Memo.pdf.
\28\ See the ``2019 Benefit Year HHS-RADV Activities Timeline''
https://www.regtap.info/uploads/library/HRADV_Timeline_091020_5CR_091020.pdf.
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Comments: Some commenters who supported the proposed error rate
calculation changes asked HHS to also apply the changes to the 2017 and
2018 benefit years of HHS-RADV. A different commenter opposed applying
the proposed changes starting with the 2019 benefit year HHS-RADV,
expressing the belief it would be retroactive to do so, and instead
supporting the adoption of these proposals for future benefit years.
Other commenters supported policies in the rule applying beginning with
the 2019 benefit year.
Response: The policies being finalized in this rule only impact the
calculation of error rates and the application of the HHS-RADV results
that occur at the end of the HHS-RADV process. Because the 2019 benefit
year of HHS-RADV has not begun \29\ and, under the updated timeline,
the calculation of the error rates for 2019 benefit year of HHS-RADV
will not occur until February 2022, we disagree that applying the error
rate calculation refinements finalized in this rule to the 2019 benefit
year would be retroactive. Further, for the reasons outlined in the
proposed rule and this rule, we believe these refinements are important
and should be applied as soon as practicable. However, we believe that
application of this rule to 2017 and 2018 benefit years of HHS-RADV
would not be appropriate because the applicable error rate calculations
are complete.30 31 We are therefore applying the error rate
calculation modifications finalized in this rule beginning with the
2019 benefit year of HHS-RADV, as proposed. Similarly, for the
application of HHS-RADV results, in light of the delay of 2019 benefit
year HHS-RADV and for the reasons outlined below in Section II.B., we
are finalizing the policy to begin applying HHS-RADV results to the
benefit year audited beginning with the 2020 benefit year which is as
soon as practicable.\32\
---------------------------------------------------------------------------
\29\ As noted above, the start of the 2019 benefit year HHS-RADV
process was postponed until the 2021 calendar year due to the COVID-
19 public health emergency.
\30\ See the 2017 HHS-RADV timeline, available at: https://www.regtap.info/uploads/library/HRADV_JobAid_timeline_5CR_032819.pdf; and https://www.regtap.info/uploads/library/HRADV_Timeline_073119_5CR_120219.pdf. Also see the
2018 HHS-RADV timeline, available at: https://www.regtap.info/uploads/library/HRADV_Timeline_030420_V1_RETIRED_5CR_041320.pdf.
\31\ See the 2017 and 2018 HHS-RADV results memos, available at:
https://www.cms.gov/CCIIO/Programs-and-Initiatives/Premium-Stabilization-Programs/Downloads/2017-Benefit-Year-HHS-Risk-Adjustment-Data-Validation-Results.pdf and https://www.cms.gov/CCIIO/Programs-and-Initiatives/Premium-Stabilization-Programs/Downloads/2018_BY_RADV_Results_Memo.pdf.
\32\ As detailed below, to effectuate the transition beginning
with the 2020 benefit year, we will aggregate results from the 2019
and 2020 benefit years of HHS-RADV for non-exiting issuers using the
average error rate approach and apply the aggregated results to 2020
benefit year risk scores and transfers.
---------------------------------------------------------------------------
A. Error Rate Calculation Methodology
HHS recognizes that variation in provider documentation of
enrollees' health status across provider types and groups results in
natural variation and validation errors. Therefore, in the 2019 Payment
Notice final rule,\33\ HHS adopted the current error rate calculation
methodology to evaluate material statistical deviation in failure
rates. The current methodology was adopted to avoid adjusting issuers'
risk scores and transfers due to expected variation and error. Instead,
HHS amends an issuer's risk score only when the issuer's failure rate
materially deviates from a statistically meaningful national metric.
HHS defines the national statistically meaningful metric as the
weighted mean and standard deviation of the failure rate calculated
based on all issuers' HHS-RADV results. Each issuer's failure rates are
compared to these national metrics to determine whether the issuer's
failure rate is an outlier. Based on outlier issuers' failure rate
results, their error rates are calculated and applied to their plan
liability risk scores.\34\
---------------------------------------------------------------------------
\33\ See 83 FR 16930 at 16961 through 16965.
\34\ As detailed further below, these risk score changes are
then used to adjust risk adjustment transfers for the applicable
state market risk pool.
---------------------------------------------------------------------------
In response to comments received on the 2019 RADV White Paper and
to help put the proposed changes in context, the proposed rule outlined
the current error rate calculation methodology.\35\ This included
information on how HHS uses outlier issuer group failure rates to
adjust enrollee risk scores, calculates an outlier issuer's error rate,
and applies that error rate to the outlier issuer's plan liability risk
score.
---------------------------------------------------------------------------
\35\ See 85 FR at 33599-33600. Also see, supra, note 26.
---------------------------------------------------------------------------
Consistent with 45 CFR 153.350(c), HHS applies the outlier issuer's
error rate to adjust that issuer's applicable benefit year plan
liability risk score.\36\ This risk score change, which also impacts
the state market average risk score, is then used to adjust the
applicable benefit year's risk adjustment transfers for the applicable
state market risk pool. Due to the budget-neutral nature of the HHS-
operated risk adjustment program, adjustments to one issuer's risk
scores and risk adjustment transfers based on HHS-RADV findings will
affect other issuers in the state market risk pool (including those who
were not identified as outliers) because the state market average risk
score is recalculated to reflect the change in the outlier issuer's
plan liability risk score. This also means that issuers that are exempt
from HHS-RADV for a given benefit year may have their risk adjustment
transfers adjusted based on other issuers' HHS-RADV results.
---------------------------------------------------------------------------
\36\ Exiting positive error rate outlier issuer risk score error
rates are currently applied to the plan liability risk scores and
risk adjustment transfer amounts for the benefit year being audited.
As detailed in Section II.B, we are finalizing the proposed
transition from the prospective application of HHS-RADV results such
that risk score error rates will also be applied to the benefit year
being audited beginning with the 2020 benefit year of HHS-RADV for
non-exiting issuers.
---------------------------------------------------------------------------
In response to stakeholder concerns, comments to the 2019 RADV
White Paper, and our analyses of 2017 benefit year HHS-RADV results,
HHS proposed to modify the HCC grouping methodology used to calculate
failure rates by combining certain HCCs with the same risk score
coefficient for grouping purposes, and to refine the error estimation
methodology to mitigate the impact of the ``payment cliff'' effect, in
which some issuers with similar HHS-RADV findings may experience
different adjustments to their risk scores and subsequently adjusted
transfers. We also proposed changes to mitigate the impact of HHS-RADV
[[Page 76984]]
adjustments that result from negative error rate outlier issuers with
negative failure rates. After consideration of comments, we are
finalizing the refinements to the error rate calculation, as proposed,
beginning with the 2019 benefit year of HHS-RADV. These targeted
policies are intended as interim, incremental measures while we
continue to analyze HHS-RADV results and consider potential further
refinements and changes to the HHS-RADV methodology, including
potential significant changes to the outlier determination process and
the error rate methodology, for future benefit years.
1. HCC Grouping for Failure Rate Calculation
HHS groups medical conditions in multiple distinct ways during the
risk adjustment and HHS-RADV processes.\37\ For risk adjustment model
development, this includes: (1) The hierarchies of HCCs, (2) HCC
coefficient estimation groups, (3) a priori stability constraints, and
(4) hierarchy violation constraints. For HHS-RADV, medical conditions
are grouped for the HHS-RADV HCC failure rate groups. These grouping
processes are not concurrent. More specifically, the grouping processes
related to model development are implemented prior to the benefit year
and the HHS-RADV HCC failure rate groups are implemented after the
benefit year. Our experience in the initial years of HHS-RADV found
that differences among these grouping processes interact in varying
ways and may result in greater or lesser HHS-RADV adjustments than may
be warranted in certain circumstances.
---------------------------------------------------------------------------
\37\ See 85 FR at 33601.
---------------------------------------------------------------------------
The first grouping of medical conditions--HCCs--is used to
aggregate thousands of standard disease codes into medically meaningful
but statistically manageable categories. HCCs in the 2019 benefit year
HHS risk adjustment models were derived from ICD-9-CM codes \38\ that
are aggregated into diagnostic groups (DXGs), which are in turn
aggregated into broader condition categories (CCs). Then, clinical
hierarchies are applied to the CCs, so that an enrollee receives an
increase to their risk score for only the most severe manifestation
among related diseases that may appear in their medical claims data on
an issuer's EDGE server.\39\ Condition categories become HCCs once
these hierarchies are imposed.
---------------------------------------------------------------------------
\38\ In the 2021 Payment Notice, we finalized several updates to
the HHS-HCC clinical classification by using more recent claims data
to develop updated risk factors that apply beginning with the 2021
benefit year risk adjustment models. See 85 FR at 29175.
\39\ The process for creating hierarchies is an iterative
process that considers severity, as well as costs of the HCCs in the
hierarchies and clinical input, among other factors. For information
on this process, see section 2.3 of the June 17, 2019 document
``Potential Updates to HHS-HCCs for the HHS-operated Risk Adjustment
Program'' (2019 HHS-HCC Potential Updates Paper), available at
https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/Potential-Updates-to-HHS-HCCs-HHS-operated-Risk-Adjustment-Program.pdf#page=11.
---------------------------------------------------------------------------
As noted previously, for a given hierarchy, if an enrollee has more
than one HCC recorded in an issuer's EDGE server, only the most severe
of those HCCs will be applied for the purposes of the risk adjustment
model and plan liability risk score calculation. Although HCCs reflect
hierarchies among related disease categories, multiple HCCs can
accumulate for enrollees with unrelated diseases; that is, the model is
``additive.'' For example, an enrollee with both diabetes and asthma
would have (at least) two separate HCCs coded and the predicted cost
for that enrollee will reflect increments for both conditions.
In the risk adjustment models, estimated coefficients of the
various HCCs within a hierarchy ensure that more severe and expensive
HCCs within that hierarchy receive higher risk factors than less severe
and less expensive HCCs. Additionally, as a part of the recalibration
of the risk adjustment models, HHS has grouped some HCCs such that the
coefficients of two or more HCCs are equal in the fitted risk
adjustment models and only one model factor is assigned to an enrollee
regardless of the number of HCCs from that group present for that
enrollee on the issuer's EDGE server,\40\ giving rise to the second set
of condition groupings used in risk adjustment. We impose these HCC
coefficient estimation groups for a number of reasons, including the
limitation of diagnostic upcoding by severity within an HCC hierarchy
and the reduction of additivity within disease groups (but not across
disease groups) in order to decrease the sensitivity of the models to
coding proliferation.
---------------------------------------------------------------------------
\40\ As described in the ``Potential Updates to HHS-HCCs for the
HHS-operated Risk Adjustment Program'' Paper, available at ``https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/Potential-Updates-to-HHS-HCCs-HHS-operated-Risk-Adjustment-Program.pdf#page=11.
---------------------------------------------------------------------------
Although some of these HCC coefficient estimation groups occur
within hierarchies, some HCC coefficient estimation groups include HCCs
that do not share a hierarchy. Within an HCC coefficient estimation
group, each HCC will have the same coefficient in our risk adjustment
models. However, as with hierarchies, only one risk marker is triggered
by the presence of one or more HCCs in the HCC coefficient estimation
groups. These HCC coefficient estimation groups are identified in DIY
Software Table 6 for the adult models and DIY Software Table 7 for the
child models. The adult model HCC coefficient estimation groups for the
V05 risk adjustment models \41\ are displayed in Table 1:
---------------------------------------------------------------------------
\41\ The shorthand ``V05'' refers to the current HHS-HCC
classification for the HHS risk adjustment models, which applies
through the 2020 benefit year. V07 is the HHS-HCC classification for
the HHS risk adjustment models, which applies beginning with the
2021 benefit year.
Table 1--HCC Coefficient Estimation Groups From Adult Risk Adjustment
Models V05
------------------------------------------------------------------------
Adult model HCC
HHS HCC V05 HHS-HCC label coefficient
estimation group
------------------------------------------------------------------------
19........................ Diabetes with Acute G01
Complications.
20........................ Diabetes with Chronic G01
Complications.
21........................ Diabetes without G01
Complication.
26........................ Mucopolysaccharidosis.... G02A
27........................ Lipidoses and G02A
Glycogenosis.
29........................ Amyloidosis, Porphyria, G02A
and Other Metabolic
Disorders.
30........................ Adrenal, Pituitary, and G02A
Other Significant
Endocrine Disorders.
54........................ Necrotizing Fasciitis.... G03
55........................ Bone/Joint/Muscle G03
Infections/Necrosis.
61........................ Osteogenesis Imperfecta G04
and Other
Osteodystrophies.
[[Page 76985]]
62........................ Congenital/Developmental G04
Skeletal and Connective
Tissue Disorders.
67........................ Myelodysplastic Syndromes G06
and Myelofibrosis.
68........................ Aplastic Anemia.......... G06
69........................ Acquired Hemolytic G07
Anemia, Including
Hemolytic Disease of
Newborn.
70........................ Sickle Cell Anemia (Hb- G07
SS).
71........................ Thalassemia Major........ G07
73........................ Combined and Other Severe G08
Immunodeficiencies.
74........................ Disorders of the Immune G08
Mechanism.
81........................ Drug Psychosis........... G09
82........................ Drug Dependence.......... G09
106....................... Traumatic Complete Lesion G10
Cervical Spinal Cord.
107....................... Quadriplegia............. G10
108....................... Traumatic Complete Lesion G11
Dorsal Spinal Cord.
109....................... Paraplegia............... G11
117....................... Muscular Dystrophy....... G12
119....................... Parkinson's, G12
Huntington's, and
Spinocerebellar Disease,
and Other
Neurodegenerative
Disorders.
126....................... Respiratory Arrest....... G13
127....................... Cardio-Respiratory G13
Failure and Shock,
Including Respiratory
Distress Syndromes.
128....................... Heart Assistive Device/ G14
Artificial Heart.
129....................... Heart Transplant......... G14
160....................... Chronic Obstructive G15
Pulmonary Disease,
Including Bronchiectasis.
161....................... Asthma................... G15
187....................... Chronic Kidney Disease, G16
Stage 5.
188....................... Chronic Kidney Disease, G16
Severe (Stage 4).
203....................... Ectopic and Molar G17
Pregnancy, Except with
Renal Failure, Shock, or
Embolism.
204....................... Miscarriage with G17
Complications.
205....................... Miscarriage with No or G17
Minor Complications.
207....................... Completed Pregnancy With G18
Major Complications.
208....................... Completed Pregnancy With G18
Complications.
209....................... Completed Pregnancy with G18
No or Minor
Complications.
------------------------------------------------------------------------
The HHS-HCC model also incorporates a small number of ``a priori
stability constraints'' to stabilize estimates that might vary greatly
due to small sample size. These a priori stability constraints differ
from the HCC coefficient estimation groups in how the corresponding
estimates are counted. In contrast to HCC coefficient estimation
groups, with a priori stability constraints, a person can have more
than one indicated condition (each with the same coefficient value) as
long as the HCCs are not in the same hierarchy. Prior to the 2021
benefit year recalibration, only one a priori stability constraint was
applied to the models, and this constraint was only applied to the
child models.\42\
---------------------------------------------------------------------------
\42\ In the 2021 Payment Notice (85 FR at 29178), we finalized
an additional a priori stability constraint to the child models,
constraining HCC 218 Extensive Third Degree Burns and HCC 223 Severe
Head Injury to have the same risk adjustment coefficient due to
small sample size, and revised the single transplant stability
constraint in the child models to be two stability constraints to
better distinguish transplant cost differences.
---------------------------------------------------------------------------
HCC coefficient estimation groups and a priori stability
constraints are both applied in the initial phase of risk adjustment
regression modeling. Other constraints may be applied in later stages
depending on regression results. For example, HCCs may be constrained
equal to each other if there is a hierarchy violation (a lower severity
HCC has a higher estimate than a higher severity HCC in the same
hierarchy). HCC coefficients may also be constrained to 0 if the
estimates fitted by the regression model are negative.
The final set of groupings is imposed during the error estimation
stage of the HHS-RADV process. In this process, HCCs are categorized
into low, medium, and high HCC failure rate groups. To create the HCC
failure rate groupings for HHS-RADV, the first step is to calculate the
national average failure rate for each HCC individually. The second
step involves ranking HCCs in order of their failure rates and then
dividing them into three groups--a low, medium, and high failure rate
group--such that the total frequency of HCCs in each group nationally
as recorded in EDGE data across all IVA samples (or SVA samples, if
applicable) are roughly equal. These HCC failure rate groups form the
basis of the failure rate outlier determination process, with each
failure rate group receiving an independent assessment of outlier
status for each issuer.\43\
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\43\ For a table of the HCC failure rate groupings for 2017
benefit year HHS-RADV, see the 2019 RADV White Paper, Appendix E.
