U.S. Department of Health and Human Services 
Office of Inspector General 
 
 
Billions in Estimated 
Medicare Advantage 
Payments From 
Diagnoses Reported Only 
on Health Risk 
Assessments Raise 
Concerns 
 
                   
Christi A. Grimm 
OEI-03-17-00471 
Principal Deputy 
September 2020 
Inspector General 
 
oig.hhs.gov  Report in Brief  U.S. Department of Health and Human Services 
September 2020 
Office  of Inspector General 
OEI-03-17-00471 
 
 
Billions in Estimated Medicare Advantage  Why OIG Did This Review 
We  undertook this study because of 
Payments From Diagnoses Reported Only on 
concerns that MAOs may use HRAs to 
Health Risk Assessments Raise Concerns  increase risk-adjusted payments 
inappropriately.  The MA program 
provided coverage to 23 million 
The risk adjustment program is an important payment 
Key Takeaway  beneficiaries in  2019 at a cost of 
mechanism for Medicare Advantage (MA).  It levels the 
$264 billion.  Unsupported 
Billions in estimated  
playing field for MA organizations (MAOs) that enroll  
risk-adjusted payments have been a 
beneficiaries who need a costlier level of care, which helps  risk-adjusted 
major driver of improper payments in 
to ensure these beneficiaries  have continued access to MA   payments supported 
the MA program. 
plans.  Health risk assessments (HRAs) can be a tool for  solely through  HRAs 
early identification of health risks to improve beneficiaries  raise concerns about  CMS risk-adjusts payments by using 
care and health outcomes.  However, some MAOs may be  the completeness of  beneficiaries diagnoses to pay higher 
initiating and using HRAsoften by hiring companies to  payment data,  capitated payments to MAOs for 
conduct HRAs  in beneficiaries homesto collect diagnoses  validity of diagnoses  beneficiaries expected to have greater 
and maximize risk-adjusted payments without improving  on HRAs, and  quality  health care needs.  This may create 
beneficiary care.   of care coordination  financial incentives for MAOs to make  
for beneficiaries.  beneficiaries appear as sick as 
What OIG Found  possible.  For CMS to risk-adjust  
Our findings highlight concerns  about the extent to which  payments, MAOs report beneficiaries 
MAOs are using HRAs to improve care and health outcomes, as intended, and about  diagnosesbased on services 
the sufficiency of the oversight by the Centers for Medicare & Medicaid Services (CMS).   provided to beneficiariesto CMSs 
From our analysis of 2016 MA  encounter data, we found that:  MA encounter data system and the 
   Diagnoses that MAOs reported only on HRAs, and on no other encounter records,  Risk Adjustment Processing System. 
resulted in an  estimated $2.6 billion in risk-adjusted payments for 2017.  
   In-home HRAs generated 80  percent of these estimated payments.  Most  in-home  HRAs are an allowable source of 
HRAs were conducted by companies that partner with or are hired by MAOs to  diagnoses for risk adjustment.  An 
conduct these  assessmentsand therefore are not likely conducted by the  HRA occurs when a physician or other 
beneficiarys own primary care provider.  health care professional collects 
   Twenty MAOs generated millions in payments from in-home HRAs for  information from beneficiaries about 
beneficiaries for whom there was not a single record of any other service being  their health to diagnose and identify 
provided in 2016.  gaps in care.  However, CMS and the 
Medicare Payment Advisory 
These findings  raise three types of concerns: (1) a data integrity concern that MAOs are 
Commission have raised concerns 
not submitting all service records as required; (2) a care coordination concern that 
that MAOs may use HRAs mainly as a 
beneficiaries are not receiving followup care  to address diagnoses identified during 
tool to collect diagnoses and increase 
HRAs; and (3) a payment integrity concern that if diagnoses are inaccurate or 
payments to MAOs rather than to 
unsupported, the associated risk-adjusted payments would then be inappropriate.  
improve the health of beneficiaries.  
Despite potential issues regarding HRAs, CMS has not yet reviewed the impact of HRAs  
on risk-adjusted payments or quality of care.  
How OIG Did This Review 
We analyzed 2016 MA encounter data 
What OIG Recommends 
to determine the 2017 financial impact 
We recommend that CMS: (1) require MAOs to implement best practices to ensure care 
of diagnoses that were reported only 
coordination for HRAs; (2) provide targeted  oversight of the 10 parent organizations 
on HRAs and not on any other service 
that drove most of the risk-adjusted payments resulting from in-home HRAs; (3) 
records in the encounter data that 
provide targeted oversight of the 20 MAOs that drove risk-adjusted payments resulting 
year.  We also analyzed CMS 
from in-home HRAs for beneficiaries who had no other service records in the encounter 
responses to a structured 
data; (4) reassess the risks and benefits of allowing in-home HRAs to be used as sources 
questionnaire to identify actions taken 
of diagnoses for risk adjustment; and (5) require MAOs to flag any MAO-initiated HRAs 
by CMS to review the impact  of HRAs 
in their MA encounter data.  CMS concurred with recommendations two and three. 
on MA payments. 
Full report can be found at oig.hhs.gov/oei/reports/oei-03-17-00471.asp  
   
TABLE OF CONTENTS 
BACKGROUND   1 
Methodology  8 
   
FINDINGS    
Diagnoses that MAOs reported only on health risk assessments (HRAs), and  not on any other  12   
encounter records, resulted in an estimated $2.6 billion in risk-adjusted payments for 2017 
In-home HRAs generated 80 percent of the estimated payments from diagnoses reported only  14 
on HRAs, and most in-home HRAs were conducted by companies that partner with MAOs 
Twenty MAOs generated millions in payments from in-home HRAs for beneficiaries for whom  17 
there was not a single record of any other service being provided in 2016 
CMS has not reviewed the impact of HRAs on risk-adjusted payments or quality of care   18 
   
CONCLUSION AND RECOMMENDATIONS   
Require MAOs to implement best practices to ensure care coordination for HRAs  20 
Provide targeted oversight of the 10  parent organizations that drove most of the risk-adjusted  21 
payments resulting from  in-home HRAs 
Provide targeted oversight of the 20 MAOs that drove risk-adjusted payments resulting from  21 
in-home HRAs for beneficiaries who had no other service records in the 2016 encounter  data  
Reassess the risks and benefits of allowing in-home HRAs to be used as  sources of diagnoses    22 
for risk adjustment, and reconsider excluding such diagnoses from risk adjustment 
Require MAOs to flag any MAO-initiated HRAs in their MA encounter data   22 
   
AGENCY COMMENTS AND OIG RESPONSE   24  
   
APPENDICES    
A: Detailed Methodology  26 
B: Estimated Payments Resulting From Diagnoses Reported on Health Risk Assessments, by  33  
Hierarchical Condition Category  
C: Estimated Payments From Health Risk Assessments for Hierarchical Condition Categories at  37  
High Risk for Improper Payments 
D: Agency Comments  38  
   
ACKNOWLEDGMENTS   41 
 
   
BACKGROUND 
Objectives 
1.   To determine the extent to which diagnoses reported only on 
health risk assessments (HRAs) increased risk-adjusted payments 
in the Medicare Advantage (MA) program. 
2.  To identify actions that the Centers for Medicare  & Medicaid 
Services (CMS) has taken to review the impact of HRAs on MA 
risk-adjusted payments. 
 
Ensuring that MA organizations (MAOs) receive accurate payments to 
provide appropriate care to Medicare beneficiaries is critically important.  
Toward this end, CMS makes risk-adjusted payments using beneficiaries 
diagnoses to pay higher capitated rates to MAOs for beneficiaries with  
higher risk scores.  However, this may create financial incentives for MAOs to 
make beneficiaries appear as sick as possible to obtain higher payments.  
CMS and the Medicare Payment Advisory Commission (MedPAC) have also 
identified vulnerabilities related to MAOs inflating their beneficiaries risk 
scores.1  This OIG evaluation analyzed data and trends related to HRAs, an 
allowable source of diagnoses that may provide MAOs with opportunities to 
inflate risk scores inappropriately. 
The Medicare Advantage Program 
Under MA, also known as Medicare Part C, CMS contracts with private 
companies,  known as  MAOs, to provide coverage of Parts A and B services 
through private health plan options.2  In 2019, one-third of Medicare 
beneficiaries23 millionelected to enroll with approximately 733 MAOs 
rather than receive services through the Medicare fee-for-service program.3   
 
1 CMS, Advanced Notice of Methodological Changes for Calendar Year (CY) 2014 for Medicare 
Advantage (MA) Capitation Rates, Part C and Part D Payment Policies and 2014 Call Letter, 
February 2013 and CMS, Advanced Notice of Methodological Changes for Calendar Year (CY) 
2015 for Medicare Advantage (MA) Capitation Rates, Part C and Part D Payment Policies and 
2015 Call Letter, February 2014.  Accessed at https://www.cms.gov/Medicare/Health-
Plans/MedicareAdvtgSpecRateStats/Announcements-and-Documents on November 21, 2019.   
MedPAC, Report to the Congress: Medicare Payment Policy, March 2016.  Accessed at 
http://www.medpac.gov/docs/default-source/reports/march-2016-report-to-the-congress-
medicare-payment-policy.pdf on July 9, 2019.  
2 Each MAO may offer multiple plans.  Medicare Parts A and B include hospital care, skilled 
nursing facility care, hospice care, home health care, physician services, and durable medical 
equipment, prosthetics, orthotics, and supplies.  Many MA plans also offer prescription drug 
coverage under Medicare Part D. 
3 We use the term MAO to refer to a unique MA contract.  
 
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MA program costs were $264 billion of the total $758 billion in Medicare 
program costs in fiscal year 2019.4  
MA Risk-Adjusted Payments 
For each beneficiary enrolled, MAOs  receive a capitated payment that 
reflects CMSs predicted cost of providing care to an MA beneficiary.  CMS 
risk-adjusts payments to pay MAOs more for beneficiaries with higher 
expected health-care costs.  CMS bases risk adjustments on MA 
beneficiaries demographic information and diagnoses from the prior year.  
As outlined in Exhibit 1, CMSs risk-adjustment process relies on diagnoses 
reported by MAOs. 
Exhibit 1: Risk-adjustment process for MA encounter data 
Beneficiary  Provider submits  MAO submits a  
receives a  service information  service record to the 
service.   to the MAO.   CMS encounter data 
system. 
CMS performs data  CMS identifies   CMS risk-adjusts  
integrity checks on the  risk-adjustment-eligible  payments and 
service record  diagnoses from the  pays the MAO. 
submitted by the MAO.  submitted service record.   
 
MAOs Report Diagnoses  to CMS.   The risk-adjustment process begins when 
the beneficiary receives a service or medical item from a provider.  The 
provider submits claims information, including diagnoses, to the MAO 
based on the service or medical item provided.  The MAO submits a record  
of the service (hereafter service record) to CMSs MA encounter data system  
 
4 CMS, CMS Financial Report Fiscal Year 2019, November 2019, p. 46.  Accessed at 
https://www.cms.gov/files/document/cms-financial-report-fiscal-year-2019 on 
November 25, 2019. 
 