---------------------------------------------------------------------------
Based on our experience with the initial years of HHS-RADV, HHS
observed that, in certain situations, the risk adjustment HCC
hierarchies and HCC coefficient estimation groups can influence and
interact with the HHS-RADV HCC failure rate groupings in ways that
could result in misalignments.\44\
---------------------------------------------------------------------------
\44\ See 85 FR at 33603-33604. Also see Section 3.3 of the 2019
RADV White Paper.
---------------------------------------------------------------------------
Based on HHS's initial analysis of the 2017 benefit year HHS-RADV
results, and in response to comments to the 2019 RADV White Paper, HHS
considered an option in the proposed rule to address the influence of
the HCC hierarchies and HCC coefficient estimation groups on the HCC
failure rate groupings in HHS-RADV. We proposed to modify the creation
of HHS-RADV HCC failure rate groupings to place all HCCs that share an
HCC coefficient estimation group in the adult risk adjustment models
(see Table 1 for the list of the HCC coefficient estimation groups in
the V05 classification) into the same HCC failure rate grouping.
Specifically, we proposed that, when HHS calculates EDGE and IVA
frequencies for each individual HCC, we would aggregate HCCs that are
in the same HCC coefficient estimation group
[[Page 76986]]
in the adult risk adjustment models (and, therefore, have coefficients
constrained to be equal to one another) into one ``Super'' HCC, prior
to calculating individual HCC failure rates and sorting the HCCs into
low, medium, and high failure rate groups for HHS-RADV. These new
frequencies, including the aggregated frequencies of HCC coefficient
estimation groups and the individual frequencies of all other HCCs that
are not aggregated with other HCCs because they are not in any
coefficient estimation groups, would be considered frequencies of
``Super HCCs.''
Under the proposed methodology, we would modify the current HCC
failure rate grouping methodology as follows:
[GRAPHIC] [TIFF OMITTED] TR01DE20.006
Where:
c is the index of the cth Super HCC;
freqEDGEh is the frequency of an HCC h occurring in EDGE
data; that is, the number of sampled enrollees recording HCC h in
EDGE data across all issuers participating in HHS-RADV;
freqEDGEc is the frequency of a Super HCC c occurring in
EDGE data across all issuers participating in HHS-RADV; that is, the
sum of freqEDGEh for all HCCs that share an HCC
coefficient estimation group in the adult models:
[GRAPHIC] [TIFF OMITTED] TR01DE20.007
When an HCC is not in an HCC coefficient estimation group in the
adult risk adjustment models, the freqEDGEc for that HCC
will be equivalent to freqEDGEh;
freqIVAh is the frequency of an HCC h occurring in IVA
results (or SVA results, as applicable); that is, the number of
sampled enrollees recording HCC h in IVA (or SVA, as applicable)
results across all issuers participating in HHS-RADV;
freqIVAc is the frequency of a Super HCC c occurring in
IVA results (or SVA results, as applicable) across all issuers
participating in HHS-RADV; that is, the sum of freqIVAh
for all HCCs that share an HCC coefficient estimation group in the
adult risk adjustment models:
[GRAPHIC] [TIFF OMITTED] TR01DE20.008
And;
FRc is the national overall (average) failure rate of
Super HCC c across all issuers participating in HHS-RADV.
Then, the failure rates for all Super HCCs would be grouped according
to the current HHS-RADV failure rate grouping methodology.
This approach would ensure that HCCs with the same estimated costs
in the adult risk adjustment models that share an HCC coefficient
estimation group do not contribute independently and additively to an
issuer's failure rate in a HCC failure rate grouping. This proposal
would refine the current methodology to better identify and focus HCC
failure rates used in outlier determination on actual differences in
risk and costs. Our tests of this proposed policy on HHS-RADV results
data revealed that between an estimated 85.2 percent (2018 data) and
98.1 percent (2017 data) of the occurrences of HCCs on EDGE belong to
HCCs that would be assigned to the same failure rate groups under the
proposed ``Super HCC'' methodology as they have been under the current
methodology as seen in Table 2. Although the impact on individual
issuer results may vary depending upon the accuracy of their EDGE data
submissions and the rate of occurrence of various HCCs in their
enrollee population, the national metrics used for HHS-RADV, that is,
the weighted means and weighted standard deviations, would only be
slightly affected, as seen in Table 3. The stability of these metrics
and high proportion of EDGE frequencies of HCCs that would be assigned
to the same failure rate group under the proposed and current sorting
methodologies reflects that the most common conditions would have
similar failure rates under both methodologies. However, the failure
rate estimates of less common conditions may be stabilized with the
proposed creation of Super HCCs by ensuring these conditions are
grouped alongside more common, related conditions.
[GRAPHIC] [TIFF OMITTED] TR01DE20.009
[[Page 76987]]
In testing this proposal to create Super HCCs in HHS-RADV, we
grouped HCCs in the same HCC coefficient estimation group in the adult
risk adjustment models. We chose to use the adult risk adjustment
models for testing because the majority of the population with HCCs in
the HHS-RADV samples are subject to the adult models (88.3 percent for
the 2017 benefit year; 89.1 percent for the 2018 benefit year).\45\ As
such, the adult models' HCC coefficient estimation groups will be
applicable to the vast majority of enrollees and we believe that the
use of HCC coefficient estimation groups present in the adult risk
adjustment models sufficiently balances the representativeness and
accuracy of HCC failure rate estimates across the entire population in
aggregate. Therefore, we proposed to use HCC coefficient estimation
groups in the adult risk adjustment models to define Super HCCs for all
HHS-RADV sample enrollees, regardless of the risk adjustment model to
which they are subject.
---------------------------------------------------------------------------
\45\ For 2017, this was calculated after removing issuers in
Massachusetts and incorporating cases where issuers failed pairwise
and the SVA sub-sample was used.
---------------------------------------------------------------------------
In developing this policy, we limited the grouping of risk
adjustment HCCs into Super HCCs for HHS-RADV to HCC coefficient
estimation groups alone and did not consider including a priori
stability constraints or hierarchy violation constraints in the
aggregation of Super HCCs.\46\ We also did not consider hierarchy
violation constraints as a part of the sorting algorithm in order to
balance complexity and consistency. For example, if, in a given benefit
year, the magnitudes of two coefficients that share a hierarchy happen
to decrease in order of their conditions' theoretical severity, the
coefficients would violate the assumptions of the hierarchy structure
and would be subject to a hierarchy violation constraint in that year's
risk adjustment models. However, if the magnitude of those two
coefficients increase in the order of their conditions' severity in the
subsequent year, as would generally be expected, the coefficients would
be consistent with the assumptions of the hierarchy structure and would
not be constrained to be equal as a part of a hierarchy violation
constraint. Because these year-to-year changes in hierarchy violation
constraints are based solely on the magnitude of each year's initial
coefficient estimates, using them in the grouping of Super HCCs would
make those groupings less stable and transparent, and would reduce
predictability for issuers.
---------------------------------------------------------------------------
\46\ Both a priori stability constraints and hierarchy violation
constraints are described earlier in this section (Section II.A.1)
of the rule. Also see 85 FR at 33602-33603.
---------------------------------------------------------------------------
Due to these considerations, we proposed to combine HCCs into Super
HCCs defined only by HCC coefficient estimation groups in the adult
risk adjustment models prior to sorting the HCCs into low, medium and
high failure rate groups for HHS-RADV, starting with the 2019 benefit
year of HHS-RADV. As proposed, these Super HCC groupings would apply to
all HHS-RADV sample enrollees, regardless of the risk adjustment models
to which they are subject. Once sorted into failure rate groups, the
failure rates for all Super HCCs, both those composed of a single HCC
and those composed of the aggregate frequencies of HCCs that share an
HCC coefficient estimation group in the adult risk adjustment models,
would be grouped according to the current HHS-RADV failure rate
grouping methodology. We solicited comment on all aspects of this
proposal. We also solicited comments on whether, in addition to the
Super HCCs based on the adult risk adjustment models, HHS should create
separate infant Super HCCs for each maturity and severity type in the
infant risk adjustment models. Additionally, we solicited comments on
whether we should consider incorporating a priori stability constraints
from the child models or hierarchy violation constraints from the adult
models when defining Super HCCs.
After consideration of the comments received, we are finalizing
this policy as proposed, and will combine HCCs in HCC coefficient
estimation groups in the adult risk adjustment models, which
effectively have equal coefficients, into Super HCCs prior to sorting
the HCCs into low, medium and high failure rate groups for HHS-RADV.
This refinement to the error rate calculation will apply starting with
the 2019 benefit year of HHS-RADV. These Super HCC groupings will apply
to all HHS-RADV sample enrollees, regardless of the risk adjustment
models to which they are subject. Therefore, although the aggregation
will be based upon the adult models, enrollees subject to the child and
infant models will have their HCCs included in the aggregated counts
when they have an HCC that is listed as sharing a coefficient
estimation group with other HCCs in the adult models. The resulting
Super HCCs will then be sorted into high, medium, and low failure rate
groups using the sorting process described in the applicable benefit
year's HHS-RADV Protocols.\47\ Once sorted into failure rate groups,
the failure rates for all Super HCCs, both those composed of a single
HCC and those composed of the aggregate frequencies of HCCs that share
an HCC coefficient estimation group in the adult risk adjustment
models, will be grouped according to the current HHS-RADV failure rate
grouping methodology.
---------------------------------------------------------------------------
\47\ See Section 11.3.1 of the 2018 HHS-RADV Protocols at
https://www.regtap.info/uploads/library/HRADV_2018Protocols_070319_RETIRED_5CR_070519.pdf for a description
of the process prior to the introduction of Super HCCs. Beginning
with the 2019 benefit year of HHS-RADV, Super HCCs would take the
place of HCCs in the process. The 2019 HHS-RADV Protocols have thus
far only been published in part at https://www.regtap.info/uploads/library/HRADV_2019_Protocols_111120_5CR_111120.pdf. The section of
the 2019 HHS-RADV Protocols pertaining to HCC grouping for failure
rate calculations is not included in the current version. Once
published, this section will be updated to include steps related to
creation of Super HCCs.
---------------------------------------------------------------------------
Comments: All comments on this policy supported the proposal to
adjust the HCC failure rate grouping methodology to define Super HCCs
based upon the HCC coefficient estimation groups in the adult risk
adjustment models. Several commenters requested we expand the proposed
definition of Super HCCs to include the grouping of conditions used to
create the variables for the infant models. Some of these commenters
added that implementing this expansion for the infant models should be
done in a way that avoids year-to-year stability concerns, if possible,
while other comments requested that we publish an analysis on the
impacts of such an expansion prior to implementing it.
In addition, some commenters agreed that the inclusion of a priori
stability constraints from the child models would be inappropriate due
to their additive nature, with a few of these commenters also agreeing
that hierarchy violation constraints should not factor into the
definitions of Super HCCs. However, other commenters requested that HHS
include HCCs involved in a hierarchy violation constraint in the same
Super HCC. Some commenters requested we publish an analysis on
including a priori stability constraints as part of the process to
create Super HCCs.
Response: We are finalizing the refinement to the HCC failure rate
grouping methodology as proposed and will place all HCCs that share an
HCC coefficient estimation group in the adult risk adjustment models
into the same HCC failure rate grouping beginning with the 2019 benefit
year of HHS-RADV. Although the aggregation will be based upon the adult
models, the child
[[Page 76988]]
and infant models will have their HCCs included in the aggregated
counts when they have an HCC that is listed as sharing a coefficient
estimation group with other HCCs in the adult models. As explained in
the proposed rule and in this rule, we believe this change mitigates
the misalignments that occur when HCCs with the same risk score
coefficient are sorted into different HCC failure rate groupings while
increasing the stability of year-to-year HCC failure rate grouping
assignments. To promote fairness and ensure the integrity of the
program, we do not believe that a RADV finding that reflects an EDGE
data miscoding of one condition as another condition from the same
coefficient estimation group should contribute to any of an issuer's
three failure rates. This refinement to the HHS-RADV failure rate
grouping methodology ensures that these types of HCC miscodings with no
risk score impact do not impact an issuer's HHS-RADV error rate.
We appreciate the comments about the creation of separate infant
Super HCCs and investigated the potential adoption of separate infant
model terms. Our analysis found that such an approach would likely
result in more year-to-year uncertainty and instability due to the
relatively small sample size for some infant model terms--notably, only
around 5 percent of 2017 \48\ and 2018 HHS-RADV sample enrollees in
strata 1 through 9 with EDGE HCCs were infants. As a result, HCC counts
and failure rates for potential infant-only Super HCCs would be more
likely to vary due to random selection, yielding less year-to-year
stability among HCC failure rate group assignments. Therefore, in the
interest of stability, we believe that basing the definitions of Super
HCCs on coefficient estimation groups from the adult risk adjustment
models is more appropriate. As noted earlier, the majority of the
population with HCCs in the HHS-RADV samples are subject to the adult
models (88.3 percent for the 2017 benefit year; 89.1 percent for the
2018 benefit year).\49\
---------------------------------------------------------------------------
\48\ For 2017, this was calculated after removing issuers in
Massachusetts and incorporating cases where issuers failed pairwise
agreement and the SVA sub-sample was used.
\49\ Ibid.
---------------------------------------------------------------------------
We also appreciate the comments regarding inclusion of hierarchy
violation constraints when creating Super HCCs, such that HCCs involved
in a hierarchy violation constraint would be included in the same Super
HCC. As explained in the proposed rule, we did not consider hierarchy
violation constraints when developing the Super HCC proposal in order
to balance complexity and consistency, since these constraints can
change from year-to-year as a natural result of the annual
recalibration updates to the model coefficients. Similar to the
concerns for the separate infant model Super HCCs, these year-to-year
changes would make HCC groupings for these HCCs less stable and
transparent, and would reduce predictability for issuers. Further, we
note that hierarchy violation constraints may occur in a single metal-
level and age group in just one of the three data years used to create
the blended coefficients. For example, the 2021 benefit year
coefficients reflect a weighted average of coefficients calculated
separately from 2016, 2017, and 2018 benefit year EDGE data. If there
is a hierarchy violation among three HCCs that share a hierarchy in the
silver adult model fitted to 2018 EDGE data, a hierarchy violation
constraint would be applied to the three coefficients calculated from
that data set alone, excluding any coefficients from the 2016 and 2017
benefit years, and any other metal levels and age groups from the 2018
benefit year. As a result, when the coefficients from the separate data
years are blended, the hierarchy violation constraint may not be
apparent in the final coefficients and the final coefficients for the
HCCs in the affected hierarchy may differ from one another.
Additionally, even if a hierarchy violation constraint is necessary
for the same hierarchy in all three data years, and is therefore
apparent in the final risk adjustment coefficients, the hierarchy
violation constraint could involve a very small number of enrollees
specific to a particular metal level and age group model (for example,
the gold metal level child model). Although the coefficients involved
in such a hierarchy violation constraint would all be equal to one
another, the coefficients from age group models unaffected by hierarchy
violation constraints are likely to differ according to the severity of
the HCCs in the hierarchy, and it would be appropriate to capture the
resulting risk score differences in HHS-RADV. Therefore, a methodology
that included hierarchy violation constraints in the definition of
Super HCCs would have to keep the relevant HCCs in the applicable metal
level and age group model affected by the hierarchy violation
constraints separate from the same HCCs in metal levels and age group
models that are unaffected. This would result in individual Super HCCs
dedicated to only the HCCs affected by a given hierarchy violation
constraint from HHS-RADV sample enrollees subject to the affected metal
level and age group model. As such, the individual Super HCC failure
rate calculation for that hierarchy violation constraint would be based
on a very small sample, leading to instability for the HCC failure rate
group assignment for that hierarchy violation constraint. It would also
increase the complexity associated with adoption of this refinement to
the HCC failure rate grouping methodology. In contrast, coefficient
estimation groups are consistent across all five metal level adult
models, and are almost identical to the coefficient estimation groups
across all five metal level child models. As such, it is much more
appropriate to define Super HCCs for all enrollees based on the adult
coefficient estimation groups, because nearly all enrollees with an
EDGE miscoding between two HCCs in a coefficient estimation group would
be assigned the same risk score for either HCC. This consistency allows
us to utilize a much larger sample size during the calculation of Super
HCC-specific failure rates, namely, the entire HHS-RADV sample,
resulting in more stable failure rate estimates and HCC failure rate
group assignments. Defining Super HCCs based on the adult coefficient
estimation groups is also easy to implement as an interim measure to
address the identified misalignment that occurs in situations where
HCCs in the same HCC coefficient estimation group are sorted into
different HCC failure rate groupings.
Finally, we appreciate the comments requesting more analysis on
including a priori stability constraints from the child models in the
definition of Super HCCs. For similar reasons to those noted in the
discussion of the hierarchy violation constraints and variables from
infant models, including a priori stability constraints from the child
models in the definition of Super HCCs would result in very small
sample sizes for the purposes of determining the Super HCC-level
failure rate prior to sorting into HCC failure rate groups. As such,
our analysis of the inclusion of a priori stability constraints for the
child models found that it would likely result in less year-to-year
uncertainty in that model than basing Super HCCs on coefficient
estimation groups alone. Moreover, HCCs subject to a priori stability
constraints are additive in the risk adjustment models, whereas HCCs
within coefficient estimation groups are not.\50\ This difference is
due to the fact
[[Page 76989]]
that many of the a priori stability constraints reflect unrelated
conditions, and therefore, a miscoding of one HCC within an a priori
stability constraint would not be expected to impact the likelihood
that another HCC in that a priori stability constraint would also be
miscoded. In contrast, coefficient estimation groups reflect related
conditions that could conceivably be miscoded as one another on EDGE.
Therefore, we do not believe that it is appropriate to include a priori
stability constraints from the child models in the definition of Super
HCCs.
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\50\ The additive nature of HCCs subject to a priori stability
constraints as opposed to other groupings of HCCs in the risk
adjustment models is discussed in greater detail in the proposed
rule (85 FR 33605). We have also previously discussed this feature
of a priori stability constraints in the 2019 HHS-HCC Potential
Updates Paper, available at: https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/Potential-Updates-to-HHS-HCCs-HHS-operated-Risk-Adjustment-Program.pdf#page=11.
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Comments: A few commenters supported the proposed changes as
valuable interim measures, but stated that the HCC failure rate
grouping methodology may require additional improvements in the future
and asked that HHS continue to analyze and propose refinements to the
HCC grouping process for HHS-RADV. Some of these commenters emphasized
that stability of HCC failure rate group assignment from year-to-year
should be a priority when considering potential future changes.
Response: We appreciate these comments. As noted in the proposed
rule, the Super HCC refinement is intended to address the misalignment
that occurs in situations where HCCs in the same HCC coefficient
estimation group are sorted into different HCC failure rate groupings
on an interim basis while we continue to assess different longer-term
options. We remain committed to ensuring the integrity and reliability
of HHS-RADV and agree that year-to-year stability is an important
factor to consider when analyzing potential future changes. We continue
to explore potential modifications to this program, including to the
HCC grouping methodology, for future benefit years and will propose any
such changes through notice-and-comment rulemaking.
Comments: Several commenters requested that HHS release more
information about the HCC failure rate grouping proposal to create
Super HCCs. This included requests for more information about the
degree to which validation failures relate to hierarchies for 2018 HHS-
RADV, analysis on year-to-year stability, and a further explanation of
the proposed refinement to the HCC failure rate grouping methodology.
Response: Once the data became available, we conducted an
additional analysis of the Super HCC proposal using 2018 benefit year
HHS-RADV results. This further analysis provided roughly the same
figure for the proportion of newly identified HCCs which could be
attributed to a miscoding of an HCC in the same hierarchy, or in the
same coefficient estimation group, as the analysis of 2017 benefit year
HHS-RADV results used to develop the Super HCC proposal, namely, about
1/3rd of newly identified HCCs. Among non-validated HCCs, the rate that
could be attributed to miscoding of an HCC in the same hierarchy was
slightly higher in our analysis of 2018 data (about 1/7th of non-
validated HCCs) than it was for 2017 data (about 1/8th of non-validated
HCCs). Additionally, in response to comments, we note that in both 2017
and 2018 HHS-RADV results, approximately 1/3rd of HCCs that could be
attributed to miscoding of an HCC in the same hierarchy also shared a
coefficient estimation group.\51\ The refinement to the HCC failure
group rate methodology finalized in this rule will ensure that these
HCCs will have no impact on failure rates. More specifically, adoption
of this change for HCCs in the same coefficient group ensures they are
not sorted into different HCC failure rate groupings and avoids making
HHS-RADV adjustments to risk scores when they are not conceptually
warranted.
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\51\ See Table 2 for a further comparison and analysis of the
estimated changes reflecting implementation of the Super HCC
refinement using 2017 and 2018 HHS-RADV data. Also see Tables 3 and
4 for a further analysis and comparison of the estimated changes
reflecting implementation of the policies finalized in this rule
using both 2017 and 2018 benefit year HHS-RADV results.
---------------------------------------------------------------------------
In response to the comments, we also provide the following
additional example regarding the calculation of a Super's HCC failure
rate using freqEDGEc, freqIVAc, and
FRc values for Super HCCs.\52\ HCC 54 Necrotizing Fasciitis
and HCC 55 Bone/Joint/Muscle Infections/Necrosis share a HCC
coefficient estimation group, and therefore those HCC failure rates
would be grouped together to form a Super HCC. For example, if
freqEDGEh54 is 30 and freqEDGEh55 is 70,
nationally, and if freqIVAh54 is 15 and
freqIVAh55 is 65, nationally, then freqEDGEc54&55
is 100 and freqIVAc54&55 is 80, yielding FRc54&55
= 1-80/100 = 20%. This is in contrast to cases such as HCC 1 HIV/AIDS,
which does not share a coefficient estimation group with any other
HCCs. In this second example, freqEDGEc will be equal to
freqEDGEh, freqIVAc will be equal to
freqIVAh, and FRc will be equal to
FRh, the value of the national failure rate for HCC 1.
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\52\ Commenters should also refer to the illustrative example in
the proposed rule. See 85 FR at 33605.
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As explained in the proposed rule, after the calculation of
freqEDGEc, freqIVAc, and FRc, we will
sort the Super HCCs--both those composed of a single HCC and those
composed of the aggregate frequencies of HCCs that share an HCC
coefficient estimation group in the adult models--using the sorting
process under the current HHS-RADV failure rate grouping methodology.
The sorting process and failure rate grouping methodology are described
in the HHS-RADV Protocols.\53\ Specifically, HHS will calculate the HCC
failure rate group for each Super HCC using the following method:
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\53\ See Section 11.3.1 of the 2018 HHS-RADV Protocols at
https://www.regtap.info/uploads/library/HRADV_2018Protocols_070319_RETIRED_5CR_070519.pdf for a description
of the process prior to the introduction of Super HCCs. Beginning
with the 2019 benefit year of HHS-RADV, Super HCCs would take the
place of HCCs in the process. The 2019 HHS-RADV Protocols have thus
far only been published in part at https://www.regtap.info/uploads/library/HRADV_2019_Protocols_111120_5CR_111120.pdf. The section of
the 2019 HHS-RADV Protocols pertaining to HCC grouping for failure
rate calculations is not included in the current version. Once
published, this section will be updated to include steps related to
creation of Super HCCs.
---------------------------------------------------------------------------
Create a list containing each Super HCC and its associated
failure rate.
Sort Super HCCs from lowest to highest failure rate
(FRc).
Put the Super HCC with the lowest failure rate in the low
failure rate group, and update the size of this group
(freqEDGElow) so that it is equal to freqEDGEc1,
that is, the value of freqEDGEc for the first Super HCC from
the sorted list. Put the next Super HCC from the sorted list in the low
failure rate group, and update the group size to freqEDGElow
+ freqEDGEci, the value of freqEDGEc for the i-th
Super HCC from the sorted list. Repeat this sorting process until the
size of freqEDGElow reaches or exceeds 1/3rd of the total
frequency of HCCs recorded on EDGE ([sum]freqEDGEh across
all HCCs, which is equal to [sum]freqEDGEc across all Super
HCCs).
After the low failure rate group has reached the 1/3rd cut
off, HHS will put the next Super HCC from the sorted list into the
medium failure rate group, and will update the size of this group
(freqEDGEmedium) so that it is equal to
freqEDGEci. We will then put the next Super HCC from the
sorted list into the medium failure rate group, and update the group
size to freqEDGEmedium +
[[Page 76990]]
freqEDGEci. We will repeat this process until
freqEDGElow + freqEDGEmedium reaches or exceeds
2/3rds of the total number of HCCs recorded on EDGE
([sum]freqEDGEh across all HCCs, which is equal to
[sum]freqEDGEc across all Super HCCs).
The remaining Super HCCs, those with the highest failure
rates, will then be assigned to the high failure rate group.
Because the inclusion of the final freqEDGEci in a given
failure rate group may result in the total frequency for that group
going beyond 1/3rd of the total [sum]freqEDGEc, consistent
with the current sorting process and methodology, HHS will then
reexamine the HCC allocations between failure rate groups to ensure an
even distribution of HCCs between failure rate groups such that each
HCC failure rate group contains as close as possible to 1/3rd of the
HCCs reported in EDGE. To accomplish this, we will first identify the
final Super HCCs in the low and medium failure rate groups that result
in a total freqEDGElow or freqEDGEmedium that
exceeds 1/3rd of the total [sum]freqEDGEc. Then we will
generate multiple grouping scenarios such that the identified Super
HCCs that cause freqEDGElow or freqEDGEmedium to
exceed 1/3rd of the total [sum]freqEDGEc are instead
included in the next higher failure rate group. These multiple grouping
scenarios will contain all possible assignments of the two Super HCCs
that cross the 1/3rd boundary for the low and medium failure rate
groupings. For each grouping scenario, we will then calculate the
potential values of freqEDGElow, freqEDGEmedium,
and freqEDGEhigh and then calculate the absolute distance
between in each HCC failure rate group and 1/3rd. HHS will then choose
the scenario that is closest to an exact 1/3rd split of HCC frequencies
across groups. This scenario will be used as the final HCC failure rate
grouping assignment for that HHS-RADV benefit year.
2. ``Payment Cliff'' Effect
The HHS-RADV error rate calculation methodology is based on the
identification of outliers, as determined using certain national
thresholds. Those thresholds are used to determine whether an issuer is
an outlier and the error rate that will be used to adjust outlier
issuers' risk scores. Under the current methodology, 1.96 standard
deviations on both sides of the confidence interval around the weighted
HCC group means are the thresholds used to determine whether an issuer
is an outlier. In practice, these thresholds mean that an issuer with
failure rates outside the 1.96 standard deviations range for any of the
HCC failure groups is deemed an outlier and receives an adjustment to
its risk score, while an issuer with failure rates inside the 1.96
standard deviations range for all groups receives no adjustment to its
risk score.\54\
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\54\ An issuer with no error rate would not have its risk score
adjusted due to HHS-RADV, but that issuer may have its risk
adjustment transfer impacted if there is another issuer(s) in the
state market risk pool that is an outlier.
---------------------------------------------------------------------------
Some stakeholders have expressed concern that issuers with failure
rates that are just outside of the confidence intervals receive an
adjustment to their risk scores, even though these issuers' failure
rates may not be significantly different from the failure rates of
issuers just inside the confidence intervals who receive no risk score
adjustment, creating a ``payment cliff'' or ``leap frog'' effect. For
example, an issuer with a low HCC group failure rate of 23.9 percent
would be considered a positive error rate outlier for that HCC group
based on the 2017 benefit year national failure rate statistics,
because the upper bound confidence interval for the low HCC group is
23.8 percent. At the same time, another issuer with a low HCC group
failure rate of 23.7 percent would receive no adjustment to its risk
score as a result of HHS-RADV. While this result is due to the nature
of establishing and using a threshold to identify outliers, some
stakeholders suggested that HHS could mitigate this effect by
calculating error rates based on the position of the bounds of the
confidence interval for the HCC group and not on the position of the
weighted mean for the HCC group.
While HHS considered several possible methods to address the
payment cliff,\55\ we proposed to address the payment cliff by adding a
sliding scale adjustment to the current error rate calculation, such
that the adjustments applied would vary based on the outlier issuer's
distance from the mean and the farthest outlier threshold. This
proposed approach would employ additional thresholds to create a
smoothing of the error rate calculation beyond what the current
methodology allows and help reduce the disparity of risk score
adjustments by using a linear adjustment.\56\ We proposed to make this
modification beginning with 2019 benefit year HHS-RADV.
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\55\ See, e.g., section 4.4.4 and 4.4.5 of the 2019 RADV White
Paper.
\56\ In the 2020 Payment Notice, we stated that we may consider
alternative options for error rate adjustments, such as using
multiple or smoothed confidence intervals for outlier identification
and risk score adjustments. See 84 FR at 17507.
---------------------------------------------------------------------------
To apply the sliding scale adjustment, we proposed to modify the
calculation of the group adjustment factor (GAF) by providing a linear
sliding scale adjustment for issuers whose failure rates are near the
point at which the payment cliff occurs. To implement this policy, we
needed to select the thresholds of the range (innerZr and
outerZr) to calculate and apply the sliding scale
adjustment.\57\ In the proposed rule, we proposed to calculate and
apply a sliding scale adjustment between the 90 and 99.7 percent
confidence interval bounds (from +/- 1.645 to 3 standard deviations).
Under this proposal, the determination of outliers in HHS-RADV for each
HCC grouping would no longer be based on a 95 percent confidence
interval or 1.96 standard deviations from the mean, and would instead
be based on a 90 percent confidence interval or 1.645 standard
deviations from the mean. Specifically, this approach would adjust the
upper and lower bounds of the confidence interval to be at 1.645
standard deviations from the mean, meaning that issuers with group
failure rates outside of the 90 percent confidence interval in any HCC
failure rate group will have their risk scores adjusted. This would
result in more issuers being considered outliers under this methodology
than under the current methodology, which uses a 95 percent confidence
interval to detect outlier issuers, but these additional outlier
issuers would face smaller GAFs due to the application of the sliding
scale.
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\57\ In the 2019 RADV White Paper, we considered four different
options for calculating and applying additional thresholds for the
sliding scale adjustment to the error rate calculation. See section
4.4.4 and 4.4.5 of the 2019 RADV White Paper.
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To calculate the sliding scale adjustment, we proposed to add an
additional step to the calculation of issuers' GAFs that takes into
consideration the distance of their group failure rates (GFRs) to the
confidence interval. The present formula for an issuer's GAF,
GAFG,i = GFRG,i-[mu]{GFRG{time} would
be modified by replacing the GFRG,i with a decomposition of
this value that uses the national weighted mean and national weighted
standard deviation for the HCC failure rate group, as well as
zG,i, the z-score associated with the GFRG,i,
where:
[[Page 76991]]
[GRAPHIC] [TIFF OMITTED] TR01DE20.010
The z-score would then be discounted using the general formula:
where disZG,i,r = a * zG,i + br, where
disZG,i,r is the confidence-level discounted z-score for
that value of zG,i according to the parameters of the
positive or negative sliding scale range (from +/-1.645 to 3 standard
deviations). This disZG,i,r value will replace the
zG,i value in the GAFG,i formula to provide the
value of the sliding scale adjustment for the positive or negative side
of the confidence interval:
[GRAPHIC] [TIFF OMITTED] TR01DE20.011
In the calculation of disZG,i,r, the coefficient a would
be the slope of the linear adjustment, which shows the adjustment
increase rate per unit increase of GFRG,i, and br
is the intercept of the linear adjustment for either the negative or
positive sliding scale range. The coefficients would be determined
between +/-1.645 to 3 standard deviations. Specifically, coefficient a
would be defined as:
[GRAPHIC] [TIFF OMITTED] TR01DE20.012
Where:
a is the slope of the sliding scale adjustment
r indicates whether the GAF is being calculated for a
negative or positive outlier
outerZr is the greater magnitude z-score
selected to define the edge of a given sliding scale range r (3.00
for positive outliers; and -3.00 for negative outliers)
innerZr is the lower magnitude z-score selected
to define the edge of a given sliding scale range r (1.645 for
positive outliers; and -1.645 for negative outliers)
The value of intercept br would differ based on whether
the sliding scale is calculated for a positive or negative outlier and
would be defined as:
[GRAPHIC] [TIFF OMITTED] TR01DE20.013
In the absence of the constraints on negative failure rates that is
being finalized later in this final rule, the final formula for the
group adjustment when an outlier issuer is subject to the sliding scale
(GAFG,i,r above) would be simplified to:
[GRAPHIC] [TIFF OMITTED] TR01DE20.014
This sliding scale GAFG,i,r would be applied to the HCC
coefficients in the applicable HCC failure rate group when calculating
each enrollee with an HCC's risk score adjustment factor for an issuer
that had a failure rate with a z score within the range of values (from
+/-1.645 to 3 standard deviations) selected for the sliding scale
adjustment (innerZr and outerZr). All other enrollee adjustment factors
would be calculated using the current formula for the
GAFG,i,r. Under this approach, the above formulas would be
implemented as follows:
[[Page 76992]]
[GRAPHIC] [TIFF OMITTED] TR01DE20.015
Where disZG,i,r is calculated using 3.00 (or -3.00, for
negative outliers) as the value of outerZr and 1.645 (or -
1.645, for negative outliers) as the value of innerZr.
We sought comment on this proposal, including the proposed
calculation of the sliding scale adjustment and the thresholds used to
calculate and apply it. We also considered retaining the 95 percent
confidence interval (1.96 standard deviations) as an alternative way to
smooth the payment cliff. However, as noted in the proposed rule, while
we recognize this option would also mitigate the payment cliff, we were
concerned it would weaken the HHS-RADV program by reducing its overall
impact and the magnitude of HHS-RADV adjustments to risk scores of
outlier issuers.\58\
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\58\ See 85 FR at 33608.