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that contains this claims information, including the diagnoses.5  MAOs  also 
submit data on beneficiaries diagnoses to CMS through the Risk 
Adjustment Processing System (RAPS).  The RAPS data include only select 
information for services provided by a limited set of provider types.  For 
RAPS, MAOs submit only the beneficiary identifier (i.e., health insurance 
claim number or Medicare beneficiary identifier), diagnosis/diagnoses, 
provider type, and date(s) of service for services provided by hospital 
inpatient facilities, hospital outpatient facilities, and physicians.  CMS began 
collecting the more comprehensive encounter data from MAOs in 2012 as 
part of an effort to improve MA payment accuracy and better perform MA 
quality reviews. 
CMS Performs Activities To Safeguard the Integrity of Reported Diagnoses.   
CMS requires MAOs to certify the accuracy, completeness, and truthfulness 
of their encounter data submissions.6  In addition, CMS performs activities 
to safeguard the integrity of the encounter data.  During the data 
submission process, CMS performs automated checks, or edits, that reject 
service records containing incorrect information (e.g., service records with 
improperly formatted data or missing fields) that CMS deems key to MA 
program payment and data integrity.   After records pass these edits, CMS 
conducts analyses to review the stored data.  If these analyses  identify data 
errors, CMS may perform outreach to MAOs or introduce new edits to 
prevent incorrect data from being included in the encounter data.7  
CMS Identifies Eligible Diagnoses for Risk Adjustment.  For CMS to permit a 
diagnosis to be eligible for risk-adjusted payment, it must be:  
(1) documented in a medical record from a hospital inpatient stay, hospital 
outpatient visit, or visit with a physician or other eligible health care 
professional during the prior year; and 
(2) documented as a result of a face-to-face visit between the  beneficiary 
and the provider.8, 9  
 
5  Encounter records include service records and  chart reviews.  In addition to reporting 
diagnoses to the MA encounter data system through service records, MAOs may also report 
diagnoses through chart reviewsretrospective reviews of beneficiaries medical record 
documentation.  
6 42 CFR  422.504(l). 
7 CMS plans to implement additional compliance actions such as issuing notices of 
noncompliance, warning letters, and corrective action plans for MAOs that fail to satisfy 
certain performance thresholds related to the integrity of the encounter data.  
8 CMS, Medicare Managed Care Manual, Pub. No. 100-16 (Rev.118, September 19, 2014), ch. 7, 
 40.  Accessed at https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/ 
Downloads/mc86c07.pdf on December 17, 2018. 
9 Diagnoses resulting from telehealth services can meet the risk adjustment face-to-face 
requirement when the services are provided using an interactive audio and video 
telecommunications system that permits real-time interactive communication.  CMS, 
Applicability of Diagnoses from Telehealth Services for Risk Adjustment, April 2020.  
 
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To identify which diagnoses meet these eligibility criteria, CMS extracts, or 
filters, diagnoses in the encounter data based on whether the  service record 
contains an acceptable procedure code (i.e., Current Procedural 
Terminology (CPT) or Healthcare Common Procedure Coding System 
(HCPCS) code) and/or type of bill code.10, 11  
CMS Risk-Adjusts Payments.   To risk-adjust payments to MAOs based on 
eligible diagnoses, CMS employs a health-based risk adjustment model 
known as the CMS hierarchical condition categories (CMS-HCC) model.  The 
model groups certain medical conditions into HCCs that are categories of 
clinically related diagnoses.12  The model also ranks related groups of 
risk-adjustment-eligible diagnoses on the basis of disease severity and costs 
associated with treatment.  Each HCC has relative  numerical values (i.e., 
relative factors) that represent CMSs predicted costs associated with treating 
the medical conditions in the category.13  
A beneficiary may have multiple HCCs.  A beneficiarys risk score equals the 
sum of the relative factors that correspond with his or her HCCs and 
demographic characteristics.14  The total risk-adjusted payment to an MAO 
for an enrolled beneficiary equals the  risk score multiplied by the MA plans 
base payment rate.15  
In addition to diagnoses reported by  MAOs in the RAPS data, CMS began 
incorporating diagnoses from the encounter data into risk scores in 2015.  
To determine risk-adjusted payments for 2017, CMS calculated a blended 
risk score for each beneficiary by combining 25 percent of the risk score 
calculated from diagnoses in the encounter data and 75 percent of the risk 
score calculated from diagnoses in the RAPS data.  CMS requires MAOs to 
submit records to the encounter data system for all services provided to 
beneficiaries.  Therefore, MAOs should report the same 
risk-adjustment-eligible diagnoses in both the RAPS and encounter data.  
 
10 For institutional outpatient services, CMS uses type of bill and procedure codes to identify 
which diagnoses are eligible for risk-adjusted payment.  For hospital inpatient services, CMS 
uses type of bill codes.  For professional services, CMS uses procedure codes to identify which 
diagnoses are eligible for risk-adjusted payment. 
11 CMS, Final Encounter Data Diagnosis Filtering Logic, December 22, 2015.  Accessed at 
https://www.csscoperations.com/internet/cssc3.nsf/files/Final%20Industry%20Memo%20Medic
are%20Filtering%20Logic%2012%2022%2015.pdf on November 26, 2019. 
12 42 CFR  422.2.  
13 CMS uses the average expected costs in Medicare fee-for-service to determine the 
predicted costs associated with  treating the medical conditions in the category. 
14 The CMS-HCC model also includes relative factors for certain combinations of coexisting 
diagnoses (i.e., disease interactions) and interactions between certain diseases and a 
beneficiarys disabled status (i.e., disabled interactions), which are added to a beneficiarys risk  
score.  For the purposes of this evaluation, we use the term HCCs to refer to all HCCs, disease 
interactions, and disabled interactions.  
15 An MA plans base payment rate is the plans standardized bid adjusted by the county 
Intra-Service Area Rate factor for the beneficiarys county of residence.  
 
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Ultimately, CMS plans to rely exclusively on encounter data to identify 
diagnoses for risk-adjusted payments.  
CMS Conducts Audits To Validate Diagnoses Used  in Risk Adjustment.   After  
making risk-adjusted payments to MAOs, CMS determines whether a 
sample of diagnoses reported by MAOs can be validated by supporting 
medical record documentation using contract-level and national 
risk-adjustment data validation (RADV) audits.16  CMS has conducted these 
audits of diagnoses submitted to CMS through RAPS since payment year 
2007.  When RADV audits cannot validate a diagnosis, CMS uses this 
information to recover overpayments from MAOs and calculate a payment 
error rate.  As part of the RADV audit process, CMS identified the HCCs that 
had the highest rates of errors for that payment year.  CMS estimates that  
from 2013 through 2017, Medicare paid $50 billion in overpayments that 
resulted from plan-submitted diagnoses that were not supported by 
beneficiaries medical records.  (These were diagnoses submitted for all 
types of encounter records, not HRAs specifically.) 
Health Risk Assessments  
Physicians or other health care professionals administer HRAs to collect 
information from beneficiaries about their health status, health risks, and 
daily activities.  In the Medicare fee-for-service program, HRAs are part of 
beneficiaries annual wellness visits, which typically occur in physician offices 
or other health care facilities.17  In the MA program, HRAs should be part of 
annual wellness visits,18 or they may also be conducted during other visits 
with beneficiariesincluding visits to beneficiaries homes performed by 
 
16 CMS, Contract-Level Risk Adjustment Data Validation Medical Record Reviewer Guidance, 
September 2017, p. 5.  Accessed at https://www.cms.gov/Research-Statistics-Data-and-
Systems/Monitoring-Programs/Medicare-Risk-Adjustment-Data-Validation-Program/Other-
Content-Types/RADV-Docs/Coders-Guidance.pdf on November 23, 2017.  CMS, Medicare 
Risk Adjustment Data Validation Program History, September 2017.  Accessed at 
https://www.cms.gov/Research-Statistics-Data-and-Systems/Monitoring-Programs 
/Medicare-Risk-Adjustment-Data-Validation-Program/Program-History on June 28, 2019. 
17 An annual wellness visit should incorporate reviews of a beneficiarys health risks, medical 
history, and current providers, along with routine measurements and personalized health 
advice.  CMS, Annual Wellness Visit, August 2018.  Accessed at  https://www.cms.gov 
/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts 
/Downloads/AWV-Chart-ICN905706TextOnly.pdf on September 16, 2019. 
18 CMS, Advanced Notice of Methodological Changes for Calendar Year (CY) 2014 for Medicare 
Advantage (MA) Capitation Rates, Part C and Part D Payment Policies and 2014 Call Letter, 
February 2013.  Accessed at https://www.cms.gov/Medicare/Health-
Plans/MedicareAdvtgSpecRateStats/Downloads/Advance2014.pdf on November 21, 2019, 
p. 22. 
 
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companies hired by MAOs.19, 20  Care coordination that results from  
assessing a beneficiarys health risks may include developing a plan of care, 
arranging services, delivering interventions, and reassessing and adjusting 
the plan of care as needed. 
Concerns Raised About MAO-Initiated HRAs.  CMS and MedPAC have 
questioned  whether MAOs use HRAs primarily as a strategy to find and 
submit more diagnoses to increase payments rather than as a means to 
improve the care provided to beneficiaries.  MAOs may receive financial 
benefits without improving beneficiaries health if MAOs initiate HRAs and 
use them to collect diagnoses without ensuring that beneficiaries receive 
needed followup care.  In 2015, CMS stated that it had observed an increase 
in in-home visits to assess MA enrollees.21  According to CMS, non-physician 
practitioners working for  companies hired by MAOs usually performed 
these in-home HRAs, and the resulting care coordination appeared to vary 
across plans.  We use the term MAO-initiated HRA to refer to HRAs that 
are performed by health care professionals who are employed or contracted 
by the MAO for this purpose and are  not the beneficiarys provider. 
CMS Guidance on In-Home HRAs.  In 2013 and 2014, CMS proposed 
excluding from risk adjustment any diagnoses that MAOs collected from 
in-home HRAs that were not confirmed by a subsequent clinical 
encounter.22  CMS did not finalize either proposal.  Instead, beginning in 
2014, CMS required MAOs to flag diagnoses submitted to the RAPS data 
that resulted from a home setting, including whether or not an annual 
wellness visit took place.23   However, this RAPS flag does not require MAOs 
to specify whether diagnoses resulted from HRAs.  CMS stated that it would 
track and analyze care provision following in-home visits beginning in 2015. 
 
19 HRAs performed in beneficiaries homes are also known as enrollee risk assessments or 
home assessments.  In this report, we refer to HRAs performed in beneficiaries homes as 
in-home HRAs.  
20 MAOs offering Special Needs Plans must conduct a comprehensive initial health risk 
assessment of the individual's physical, psychosocial, and functional needs as well as annual 
HRA, using a comprehensive risk assessment tool that CMS may review during oversight 
activities.  Social Security Act,  1859(f)(5)(B); 42 CFR  422.101(f)(1)(i).  
21 CMS, Announcement of Calendar Year (CY) 2016 Medicare Advantage Capitation Rates and  
Medicare Advantage and Part D Payment Policies and Final Call Letter, April 2015, p. 144.  
Accessed at https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/ 
Downloads/Announcement2016.pdf on November 19, 2019.  
22  CMS, Advanced Notice of Methodological Changes for Calendar Year (CY) 2014 for Medicare 
Advantage (MA) Capitation Rates, Part C and Part D Payment Policies and 2014 Call Letter, 
February 2013 and CMS, Advanced Notice of Methodological Changes for Calendar Year (CY) 
2015 for Medicare Advantage (MA) Capitation Rates, Part C and Part D Payment Policies and 
2015 Call Letter, February 2014.  Accessed at https://www.cms.gov/Medicare/Health-Plans  
/MedicareAdvtgSpecRateStats/Announcements-and-Documents on November 21, 2019. 
23 CMS did not require MAOs to flag diagnosis codes submitted to the encounter data that 
result from in-home visits.  
 
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In 2015, CMS provided guidance to MAOs on best practices that promote 
the primary use of in-home HRAs as tools for improving care for MA 
enrolleesnot just as a process for collecting diagnoses to increase 
risk-adjusted payments.24   CMS encouraged MAOs to adopt best practices 
that support care coordination when implementing in-home HRA programs, 
including processes to:  
   schedule appointments with appropriate providers; 
   make referrals to appropriate community resources; 
   verify that needed followup care is provided; 
   verify that information obtained during the assessment was 
provided to the appropriate providers; 
   provide a summary to the beneficiary that includes their diagnoses, 
medications, scheduled followup appointments, plan for care 
coordination, and contact information for community resources; and 
   enroll the beneficiary in the MAOs disease management or case 
management program, as appropriate. 
In 2016, CMS issued guidance to MAOs related to ensuring coordination of 
care for services provided to MA beneficiaries.25  This guidance states that 
MAOs must ensure continuity of services, including implementing 
procedures to make a best effort to conduct HRAs annually and to ensure 
an appropriate and timely exchange of clinical information among 
providers.  
MedPAC Recommendation To Exclude HRAs From Risk Adjustment.   In 2016, 
MedPAC recommended that the Secretary eliminate HRAs as a source of 
diagnoses for MA risk adjustment because it contended that a small number 
of MAOs were using HRAs to increase Medicare  payment without providing 
followup care.26  MedPAC stated that it recognized the value of HRAs that 
MAOs administer as part of a care plan that includes: (1) providing 
information to beneficiaries primary care providers; and (2) ensuring that 
beneficiaries receive needed treatment.  However, MedPAC analyzed 
encounter data from 2012 and found that more than one-third of the health  
 
24 CMS, Announcement of Calendar Year (CY) 2016 Medicare Advantage Capitation Rates and  
Medicare Advantage and Part D Payment Policies and Final Call Letter, April 2015.  Accessed 
at https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Downloads/ 
Announcement2016.pdf on November 19, 2019. 
25 CMS, Medicare Managed Care Manual, Pub. No. 100-16 (Rev. 121, April 22, 2016), ch. 4, 
 110.6.  Accessed at https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals 
/Downloads/mc86c04.pdf on November 29, 2019. 
26 As part of this recommendation, MedPAC recommended that the Secretary develop a risk 
adjustment model that uses two years of Medicare fee-for-service and MA diagnostic data.  
MedPAC, Report to the Congress: Medicare Payment Policy, March 2016, p. 352.  Accessed at 
http://www.medpac.gov/docs/default-source/reports/march-2016-report-to-the-congress-
medicare-payment-policy.pdf on July 9, 2019.  
 
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conditions documented on HRAs had no other documentation showing that 
health care was provided.27  In addition, MedPAC questioned the accuracy 
of diagnoses identified only through in-home HRAs because these 
diagnoses are often based on beneficiary self-reporting or may require 
verification by diagnostic equipment not present during the visit.  MedPAC 
also raised concerns about the tactics MAOs use to recruit beneficiaries for 
in-home HRAs, which some beneficiaries have reported to be 
uncomfortably aggressive. 
Prior OIG Work  
Prior OIG work analyzed data and trends related to chart reviews, which are 
another allowable source of diagnoses that may provide MAOs with 
opportunities to inflate risk scores inappropriately.28  This work found that 
diagnoses that MAOs reported only on chart reviewsand not on any 
service recordsresulted in an estimated $6.7 billion in risk-adjusted 
payments for 2017.  CMS based an estimated $2.7 billion of these 
risk-adjusted payments on chart review diagnoses that MAOs did not link to  
a specific service provided to the beneficiary.  These findings raised 
potential concerns about the completeness of payment data submitted to 
CMS, the validity of diagnoses on chart reviews, and the quality of care 
provided to beneficiaries.  CMS concurred with this reports 
recommendations, which were to: 
   provide oversight of MAOs that had risk-adjusted payments 
resulting from unlinked chart reviews for beneficiaries who had no 
service records in 2016;  
   conduct audits that validate diagnoses reported on chart reviews in 
the encounter data; and 
   reassess the risks and benefits of allowing the use of unlinked chart 
reviews as sources of diagnoses for risk adjustment. 
 
Methodology  We reviewed HRAs from the 2016 MA encounter data stored in CMSs 
Integrated Data Repository (IDR) to determine the amount of 2017 MA 
risk-adjusted payments that would have resulted from diagnoses reported 
only on HRAs.  Although CMS incorporates diagnoses from both RAPS and 
encounter data in actual risk-adjusted payments,  we did not incorporate 
diagnoses stored in CMSs RAPS data into our payment calculations.  We 
did not include RAPS data because there is no mechanism in RAPS to 
 
27 Because CMS does not require MAOs to flag diagnoses that resulted from HRAs in the 
encounter data, MedPAC identified HRAs as records with (1) procedure codes G0438, G0439, 
99420, or (2) a procedure code for an evaluation and management visit and a place of 
service code of home. 
28 OIG, Billions in Estimated Medicare Advantage Payments From Chart Reviews Raise 
Concerns, OEI-03-17-00470, December 2019. 
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definitively identify diagnoses reported on HRAs.  However, we found that 
99.5 percent of the diagnoses included in our payment analysis were also 
reported in RAPS.  In addition, among diagnoses that appeared in RAPS, 
almost all (96.3 percent) appeared only once.  Appendix A contains a 
detailed description of our methodology. 
Because CMS does not require MAOs to flag diagnoses that resulted from 
HRAs in the encounter data, we identified diagnoses reported on HRAs 
using a two-fold process: 
   First, we aggregated records from the IDR that identify potential 
HRAs, including annual wellness visits, initial preventive physical  
exams, and evaluation and management home visits.29  
   Second, among these types of records, we excluded from our 
analysis any beneficiaries who had more than one procedure code 
in 2016 that met our criteria for a potential HRA. 
We included HRAs only for beneficiaries enrolled in the same  MA plan for 
all 12 months of 2016 in our evaluation.30  In addition, we excluded cost 
plans, demonstration plans, programs of all-inclusive care for the elderly 
(PACE) organizations, and Medicare medical savings account plans.  Using 
these criteria, we analyzed risk-adjustment-eligible diagnoses reported on 
3.5 million HRAs submitted by 67 percent of MAOs (462 of 690) to calculate 
the impact of HRAs on risk-adjusted payments for 2017.31  
Financial Impact of HRAs 
To estimate the amount of payments from HRAs, we identified beneficiaries 
who had risk-adjustment-eligible diagnoses reported on HRAs that were not 
reported on any other encounter records in 2016.  We used the  
2017 CMS-HCC model to identify the HCCs that would not have been 
generated if  MAOs had not added these diagnoses.  For each HCC, we 
calculated the risk-adjusted payment by multiplying the MA plans base 
payment rate by the HCCs relative factor. 
 
29 We identified annual wellness visits using procedure codes G0438 and G0439, initial 
preventive physical exams using procedure code G0402, and evaluation and management 
home visits using procedure codes 99341-99345 and 99347-99350.  For evaluation and 
management home visits, we also ensured that the place of service was home. 
30 We use the term MA plan to represent each unique combination of an MA  contract 
number and plan number. 
31 We use the term MAO to represent each unique MA contract number.  As of 
January 2016, CMS contracted with 690 MAOs  to provide Parts A and B services to MA 
beneficiaries.  CMS, Medicare Advantage, Cost, PACE, Demo, and Prescription Drug Plan 
Contract Report -Monthly Summary Report (Data as of January 2016), January 2016.  Accessed 
at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-
Reports/MCRAdvPartDEnrolData/Monthly-Contract-and-Enrollment-Summary-Report-
Items/Contract-Summary-2016-01 on November 19, 2019. 
 
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We summarized the number and type of HCCs that increased payments and 
compared our list of HCCs to the high-risk HCCs that CMS identified for 
2014.  We checked for variation across MAOs and  their parent organizations 
to see whether certain MAOs and parent organizations had higher or lower 
amounts of risk-adjusted payments due to diagnoses reported only on 
HRAs.32  
We also performed these analyses by place of service (i.e., in-home HRAs 
versus facility-based HRAs).33  In addition, we identified the 10 providers (i.e., 
National Provider Identifiers) that billed for the most in-home  HRAs.  We  
determined  whether these billing providers were  companies that partnered 
with or were hired by MAOs to conduct in-home HRAs using information 
from company websites  and other publicly available sources. 
CMS Oversight of the Financial Impact of HRAs  
To identify the actions taken by CMS to review the impact of HRAs on MA 
risk-adjusted payments, we analyzed CMSs responses to a structured 
questionnaire and reviewed relevant documentation related to instructions, 
policies and/or procedures that CMS has in place to identify and address 
concerns related to the financial impact of HRAs. 
Limitations  
We estimated risk-adjusted payments that resulted from HRAs based solely 
on diagnoses contained in the MA encounter data.  We did not estimate 
payments based on RAPS.  CMS actual risk-adjusted payments to MAOs for 
2017 incorporate diagnoses from both RAPS and encounter data.34  Because  
CMS requires MAOs to submit records of all services provided for 
beneficiaries to the encounter data system, MAOs should submit the same 
risk-adjustment-eligible diagnoses in both the RAPS and encounter data.  
When we conducted a review of the diagnoses included in RAPS, we found 
that 99.5 percent of the diagnoses included on our payment analysis were 
also reported in RAPS.  Among diagnoses that appeared in RAPS, almost all 
(96.3 percent) appeared only once.  However, our analysis underestimates 
the risk-adjusted payments from HRAs if MAOs reported in RAPS additional  
diagnoses from HRAs and did not submit corresponding encounter records 
for those HRAs in the encounter data.  
Because CMS does not require MAOs to flag in the encounter data that a 
diagnosis resulted from an HRA, we had to reasonably approximate our 
identification of these diagnoses.  Our approximation may include 
 
32 A parent organization is an entity that owns or has controlling interest in at least one MAO.  
33 We considered the following places of service to be in-home HRAs: home, temporary 
lodging, custodial care facility, group home, and homeless shelter.  We considered all other 
places of serviceincluding a physicians office, clinic, and hospitalto be facility-based 
HRAs.  
34 For 2017, CMS calculated a blended risk score by combining 25 percent of the risk score 
calculated from encounter data and 75 percent of the risk score calculated from RAPS data.  
Ultimately, CMS plans to rely exclusively on encounter data to calculate risk scores. 
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diagnoses reported on home visits during which medical care was provided 
and an HRA was not administered.  Alternatively, our approximation may 
have missed diagnoses that resulted from HRAs that MAOs reported on 
types of records that we did not include.  
CMS bases risk-adjusted payments for a given year on diagnoses from 
specified face-to-face visits provided to the beneficiary in the previous year.  
Thus, we estimated the potential impact of HRAs on the MA program for 
2017 by using the encounter data submitted by MAOs for 2016.  We did not 
review CMSs final payment data to  MAOs for 2017.  CMSs actual monthly 
payments to MAOs may change each month if there are changes in certain 
beneficiary characteristics, such as long-term institutional status, 
dual-eligibility status, and county of residence.  For analytic efficiency, we 
calculated payment estimates for the entire year using 2016 encounter data 
and beneficiaries characteristics as of January 2016.  We believe that 
selecting a point in time resulted in reasonable payment estimates because 
changes to these characteristics during the year can cause both payment 
increases and decreases, which could balance out across the population.  
We also assumed that 2016 MA beneficiaries remained enrolled in MA in 
2017.  
We also did not determine whether diagnoses reported only on HRAs were 
supported by documentation in beneficiaries medical records.  In addition, 
we did not determine whether each MAO had submitted all required 
encounter records.  Finally, our review did not include any services provided 
to MA beneficiaries but not covered or paid under Medicare Part C by an 
MAO, such as services provided through the Veterans Health 
Administration. 
 