---------------------------------------------------------------------------
After consideration of comments received, we are finalizing the
proposed sliding scale adjustment to smooth the payment cliff effect
for those issuers whose failure rates are near the point at which the
payment cliff occurs. We will calculate and apply a sliding scale
adjustment between the 90 and 99.7 percent confidence interval bounds
(from +/-1.645 to 3 standard deviations) beginning with 2019 benefit
year HHS-RADV. For outlier issuers with failure rates more than 3
standard deviations from the mean, the GAF will not be impacted by the
sliding scale adjustment, but will instead continue to be calculated as
the difference between the weighted mean group failure rate and the
issuers' group failure rate.
Comments: Some commenters supported the proposal to apply the
sliding scale adjustment between the 90-99.7 percent confidence
interval. Several commenters supported the adoption of a sliding scale
adjustment but wanted to retain the current confidence intervals and
start the adjustment at the 95 percent confidence interval. These
commenters were concerned with the increased number of outliers under
the proposed sliding scale adjustment, which would result in more risk
adjustment transfers being impacted by HHS-RADV results, arguing this
would reduce predictability and stability of HHS-RADV. Other commenters
expressed concern about the identification of more outliers under the
proposed sliding scale adjustment, arguing it would be more disruptive
especially during COVID-19. Some commenters stated that they did not
believe that identifying outliers at the proposed 90 percent confidence
interval would more accurately capture issuers' actuarial risk and some
thought the proposed 90 percent confidence interval could lead to an
increase in ``false positives'' when identifying outliers. These
commenters stated that the 95 percent confidence interval imposes a
more robust confidence interval for identifying ``true outliers.''
Some commenters wanted HHS to calculate error rates based on the
difference between the edge of the confidence intervals and the outlier
issuer's failure rate (instead of the difference between the weighted
group mean or a sliding scale adjustment and the outlier issuer's
failure rate). However, these commenters also supported the adoption of
a sliding scale adjustment starting at the 95 percent confidence
intervals, if HHS were to finalize a sliding scale adjustment. One
commenter wanted HHS to identify outliers and calculate their GAF based
on state specific group means to address potential over and under
adjustments of outlier issuers relative to their state-based
competitors. One commenter supported the current methodology without a
sliding scale adjustment, noting that the payment cliff effect resulted
from the policy of only adjusting for outliers and that any measures to
address the payment cliff would dampen the impact of HHS-RADV. Other
commenters stated that it is appropriate for issuers who fall outside
of the 99.7 percent confidence interval (beyond 3 standard deviations)
to be assessed a full penalty. Another commenter, that supported the
adoption of a sliding scale adjustment, expressed concerns that even
with the proposed adjustment there would still be a payment cliff
effect for issuers with very similar error rates. This commenter also
asked HHS to address this effect for the current benefit year and
beyond, as well as prior years, of HHS-RADV.
Response: We are finalizing the sliding scale approach for
calculating an outlier issuer's error rate using modified group
adjustment factors for issuers' group failure rates between 1.645 to 3
standard deviations from the mean on both sides of the confidence
interval as proposed. We will apply this adjustment to the error rate
calculation beginning with the 2019 benefit year of HHS-RADV. We
believe that using a linear sliding scale adjustment will provide a
smoothing effect in the current error rate calculation for issuers with
failure rates just outside of the confidence interval of an HCC group
and will retain the current significant adjustment to the HCC group
weighted mean for issuers beyond three standard deviations. This
approach ensures that the mitigation of the payment cliff for those
issuers close to the confidence intervals does not impact situations
where outlier issuers' failure rates are not close to the confidence
intervals and a larger adjustment is warranted.
We appreciate the comments supporting an alternative sliding scale
[[Page 76993]]
adjustment that would begin at 1.96 standard deviations. As detailed in
the proposed rule, we recognize this alternative adjustment would also
address the payment cliff and would provide stability by maintaining
the current thresholds used in the error rate calculation. However,
these benefits are outweighed by the concerns that such an adjustment
would weaken HHS-RADV by reducing its overall impact and the magnitude
of HHS-RADV adjustments to outlier issuer's risk scores. As noted
previously, the sliding scale adjustment that is finalized in this rule
will mitigate the payment cliff effect while not impacting the error
rate calculation for those outlier issuers who are not close the
confidence intervals.
While we did not propose adjusting issuers' error rates to the
state-specific means, we considered such an approach in response to
comments. However, we do not believe that using state-specific means
would address the payment cliff in the current error rate methodology.
We also have concerns about using national metrics to determine
outliers and then switching to state-specific means to calculate the
GAFs. In addition, the adoption of a state-specific approach to
calculate the GAF could create other issues, if states have small
sample sizes (that is, a small number of issuers participated in HHS-
RADV), this would create less confidence in the state mean metric being
used to adjust issuers, and would introduce new complexities as each
state would have a different calculation for the GAF. We therefore
decline to adopt such an approach in this final rule. We also
considered adjusting to the confidence intervals,\59\ but we have
concerns that this option minimizes the impact of HHS-RADV adjustments
on risk scores and risk adjustment transfers--including those outlier
issuers with high error rates who are furthest away from the confidence
intervals.
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\59\ See section 4.4.2 of the 2019 RADV White Paper.
---------------------------------------------------------------------------
While any outlier threshold by definition has the risk of flagging
false positives, and that risk may be slightly greater at the 90
percent confidence interval, we believe that the 90 percent confidence
interval will better encourage issuers to ensure accurate EDGE data
reporting and the risk of flagging false positives is mitigated by the
fact that the adjustments to these issuers will be small since they
will be subject to the sliding scale adjustment. Furthermore, while we
understand the concerns that use of the 90 percent confidence interval
will increase the number of outliers, we have found that the overall
impact of the proposed approach on risk adjustment transfers is less
than the current methodology despite the increased number of outliers.
As discussed in the 2019 RADV White Paper, we tested various potential
sliding scale adjustments between the 90 and 99.7 percent confidence
interval bounds using 2017 HHS-RADV results.\60\ We found that even
though including issuers whose failure rates fell between 1.645 and
1.96 standard deviations from the mean would increase the number of
outliers, the sliding scale adjustment lowers the overall impact of
HHS-RADV adjustments to transfers and results in the distribution of
issuers' error rates moving closer to zero compared to the current
methodology.\61\ We also tested this policy on the 2018 benefit year
HHS-RADV data once it became available and found similar results. We
found that the sliding scale adjustment option between 1.645 and 1.96
standard deviations generally resulted in lower overall impact of HHS-
RADV adjustment to risk adjustment transfers and the distribution of
issuers' error rates moving closer to zero compared to the current
methodology. Furthermore, we believe that the 90 percent confidence
interval will maintain the program integrity impact of HHS-RADV despite
the estimated reduced impact of HHS-RADV on risk adjustment transfers
using the 90 percent confidence interval, and we are not concerned that
increasing the number of outliers will be more disruptive during the
COVID-19 public health emergency. More importantly, we believe that
using the 90 percent confidence interval will preserve a strong
incentive for issuers to submit accurate EDGE data that can be
validated in HHS-RADV because it increases the range in which issuers
can be flagged as outliers, while lowering the magnitude of that
adjustment amount for those outlier issuers close to the confidence
intervals and maintaining a larger adjustment for those who are not
close to the confidence intervals. For these reasons, we believe that
this methodology for calculating and applying the sliding scale
adjustment provides a balanced approach to mitigating the payment cliff
effect in the current methodology and disagree that adoption of the
adjustment would reduce predictability and stability of HHS-RADV.
---------------------------------------------------------------------------
\60\ See section 4.4.5 and Appendix C of the 2019 RADV White
Paper.
\61\ Ibid.
---------------------------------------------------------------------------
We recognize the sliding scale adjustment finalized in this rule
does not eliminate the payment cliff because the identification of
outliers will still be based on the establishment and use of
thresholds. As noted earlier, we are finalizing the targeted policies
in this rule, such as the sliding scale adjustment, as incremental
refinements to the current error rate methodology to address
stakeholder feedback and our experience from the first payment year of
HHS-RADV on these issues. We will continue to consider other potential
changes to the error rate methodology for future benefit years,
including potential significant changes to the outlier determination
process, and as part of that process, we will also consider whether
additional measures are necessary or appropriate to further mitigate
the impact of the payment cliff after we have experience with the
sliding scale adjustment finalized in this rule.
We will apply the sliding scale adjustment beginning with the 2019
benefit year of HHS-RADV, as proposed. We believe that application of
this rule to the 2017 and 2018 HHS-RADV would not be appropriate
because the error rate calculations for those benefit years are
complete.\62\ Further, it would disrupt issuers' well-settled
expectations with respect to the calculation of HHS-RADV error rates
and adjustments if we were to extend this new policy to the 2017 and
2018 benefit years. In addition, there is no need to apply the sliding
scale adjustment to the earlier benefit years because HHS-RADV was not
conducted for the 2014 benefit year and HHS-RADV was treated as a pilot
for the 2015 and 2016 benefit years.\63\
---------------------------------------------------------------------------
\62\ See, supra, notes 30 and 31.
\63\ See FAQ ID 11290a (March 7, 2016) available at: https://www.regtap.info/faq_viewu.php?id=11290 and HHS-Operated Risk
Adjustment Data Validation (HHS-RADV)--2016 Benefit Year
Implementation and Enforcement (May 3, 2017) available at: https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/HHS-Operated-Risk-Adjustment-Data-Validation-HHS-RADV-%E2%80%93-2016-Benefit-Year-Implementation-and-Enforcement.pdf.
---------------------------------------------------------------------------
Comments: A few commenters noted that the increase in the number of
issuers identified as outliers due to the introduction of the sliding
scale adjustment could increase volatility by increasing the likelihood
that an issuer would be an outlier in three HCC failure rate groups,
leading to larger overall error rates despite the smaller GAF in each
group, or by creating several negative outliers in one state market
risk pool. One commenter, who was concerned about the increased number
of outliers, noted that issuers can have a larger HHS-RADV adjustment
under the proposed sliding scale adjustment than under the current
methodology.
[[Page 76994]]
Some commenters were concerned that this volatility from the increased
number and type of outliers could increase premiums or adversely affect
issuers' finanical planning.
Response: We recognize that the sliding scale adjustment finalized
in this rule will result in more issuers being identified as outliers
than the current methodology.\64\ However, when testing various
potential sliding scale adjustment options, we found that even though
including issuers whose failure rates fell between 1.645 and 1.96
standard deviations from the mean would increase the number of
outliers, the sliding scale adjustment we are finalizing in this rule
lowers the overall impact of HHS-RADV adjustments to risk adjustment
transfers and results in the distribution of issuers' error rates
moving closer to zero compared to the current methodology.\65\
Therefore, we do not believe that using the sliding scale adjustment
starting with the 1.645 confidence interval will increase volatility or
impact premiums more than the previous methodology. Instead, we believe
that the sliding scale adjustment finalized in this rule will preserve
a strong incentive for issuers to submit accurate EDGE data that can be
validated in HHS-RADV because it increases the range in which issuers
can be flagged as outliers, while lowering the calculation of that
adjustment amount for those outlier issuers close to the confidence
intervals and maintaining a larger adjustment for those who are not
close to the confidence intervals. For these reasons, we believe that
the incorporation of the sliding scale adjustment as proposed provides
a balanced approach to mitigating the payment cliff effect.
---------------------------------------------------------------------------
\64\ See, e.g., 85 FR at 33608.
\65\ See section 4.4.5 and Appendix C of the 2019 RADV White
Paper.
---------------------------------------------------------------------------
Under the new confidence intervals with the sliding scale
adjustment beginning at 90 percent finalized in this rule, it is
possible for an issuer to fail more HCC groups resulting in larger
error rates than the previous methodology or for there to be more
negative error rate outliers in a state market risk pool compared to
the current methodology. In those cases, outlier issuers could have a
higher error rate, or non-outlier issuers could be impacted by more
outliers in their state market risk pool than under the current
methodology that does not include a sliding scale adjustment. However,
failure rates for the issuers newly identified as outliers due to the
adoption of the sliding scale adjustment would be between 1.645 to 1.96
standard deviations. Since these issuers' failure rates are closer to
the mean, the increase in error rates based on outlier status in
several HCC failure rate groups would likely be small and could
potentially be offset by reduced transfers from other issuers with
failure rates between 1.96 and 3 standard deviations in the same state
market risk pool.
Comments: Some commenters expressed concern that issues other than
actual HCC validation errors that impact the measurement of actuarial
risk, such as medical record retrieval issues or incorrect provider
coding, may contribute to the variance in failure rates, and that it is
therefore not appropriate to adjust outlier issuers to the mean. Other
commenters noted that changing the confidence intervals does not ensure
that validation of HCCs that contribute to actuarial risk is accurately
measured through HHS-RADV; these commenters supported maintaining the
current confidence intervals.
Response: HHS-RADV validates risk based upon the enrollee's medical
record which generally aligns with how the Medicare Advantage risk
adjustment data validation (MA-RADV) program operates. Specifically,
Sec. 153.630(b)(7)(ii) requires that the validation of enrollee health
status (that is, the medical diagnoses) occur through medical record
review, that the validation of medical records include a check that the
records originate from the provider of the medical services, that they
align with the dates of service for the medical diagnosis, and that
they reflect permitted providers and services. When an issuer fails to
submit a medical record or has submitted an inaccurate medical record,
the issuer has failed to validate the issuer's risk under our
regulations. We do not treat these medical record issues differently
than other errors that can occur in HHS-RADV nor would we treat them
differently for purposes of calculating GAF using the weighted group
mean.
While we are amending the calculation of the GAF, we did not
propose and are not finalizing any changes to no longer use the mean in
the calculation of the GAF. The purpose of the sliding scale adjustment
is to mitigiate the payment cliff effect that was occuring by adjusting
outlier issuers just outside the confidence interval to the weighted
group mean. To ensure that the validation of HCCs that contribute to
actuarial risk is accurately measured through HHS-RADV, we proposed the
HCC failure rate grouping policy being finalized in this rule. That
policy is another targeted refinement to the current methodology and it
is focused on ensuring that miscoding of HCCs in the same coefficient
estimation group with the same risk scores does not contribute to an
issuer's group failure rate. Additionally, in this rule, we are
finalizing the application of HHS-RADV results to the benefit year
being audited in response to stakeholder concerns about changes in
population and risk score between benefit years.
Comments: A commenter requested that HHS release prior HHS-RADV
results and data if the sliding scale adjustment policy is finalized.
Response: Summary information on issuers' 2017 and 2018 benefit
years HHS-RADV results are available on the Premium Stabilization
Program page of the CCIIO website, which can be accessed at https://www.cms.gov/CCIIO/Programs-and-Initiatives/Premium-Stabilization-Programs. Issuers who participated in HHS-RADV for these benefit years
also received issuer-specific and enrollee-specific results in the
Audit Tool at the same time the summary information was released.
Additionally, HHS conducted two pilot years of HHS-RADV for the 2015
and 2016 benefit years to give HHS and issuers experience with how the
audits would be conducted prior to applying HHS-RADV results to adjust
issuers' risk scores and risk adjustment transfers in the applicable
state market risk pool and for the 2016 benefit year, participating
issuers were provided illustrative 2016 benefit year HHS-RADV results
based on the application of the current error rate methodology. As
noted previously, HHS-RADV was not conducted for the 2014 benefit year
so there were no results to release or otherwise share. We also point
this commenter to the analysis in the proposed rule,\66\ as well as the
results of the evaluation of the sliding scale adjustment options in
the 2019 RADV White Paper, using 2017 benefit year HHS-RADV
results.\67\ In addition, Tables 3 and 4 in this rule share an analysis
and comparison of the estimated changes reflecting implementation of
this policy using both 2017 and 2018 benefit year HHS-RADV results.
---------------------------------------------------------------------------
\66\ See 85 FR at 33613.
\67\ See section 4.4.5 and Appendix C of the 2019 RADV White
Paper.
---------------------------------------------------------------------------
3. Negative Error Rate Issuers With Negative Failure Rates
HHS-RADV uses a two-sided outlier identification approach because
the long-standing intent has been to account for identified material
risk differences between what issuers submitted to their EDGE servers
and what was validated in
[[Page 76995]]
medical records through HHS-RADV, regardless of the direction of those
differences.\68\ In addition, the two-sided adjustment policy penalizes
issuers who validate HCCs in HHS-RADV at much lower rates than the
national average and rewards issuers in HHS-RADV who validate HCCs in
HHS-RADV at rates that are much higher than the national average,
encouraging issuers to ensure that their EDGE-reported risk scores
reflect the true actuarial risk of their enrollees. Positive and
negative error rate outliers represent these two types of adjustments,
respectively.
---------------------------------------------------------------------------
\68\ An exception to this approach was established, beginning
with the 2018 benefit year of HHS-RADV, for exiting issuers who are
negative error rate outliers. See 84 FR at 17503-17504.