Standards  We conducted this study in accordance with the Quality Standards for  
Inspection and Evaluation issued by the Council of the Inspectors General on 
Integrity and Efficiency. 
 
 
 
 
 
 
 
 
 
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FINDINGS 
Diagnoses that  Diagnoses that MAOs reported only on HRAs in the encounter data totaled 
an estimated $2.6 billion in risk-adjusted payments for 2017. 
MAOs reported 
only on HRAs, and  MAOs potentially received billions in payments for diagnoses 
collected on HRAs without evidence of these beneficiaries 
not on any other 
receiving any care to treat those conditions 
service records, 
MAOs reported diagnoses on HRAs for 3.5 million beneficiaries with no 
resulted in an 
other encounter records of visits, procedures, tests, or supplies that 
estimated  contained the diagnosis reported on the HRA.  This means that, for the 
entire year, these beneficiaries may not have received other services for the  
$2.6 billion in 
medical conditions indicated by the  diagnoses.  This raises questions about 
risk-adjusted 
whether these HRAs were administered as part of a care plan that included 
payments for 2017 
care coordination.  When HRAs lack care coordinationsuch as providing 
information to beneficiaries primary care providers and ensuring that 
beneficiaries receive needed treatmentthey could become vehicles for 
MAOs to collect diagnoses rather than function as tools to improve 
beneficiary health. 
We estimated that MAOs received risk-adjusted payments for 
617,652 beneficiaries (out of 3.5 million) based solely on HRA diagnoses.  In 
an extreme case, a beneficiary had diagnoses reported only on an HRA that 
resulted in an estimated $51,804 in risk-adjusted payments to the MAO for 
2017.  This beneficiarys diagnoses correspond to serious conditions including 
heart arrhythmias, severe  hematological disorders, and major 
depression/bipolar/paranoid disorders that trigger risk-adjusted payments.  
However, these diagnoses did not appear on any other encounter record for 
this beneficiary in 2016. 
The HCCs generated by diagnoses reported only on HRAs included serious 
illnesses, such as diabetes and heart disease.   Appendix B provides the 
amount of risk-adjusted payments for each HCC that resulted  from 
diagnoses reported only on HRAs.  The 10 HCCs that CMS identified as 
having the highest payment error rates for 2014 (the most recent year for 
which CMS identified high-risk HCCs) accounted for $152.3 million of the 
estimated risk-adjusted payments from HRAs for 2017.  Appendix C lists the 
impact on risk-adjusted payments from HRAs for these HCCs. 
Ten MAOs had almost one-half of the payments resulting solely 
from HRAs 
Ten MAOs had an estimated $1.2 billion of the risk-adjusted payments that 
resulted from diagnoses only on HRAs, as shown in Exhibit 2.  Overall, 
462 MAOs reported diagnoses only on  HRAs.  Ninety-five percent of these 
MAOs (438 of 462) had a payment resulting solely from HRAs.  For these  
438 MAOs, risk-adjusted payments due solely to diagnoses reported on 
Billions in Estimated Medicare Advantage Payments From  Diagnoses Reported Only on HRAs Raise Concerns  12 
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HRAs varied significantly, ranging from a high of $243.9 million to a low of 
$1,558 across MAOs.  
These 10 MAOs belonged to two parent organizations that had $2.1 billion 
in payments resulting solely from HRAs.  These top two parent 
organizations had 81 percent of risk-adjusted payments from  HRAs but 
enrolled just 40 percent of all MA beneficiaries, as shown in Exhibit 2.35  
Exhibit 2: Almost all MAOs in our review had estimated risk-adjusted 
payments that resulted solely from HRAs, but a few drove a large 
portion of those payments 
 
462 MAOs   reported  diagnoses only on HRAs  
 
438 MAOs   had $2.6B in payments  resulting solely from HRAs 
 
            These risk-adjusted payments  varied significantly, 
            ranging from  $1,558 to $244M  
 
 
 
  10 MAOs   drove almost half of  these payments, totaling an  
                        estimated  $1.2B  
 
  These 10 MAOs belonged to  2 parent organizations  that generated 
  81% of the $2.6B but enrolled only 40% of beneficiaries  
 
         
Source: OIG  estimation of  2017 payments using 2016 MA encounter data from CMSs IDR  
         
 
 
 
 
 
 
 
 
 
     
 
 
35 For parent organizations, we determined their percentage of all MA beneficiaries enrolled, 
as of January 1, 2016, in the types of plans included in our analysis.  
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Although HRAs conducted in beneficiaries homes represented only 
In-home HRAs 
one-third of the HRAs we reviewed, they generated most of the estimated 
generated 80 
payments that resulted from diagnoses only reported on HRAs.   The 
percent of the  number of in-home HRAs totaled 37 percent (1.3 million of 3.5 million) of 
estimated payments  the total number of HRAs included in our review.  However, these in-home 
HRAs resulted in 80 percent ($2.05 billion of $2.56 billion) of the estimated 
from diagnoses 
risk-adjusted payments to MAOs for  diagnoses reported only on HRAs, as 
reported only on 
shown in Exhibit 3.36  
HRAs, and most 
Exhibit 3:  Most  HRAs reviewed were conducted in health care 
in-home HRAs were 
facilities, but those conducted in  beneficiaries homes generated 
conducted by  most of the  estimated risk-adjusted payments  
 
companies that 
partner with MAOs  HRAs Conducted   Estimated Payments From HRAs 
   
Facility 
 
20% 
Home  
   
Facility   37%  Home  
    80% 
63% 
   
   
 
 Source: OIG estimation of 2017  payment amounts using 2016 MA encounter data 
 
Estimated payments to MAOs for diagnoses reported only on in-home 
HRAs were wide-ranging and driven by a few MAOs.  These estimated 
payments ranged from a high of $235 million to a low of $747.  Eight MAOs 
generated one-half of these payments, totaling an estimated $1.1 billion.  
Among parent organizations, 10 parent organizations enrolled 52 percent of 
MA beneficiaries but were responsible for 97 percent ($2 billion) of all 
estimated payments resulting from in home HRAs, as shown in Exhibit 4. 
 
36 The remaining 20 percent of risk-adjusted payments resulted from diagnoses reported 
only on facility-based HRAs.  These HRAs took place in health care facilities, such as physician 
offices, clinics, and hospitals.  
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Exhibit 4: Ten MA parent organizations drove almost all of the 
risk-adjusted payments from in-home HRAs but enrolled only 
52 percent of MA beneficiaries  
 
Source: OIG estimation of 2017  payments using 2016 MA encounter data from CMSs IDR  
 
 
Most of the in-home HRAs that resulted in risk-adjusted 
payments were conducted by companies that MAOs partner with 
or hire to conduct HRAs 
Of the top 10 providers that billed for in-home HRAs, 8 were companies that 
partner with or are hired by MAOs to  conduct home assessments.  (The 
remaining 2 of the top 10 providers that billed for in-home HRAs appear to 
be affiliated with one of the 8 companies that partner with MAOs to 
conduct home assessments.)  These 8 companies conducted 89 percent 
(415,485 of 466,481) of the in-home HRAs that contained diagnoses that 
resulted in risk-adjustment payments, and yet these diagnoses were not 
reported on any other encounter record for the beneficiaries. 
Overall, $1.8 billionor 90 percentof the estimated $2 billion in 
risk-adjusted payments from in-home HRAs resulted from home visits 
conducted by these eight companies.  The top company conducted 
in-home HRAs that generated $1.2 billion in risk-adjusted payments to 
48 MAOs that are owned by 1 parent organization. 
For the 415,485 beneficiaries who received an in-home HRA from these  
companies, the assessment was likely conducted by a health care 
professional who was not their primary care provider.  The lack of other 
encounter records that contain the diagnoses identified by the companies 
providers raises questions about whether MAOs ensured that (1) the results 
of these HRAs were forwarded to beneficiaries primary care providers; 
(2) beneficiaries received appropriate followup care and treatment; and 
(3) the diagnoses reported only on in-home HRAs were accurate. 
The diagnoses that MAOs reported only on in-home HRAs 
corresponded to some serious and chronic health conditions 
In-home HRAs have the potential to  address the health care needs of 
beneficiaries with serious diagnoses.  However, we found that among the 
1.3 million beneficiaries who had an in-home HRA that added diagnoses to 
Billions in Estimated Medicare Advantage Payments From  Diagnoses Reported Only on HRAs Raise Concerns  15 
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the encounter data, there were no other encounter records of visits, 
procedures, tests, or supplies that contained the diagnoses reported on the 
HRA. 
These in-home HRAs were associated with some serious and chronic 
medical conditions for beneficiaries.  For 117,620 beneficiaries,  diagnoses 
that MAOs reported only on in-home HRAs corresponded to having major 
depressive, bipolar, and paranoid disorders.  However, there were no 
encounter records directly demonstrating that they received treatment for 
these serious health diagnoses.  Almost 75 percent of risk-adjusted 
payments ($1.5 billion of $2 billion) from diagnoses reported only on 
in-home HRAs were concentrated among 10 of 101 possible HCCs, as shown 
in Exhibit 5. 
 