---------------------------------------------------------------------------
If an issuer is a positive error rate outlier, its risk score will
be adjusted downward. Assuming no changes to risk scores for the other
issuers in the same state market risk pool, this downward adjustment
increases the issuer's charge or decreases its payment for the
applicable benefit year, leading to a decrease in charges or an
increase in payments for the other issuers in the state market risk
pool. If an issuer is a negative error rate outlier, its risk score
will be adjusted upward. Assuming no changes to risk scores for the
other issuers in the same state market risk pool, this upward
adjustment reduces the issuer's charge or increases its payment for the
applicable benefit year, leading to an increase in charges or a
decrease in payments for the other issuers in the state market risk
pool. The increase to risk score(s) for negative error rate outliers is
consistent with the upward and downward risk score adjustments
finalized as part of the original HHS-RADV methodology in the 2015
Payment Notice \69\ and the HCC failure rate approach to error
estimation finalized in the 2019 Payment Notice.\70\
---------------------------------------------------------------------------
\69\ For example, we stated that ``the effect of an issuer's
risk score error adjustment will depend upon its magnitude and
direction compared to the average risk score error adjustment and
direction for the entire market.'' See 79 FR 13743 at 13769.
\70\ See 83 FR 16930 at 16962. The shorthand ``positive error
rate outlier'' captures those issuers whose HCC coefficients are
reduced as a result of being identified as an outlier, while
``negative error rate outlier'' captures those issuers whose HCC
coefficients are increased as a result of being identified as an
outlier.
---------------------------------------------------------------------------
In response to stakeholder feedback about the impact of negative
error rate issuer HHS-RADV adjustments on issuers who are not outliers,
we proposed to adopt a constraint to the calculation of negative error
rate outlier issuers' error rates in cases when an outlier issuer's
failure rate is negative. An issuer can be identified as a negative
error rate outlier for a number of reasons. However, the current error
rate methodology does not distinguish between low failure rates due to
accurate data submission and failure rates that have been depressed
through the presence of found HCCs (that is, HCCs in the audit data
that were not present in the EDGE data). If a negative failure rate is
due to a large number of found HCCs, it does not reflect accurate
reporting through the EDGE server for risk adjustment. For this reason,
we proposed to refine the error rate calculation to mitigate the impact
of adjustments that result from negative error rate outliers that are
driven by newly found HCCs rather than by high validation rates.
Beginning with 2019 benefit year HHS-RADV, we proposed to adopt an
approach that constrains negative error rate outlier issuers' error
rate calculations in cases when an issuer's failure rate is negative.
For negative error rate outlier issuers with negative failure rates,
the proposed constraint would be applied to the GAF such that this
value would be calculated as the difference between the weighted mean
failure rate for the HCC grouping (if positive) and zero (0). This
would be calculated by substituting the following
[verbar][verbar]double barred[verbar][verbar] terms and definitions
into the error rate calculation \71\ process:
---------------------------------------------------------------------------
\71\ This calculation sequence is expressed here in a revised
order compared to how the sequence is published in the 2021 Payment
Notice (85 FR at 29196-29198). This change was made to simplify the
illustration of how this sequence will be combined with proposals
finalized in this rule. The different display does not modify or
otherwise change the amendments to the outlier identification
process finalized in the 2021 Payment Notice.
[GRAPHIC] [TIFF OMITTED] TR01DE20.016
[[Page 76996]]
Where:
GFRG,i is an issuer's failure rate for the HCC failure rate grouping
[verbar][verbar]GFRG,i,constr is an issuer's failure rate for the
HCC failure rate grouping, constrained to 0 if is less than 0. Also
expressed as:
[GRAPHIC] [TIFF OMITTED] TR01DE20.017
UBG and LBG are the upper and lower bounds of
the HCC failure rate grouping confidence interval, respectively.
FlagG,i is the indicator if issuer i's group failure rate
for group G locates beyond a calculated threshold that we are using
to classify issuers into ``outliers'' or ``not outliers'' for group
G.
GAFG, is the group adjustment factor for HCC failure rate
group G for an issuer i.
We would then compute total adjustments and error rates for each
outlier issuer based on the weighted aggregates of the
GAFG,i.\72\
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\72\ See, for example, the 2018 Benefit Year Protocols: PPACA
HHS Risk Adjustment Data Validation, Version 7.0 (June 24, 2019),
available at: https://www.regtap.info/uploads/library/HRADV_2018Protocols_070319_RETIRED_5CR_070519.pdf.
---------------------------------------------------------------------------
We are finalizing this refinement to the error rate calculation as
proposed. We will adjust the GAF calculation to be the difference
between the weighted group mean and zero for negative error rate
issuers with negative failure rates beginning with the 2019 benefit
year of HHS-RADV.
Comments: Most commenters supported the proposed negative failure
rate constraint. These commenters tended to be concerned that the
current methodology rewards issuers who fail to submit accurate data to
the EDGE server, were concerned about predictability of HHS-RADV
adjustments, or thought that the proposed constraint would result in
more equitable HHS-RADV adjustments. A few commenters opposed the
proposed negative failure rate constraint. These commenters, as well as
another commenter that was not opposed to the negative failure rate
constraint, expressed concerns that the proposed negative failure rate
constraint would treat issuers with different validation rates and the
same rate of found HCCs the same for calculating error rates,
potentially penalizing issuers that submitted more verifiable HCCs.
Some commenters argued that the potential for underreporting of risk in
risk adjustment was minor, and one supported allowing issuers to get
credit for the risk that they incurred including through newly found
HCCs.
Other commenters generally agreed that a change in methodology is
needed to reduce the magnitude of HHS-RADV adjustments due to negative
error rate issuers and the impact of these adjustments on non-outlier
issuers in the same state market risk pool. Some commenters wanted HHS
to abandon the two-sided nature of the outlier identification process
and not adjust for any negative error rate outliers or urged HHS to
look for ways to minimize adverse impact of negative error rate
outliers on non-outliers. Other commenters recommended that HHS analyze
the failure rates for negative error rate outliers without including
found HCCs (meaning that only non-validated EDGE HCCs would be
contributing to the issuer's failure rate) and compare the results with
the current methodology to assess if negative error rate outliers had
better validation rates. Another commenter requested that HHS monitor
data on the policy's impact, if finalized.
Response: We are finalizing the proposed approach to constrain
negative error rate outlier issuers' error rate calculations in cases
when an outlier issuer's failure rate is negative and will apply this
constraint beginning with the 2019 benefit year of HHS-RADV. We believe
that the negative failure rate constraint to the GAF calculation in the
error rate calculation will reduce potential incentives for issuers to
use HHS-RADV to identify more HCCs than were reported to their EDGE
servers and provide additional incentives for issuers to submit the
most accurate data to the EDGE server. It also will mitigate the impact
of HHS-RADV adjustments to transfers in the case of negative error rate
issuers with negative failure rates and improve predictability.
Specifically, this approach would limit the financial impact that
negative error rate outliers with negative failure rates will have on
other issuers in the same state market risk pool and can be easily
implemented under the current error rate methodology.
We understand that this constraint has limitations. We used 2017
and 2018 benefit year HHS-RADV results to analyze the failure rates of
negative error rate outliers and explore the impact of excluding found
HCCs. We found that negative error rate outliers tended to have better
than average validation rates, particularly when the HCC grouping
methodology finalized in this rule is applied and those issues get
credit for IVA findings that substitute for EDGE HCCs in the same HCC
coefficient estimation group. However, at the same time, we recognize
that there are limitations to the negative failure rate constraint
policy as it does not distinguish between issuers with different
validation rates and the same rate of found HCCs. Thus, as previously
noted, this policy and the other changes to the error rate calculation
in this rule are targeted refinements to the current methodology as we
consider other potential long-term approaches. In proposing and
finalizing these changes, we sought to balance the goals of promoting
stability and predictability of HHS-RADV adjustments and adopting
refinements as expeditiously as possible. The negative error rate
constraint was designed with these goals in mind, as it builds on the
current methodology, which issuers now have several years of experience
with, and is easy to implement. It is an interim measure that will
limit the financial impact that negative error rate outliers with
negative failure rates have on other issuers in the same state market
risk
[[Page 76997]]
pool. We remain committed to continuing to explore different longer-
term options, including approaches that involve significant
methodological changes, such as those described in the 2019 RADV White
Paper that would switch to identifying outliers based on risk score
instead of number of HCCs.\73\
---------------------------------------------------------------------------
\73\ See Section 3.3 on addressing the influence of HCC
hierarchies on failure rate outlier determination (Pages 63-71).
https://www.cms.gov/files/document/2019-hhs-risk-adjustment-data-validation-hhs-radv-white-paper.pdf.
---------------------------------------------------------------------------
We also decline to abandon the two-sided nature of the outlier
identification process. The long-standing intent of HHS-RADV has been
to account for identified material risk differences between what
issuers submitted to their EDGE servers and what was validated in
medical records through HHS-RADV, regardless of the direction of those
differences. The increase to risk scores for negative error rate
outliers is consistent with the upward and downward risk score
adjustments finalized as part of the original HHS-RADV methodology in
the 2015 Payment Notice \74\ and the HCC failure rate approach to error
estimation finalized in the 2019 Payment Notice.\75\ The two-sided
approach also encourages issuers to ensure that their EDGE-reported
risk scores reflect the true actuarial risk of their enrollees.
---------------------------------------------------------------------------
\74\ For example, we stated that ``the effect of an issuer's
risk score error adjustment will depend upon its magnitude and
direction compared to the average risk score error adjustment and
direction for the entire market.'' See 79 FR 13743 at 13769.
\75\ See 83 FR 16930 at 16962.
---------------------------------------------------------------------------
We agree with the commenter that supported allowing issuers to get
credit for the risk that they incurred including through newly found
HCCs. It ensures that risk adjustment transfers are made based on
documented risk and that, consistent with the statute, the HHS-operated
program assesses charges to plans with lower-than-average actuarial
risk while making payments to plans with higher-than-average actuarial
risk. As such, even with the adoption of this constraint, the
calculation of error rates will still include found HCCs. The negative
failure rate constrained value in the calculation of the GAF will only
impact the negative failure rate portion of an issuer's GAF. Therefore,
this policy ensures that negative error rate outlier issuers with
negative failure rates will only get credit in their error rate
calculation for finding HCCs at a similar rate as they reported to EDGE
and will not get credit for finding more HCCs in HHS-RADV than they
reported on EDGE. We believe that any issuer with a negative failure
rate is likely to review their internal processes to better capture
missing HCCs in future EDGE data submissions. We intend to monitor the
impact of this policy on future benefit years of HHS-RADV data.
Comments: One commenter noted that it is not evident that issuers
with negative failure rates in one HCC group are adding more diagnoses
given that the three HCC grouping structure allows for HCCs to be found
in one grouping and missing in another grouping. One commenter noted
that the proposal to calculate the GAF between zero and the weighted
mean for negative failure rate issuers does not reflect the outlier
portion of the negative error rate outlier (because the adjustment is
within the confidence intervals for two of three HCC groupings).
Another commenter expressed concerns that the national mean is not
adjusted for found HCCs under the proposal leading to concerns that the
national mean is being inflated and proposed adjusting negative error
rate outliers to the edge of the confidence intervals as an alternative
to the proposed negative failure rate constraint.
Response: The purpose of this negative failure rate constraint
policy is to mitigate the impact of HHS-RADV adjustments due to
negative error rate issuers with negative failure rates. We understand
that the HCC failure rate grouping methodology can result in an issuer
finding HCCs in one HCC failure rate group when the HCC may be missing
in another HCC failure rate grouping. We are finalizing the HCC
grouping refinement discussed earlier in this rule to help prevent
those cases from occurring when the HCCs are in the same HCC
coefficient estimation group in the adult risk adjustment models. We
also acknowledge that this constraint would not affect the calculation
of the national mean, which would continue to consider all found HCCs
and that the calculation of the GAF under this constraint policy may
not fully reflect the outlier portion. We considered these limitations
and weighted them against the benefits of this policy. While we do have
concerns about the impact of adjustments resulting from negative error
rate issuers with negative failure rates, we believe that issuers
should retain the ability to find HCCs in HHS-RADV. Having the ability
to find HCCs in HHS-RADV is important to ensure that issuers' actual
actuarial risk is reflected in HHS-RADV, especially when those HCCs
replace related HCCs that were reported to EDGE. As such, we believe
that found HCCs should continue to contribute to the national mean. At
the same time, given the number of negative error rate issuers with
negative failure rates, we believe that it is important to refine the
current methodology to reduce the incentives for issuers to find HCCs
in HHS-RADV that are not reported in EDGE. We intend to monitor the
impact of this policy on HHS-RADV adjustments and will continue to
explore potential further refinements and changes to the HHS-RADV
methodology and program requirements for future benefit years.
Comment: Some commenters stated that the HHS-RADV Protocols and the
applicable EDGE data submission requirements did not align and
recommended that HHS align these documents. One of these commenters
recommended aligning these rules as an alternative to constraining
negative error rate outliers with negative failure rates.
Response: We did not propose and are not finalizing any changes to
the EDGE data submission requirements. As noted earlier, the long-
standing intent of HHS-RADV has been to account for identified material
risk differences between what issuers submitted to their EDGE servers
and what was validated in medical records through HHS-RADV, regardless
of the direction of those differences. This includes allowing issuers
to get credit for the risk that they incurred including through newly
found HCCs. However, in response to stakeholder feedback, we are
adopting the negative failure rate constraint to limit the impact of
HHS-RADV adjustments due to negative error rate issuers with negative
failure rates beginning with the 2019 benefit year of HHS-RADV. We
disagree that the HHS-RADV Protocols and the EDGE data submission are
not appropriately aligned as the EDGE data submissions and HHS-RADV
Protocols are different processes. Specifically, the EDGE data
submission process for risk adjustment requires issuers to submit all
paid claims to their respective EDGE servers, regardless of provider
type, for the applicable benefit year. These paid claims provide the
diagnoses that are used to calculate risk adjustment transfers at the
state market risk pool level under the state payment transfer
formula.\76\ HHS-RADV is a review of an enrollee's medical records to
confirm the diagnoses used to perform the
[[Page 76998]]
calculations under the state payment transfer formula. HHS- RADV allows
issuers to take into account an issuer's paid claims for the applicable
benefit year for medical record review and this process also allows
issuers to take into account certain diagnoses found during the review
of the medical records of the enrollee to provide a more complete and
accurate picture of an enrollee's risk to the issuer. Further, while
HHS-RADV Protocols allow IVA and SVA auditors to abstract documented
``Lifelong Permanent Conditions'' \77\ that may not be captured in EDGE
data submissions, we disagree that such an approach is inappropriate.
The list of Lifelong Permanent Conditions is a set of health conditions
that require ongoing medical attention and where all associated
diagnoses are typically unresolved once diagnosed. Allowing abstraction
of diagnosis codes for those conditions from medical records submitted
during HHS-RADV if the Lifelong Permanent Condition is identified in
the enrollee's medical history included in a medical record for the
applicable benefit year ensures that an enrollee's full health risk is
captured and reflected in risk adjustment transfers for that state
market risk pool.
---------------------------------------------------------------------------
\76\ For the 2014 through 2016 benefit years, EDGE data was also
used for the transitional reinsurance program established under
section 1341 of the PPACA. The reinsurance program provided
reimbursement based on the total amount of claims paid. Beginning
with the 2018 benefit year, EDGE data is also used for calculating
payments under the high-cost risk pool (HCRP) parameters added to
the HHS risk adjustment methodology. Similar to the reinsurance
program, HCRP payments are based on the amount of paid claims.
Therefore, information on all claims paid--from all provider types--
for a given benefit year should be submitted by issuers to their
EDGE servers.
\77\ See, for example, Appendix E of the 2018 Benefit Year HHS-
RADV Protocols, which describes the guidelines for abstracting
Lifelong Permanent Conditions from medical records for purposes of
the 2018 benefit year of HHS-RADV.
---------------------------------------------------------------------------
a. Combining the HCC Grouping Constraint, Negative Failure Rate
Constraint and the Sliding Scale Proposals
As discussed elsewhere in this final rule, we are finalizing as
proposed each of the three constituent proposals to refine the current
error rate calculation. To illustrate the interaction of the finalized
policies to create Super HCCs for HHS-RADV grouping purposes, apply the
sliding scale adjustment, and constrain negative failure rates for
negative error rate outliers, this section outlines the complete
finalized revised error rate calculation methodology formulas that will
apply beginning with the 2019 benefit year of HHS-RADV, integrating all
the changes finalized in this rule.\78\
---------------------------------------------------------------------------
\78\ The illustration of the error rate calculation methodology
formulas that will apply beginning with the 2019 benefit year of
HHS-RADV also includes the policy finalized in the 2021 Payment
Notice to not consider issuers with fewer than 30 HCCs in an HCC
failure rate group to be outliers in that HCC failure rate group but
continue to include such issuers in the calculation of national
metrics. See 85 FR at 29196-29198.
---------------------------------------------------------------------------
First, HHS will use the failure rates for Super HCCs to group each
HCC into three HCC groupings (a high, medium, or low HCC failure rate
grouping). Under the finalized approach, Super HCCs will be defined as
HCCs that have been aggregated such that HCCs that are in the same HCC
coefficient estimation group in the adult models are aggregated
together and all other HCCs each compose a Super HCC individually.
Using the Super HCCs, we will calculate the HCC failure rate as
follows:
[GRAPHIC] [TIFF OMITTED] TR01DE20.018
Where:
c is the index of the cth Super HCC;
freqEDGEc is the frequency of a Super HCC c occurring in EDGE data;
that is, the sum of freqEDGEh for all HCCs that share an HCC
coefficient estimation group in the adult risk adjustment models:
[GRAPHIC] [TIFF OMITTED] TR01DE20.019
When an HCC is not in an HCC coefficient estimation group in the
adult risk adjustment models, the freqEDGEc for that HCC will be
equivalent to freqEDGEh;
freqIVAc is the frequency of a Super HCC c occurring in IVA results
(or SVA results, as applicable); that is, the sum of freqIVAh for
all HCCs that share an HCC coefficient estimation group in the adult
risk adjustment models:
[GRAPHIC] [TIFF OMITTED] TR01DE20.020
And;
FRc is the national overall (average) failure rate of Super HCC c
across all issuers.
Then, the failure rates for all Super HCCs, both those composed of
a single HCC and those composed of the aggregate frequencies of HCCs
that share an HCC coefficient estimation group in the adult models,
will be grouped according to the current sorting algorithm in the
current HHS-RADV failure rate grouping methodology.\79\ These HCC
groupings will be determined by first ranking all Super HCC failure
rates and then dividing the rankings into the three groupings weighted
by total observations of that Super HCC across all issuers' IVA
samples, thereby assigning each Super HCC into a high, medium, or low
HCC failure rate grouping. This process ensures that all HCCs in a
Super HCC are grouped into the same HCC failure rate grouping in HHS-
RADV.
---------------------------------------------------------------------------
\79\ See Section 11.3.1 of the 2018 HHS-RADV Protocols at
https://www.regtap.info/uploads/library/HRADV_2018Protocols_070319_RETIRED_5CR_070519.pdf for a description
of the process prior to the introduction of Super HCCs. Beginning
with the 2019 benefit year of HHS-RADV, Super HCCs would take the
place of HCCs in the process. The 2019 HHS-RADV Protocols have thus
far only been published in part at https://www.regtap.info/uploads/library/HRADV_2019_Protocols_111120_5CR_111120.pdf. The section of
the 2019 HHS-RADV Protocols pertaining to HCC grouping for failure
rate calculations is not included in the current version. Once
published, this section will be updated to include steps related to
creation of Super HCCs.
---------------------------------------------------------------------------
Next, an issuer's HCC group failure rate would be calculated as
follows:
[GRAPHIC] [TIFF OMITTED] TR01DE20.021
Where:
freqEDGEG,i is the number of occurrences of HCCs in group G that are
recorded on EDGE for all enrollees sampled from issuer i.
freqIVAG,i is the number of occurrences of HCCs in group G that are
identified by the IVA (or SVA, as applicable) for all enrollees
sampled from issuer i.
GFRG,i is issuer i's group failure rate for the HCC group G.
HHS calculates the weighted mean failure rate and the standard
deviation of each HCC group as:
[GRAPHIC] [TIFF OMITTED] TR01DE20.022
[[Page 76999]]
Where:
[mu]{GFRG{time} is the weighted mean of GFRG,i of all issuers for
the HCC group G weighted by all issuers' sample observations in each
group.
Sd{GFRG{time} is the weighted standard deviation of GFRG,i of all
issuers for the HCC group G.
Each issuer's HCC group failure rates will then be compared to the
national metrics for each HCC failure rate grouping. If an issuer's
failure rate for an HCC failure rate group falls outside of the two-
tailed 90 percent confidence interval with a 1.645 standard deviation
cutoff based on the weighted mean failure rate for the HCC failure rate
group, the failure rate for the issuer's HCCs in that group will be
considered an outlier (if the issuer meets the minimum number of HCCs
for the HCC failure rate group). Based on issuers' failure rates for
each HCC failure rate group, outlier status will be determined for each
issuer independently for each issuer's HCC failure rate group such that
an issuer may be considered an outlier in one HCC failure rate group
but not an outlier in another HCC failure rate group. Beginning with
the 2019 benefit year, issuers will not be considered an outlier for an
HCC group in which the issuer has fewer than 30 EDGE HCCs. If no
issuers' HCC group failure rates in a state market risk pool materially
deviate from the national mean of failure rates or if those issuers
whose failure rates do materially deviate from the national mean do not
also meet the minimum HCC frequency requirement (that is, if no issuers
in the state market risk pool are outliers), HHS will not apply any
HHS-RADV adjustments to issuers' risk scores or to transfers in that
state market risk pool.
Then, once the outlier issuers are determined, we will calculate
the GAF taking into consideration the outlier issuer's distance from
the confidence interval and limiting calculation of the GAF when if the
issuer is a negative error rate outlier with a negative failure rate.
The formula \80\ will apply as follows:
---------------------------------------------------------------------------
\80\ This calculation sequence is expressed here in a revised
order compared to how the sequence is published in the 2021 Payment
Notice (85 FR at 29196-29198). This change was made to simplify the
illustration of how this sequence would be combined with proposals
finalized in this rule. The different display does not modify or
otherwise change the amendments to the outlier identification
process finalized in the 2021 Payment Notice.
[GRAPHIC] [TIFF OMITTED] TR01DE20.023
---------------------------------------------------------------------------
Where:
r indicates whether the GAF is being calculated for a
negative or positive outlier;
a is the slope of the sliding scale adjustment, calculated
as:
[GRAPHIC] [TIFF OMITTED] TR01DE20.024
With outerZr defined as the greater magnitude z-score selected to
define the edge of the sliding scale range r (3.00 for positive
outliers; and -3.00 for negative outliers) and innerZr defined as the
lower magnitude z-score selected to define the edge of the range r
(1.645 for positive outliers; and -1.645 for negative outliers);
br is the intercept of the sliding scale adjustment for a
given sliding scale range r, calculated as:
[GRAPHIC] [TIFF OMITTED] TR01DE20.025
[[Page 77000]]
disZG,i,r is the z-score of issuer i's GFRG,i, for HCC
failure rate group G discounted according to the sliding scale
adjustment for range r, calculated as:
[GRAPHIC] [TIFF OMITTED] TR01DE20.026
With zG,i defined as the z-score of i issuers' GFRG,i:
[GRAPHIC] [TIFF OMITTED] TR01DE20.027
GAFG,i is the group adjustment factor for HCC failure rate
group G for an issuer i;
Sd{GFRG{time} is the weighted national standard deviation
of all issuers' GFRs for HCC failure rate group G;
[micro]{GFRG{time} is the weighted national mean of all
issuers' GFRs for HCC failure rate group G.
Once an outlier issuer's GAF is calculated, the enrollee adjustment
will be calculated by applying the GAF to an enrollee's individual EDGE
HCCs. For example, if an issuer has an enrollee with the HIV/AIDS HCC
and the issuer's HCC group adjustment rate is 10 percent for the HCC
group that contains the HIV/AIDS HCC, the enrollee's HIV/AIDS
coefficient would be reduced by 10 percent. This reduction would be
aggregated with any reductions to other EDGE HCC risk score
coefficients for that enrollee to arrive at the overall enrollee
adjustment factor. This value would be calculated according to the
following formula for each sample enrollee in strata 1 through 9 with
EDGE HCCs: \81\
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\81\ Some enrollees sampled in Strata 1-3 will only have RXCs,
which are not considered as part of the determination of an enrollee
adjustment factor.
[GRAPHIC] [TIFF OMITTED] TR01DE20.028
---------------------------------------------------------------------------
Where:
RSh,G,i,e is the risk score component of a single HCC h (belonging
to HCC group G) recorded on EDGE for enrollee e of issuer i.
GAFG,i is the group adjustment factor for HCC failure rate group G
for an issuer i;
Adjustmenti,e is the calculated adjustment amount to adjust enrollee
e of issuer i's EDGE risk scores.
The calculation of the enrollee adjustment factor only considers
risk score factors related to the HCCs and ignores any other risk score
factors (such as demographic factors and RXC factors). Furthermore,
because this formula is concerned exclusively with EDGE HCCs, HCCs
newly identified by the IVA (or SVA as applicable) would not contribute
to enrollee risk score adjustments for that enrollee and adjusted
enrollee risk scores are only computed for sampled enrollees with EDGE
HCCs in strata 1 through 9.
Next, for each sampled enrollee with EDGE HCCs, HHS will calculate
the total adjusted enrollee risk score as:
[GRAPHIC] [TIFF OMITTED] TR01DE20.029
Where:
EdgeRSi,e is the risk score as recorded on the EDGE server of
enrollee e of issuer i.
AdjRSi,e is the amended risk score for sampled enrollee e of issuer
i.
Adjustmenti,e is the adjustment factor by which we
estimate whether the EDGE risk score exceeds or falls short of the
IVA or SVA projected total risk score for sampled enrollee e of
issuer i.
The calculation of the sample enrollee's adjusted risk score
includes all EDGE server components for sample enrollees in strata 1
through 9 with EDGE HCCs.
After calculating the outlier issuers' sample enrollees with HCCs'
adjusted EDGE risk scores, HHS will calculate an outlier issuer's error
rate by extrapolating the difference between the amended risk score and
EDGE risk score for all enrollees (strata 1 through 10) in the sample.
The extrapolation formula will be weighted by determining the ratio of
an enrollee's stratum size in the issuer's population to the number of
sample enrollees in the same stratum as the enrollee. Sample enrollees
with no EDGE HCCs will be included in the extrapolation of the error
rate for outlier issuers with the EDGE risk score unchanged for these
sample enrollees. The formulas to compute the error rate using the
stratum-weighted risk score before and after the adjustment will be:
[[Page 77001]]
[GRAPHIC] [TIFF OMITTED] TR01DE20.030
Consistent with 45 CFR 153.350(b), HHS then will apply the outlier
issuer's error rate to adjust that issuer's applicable benefit year's
plan liability risk score.\82\ This risk score change, which also will
impact the state market average risk score, will then be used to adjust
the applicable benefit year's risk adjustment transfers for the
applicable state market risk pool.\83\ Due to the budget-neutral nature
of the HHS-operated risk adjustment program, adjustments to one
issuer's risk scores and risk adjustment transfers based on HHS-RADV
findings affect other issuers in the state market risk pool (including
those who were not identified as outliers) because the state market
average risk score changes to reflect the outlier issuer's change in
its plan liability risk score. This also means that issuers that are
exempt from HHS-RADV for a given benefit year will have their risk
adjustment transfers adjusted based on other issuers' HHS-RADV results
if any issuers in the applicable state market risk pool are identified
as outliers.
---------------------------------------------------------------------------
\82\ Exiting outlier issuer risk score error rates are currently
applied to the plan liability risk scores and risk adjustment
transfer amounts for the benefit year being audited if they are a
positive error rate outlier. For all other outlier issuers, risk
score error rates are currently applied to the plan liability risk
scores and risk adjustment transfer amounts for the current transfer
year. As detailed in Section II.B, we are finalizing the transition
to the concurrent application of HHS-RADV results such that issuer
risk score error rates for non-exiting issuers will also be applied
to the risk scores and transfer amounts for the benefit year being
audited beginning with the 2020 benefit year of HHS-RADV.
\83\ See 45 CFR 153.350(c).
\84\ These estimates reflect the exclusion from outlier status
of those issuers with fewer than 30 HCCs in an HCC group, consistent
with the policy finalized in the 2021 Payment Notice (85 FR 29164),
which was not in effect for 2017 or 2018 benefit year HHS-RADV. We
excluded issuers with fewer than 30 HCCs from outlier status in
these estimates to provide a sense of the impact of the proposed
changes when compared to the methodology presently in effect for
2019 benefit year HHS-RADV and beyond.
\85\ This analysis reflects the sliding scale policy finalized
in Section II.A.2. of this rule which creates a sliding scale
adjustment from +/-1.645 to 3 standard deviations.
---------------------------------------------------------------------------
In the proposed rule, we estimated the combined impact of applying
the proposed sliding scale adjustment, the proposed negative failure
rate constraint and the proposed Super HCC aggregation using 2017
benefit year HHS-RADV results. We performed a similar analysis using
2018 benefit year HHS-RADV results, once the data became available.
Table 3 provides a comparison of the national failure rate metrics
under the current and new, finalized methodologies using 2017 and 2018
benefit year HHS-RADV results. Additionally, using the 2017 and 2018
HHS-RADV data, Table 4 provides a comparison between the estimated mean
error rates using the current methodology for sorting HCCs for HHS-RADV
grouping or the finalized Super HCC aggregation for sorting of HCCs for
HHS-RADV groupings, with the finalized negative failure rate constraint
and the finalized sliding scale adjustment also being applied. As shown
in Tables 3 and 4, the analysis of 2018 HHS-RADV results provided
roughly the same figures as the 2017 HHS-RADV results, and offers
further support for finalizing these refinements to the error rate
calculation.
Table 3--A Comparison of HHS-RADV National Failure Rate Metrics Based on Prior Benefit Year HHS-RADV Data
--------------------------------------------------------------------------------------------------------------------------------------------------------
Weighted mean failure Weighted std. dev. Lower threshold Upper threshold
rate -----------------------------------------------------------------------------
HHS-RADV data benefit year Group -------------------------- Current New Current New
Current New Current New grouping grouping grouping grouping
grouping grouping grouping grouping and 95% CI and 90% CI and 95% CI and 90% CI
--------------------------------------------------------------------------------------------------------------------------------------------------------
2017 Data..................... Low............. 0.0476 0.0496 0.0973 0.0959 -0.1431 -0.1082 0.2382 0.2074
Med............. 0.1549 0.1557 0.0992 0.0994 -0.0395 -0.0078 0.3493 0.3192
High............ 0.2621 0.2595 0.1064 0.1065 0.0536 0.0843 0.4706 0.4347
2018 Data..................... Low............. 0.0337 0.0369 0.0884 0.0856 -0.1396 -0.1038 0.2070 0.1777
Med............. 0.1198 0.1225 0.0862 0.0856 -0.0490 -0.0184 0.2887 0.2633
High............ 0.2262 0.2283 0.0919 0.0914 0.0461 0.0779 0.4062 0.3787
--------------------------------------------------------------------------------------------------------------------------------------------------------
Table 4--A Comparison of HHS-RADV Error Rate (ER) Estimated Changes Based on Prior Benefit Year 84 HHS-RADV Data
--------------------------------------------------------------------------------------------------------------------------------------------------------
2017 Data 2018 Data
-------------------------------------------------------------------------------------------------------
Current sorting method New sorting method Current sorting method New sorting method
Scenario -------------------------------------------------------------------------------------------------------
Mean neg. Mean pos. Mean neg. Mean pos. Mean neg. Mean pos. Mean neg. Mean pos.
ER (%) ER (%) ER (%) ER (%) ER (%) ER (%) ER (%) ER (%)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Sorting Method Only............................. -5.68 9.96 -5.98 9.91 -6.92 5.43 -7.06 5.71
Sorting Method with Negative Constraint......... -3.11 9.96 -3.38 9.91 -3.35 5.43 -3.16 5.89
Sorting Method with Sliding Scale \85\.......... -2.27 5.28 -2.49 5.32 -3.07 2.21 -3.21 2.45
Sorting Method, Sliding Scale & Negative -1.50 5.28 -1.66 5.32 -1.71 2.21 -1.86 2.47
Constraint (Finalized).........................
--------------------------------------------------------------------------------------------------------------------------------------------------------
[[Page 77002]]
B. Application of HHS-RADV Results
In the 2014 Payment Notice, HHS finalized a prospective approach
for making adjustments to risk adjustment transfers based on findings
from the HHS-RADV process.\86\ Specifically, we finalized using an
issuer's HHS-RADV error rates from the prior year to adjust the
issuer's average risk score in the current benefit year. As such, we
used the 2017 benefit year HHS-RADV results to adjust 2018 benefit year
risk adjustment plan liability risk scores for non-exiting issuers,
resulting in adjustments to 2018 benefit year risk adjustment transfer
amounts.87 88
---------------------------------------------------------------------------
\86\ See 78 FR 15410 at 15438.
\87\ See the Summary Report of 2017 Benefit Year HHS-RADV
Adjustments to Risk Adjustment Transfers released on August 1, 2019,
available at: https://www.cms.gov/CCIIO/Programs-and-Initiatives/Premium-Stabilization-Programs/Downloads/BY2017-HHSRADV-Adjustments-to-RA-Transfers-Summary-Report.pdf.