 
Exhibit 5: Almost three-quarters of the estimated MA risk-adjusted payments from in-home 
HRAs corresponded to 10 HCCs  
Estimated   
Number of  Payments  Percentage of 
Beneficiaries  From    In-Home HRA 
HCC  HCC  Description  With HCC  In-Home HRAs  Payments  
HCC58  Major Depressive, Bipolar, and Paranoid Disorders  117,620   $353,868,747   17% 
HCC108  Vascular Disease   116,238   $307,397,936    15%  
HCC18   Diabetes With Chronic Complications   59,856   $173,205,866   8% 
HCC22  Morbid Obesity   58,760   $142,061,471   7% 
HCC111   Chronic Obstructive Pulmonary Disease   49,800   $141,810,708    7% 
HCC85   Congestive Heart Failure   39,422   $114,760,158    6% 
HCC75  Myasthenia  Gravis/Myoneural Disorders and Guillain-Barre  26,521  $102,878,201   5% 
Syndrome/Inflammatory and Toxic Neuropathy  
HCC88  Angina Pectoris  55,500   $67,379,225    3% 
HCC21  Protein-Calorie  Malnutrition  11,697   $61,723,441   3% 
HCC55  Drug/Alcohol Dependence   18,817   $59,412,704   3% 
Total   554,231   $1,524,498,457        74%  
Source: OIG  estim  a  t ion of  2017 payment amounts by  using 2016 MA encounter data from CMSs IDR  
 
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Twenty MAOs accounted for almost three-quarters ($43.9 million of 
Twenty MAOs 
$60.9 million) of risk-adjusted payments based on diagnoses from in-home 
generated millions 
HRAs where not a single non-HRA item or service was provided to the 
in payments from  beneficiary by the MAO in 2016.  The top five MAOs accounted for 
$23.4 million in payments.  One MAO received $6.7 million in risk-adjusted 
in-home HRAs for  
payments that resulted from diagnoses submitted only on in-home HRAs 
beneficiaries for  
for which there was not a single record of any other service being provided 
whom there was  to 1,272 of its beneficiaries.  In total, in-home HRAs submitted by 
not a single record  203 MAOs resulted in an estimated $60.9 million in risk-adjusted payments 
for 2017.  These MAOs submitted only in-home HRAs and no other service 
of any other service 
records for 12,287 beneficiaries, which calls into question the treatment and 
being provided in 
followup care provided to these beneficiaries.  The lack of any other record 
2016  aside from an in-home HRA that triggered a risk-adjusted payment raises 
concerns that these MAOs: 
   may not have ensured that the MA encounter data contained all 
records of items and services provided to beneficiaries; 
   may not have coordinated care following the HRA, including 
verifying that information was provided to the beneficiarys 
provider(s) and verifying that appropriate followup care was 
provided to the beneficiary; or  
   may have submitted diagnoses on the HRA that were not 
documented in the beneficiarys medical record and, therefore, may 
have received inappropriate payments from CMS. 
The 8 companies that partner with or are hired by MAOs to conduct most 
of the in-home HRAs reviewed conducted 10,697 of the 12,287 in-home 
HRAs (87 percent) for which there was not a single record of any other 
service being provided to the beneficiary in all of 2016.  In-home HRAs 
conducted by these 8 companies generated 88 percent ($53.8 million of 
$60.9 million) of the risk-adjusted payments to MAOs for beneficiaries with 
no other service records. 
For beneficiaries with an HRA and no other record of a service, risk-adjusted 
payments to MAOs from  in-home HRAs totaled an estimated 
$60.9 millionwhereas only $5.3 million in payments resulted from 
facility-based HRAs. 
 
 
 
 
 
 
 
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Although CMS raised concerns about MAOs use  of in-home HRAs to 
CMS has not 
increase risk-adjusted payments in 2013 and 2014, it has not analyzed the 
reviewed the  impact of HRAs on risk-adjusted payments or the quality of care provided 
impact of HRAs on  to beneficiaries who received in-home HRA visits.  However, CMS has stated  
that it plans to conduct analyses of 2014 MA overpayments resulting from 
risk-adjusted 
diagnoses reported on HRAs in RAPS. 
payments or  
quality of care   CMS does not have a definitive mechanism within the RAPS or 
encounter data to identify diagnoses that result from 
MAO-initiated HRAs  
CMS raised concerns that MAOs were using in-home HRAs mainly as a tool 
to maximize risk-adjusted payments, and it created a flag in the RAPS data 
that required MAOs to identify diagnoses that result from a home visit 
during which an annual wellness visit was completed.  CMS informed OIG  
that it has not used the RAPS flag to track and analyze the care provided to 
beneficiaries.  In addition, this RAPS flag does not require MAOs to specify 
whether diagnoses resulted from in-home HRAs or HRAs initiated by an 
MAO.37  Despite its previous plans to assess the care provided to 
beneficiaries after receiving in-home HRAs, CMS has not created a RAPS 
flag that can determine which diagnoses resulted from in-home HRAs. 
In the encounter data, CMS does not require MAOs to flag diagnoses 
resulting from HRAs that MAOs initiate with the beneficiary.  CMS has not 
used the encounter data to perform analyses related to HRAs.  CMS stated 
that it does not believe it is possible to identify the universe of visits during 
which HRAs were conducted because MAOs may code an HRA in multiple 
ways in the encounter data.38    
CMS has not assessed whether MAOs follow its recommended 
best practices for in-home HRAs 
Although CMS has provided MAOs with guidance on best practices for 
in-home HRAs, it has not assessed whether MAOs follow these best 
practices.  In guidance for payment year 2016, CMS outlined best practices 
that it recommended but did not require MAOs to adopt for conducting 
in-home HRAs that support care coordination.  These best practices include 
verifying that needed followup care is provided, verifying that information 
obtained during the assessment was provided to the appropriate providers, 
and enrolling beneficiaries in disease management or case management 
 
37 We use the term MAO-initiated HRA to refer to HRAs that are performed by health care 
professionals who are employed or contracted  by the MAO for this purpose and are not the 
beneficiarys provider. 
38 Because CMS does not require MAOs to flag in the encounter data that a diagnosis 
resulted from an HRA, we identified potential HRAs as services containing a procedure code 
for annual wellness visits, initial preventive physical exams, or evaluation and management 
home visits.  For evaluation and management home visits, we also ensured that the place of 
service was home.  For our analysis, we included only those beneficiaries with a one-time 
instance of a procedure code that identifies an HRA.  
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programs.  Since communicating these best practices in 2015, CMS has not 
taken action to assess whether MAOs are following encouraged best 
practices. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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CONCLUSION AND RECOMMENDATIONS 
HRAs can be a tool for early identification of health risks to improve 
beneficiaries care and health outcomes.  However, in the MA program 
some MAOs  may be initiating and using HRAs to collect diagnoses and 
thereby maximize risk-adjusted payments without improving beneficiary 
care.  We found that diagnoses that MAOs reported only on HRAsand on 
no other encounter recordsresulted in an estimated $2.6 billion in 
risk-adjusted payments for 2017. 
Particular concerns surround the potential for MAOs to inappropriately use 
in-home HRAs, which may be administered by companies that partner with 
or are hired by MAOs.  Although HRAs conducted in beneficiaries homes 
represented only one-third of the HRAs in our review, they generated 
$2 billionor 80 percentof the estimated payments that resulted from 
diagnoses reported only on HRAs.  In addition, most of these in-home HRAs 
were conducted by companies that partner with MAOsnot by the 
beneficiarys treating provider.  Twenty MAOs drove millions in risk-adjusted 
payments from in-home HRAs for more than 8,000 beneficiaries for whom  
there was not a single record of any service being  provided in all of 2016. 
These findings raise three types of concerns.  First, there may be a data 
integrity concern that MAOs are not submitting all service records as 
required.  Second, there may be a coordination of care concern that 
beneficiaries are not receiving followup care needed for potentially serious 
diagnoses reported only on HRAs.  Third, there may be a payment integrity 
concern that if diagnoses are inaccurate or unsupported, the associated 
risk-adjusted payments would then be inappropriate.  Despite prior 
concerns raised by CMS about in-home HRAs, CMS has not reviewed the 
financial impact of diagnoses reported on HRAs or assessed the care 
provided to beneficiaries after receiving in-home HRAs.   
Based on these findings we recommend that CMS:  
Require MAOs to implement best practices to ensure care 
coordination for HRAs  
CMS currently encouragesbut does not requireMAOs to  adopt best 
practices supporting care coordination for beneficiaries who receive 
in-home HRAs.  This review identified 3.5 million beneficiaries with 
diagnoses reported only on HRAs yet with no other encounter records for 
visits, procedures, tests, or supplies that contained the diagnosis reported 
on the HRA.  This calls into question how effectively the MAOs are using 
HRAs to help coordinate care for these beneficiaries, many of whom had 
serious conditions diagnosed through their HRAs.  For all HRAsboth 
in-home and facility-basedCMS should require  MAOs to develop and 
implement processes to verify that (1) information obtained during the HRA 
Billions in Estimated Medicare Advantage Payments From  Diagnoses Reported Only on HRAs Raise Concerns  20 
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is provided to the beneficiarys primary care provider and other appropriate 
providers; and (2) appropriate followup care is provided to the beneficiary 
after the HRA.  By requiring that MAOs develop and implement these best 
practices that ensure care coordination, CMS can promote the use of HRAs 
by MAOs as tools for improving care and health outcomes for MA enrollees.  
Provide targeted oversight of the 10 parent organizations that 
drove most of the risk-adjusted payments resulting from 
in-home HRAs 
CMS should perform targeted oversight of the 10  MA parent organizations 
that drove 97 percent of the risk-adjusted payments from diagnoses 
reported only on in-home HRAs and yet enrolled only half of the 
beneficiaries.  CMS could conduct reviews to determine the accuracy of 
diagnoses reported only on in-home HRAs submitted by the 10 parent 
organizations.  CMS also  could target  for review diagnoses from in-home 
HRAs conducted by the  eight companies that performed most of the 
in-home HRAs that resulted in risk-adjusted payments.  If CMS determines 
that the diagnoses are in fact accurate, CMS should ensure that these MAOs 
are providing appropriate care coordination to their beneficiaries.  OIG will 
provide CMS with a list of these 10  MA parent organizations and 
8 companies, including the estimated risk-adjusted payments from in-home 
HRAs. 
Provide targeted oversight of the 20 MAOs that drove 
risk-adjusted payments resulting from in-home HRAs for 
beneficiaries who had no other service records in the 
2016 encounter data 
CMS should perform ongoing oversight of the 20 MAOs that drove 
$60.9 million in risk-adjusted payments from in-home HRAs for beneficiaries 
who had no other service records in the 2016 encounter data.  To this end, 
OIG will provide CMS with a list of the beneficiaries who had in-home HRAs 
and no records to show that any services were provided in all of 2016 for 
these MAOs.  CMS should use this information to perform targeted reviews 
of the MAOs that had payments resulting from these beneficiaries in-home 
HRAs.  These reviews could include outreach to the MAOs to determine 
whether they submitted records for all services, as required.  CMS could also 
assess the MAOs requirements for care coordination after in-home HRAs.  
If CMS identifies problems with the completeness of these MAOs encounter 
data submissions, or if CMS identifies that an MAO submitted an in-home 
HRA with unsupported diagnoses, CMS should take action to remedy those 
problems. 
 