\88\ In the 2019 Payment Notice, we adopted an exception to the
prospective application of HHS-RADV results for exiting issuers,
whereby risk score error rates for outlier exiting issuers are
applied to the plan liability risk scores and transfer amounts for
the benefit year being audited. Therefore, for exiting issuers, we
used the 2017 benefit year's HHS-RADV results to adjust 2017 benefit
year plan liability risk scores, resulting in adjustments to 2017
benefit year risk adjustment transfer amounts. See 83 FR at 16965-
16966. We updated this policy to only apply HHS-RADV results for
exiting issuers that are positive error rate outliers beginning with
the 2018 benefit year. See the 2020 Payment Notice, 84 FR at 17503-
17504.
---------------------------------------------------------------------------
When we finalized the prospective HHS-RADV results application
policy in the 2014 Payment Notice, we did not anticipate the extent of
the changes that could occur in the risk profile of enrollees or market
participation in the individual and small group markets from benefit
year to benefit year. As a result of experience with these changes over
the early years of the program, and in light of the timeline for the
reporting, collection, and disbursement of HHS-RADV adjustments to
transfers \89\ and the changes to the risk adjustment holdback
policy,\90\ both of which lead to reopening of prior year risk
adjustment transfers, we proposed to switch away from the prospective
approach for non-exiting issuers. We proposed to make the transition
and apply HHS-RADV results to the benefit year being audited for all
issuers starting with the 2021 benefit year of HHS-RADV. We proposed
applying HHS-RADV results to the benefit year being audited for all
issuers in an effort to address stakeholder concerns about maintaining
actuarial soundness in the application of an issuer's HHS-RADV error
rate if an issuer's risk profile, enrollment, or market participation
changes substantially from benefit year to benefit year.
---------------------------------------------------------------------------
\89\ See 84 FR at 17504 through 17508.
\90\ See the Change to Risk Adjustment Holdback Policy for the
2018 Benefit Year and Beyond Bulletin (May 31, 2019) (May 2019
Holdback Guidance), available at: https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/Change-to-Risk-Adjustment-Holdback-Policy-for-the-2018-Benefit-Year-and-Beyond.pdf.
---------------------------------------------------------------------------
In the proposed rule, we explained that if we finalized and
implemented the policy to adjust the benefit year being audited
beginning with the 2021 benefit year HHS-RADV, we would need to adopt
transitional measures to move from the current prospective approach to
one that applies the HHS-RADV results to the benefit year being
audited. More specifically, 2021 benefit year risk adjustment plan
liability risk scores and transfers would need to be adjusted first to
reflect 2020 benefit year HHS-RADV results, and adjusted again based on
2021 benefit year HHS-RADV results. Then, for the 2022 benefit year of
HHS-RADV and beyond, risk adjustment plan liability risk scores and
transfers would only be adjusted once based on the same benefit year's
HHS-RADV results (that is, 2022 benefit year HHS-RADV results would
adjust 2022 benefit year risk adjustment plan liability risk scores and
transfers).\91\
---------------------------------------------------------------------------
\91\ As discussed in the May 2019 Holdback Guidance, a
successful HHS-RADV appeal may require additional adjustments to
transfers for the applicable benefit year in the impacted state
market risk pool.
---------------------------------------------------------------------------
In order to effectuate this transition, we proposed an ``average
error rate approach,'' as set forth in the 2019 RADV White Paper, under
which HHS would calculate an average value for the 2021 and 2020
benefit years' HHS-RADV error rates and apply this average error rate
to 2021 plan liability risk scores and risk adjustment transfers.\92\
This approach would result in one final HHS-RADV adjustment to 2021
benefit year plan liability risk scores and risk adjustment transfers,
reflecting the average value for the 2021 and 2020 benefit years' HHS-
RADV error rates. The adjustments to transfers would be collected and
paid in accordance with the 2021 benefit year HHS-RADV timeline.\93\
---------------------------------------------------------------------------
\92\ See Section 5.2 of the 2019 RADV White Paper.
\93\ For a general description of the current timeline for
reporting, collection, and disbursement of HHS-RADV adjustments to
transfers, see 84 FR at 17506 through 17507.
---------------------------------------------------------------------------
However, in an effort to be consistent with our current risk score
error rate application and calculation and ensure that both years of
HHS-RADV results were taken into consideration in calculating risk
adjustment plan liability risk scores, we also proposed an alternative
approach: the ``combined plan liability risk score option.'' Under the
combined plan liability risk score option, we would apply 2020 benefit
year HHS-RADV risk score adjustments to 2021 benefit year plan
liability risk scores, and then apply 2021 benefit year HHS-RADV risk
score adjustments to the adjusted 2021 plan liability risk scores. We
would then use the final adjusted plan liability risk scores
(reflecting both the 2020 and 2021 HHS-RADV adjustments to risk scores)
to adjust 2021 benefit year transfers. Under this proposal, HHS would
calculate risk score adjustments for 2020 and 2021 benefit year HHS-
RADV sequentially and incorporate 2020 and 2021 benefit year HHS-RADV
results in one final adjustment amount to 2021 benefit year transfers.
We sought comment on both of these approaches to transition from the
current prospective approach to one that applies the HHS-RADV results
to the benefit year being audited.
We also explained in the proposed rule that the transition to a
policy to apply HHS-RADV results to the benefit year being audited for
all issuers would remove the need to continue the current policy on
issuers entering sole issuer markets finalized in the 2020 Payment
Notice.\94\ As finalized in the 2020 Payment Notice, new issuer(s) that
enter a new market or a previously sole issuer market have their risk
adjustment transfers in the current benefit year adjusted if there was
an outlier issuer in the applicable state market risk pool in the prior
benefit year's HHS-RADV.\95\ We further explained that if the proposal
to apply HHS-RADV results to the benefit year being audited for all
issuers is finalized, new issuers, including new issuers in previously
sole issuer markets, would no longer be impacted by HHS-RADV results
from a previous benefit year; rather, the new issuer would only have
their current benefit year risk scores (and subsequently, risk
adjustment transfers) impacted if there was an outlier issuer in the
same state market risk pool.
---------------------------------------------------------------------------
\94\ 84 FR at 17504.
\95\ Ibid.
---------------------------------------------------------------------------
We also sought comment on an alternative timeline, in which HHS
would apply HHS-RADV results to the benefit year being audited for all
issuers starting with the 2020 benefit year of HHS-RADV, rather than
the 2021 benefit year. We explained that under the alternative
timeframe, 2020 benefit year risk adjustment plan liability risk scores
and transfers would need to be adjusted twice--first to reflect 2019
benefit year HHS-RADV results and again based on 2020 benefit year HHS-
RADV results. Lastly, we sought
[[Page 77003]]
comment on whether, if we finalized and implemented either of the
transition options using the alternative timeline, we should also pilot
RXCs for the 2020 benefit year HHS-RADV.
We are finalizing the proposed transition from the current
prospective application of HHS-RADV results for non-exiting issuers and
will apply HHS-RADV audit findings to the benefit year being audited
for all issuers, starting with the 2020 benefit year HHS-RADV, by
combining 2019 and 2020 benefit years HHS-RADV results for non-exiting
issuers following the average error rate approach. We also reaffirm
that, as a result of finalizing these changes, we will not need to
continue the current policy on issuers entering sole issuer markets
after the transition is effectuated. Therefore, if a new issuer entered
a state market risk pool in 2020, its risk adjustment plan liability
risk score(s) and transfer for 2020 benefit year risk adjustment could
be impacted by the new issuer's own 2020 HHS-RADV results and the
combined 2019 and 2020 HHS-RADV results of other issuers in the same
state market risk pool. For exiting issuers, HHS will continue to
adjust only for positive error rate outliers, as opposed to both
positive and negative error rate outliers.\96\ Beginning with the 2021
benefit year of HHS-RADV, plan liability risk scores and risk
adjustment transfers will only be adjusted once based on the same
benefit year's HHS-RADV results (that is, 2021 benefit year HHS-RADV
results would adjust 2021 benefit year plan liability risk scores and
transfers for all issuers).\97\ Additionally, HHS will continue to
pilot RXCs for the 2020 benefit year.
---------------------------------------------------------------------------
\96\ In addition, positive error rate outlier issuers' 2019 and
2020 HHS-RADV results will be applied to the risk scores and
transfers for the benefit year being audited. The average error rate
approach is not applicable because exiting issuers who participated
in 2019 HHS-RADV would not have 2020 benefit year risk scores or
transfers to adjust.
\97\ As discussed in the May 2019 Holdback Guidance, a
successful HHS-RADV appeal may require additional adjustments to
transfers for the applicable benefit year in the impacted state
market risk pool.
---------------------------------------------------------------------------
We are finalizing this change to apply HHS-RADV results to the
benefit year being audited for all issuers to address stakeholder
concerns about maintaining actuarial soundness in the application of an
issuer's HHS-RADV error rate if an issuer's risk profile, enrollment,
or market participation changes substantially from benefit year to
benefit year. In addition, this change has the potential to provide
more stability for issuers of risk adjustment covered plans and help
them better predict the impact of HHS-RADV results. Once the transition
is effectuated, it will also prevent situations in which an issuer who
newly enters a state market risk pool, including new market entrants to
a sole issuer market, is subject to HHS-RADV adjustments from the prior
benefit year for which they did not participate.
Comments: The majority of commenters supported switching from the
prospective application of the HHS-RADV results to the benefit year
being audited. These commenters generally agreed that having a
concurrent application would maintain actuarial soundness in the
application of an issuer's HHS-RADV error rate, provide stability to
HHS-RADV results, and promote fairness in the HHS-RADV process. One
commenter suggested that HHS should consider maintaining the current
prospective application of HHS-RADV findings; another commenter
suggested HHS exempt new issuers from having their transfers adjusted
due to HHS-RADV.
Regarding the transition year, some commenters supported switching
to the concurrent application in the 2021 benefit year as proposed due
to concerns that changing the transition year to the 2020 benefit year
of HHS-RADV would heighten the already significant uncertainty
surrounding 2020 as a result of COVID-19, with one commenter noting
that issuers did not account for this change in their 2020 pricing.
However, most commenters supported switching to the concurrent
application with the 2020 benefit year, suggesting that it would be
most appropriate to transition to a concurrent application as early as
possible and one cited to the various changes to the HHS-operated risk
adjustment program beginning with the 2021 benefit year as further
support for the alternative timeline for the transition. One commenter
requested additional information on the 2020 benefit year HHS-RADV
timeline.
Response: We are finalizing the proposal to switch from the current
prospective application of the HHS-RADV results to the benefit year
being audited, starting with the 2020 benefit year. As previously
noted, when we finalized the prospective HHS-RADV results application
policy, we did not anticipate the extent of changes that could occur in
the risk profile of enrollees or market participation by issuers from
benefit year to benefit year. As a result of experience over the early
years of the program, we believe that transitioning to apply HHS-RADV
results on a concurrent basis for all issuers will provide greater
stability, promote fairness, and enhance actuarial soundness,
specifically in the event that an issuer's risk profile, enrollment, or
market participation changes significantly from benefit year to benefit
year. In light of the other changes to HHS-RADV program operations
described in this rule which will lead to reopening of prior benefit
year risk adjustment transfers,\98\ it is also no longer necessary to
apply HHS-RADV results on a prospective basis to allow time to complete
the discrepancy and appeals processes to avoid having to reopen prior
year transfers. We also agree that we should begin the application of
the results on a concurrent basis as soon as possible and will
implement the policy starting with the 2020 benefit year. We believe
that starting with the 2020 benefit year will add stability in the
midst of the COVID-19 pandemic, as the results from the 2019 and 2020
benefit years of HHS-RADV will be averaged together to calculate the
adjustment to 2020 benefit year risk adjustment risk scores. We believe
this added stability will account for concerns that issuers did not
take this proposed change into consideration when setting rates for the
2020 benefit year. We also agree with the commenter who cited the risk
adjustment program updates that apply beginning with the 2021 benefit
year as further support for effectuating the transition beginning with
the 2020 benefit year.\99\
---------------------------------------------------------------------------
\98\ Ibid.
\99\ For example, in the 2021 Payment Notice, we finalized
several updates to the HHS-HCC clinical classification to develop
updated risk factors that apply beginning with the 2021 benefit year
risk adjustment models. See 85 FR at 29175.
---------------------------------------------------------------------------
We did not propose and are not finalizing a new exemption from HHS-
RADV for new market entrants. The inclusion of new market entrants in
HHS-RADV ensures that those issuers' actuarial risk for the applicable
benefit year is accurately reflected in risk adjustment transfers, and
that the HHS-operated risk adjustment program assesses charges to plans
with lower-than-average actuarial risk while making payments to plans
with higher-than-average actuarial risk. However, new market entrants
will no longer be impacted by a prior year's HHS-RADV results and will
only be impacted by the results from the benefit year under which they
participated in the state market risk pool after the transition is
effectuated.\100\
---------------------------------------------------------------------------
\100\ As noted above, a new entrant to a state market risk pool
in 2020 would see its risk score(s) and transfer impacted by the new
issuer's own 2020 HHS-RADV results, the combined 2019 and 2020 HHS-
RADV results of other non-exiting issuers in the same state market
risk pool, and the 2020 HHS-RADV results for positive error rate
outlier exiting issuers in the same state market risk pool. However,
a new entrant to a state market risk pool in 2021 would see its risk
score(s) and transfer impacted by 2021 HHS-RADV results only.
---------------------------------------------------------------------------
[[Page 77004]]
HHS intends to provide more information on the 2020 benefit year
HHS-RADV timeline in the future, but generally anticipates it will
commence as usual with the release of samples in May 2021. As
previously noted in this rule, HHS has provided details on the updated
timeline on the activities for 2019 benefit year HHS-RADV.\101\
---------------------------------------------------------------------------
\101\ See the ``2019 Benefit Year HHS-RADV Activities Timeline''
https://www.regtap.info/uploads/library/HRADV_Timeline_091020_5CR_091020.pdf.
---------------------------------------------------------------------------
Comments: Most commenters who submitted comments on the options for
combining HHS-RADV results during the transition period supported using
the average error rate approach, noting that it would provide more
stability and transparency than the combined plan liability risk score
option. One commenter who expressed a preference for the average error
rate approach cited concerns with the amplifying effect of adjusting
risk scores twice under the plan liability risk score option. Most
commenters who supported the average error rate approach supported
effectuating the transition using 2019 and 2020 benefit years' error
rate results. These commenters noted that aggregating the results of
these 2 years could reduce volatility and smooth over potential
challenges issuers may face when conducting HHS-RADV audits for these
benefit years due to the COVID-19 public health emergency. A few
commenters who supported use of the average error rate approach urged
HHS to implement the transition and use 2020 and 2021 benefit years'
results, suggesting it would be the most straightforward approach. One
commenter requested clarification as to whether the average error rate
approach would use a weighted average error rate.
A few commenters supported the combined plan liability risk score
option for the transition years of HHS-RADV. One of these commenters
believed that the combined plan liability risk score option would be a
fairer way to provide consistency, while a different commenter that
supported the combined plan liability risk score option was concerned
that the average error rate approach would reduce the otherwise
applicable HHS-RADV adjustment. Another commenter compared the two
alternative approaches, noting that the average error rate would align
well with some issuers' practices, while the combined liability risk
score option would align better with other issuers' financial
reporting.
Response: We are finalizing the use of the average error rate
approach to transition to the concurrent application of HHS-RADV
results for non-exiting issuers by combining their 2019 and 2020
benefit years' HHS-RADV results. In response to comments we clarify
that for simplification purposes, HHS will apply an unweighted average
value of the 2019 and 2020 benefit years' HHS-RADV results to adjust
2020 benefit year risk scores and transfers. We proposed using a
combined plan liability risk score as an alternative option, believing
that it could provide a more consistent transition to a concurrent
application of HHS-RADV results. However, the majority of comments on
these transition options emphasized the extent to which they believed
an average error rate approach will actually provide greater stability
and transparency for the HHS-RADV adjustments applied during the
transition period. After consideration of comments, we agree that the
average error rate approach will be the optimal transitional approach.
More specifically, aggregating the 2019 and 2020 benefit years' results
for non-exiting issuers and using the unweighted average value of those
benefit years' HHS-RADV results to adjust transfers will allow for more
consistency, reduce potential volatility, and better accommodate any
potential disparities or challenges due to COVID-19. As noted
previously, we also believe the transition to the application of the
results on a concurrent basis should be implemented as soon as possible
and therefore will start the concurrent application of HHS-RADV results
for all issuers starting with the 2020 benefit year. We recognize that
there are advantages to the combined plan liability risk score option,
which is why we proposed it for combining HHS-RADV results for the
transition years. However, for the reasons outlined above, we believe
the average error rate method is the more balanced approach to
effectuate the transition and combine 2019 and 2020 HHS-RADV results
for non-exiting issuers.
Comments: Some commenters suggested HHS cancel either the 2019 or
2020 benefit years of HHS-RADV. One of these commenters expressed
concern that the COVID-19 pandemic could potentially skew the 2020
benefit year HHS-RADV results. Other commenters stated that COVID-19
would make it difficult for providers to respond to issuer requests for
the medical documentation needed to complete audits, which they noted
could skew HHS-RADV results.