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Reassess the risks and benefits of allowing in-home HRAs to be 
used as sources of diagnoses for risk adjustment and reconsider 
excluding such diagnoses from risk adjustment 
Once CMS has completed targeted reviews of MAOs and conducted 
2015 RADV audits, CMS should use data gathered during risk-adjustment 
data validation audits and targeted MAO reviews to determine the impact 
of in-home HRAs on MA data integrity and overpayments.  If contract-level 
RADV audits do not include a sufficient number of beneficiaries with 
diagnoses reported only on in-home HRAs, CMS should conduct a separate 
review of in-home HRAs.  These audits and/or reviews should identify the 
risks and benefits associated with in-home HRAs by: (1) determining the 
validity of diagnoses reported only on in-home HRAs; (2) analyzing the care 
provided to beneficiaries for diagnoses documented only on in-home HRAs; 
and (3) assessing the completeness of encounter data service records 
submitted by MAOs for beneficiaries with in-home HRAs and no other 
service records.  If CMS determines that the risks of allowing in-home HRAs 
as sources of diagnoses outweigh the  benefits, CMS should consider 
excluding diagnoses reported only on in-home HRAs from risk adjustment.  
Alternatively, CMS could require that the beneficiarys medical record 
contain evidence that care coordination took place, including the 
transmission of information and provision of appropriate followup care, as a 
condition for the in-home HRA to be an allowable source for risk-adjusted 
payment. 
Due to the COVID-19 pandemic, there have been increased flexibilities and 
utilization of telehealth in the MA program.  HRAs conducted remotely may 
introduce similar, and possibly additional, vulnerabilities as those associated 
with in-home HRAs.  CMS should include HRAs furnished via telehealth in its 
assessment of the risks and benefits of allowing certain types of HRAs to be 
used as sources of diagnoses for risk  adjustment.  
Require MAOs to flag any MAO-initiated HRAs in their MA 
encounter data 
CMS should develop a flag in the encounter data and require MAOs to 
indicate whether service records submitted to the encounter data contain 
HRAs administered as part of an MAOs internal HRA program or by 
companies partnered with an MAO for this purpose (i.e., MAO-initiated 
HRAs).  As a centralized repository of data for all services provided to MA 
beneficiaries, encounter data may be used to conduct reviews on program 
integrity and quality of care.  As CMS moves to rely exclusively on the 
encounter data to calculate risk-adjusted payments, the absence of a 
mechanism to clearly identify MAO-initiated HRAs (regardless of the place 
of service) limits the ability of CMS and others to validate increases in 
risk-adjusted payments that may arise solely from these HRAs.  
Furthermore, the absence of such a flag limits CMSs and other  
stakeholders ability to review the comprehensiveness of these assessments 
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in ensuring care coordination and improving health outcomes for 
beneficiaries.  Establishing an MAO-initiated HRA flag in the encounter data 
would allow CMS and other concerned stakeholders to better analyze 
subsequent care coordination for beneficiaries and assess the impact of 
HRAs on risk-adjusted payments. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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AGENCY COMMENTS AND OIG RESPONSE 
In response to our draft report, CMS stated that it is committed to ensuring 
that diagnoses used in risk adjustment are accurate.  CMS concurred with  
two of our five recommendations.  CMS agreed that it will: 
   provide targeted oversight of the 10  parent organizations that drove 
most of the risk-adjusted payments resulting from  in-home HRAs; 
and 
   provide targeted oversight of the 20 MAOs that drove risk-adjusted 
payments resulting from  in-home HRAs for the beneficiaries who 
had no other service records in the 2016 encounter data. 
CMS did not concur with our recommendation to require MAOs to 
implement best practices to ensure care coordination for HRAs.  CMSs view  
is that MAOs are in the best position to decide what HRA information is 
most appropriate to share with specific providers.  CMS also stated that 
implementing such requirements must be done through 
notice-and-comment rulemaking and there is not a regulatory medium 
available at this time.  OIGs identification of 3.5 million beneficiaries with 
diagnoses reported only on HRAs calls into question how effectively MAOs 
are using HRAs to help coordinate care for MA beneficiaries.  Therefore, 
OIG continues to recommend that CMS require MAOs to implement best 
practices that promote their primary use of HRAs as tools for improving 
care and health outcomes for MA beneficiaries. 
CMS did not concur with our recommendation to reassess the risk and 
benefits of allowing in-home HRAs to be used as sources of diagnoses for 
risk adjustment and reconsider excluding such diagnoses from risk 
adjustment.  CMS does not believe that there is a basis to reassess this 
policy because our analysis did not determine whether diagnoses reported 
only on HRAs were supported by documentation in beneficiaries medical 
records.  However, CMS noted that it  will reassess the use of HRAs as 
sources of diagnoses for risk adjustment if CMSs  own reviews indicate that  
these diagnoses reflect inaccuracies.  OIG encourages CMS to conduct its 
own reviews and reassess whether the risks of allowing diagnoses reported 
on in-home HRAs outweigh the benefits. 
Finally, CMS did not concur with our recommendation to require MAOs to 
flag any MAO-initiated HRAs in their MA encounter data.  The absence of a 
mechanism to clearly identify MAO-initiated HRAs in the encounter data 
limits the ability of CMS and others to validate increases in risk-adjusted 
payments that may arise solely from these HRAs.  Therefore, OIG continues 
to recommend that CMS establish an MAO-initiated HRA flag  in the 
encounter data that would allow CMS and other concerned stakeholders to  
Billions in Estimated Medicare Advantage Payments From  Diagnoses Reported Only on HRAs Raise Concerns  24 
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better analyze subsequent care coordination for beneficiaries and assess the 
impact of these HRAs on risk-adjusted payments. 
The full text of CMSs comments can be found in Appendix D.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX A: Detailed Methodology  
This appendix provides a more detailed description of the methodology that 
we used  to determine the  amount of 2017 MA risk-adjusted payments that 
resulted from diagnoses reported only on HRAs with 2016 service dates.  
Because CMS bases risk-adjusted payments for a given year on diagnoses 
from services provided to the beneficiary in the previous year, we estimated 
the potential impact of HRAs on 2017 payments based on encounter data 
that MAOs submitted for 2016.  We did not review CMSs final 
2017 risk-adjusted payments to MAOs.  In addition, we estimated 
risk-adjusted payments that resulted  from HRAs based solely on diagnoses 
contained in the MA encounter data.  CMSs actual risk-adjusted payments 
to MAOs incorporate diagnoses from both RAPS  and encounter data.  
However, when we conducted a review of the diagnoses included in RAPS, 
we found that 99.5 percent of the diagnoses included in our payment 
analysis were also reported in RAPS. 
Analyses of HRAs  
Identification of HRAs   We identified HRAs that contained 
risk-adjustment-eligible diagnoses in the 2016 MA encounter  data, as 
described below.  In October 2018, after the September deadline for MAOs 
to submit data for payment year 2017, we identified HRAs in the 2016 MA 
encounter data in CMSs IDR as records containing: 
   a claim through date between January 1, 2016, and 
December 31, 2016;  
   a submission date between January 1, 2016, and September 14, 2018; 
   a claim final action indicator value of Y, indicating that the record 
is the most recently accepted version of the record; 
   a claim chart review switch value of null, indicating that the record 
is an encounter data record (i.e., not a chart review record); and  
   a procedure code that identifies a potential HRA, including a 
procedure code for an annual wellness visit (G0438 or G0439), an 
Billions in Estimated Medicare Advantage Payments From  Diagnoses Reported Only on HRAs Raise Concerns  26 
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initial preventive physical exam (G0402), or an evaluation and 
management home visit (99341-99345 or 99347-99350).39, 40  
We excluded certain beneficiaries and types of MAOs.  We excluded 
beneficiaries who had end-stage renal disease, were receiving hospice care, 
or did not reside in a U.S. State based on information contained in the IDRs 
MA prescription drug (MARx) data.  We did this because CMS uses  different 
methods to calculate these payments.  We included only beneficiaries 
enrolled with the same MA plan for all 12 months of 2016.41  We excluded 
beneficiaries with inconsistencies between their MA encounter data, 
Medicare beneficiary data, and MARx data contained in the IDR to ensure 
data accuracy.  For example, we did not include beneficiaries whose MAO 
contract number was not the same across all three IDR data sources.  
In addition, we excluded from our analysis any beneficiaries who had more 
than one procedure code in 2016 that met our criteria for an HRA.  As such, 
we included only those beneficiaries with a one-time instance of a 
procedure code that identifies an HRA.  
Identification of Diagnoses Reported Only on HRAs   For each beneficiary 
with a diagnosis reported on an HRA, we identified all of their other 
encounter records contained in the IDRs 2016 MA encounter data.  For 
beneficiaries with diagnoses reported on HRAs with a claim through date 
between October 1, 2016, and December 31, 2016, we also identified all 
encounter records that had a claim through date between January 1, 2017, 
and March 31, 2017.  We then compared the diagnoses reported on the 
HRAs to the diagnoses reported on the other encounter records.  We only 
kept the diagnoses reported on HRAs that were not reported on any other 
encounter record in 2016 and, if applicable, the first quarter of 2017.42  We  
identified diagnoses reported on 3.5 million HRAs that were not reported on 
any other encounter record. 
 
39 Diagnoses reported on HRAs included in our analysis met CMSs eligibility criteria for risk 
adjustment because these procedure codes are listed on CMSs filtering list for 2016.  CMS, 
2016 Medicare Risk Adjustment Eligible CPT/HCPCS Codes.  Accessed at 
https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors-
Items/CPT-HCPCS on May 2, 2018. 
40 For evaluation and management home visits, we also ensured that the place of service was 
at home (i.e., place of service codes for home (12), temporary lodging (16), custodial care 
facility (33), group home (14), and homeless shelter (04)). 
41 We use the term MAO to represent each unique MA contract number.  We use the term 
MA plan to represent each unique combination  of an MA contract number and plan number.  
We excluded from our review all cost plans, demonstration plans, PACE organizations, and 
Medicare medical savings account plans. 
42  For the encounter records where we identified an HRA, we included in our analyses all 
diagnoses reported on those encounter records.  This is consistent with the CMS method for 
calculating risk-adjusted payments.  
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Identification of In-Home and Facility-Based HRAs   We categorized records 
of diagnoses reported only on HRAs into two groups: in-home versus 
facility-based visits.  We identified in-home HRAs as records containing a 
place of service code of home (12), temporary lodging (16), custodial 
care facility (33), group  home (14), or homeless shelter (04).  We  
considered records containing all other place of service codesincluding 
codes for physician offices, clinics, and hospitalsto be facility-based HRAs.  
Using these criteria, we identified that 1.3 million of 3.5 million HRAs were 
delivered to beneficiaries at home, as  shown in Exhibit A-1.  
Exhibit A-1: Number of HRAs containing diagnoses that were not reported on  any 
other encounter record in 2016, by procedure code  and  place of service code  
   
Procedure  In-Home   Facility-Based  All   
Code  Description of Procedure Code  HRAs  HRAs  HRAs  
G0439   Annual wellness visit; includes a personalized   133,764  1,759,433   1,893,197  
prevention plan of service, subsequent visit  
G0438   Annual wellness visit; includes a personalized   555,231  393,574   948,805  
prevention plan of service, initial visit  
99343   Evaluation and management, new patient  402,969  n/a  402,969  
home visit; typically 45 minutes 
99345   Evaluation and management, new patient  98,795  n/a  98,795  
home visit; typically 75 minutes 
G0402   Initial preventive physical exam   40  39,213   39,253  
99342   Evaluation and management, new patient  36,135  n/a  36,135  
home visit; typically 30 minutes  
99350   Evaluation and management, established  27,297  n/a  27,297  
patient home visit; typically 60  minutes  
99347   Evaluation and management, established  15,874  n/a  15,874  
patient home visit; typically 15  minutes  
99344   Evaluation and management, new patient  13,447  n/a  13,447 
home visit; typically 60 minutes  
99349   Evaluation and management, established  8,047  n/a  8,047 
patient home  visit; typically 40 minutes  
99348   Evaluation and management, established  5,310  n/a  5,310  
patient home visit; typically 25  minutes  
99341   Evaluation and management, new patient  4,986  n/a  4,986  
home visit; typically 20 minutes  
TOTAL  1,301,895   2,192,220   3,494,115  
Source: OIG analysis of 2016 MA encounter data from CMSs IDR 
 
 
 
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Of the HRAs categorized as in-home, almost all contained a place of service 
code of home (12), as shown in Exhibit A-2. 
Exhibit A-2: Place of service codes used to identify HRAs delivered 
in the  beneficiarys home 
Place of 
Description of Place of Service Code  Number of HRAs 
Service Code  
12  Home  1,299,454  
16  Temporary Lodging  1,186  
33   Custodial Care Facility   1,131  
14  Group Home  124  
04  Homeless  Shelter  0 
Total  1,301,895  
Source: OIG analysis of 2016 MA encounter data from CMSs IDR 
 
Identification of HCCs Generated by Diagnoses Reported on 
HRAs 
For beneficiaries who had risk-adjustment-eligible diagnoses reported only 
on an HRA,  we used the 2017 CMS-HCC model and CMSs CMS-HCC  
mapping software to identify the HCCs generated by the diagnoses 
reported only on HRAs.43   To identify these HCCs, we first mapped all of a 
beneficiarys risk-adjustment-eligible diagnoses (i.e., diagnoses reported on 
HRAs and all other encounter records) to HCCs.  Then we mapped just the 
risk-adjustment-eligible diagnoses reported on records not identified as 
HRAs to HCCs.  Finally, we compared the two sets of HCCs to determine the 
HCCs generated from mapping the diagnoses reported only on HRAs.  
Assignment of Relative Factors to HCCs   We assigned relative factors to 
each HCC based on the segment of the 2017 CMS-HCC model that applied 
to each beneficiary based on their characteristics as of January 2016.  These 
characteristics included the beneficiaries long-term institutional status, age, 
original reason for Medicare entitlement, and dual-eligibility status.  We 
used Medicare beneficiary data from the IDR to identify the beneficiaries 
age, original reason for Medicare entitlement, and dual-eligibility status.  
We used MARx data from the IDR to identify the beneficiaries long-term 
institutional status.  We adjusted each HCCs relative factor by  CMSs 
 
43 CMS, Announcement of Calendar Year (CY) 2017 Medicare Advantage Capitation Rates and  
Medicare Advantage and Part D Payment Policies and Final Call Letter, Table VI-1. 2017  
CMS-HCC Model Relative Factors for Community and Institutional Beneficiaries, April 2016, pp. 
78-84.  Accessed at https://www.cms.gov/Medicare/Health-Plans/ 
MedicareAdvtgSpecRateStats/Downloads/Announcement2017.pdf on December 19, 2018.  
CMS, 2017 Model Software/ICD-10 Mappings, V2217.79.O1.  Accessed at https://  
www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors.  
html on May 9, 2018. 
 
Billions in Estimated Medicare Advantage Payments From  Diagnoses Reported Only on HRAs Raise Concerns  29 
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normalization and coding adjustment factors for 2017 prior to calculating 
payment estimates.44  
Calculation of Payment Estimates 
We calculated estimates of the amount of risk-adjusted payments 
associated with each HCC by multiplying the MA plans monthly base 
payment rate by the relative factor of the HCC.  We then multiplied monthly 
amounts of payments by 12 to determine annual payment estimates. 
We determined the base payment rate for each beneficiarys plan by using 
information gathered from several data sources.  For MA plans that submit 
bids to CMS, we identified base payment rates for December 2017 in the 
Approved Bid Pricing Tool Extract from CMSs Health Plan Management 
System.  For  Employer Group Waiver Plans (EGWPs), which do not submit 
bids to CMS, we identified base payment rates using CMSs EGWP county 
level rate books for regional and local EGWPs and information on each 
EGWPs star rating.45, 46  We  then determined base payment rates for each 
beneficiarys plan based on each beneficiarys plan contract number, plan 
number, plan segment number, Part A and B entitlement status, and county 
of residence as of January 2016 in the  MARx data.  
For a small percentage of beneficiaries, we used an alternative method to 
determine their plans base payment rates.  For 14  percent of beneficiaries 
included in our analysis, we calculated payment estimates based on a 
median base payment rate for all non-EGWPs, instead of each plans actual 
base payment rate.  For almost all of these beneficiaries, the  MA plan 
enrollment information (i.e., the contract number, plan number, segment 
number, or county of residence) contained in the IDR for January 2016 did 
not match the 2017 MA plan information used in our analysis.47  The 
remaining 0.1 percent of beneficiaries were enrolled in an EGWP and were 
covered only by either Medicare Part A or Medicare Part B (and not covered 
by both Medicare Parts A and B).  For these beneficiaries, we determined 
the median Part A and/or Part B base payment rate for December 2017 for 
all plans in the Approved Bid Pricing Tool Extract. 
 
44 CMS adjusts the risk score by a normalization factor and a coding-adjustment factor.  The 
normalization factor adjusts risk scores to ensure that the average beneficiary risk score in 
any given year remains 1.0 despite annual changes in risk scores.  The coding adjustment 
factor reduces risk scores to account for differences in coding patterns between MA and 
Medicare fee-for-service.  
45 CMS, 2017 Medicare Advantage Ratebook and Prescription Drug Rate Information, 2017.  
Accessed at https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/ 
Ratebooks-and-Supporting-Data.html on January 5, 2018.  
46 We identified EGWPs 2017 star ratings using the Approved Bid Pricing Tool Extract and the 
MA Quality Bonus Payment Rating files from CMSs Health Plan Management System. 
47 The 2017 MA plan information used in our analysis included information from the 
Approved Bid Pricing Tool Extract and the Plan Benefit Package Extract in CMSs Health Plan 
Management System, as well as CMSs EGWP county level rate books. 
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We conducted a summary analysis of estimated payment amounts and 
HCCs from diagnoses reported only on HRAs.  We summarized the number  
and type of HCCs that resulted in payments and compared our list of HCCs 
to the high risk HCCs that CMS identified as having the highest rates of 
errors for 2014.  We checked for variation across MAOs (i.e., contract 
numbers) and their parent organizations to see whether certain 
organizations had higher or lower payments due to diagnoses reported on 
HRAs.  We also performed these analyses by place of service (i.e., in-home 
HRAs versus facility-based HRAs).  In addition, we  identified the 10 providers 
that billed for the highest number of in-home HRAs.  We determined 
whether these billing providers were  companies that partnered with or were 
hired by MAOs to conduct in-home HRAs using information from company 
websites and other publicly available sources. 
Identification of Diagnoses Reported in RAPS 
We analyzed the 2016 RAPS data to determine whether diagnoses reported 
only on HRAs in the encounter data were also reported in the RAPS data.  
For each beneficiary with HRA-only diagnoses that resulted in risk-adjusted 
payments, we identified their HRA-only diagnoses that could map to an 
HCC based on the 2017 CMS-HCC model.48  We then identified whether 
these diagnoses were reported on a RAPS record that contained an ending 
date of service between January 1, 2016, and December 31, 2016.  We 
calculated the percentage of diagnoses that were contained in the 
2016 RAPS data.  We determined that 99.5 percent of these diagnoses were 
also reported in RAPS.  In addition, we identified whether each of these  
diagnoses appeared more than once in RAPS, which might indicate that the 
beneficiary received additional care for the diagnosis.  We determined that 
96.3 percent of these diagnoses appeared only once in RAPS.  
CMS Oversight of the Financial Impact of Health HRAs 
To identify the actions taken by CMS to review the impact of HRAs on MA 
risk-adjusted payments, we analyzed CMSs responses to a structured 
questionnaire and reviewed relevant documentation related to:  
   instructions, procedures, and policies CMS has in place to review the 
financial impact of HRAs using MA encounter data, RADV audits, 
and/or any other data sources; 
   the use of encounter data, RADV audits, or any other data sources 
to track and analyze the care provided to MA beneficiaries for 
diagnoses added by HRAs;  
   the kinds of issues, if any, identified by CMS related to the financial 
impact of HRAs; and  
 
48 CMS, 2017 Midyear Final ICD-10-Mappings.  Accessed at 
https://www.cms.gov/Medicare/Health-Plans/MedicareAdvtgSpecRateStats/Risk-Adjustors-
Items/Risk2017  on November 19, 2019.  
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   descriptions of whether and how concerns regarding the financial 
impact of HRAs were addressed by CMS. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX B: Estimated Payments Resulting From 
Diagnoses Reported on Health Risk Assessments, 
by Hierarchical Condition Category  
For beneficiaries who had diagnoses reported only on HRAs in the 
2016 encounter data, we identified the HCCs generated by these diagnoses.  
As shown in Exhibit B-1, the estimated 2017 risk-adjusted payments for each 
HCC added by all HRAs ranged from $2,004 to $434.6 million. 
Exhibit  B-1: Estimated 2017 risk-adjusted payments resulting from diagnoses reported on HRAs, 
by HCCa  
    Risk-Adjusted  Risk-Adjusted  Risk-Adjusted 
    Payments From   Payment From  Payments From 
HCC  HCC Description   In-Home HRAs  Facility-Based HRAs  All HRAs 
HCC58   Major Depressive, Bipolar, and Paranoid  $353,868,747  $80,752,433   $434,621,180  
Disorders  
HCC108  Vascular Disease  $307,397,936  $73,541,303   $380,939,239  
HCC18   Diabetes With  Chronic Complications  $173,205,866   $30,291,494  $203,497,360  
HCC111   Chronic Obstructive Pulmonary Disease  $141,810,708   $43,594,247   $185,404,955  
HCC22  Morbid  Obesity  $142,061,471  $36,915,440   $178,976,911 
HCC85  Congestive Heart  Failure  $114,760,158  $20,451,517   $135,211,675  
HCC75  Myasthenia Gravis/Myoneural Disorders  $102,878,201  $8,805,499   $111,683,700  
and Guillain-Barre Syndrome/ 
Inflammatory  and Toxic Neuropathy  
HCC55  Drug/Alcohol Dependence  $59,412,704  $18,759,051  $78,171,755  
HCC88  Angina Pectoris  $67,379,225  $5,040,813   $72,420,038  
HCC21  Protein-Calorie Malnutrition  $61,723,441  $6,786,809   $68,510,250  
HCC40   Rheumatoid Arthritis and Inflammatory   $44,507,735  $23,191,850   $67,699,585  
Connective Tissue Disease 
HCC96  Specified Heart Arrhythmias  $39,136,202   $15,973,978   $55,110,180  
HCC103  Hemiplegia/Hemiparesis   $45,006,539  $6,192,377   $51,198,916  
HCC48   Coagulation Defects and  Other  Specified  $9,337,772  $17,065,472   $26,403,244 
Hematological Disorders  
HCC189   Amputation Status, Lower  $20,101,310  $2,175,599   $22,276,909  
Limb/Amputation Complications 
HCC8   Metastatic Cancer and  Acute Leukemia  $16,033,573   $5,384,319   $21,417,892  
HCC57   Schizophrenia  $16,944,365  $1,746,064  $18,690,429  
HCC79   Seizure Disorders and Convulsions   $14,126,220  $3,986,296   $18,112,516  
HCC84   Cardio-Respiratory Failure and Shock  $13,096,117   $3,574,971  $16,671,088  
HCC106   Atherosclerosis of the Extremities With  $13,944,640  $1,930,652   $15,875,292  
Ulceration or Gangrene  
       