Response: We appreciate the concerns related to the potential
impact of COVID-19, but are not cancelling HHS-RADV for either the 2019
or 2020 benefit year. We believe that cancelling either year of this
program would be detrimental to program integrity and would result in
future difficulties monitoring HHS-RADV trends. We acknowledge that the
COVID-19 pandemic puts a number of stressors on providers and issuers.
Recognizing the impact of the public health emergency on HHS-RADV
activities, we postponed the start of 2019 benefit year HHS-RADV
activities.\102\ As recently announced, IVA samples for 2019 benefit
year HHS-RADV will be released in January 2021 and we anticipate 2020
benefit year HHS-RADV will commence as usual.\103\ We will continue to
monitor the COVID-19 public health emergency and will consider whether
additional flexibilities for HHS-RADV are appropriate. Further, as
noted above, the adoption of the average error rate approach for the
transition to the concurrent application of HHS-RADV is intended to
help reduce volatility related to potential challenges issuers may face
when conducting HHS-RADV audits for these benefit years due to the
COVID-19 public health emergency.
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\102\ https://www.cms.gov/files/document/2019-HHS-RADV-Postponement-Memo.pdf.
\103\ See the ``2019 Benefit Year HHS-RADV Activities Timeline''
https://www.regtap.info/uploads/library/HRADV_Timeline_091020_5CR_091020.pdf.
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Comments: Most commenters supported continuing the pilot of RXCs
for the 2020 benefit year. Some of these commenters suggested that
continuing to pilot RXCs would allow for more consistency between 2019
and 2020 and support transitioning to the concurrent application of
HHS-RADV results starting with the 2020 benefit year, while another
commenter believed that it would minimize the amount of changes
occurring at once. One commenter noted that extending the RXC pilot
would benefit the issuers who are still learning how to conduct HHS-
RADV for RXCs. Another commenter did not believe it would be necessary
to continue piloting RXCs in 2020, but acknowledged that an additional
pilot period would allow issuers to focus on HHS-RADV during the COVID-
19 pandemic, rather than adjusting to new aspects of HHS-RADV
reporting.
Response: After consideration of comments, we are finalizing the
continuation of the pilot for RXCs for the 2020 benefit year. Extending
the RXC pilot an additional benefit year will increase consistency
between the
[[Page 77005]]
operations of the 2019 and 2020 benefit years' HHS-RADV and facilitate
the combination of the HHS-RADV adjustments for these benefit years as
we transition to a concurrent application of HHS-RADV results starting
with the 2020 benefit year. We agree with commenters who suggested that
an additional pilot year for RXCs would benefit issuers and provide an
opportunity to continue to improve their internal process for
conducting HHS-RADV for RXCs.
III. Collection of Information Requirements
This document does not impose information collection requirements,
that is, reporting, recordkeeping, or third-party disclosure
requirements. Consequently, there is no need for review by the Office
of Management and Budget under the authority of the Paperwork Reduction
Act of 1995 (44 U.S.C. 3501 et seq.).
Under this final rule, we are finalizing the modifications to the
calculation of error rates to modify the HCC failure rate grouping
methodology for HCCs that share an HCC coefficient estimation group in
the adult risk adjustment models; to calculate and apply a sliding
scale adjustment for cases where outlier issuers are near the
confidence intervals; and to constrain the error rate calculation for
issuers with negative failure rates. We are also finalizing the
transition from the current prospective application of HHS-RADV results
\104\ to apply the results to the benefit year being audited. These are
methodological changes to the error estimation used in calculating
error rates and changes to the application of HHS-RADV results to risk
scores and transfers. Since HHS calculates error rates and applies HHS-
RADV results to risk scores and transfers, we did not estimate a burden
change on issuers to conduct and complete HHS-RADV in states where HHS
operates the risk adjustment program for a given benefit year.\105\
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\104\ The exception to the current prospective application of
HHS-RADV results is for exiting issuers identified as positive error
rate outliers, whose HHS-RADV results are applied to the risk scores
and transfer amounts for the benefit year being audited.
\105\ Since the 2017 benefit year, HHS has been responsible for
operating risk adjustment in all 50 states and the District of
Columbia.
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IV. Regulatory Impact Statement
A. Statement of Need
This rule finalizes standards related to HHS-RADV, including
certain refinements to the calculation of error rates and a transition
from the prospective application of HHS-RADV results. The Premium
Stabilization Rule and other rulemakings noted earlier provided detail
on the implementation of HHS-RADV.
B. Overall Impact
We have examined the impact of this rule as required by Executive
Order 12866 on Regulatory Planning and Review (September 30, 1993),
Executive Order 13563 on Improving Regulation and Regulatory Review
(January 18, 2011), the Regulatory Flexibility Act (RFA) (September 19,
1980, Pub. L. 96-354), section 1102(b) of the Social Security Act (the
Act), section 202 of the Unfunded Mandates Reform Act of 1995 (March
22, 1995; Pub. L. 104-4), Executive Order 13132 on Federalism (August
4, 1999), the Congressional Review Act (5 U.S.C. 804(2)), and Executive
Order 13771 on Reducing Regulation and Controlling Regulatory Costs
(January 30, 2017).
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). A
Regulatory Impact Analysis (RIA) must be prepared for major rules with
economically significant effects ($100 million or more in any 1 year).
This rule does not reach the economic significance threshold, and thus
is not considered a major rule. For the same reason, it is not a major
rule under the Congressional Review Act.
C. Regulatory Alternatives Considered
In developing the policies contained in this final rule, we
considered numerous alternatives to the presented policies. Below we
discuss the key regulatory alternatives considered.
We considered an alternative approach to the sorting of all HCCs
that share an HCC coefficient estimation group in the adult models into
the same ``Super HCC'' for HHS-RADV HCC grouping purposes. This
alternative approach would have combined all HCCs in the same hierarchy
into the same Super HCC for HHS-RADV HCC grouping purposes even if
those HCCs had different coefficients in the risk adjustment models.
While we did analyze this option, we were concerned that it would not
account for risk differences within the HCC hierarchies, and that the
finalized approach that focuses on HCCs that share an HCC coefficient
estimation group and have the same risk scores in the adult models
would better ensure that HHS-RADV results account for risk differences
within HCC hierarchies. Additionally, by forcing all HCCs that share a
hierarchy into the same HHS-RADV failure rate grouping regardless of
whether they have different coefficients, we would not only diminish
our ability to allow for differences among various diseases within an
HCC hierarchy but would also reduce our ability to recognize
differences in the difficulty of providing medical documentation for
them.\106\
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\106\ See 83 FR 16961 and 16965.
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We considered several other options for addressing the payment
cliff effect besides the specific sliding scale adjustment that we are
finalizing. One option was returning to the original methodology
finalized in the 2015 Payment Notice, which would have adjusted almost
all issuers' risk scores for every error identified as a result of HHS-
RADV.\107\ The adjustments under the original methodology would have
used the issuer's corrected average risk score to compute an adjustment
factor, which would have been based on the ratio between the corrected
and original average risk scores. However, our analysis indicated that
the original methodology generally resulted in less stability, since
the vast majority of outlier issuers had their original failure rates
applied without the benefit of subtracting the weighted mean
difference.\108\ In addition, while the original methodology did not
specifically result in a payment cliff effect, it would have resulted
in more and larger adjustments to transfers.
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\107\ See 79 FR 13755-13770.
\108\ See the 2019 RADV White Paper at pages 78-79 and Appendix
B.
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The second option we considered to mitigate the impact of the
payment cliff was to modify the error rate calculation by calculating
the issuer's GAF using the HCC group confidence interval rather than
the distance to the weighted HCC group mean. As described in the 2019
RADV White Paper and in previous rulemaking,\109\ we had concerns that
this option would result in under-adjustments based on HHS-RADV results
for issuers farthest from the confidence intervals. Thus, although this
option could address the payment cliff effect for issuers just outside
of the confidence interval, it also could create the unintended
consequence of mitigating the payment impact for situations where
issuers are not close to the confidence intervals, potentially reducing
incentives for issuers to submit
[[Page 77006]]
accurate risk adjustment data to their EDGE servers.
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\109\ See 84 FR 17507-17508. See also the 2019 RADV White Paper
at page 80.
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An additional option suggested by some stakeholders that could
address, at least in part, the payment cliff effect that we considered
would be to modify the two-sided approach to HHS-RADV and only adjust
issuers who are positive error rate outliers. However, moving to a one-
sided outlier identification methodology would not have addressed the
payment cliff effect because it would still exist on the positive error
rate side of the methodology.\110\ In addition, the two-sided outlier
identification, and the resulting adjustments to outlier issuer risk
scores that have significantly better-than-average or poorer-than-
average data validation results, ensures that HHS-RADV adjusts for
identified, material risk differences between what issuers submitted to
their EDGE servers and what was validated by the issuers' medical
records during HHS-RADV. The two-sided outlier identification approach
ensures that an issuer who is coding well is able to recoup funds that
might have been lost through risk adjustment because its competitors
are coding badly.
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\110\ It is important to note the purpose of HHS-RADV approach
is fundamentally different from the Medicare Advantage risk
adjustment data validation (MA-RADV) approach. MA-RADV only adjusts
for positive error rate outliers, as the program's intent is to
recoup Federal funding that was the result of improper payments
under the Medicare Part C program.
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We also considered various other options for the thresholds under
the sliding scale option to mitigate the payment cliff effect. For
example, we considered as an alternative the adoption of a sliding
scale option that would adjust outlier issuers' error rates on a
sliding scale between the 95 and 99.7 percent confidence interval
bounds (from +/- 1.96 to 3 standard deviations). This alternative
sliding scale option would retain the current methodology's confidence
interval at 1.96 standard deviations, the full adjustment to the mean
failure rate for issuers outside of the 99.7 percent confidence
interval (beyond three standard deviations), and the current
significant adjustment to the HCC group weighted mean after three
standard deviations. Commenters supported this sliding scale option
because it addressed the payment cliff issue without increasing the
number of issuers identified as outliers. However, while we recognized
that this alternative also would mitigate the payment cliff effect, it
would weaken HHS-RADV by reducing its overall impact and the magnitude
of HHS-RADV adjustments to outlier issuer's risk scores.
When developing a process for implementing the transition from the
prospective application of HHS-RADV results to a concurrent application
approach, we considered three options for the transition year. In
previous sections of this rule, we described two of those options. The
third option is the ``RA transfer option.'' The RA transfer option
would separately calculate 2019 benefit year HHS-RADV adjustments to
2020 benefit year transfers and 2020 benefit year HHS-RADV adjustments
to 2020 benefit year transfers.\111\ Under this option, we would then
calculate the difference between each of these values and the
unadjusted 2020 benefit year transfers before any HHS-RADV adjustments
were applied, and add these differences together to arrive at the total
HHS-RADV adjustment that would be applied to the 2020 benefit year
transfers. That is, HHS would separately calculate adjustments for the
2019 and 2020 benefit year HHS-RADV results and incorporate 2019 and
2020 benefit year HHS-RADV results in one final adjustment to 2020
benefit year transfers that would be collected and paid in accordance
with the 2020 benefit year HHS-RADV timeline.\112\ However, we believe
this alternative is not as consistent with our current risk score error
rate application and calculation as the combined plan liability risk
score option, or as simple as the average error rate approach being
finalized.
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\111\ See section 5.2 of the 2019 RADV White Paper.
\112\ For a general description of the current timeline for
publication, collection, and distribution of HHS-RADV adjustments to
transfers, see 84 FR at 17506 -17507.
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V. Regulatory Flexibility Act
The RFA (5 U.S.C. 601 et seq.) requires agencies to prepare an
initial regulatory flexibility analysis to describe the impact of a
proposed rule on small entities, unless the head of the agency can
certify that the rule will not have a significant economic impact on a
substantial number of small entities. The RFA generally defines a
``small entity'' as (1) a proprietary firm meeting the size standards
of the Small Business Administration (SBA), (2) a not-for-profit
organization that is not dominant in its field, or (3) a small
government jurisdiction with a population of less than 50,000. States
and individuals are not included in the definition of ``small entity.''
HHS uses a change in revenues of more than 3 to 5 percent as its
measure of significant economic impact on a substantial number of small
entities.
In this final rule, we establish standards for HHS-RADV. This
program is generally intended to ensure the integrity of the HHS-
operated risk adjustment program, which stabilizes premiums and reduces
the incentives for issuers to avoid higher-risk enrollees. Because we
believe that insurance firms offering comprehensive health insurance
policies generally exceed the size thresholds for ``small entities''
established by the SBA, we do not believe that an initial regulatory
flexibility analysis is required for such firms.
We believe that health insurance issuers would be classified under
the North American Industry Classification System code 524114 (Direct
Health and Medical Insurance Carriers). According to SBA size
standards, entities with average annual receipts of $41.5 million or
less would be considered small entities for these North American
Industry Classification System codes. Issuers could possibly be
classified in 621491 (HMO Medical Centers) and, if this is the case,
the SBA size standard would be $35.0 million or less.\113\ We believe
that few, if any, insurance companies underwriting comprehensive health
insurance policies (in contrast, for example, to travel insurance
policies or dental discount policies) fall below these size thresholds.
Based on data from MLR annual report \114\ submissions for the 2017 MLR
reporting year, approximately 90 out of 500 issuers of health insurance
coverage nationwide had total premium revenue of $41.5 million or less.
This estimate may overstate the actual number of small health insurance
companies that may be affected, since over 72 percent of these small
companies belong to larger holding groups, and many, if not all, of
these small companies are likely to have non-health lines of business
that will result in their revenues exceeding $41.5 million.
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\113\ https://www.sba.gov/document/support--table-size-standards.
\114\ Available at https://www.cms.gov/CCIIO/Resources/Data-Resources/mlr.html.
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In addition, section 1102(b) of the Act requires us to prepare an
RIA if a rule may have a significant impact on the operations of a
substantial number of small rural hospitals. This analysis must conform
to the provisions of section 604 of the RFA. For purposes of section
1102(b) of the Act, we define a small rural hospital as a hospital that
is located outside of a metropolitan statistical area and has fewer
than 100 beds. This final rule would not affect small rural hospitals.
Therefore, the Secretary has determined that this final
[[Page 77007]]
rule will not have a significant impact on the operations of a
substantial number of small rural hospitals.
VI. Unfunded Mandates
Section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA)
requires that agencies assess anticipated costs and benefits and take
certain other actions before issuing a proposed rule that includes any
federal mandate that may result in expenditures in any 1 year by state,
local, or Tribal governments, in the aggregate, or by the private
sector, of $100 million in 1995 dollars, updated annually for
inflation. In 2020, that threshold is approximately $156 million.
Although we have not been able to quantify all costs, we expect the
combined impact on state, local, or Tribal governments and the private
sector to be below the threshold.
VII. Federalism
Executive Order 13132 establishes certain requirements that an
agency must meet when it issues a proposed rule that imposes
substantial direct costs on state and local governments, preempts state
law, or otherwise has federalism implications.
In compliance with the requirement of Executive Order 13132 that
agencies examine closely any policies that may have federalism
implications or limit the policymaking discretion of the states, we
have engaged in efforts to consult with and work cooperatively with
affected states, including participating in conference calls with and
attending conferences of the National Association of Insurance
Commissioners, and consulting with state insurance officials on an
individual basis.
While developing this final rule, we attempted to balance the
states' interests in regulating health insurance issuers with the need
to ensure market stability and adopt refinements to HHS-RADV standards.
By doing so, it is our view that we have complied with the requirements
of Executive Order 13132.
Because states have flexibility in designing their Exchange and
Exchange-related programs, state decisions will ultimately influence
both administrative expenses and overall premiums. States are not
required to establish an Exchange or risk adjustment program. HHS
operates risk adjustment on behalf of any state that does not elect to
do so. Beginning with the 2017 benefit year, HHS has operated risk
adjustment for all 50 states and the District of Columbia.
In our view, while this final rule would not impose substantial
direct requirement costs on state and local governments, it has
federalism implications due to direct effects on the distribution of
power and responsibilities among the state and Federal Governments
relating to determining standards about health insurance that is
offered in the individual and small group markets.
VIII. Reducing Regulation and Controlling Regulatory Costs
Executive Order 13771 requires that the costs associated with
significant new regulations ``to the extent permitted by law, be offset
by the elimination of existing costs associated with at least two prior
regulations.'' This final rule is not subject to the requirements of
Executive Order 13771 because it is expected to result in no more than
de minimis costs.
IX. Conclusion
In accordance with the provisions of Executive Order 12866, this
regulation was reviewed by the Office of Management and Budget.
Dated: November 18, 2020.
Seema Verma,
Administrator, Centers for Medicare & Medicaid Services.
Dated: November 23, 2020.
Alex M. Azar II,
Secretary, Department of Health and Human Services.
[FR Doc. 2020-26338 Filed 11-25-20; 4:15 pm]
BILLING CODE 4120-01-P