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Exhibit  B-1: Estimated 2017 risk-adjusted payments resulting from diagnoses reported on HRAs, 
by HCC (continued)  
    Risk-Adjusted  Risk-Adjusted  Risk-Adjusted 
    Payments From  Payments From  Payments From 
HCC  HCC Description   In-Home HRAs  Facility-Based HRAs  All HRAs 
HCC71   Paraplegia  $13,323,974  $827,146   $14,151,120  
HCC78   Parkinson's and Huntington's Diseases  $10,934,930   $3,028,010   $13,962,940  
HCC10   Lymphoma and Other Cancers   $9,361,041  $3,860,192   $13,221,233  
HCC104   Monoplegia, Other Paralytic Syndromes  $12,405,999   $776,218   $13,182,217  
HCC161   Chronic Ulcer of Skin, Except Pressure  $9,818,306   $2,485,962  $12,304,268  
HCC23   Other Significant Endocrine and Metabolic  $4,309,366  $7,816,139   $12,125,505  
Disorders  
HCC188   Artificial Openings for Feeding or  $9,362,986  $1,338,691  $10,701,677 
Elimination 
HCC9   Lung and Other Severe Cancers   $6,145,930   $3,479,056   $9,624,986  
HCC19   Diabetes Without Complication  $5,623,527   $3,365,097   $8,988,624  
HCC107   Vascular Disease With Complications  $3,516,182   $4,632,180   $8,148,362  
HCC29  Chronic Hepatitis  $6,776,071   $1,308,717  $8,084,788  
HCC35   Inflammatory Bowel Disease   $4,878,832  $3,069,595   $7,948,427 
HCC70  Quadriplegia  $7,236,786  $507,943   $7,744,729  
HCC11   Colorectal, Bladder, and Other Cancers  $2,663,816   $5,050,660   $7,714,476  
HCC124   Exudative Macular Degeneration  $5,247,741  $1,984,683   $7,232,424  
HCC72  Spinal Cord  Disorders/Injuries  $4,039,497   $3,074,128   $7,113,625  
HCC47  Disorders of Immunity  $1,205,292   $5,468,363   $6,673,655  
HCC46  Severe  Hematological Disorders   $3,694,816   $2,840,478   $6,535,294  
HCC122  Proliferative Diabetic Retinopathy and  $5,709,862  $710,853   $6,420,715  
Vitreous Hemorrhage  
HCC100  Ischemic or Unspecified Stroke  $316,551  $5,477,506   $5,794,057  
HCC39  Bone/Joint/Muscle  Infections/Necrosis  $4,059,432   $1,436,072   $5,495,504 
HCC12   Breast, Prostate, and Other Cancers and  $1,900,251  $3,399,249   $5,299,500  
Tumors 
HCC158   Pressure Ulcer of Skin With Full Thickness  $4,435,262  $466,371  $4,901,633  
Skin Loss  
HCC28   Cirrhosis of Liver   $3,482,110  $1,167,881  $4,649,991 
HCC112   Fibrosis of Lung and Other Chronic Lung  $1,631,709  $2,787,730   $4,419,439  
Disorders  
HCC77  Multiple Sclerosis  $2,209,468  $1,144,218   $3,353,686  
HCC27   End-Stage Liver Disease   $1,984,807  $1,286,525   $3,271,332  
HCC169  Vertebral Fractures  Without Spinal Cord   $290,664  $2,462,743   $2,753,407  
Injury  
HCC87   Unstable Angina and Other Acute  $1,661,437  $1,005,786   $2,667,223  
Ischemic Heart Disease 
HCC82  Respirator  Dependence/Tracheostomy  $2,249,000  $331,973   $2,580,973  
Status  
HCC186   Major Organ Transplant or Replacement  $2,045,476  $396,481  $2,441,957  
Status  
       
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Exhibit  B-1: Estimated 2017 risk-adjusted payments resulting from diagnoses reported on HRAs, 
by HCC (continued)  
    Risk-Adjusted  Risk-Adjusted  Risk-Adjusted 
    Payments From  Payments From  Payments From 
HCC  HCC Description   In-Home HRAs  Facility-Based HRAs  All HRAs 
HCC137   Chronic Kidney Disease, Severe (Stage 4)  $888,753   $1,450,873   $2,339,626  
HCC83  Respiratory Arrest   $1,979,686   $36,757   $2,016,443  
HCC34  Chronic Pancreatitis   $1,389,565   $621,073   $2,010,638  
HCC136   Chronic Kidney Disease, Stage 5   $1,071,463   $596,758   $1,668,221 
HCC86   Acute Myocardial Infarction  $226,739   $1,224,642   $1,451,381  
HCC80   Coma, Brain Compression/Anoxic Damage  $926,540   $309,897  $1,236,437  
HCC33  Intestinal Obstruction/Perforation  $188,966   $915,202   $1,104,168  
HCC135   Acute Renal Failure  $133,695   $940,093   $1,073,788  
HCC134  Dialysis Status   $971,864  $81,827   $1,053,691 
HCC73  Amyotrophic Lateral Sclerosis and Other  $709,507  $308,742   $1,018,249  
Motor Neuron Disease 
HCC176  Complications of Specified Implanted  $169,156  $776,370   $945,526  
Device or Graft 
HCC157   Pressure Ulcer of Skin With Necrosis  $857,757  $79,227   $936,984 
Through to Muscle, Tendon, or Bone 
HCC76  Muscular Dystrophy   $646,185   $252,039   $898,224 
HCC170  Hip Fracture/Dislocation   $411,918   $428,835   $840,753  
HCC99  Cerebral  Hemorrhage  $40,799  $769,949  $810,748 
HCC6  Opportunistic Infections  $278,656   $483,306   $761,962  
HCC17   Diabetes With Acute Complications  $99,418   $632,241  $731,659  
HCC167  Major Head Injury  $394,733   $295,285   $690,018  
HCC1  HIV/AIDS   $592,740   $65,437   $658,177 
HCC54  Drug/Alcohol Psychosis   $456,813   $180,761  $637,574 
HCC74  Cerebral Palsy   $414,403   $218,892   $633,295  
HCC114  Aspiration and Specified Bacterial  $107,586  $198,491   $306,077  
Pneumonias 
HCC110  Cystic Fibrosis   $229,687  $65,393   $295,080  
HCC2  Septicemia, Sepsis, Systemic Inflammatory  $123,552  $151,839   $275,391 
Response Syndrome/Shock 
HCC115  Pneumococcal  Pneumonia, Empyema,  $80,271  $174,772  $255,043  
Lung Abscess 
HCC173   Traumatic Amputations and Complications  $36,082   $121,905   $157,987 
HCC162   Severe Skin Burn or Condition  $41,946   $29,532   $71,478  
HCC166   Severe Head Injury   $0  $7,933   $7,933  
       
continued on next page   
 
 
 
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OEI-03-17-00471   
Exhibit  B-1: Estimated 2017 risk-adjusted payments resulting from diagnoses reported on HRAs, 
by HCC (continued)  
    Risk-Adjusted  Risk-Adjusted  Risk-Adjusted 
    Payments From  Payments From  Payments From 
HCC   HCC Description   In-Home HRAs  Facility-Based HRAs  All HRAs  
Disease Interactions      
HCC85_gCopdCF  Congestive  Heart Failure*  $35,483,345  $5,440,420   $40,923,765  
Chronic Obstructive Pulmonary 
Disease Group 
HCC85_gDiabetes Congestive Heart  Failure*  $31,147,286  $4,007,739   $35,155,025  
Mellit   Diabetes Group  
HCC85_HCC96  Congestive Heart  Failure*  $17,975,056  $2,744,374  $20,719,430  
Specified Heart Arrhythmias 
gRespDepandArre Cardiorespiratory Failure Group*  $17,357,185  $2,936,634   $20,293,819  
_gCopdCF   Chronic Obstructive Pulmonary 
Disease Group 
HCC85_gRenal   Congestive Heart Failure* Renal  $12,010,097  $1,675,394   $13,685,491 
Group 
gSubstanceAbuse Substance Abuse Group*  $9,712,974  $1,644,687   $11,357,661 
_gPsychiatric  Psychiatric Group 
HCC47_gCancer  Immune Disorders* Cancer  $1,021,149  $1,258,642   $2,279,791 
Group 
SCHIZOPHRENIA_  Schizophrenia* Chronic  $9,318  $3,310   $12,628  
gCopdCF  Obstructive Pulmonary Disease  
SCHIZOPHRENIA_  Schizophrenia* Congestive  Heart  $7,239  $2,960   $10,199  
CHF   Failure 
SCHIZOPHRENIA_  Schizophrenia* Seizure Disorders  $8,282  $0  $8,282  
SEIZURES   and Convulsions  
SEPSIS_PRESSURE  Sepsis* Pressure Ulcer  $4,173   $0  $4,173  
_ULCER  
ART_OPENINGS_P  Artificial Openings for Feeding or  $2,798  $0  $2,798  
RESSURE_ULCER  Elimination* Pressure Ulcer 
gCopdCF_ASP_SP Chronic Obstructive Pulmonary  $2,004  $0  $2,004 
EC_BACT_PNEUM   Disease* Aspiration and Specified 
Bacterial Pneumonias 
Disabled/Disease Interactions 
Disabled, Congestive Heart  $11,031  $2,577   $13,608  
DISABLED_HCC85  
Failure 
DISABLED_  Disabled, Pressure Ulcer  $5,234  $0  $5,234 
PRESSURE_ 
ULCER  
  Total  $2,047,479,702  $514,281,638   $2,561,761,340 
 
Source: OIG estimation of 2017  payment amounts using 2016 MA encounter data from CMSs IDR  
a For 7 of the 101 HCCs from  the  2017 CMS-HCC  model,  there were  no risk-adjusted payments that resulted from diagnoses reported only on HRAs.  
 
 
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APPENDIX C: Estimated Payments From Health 
Risk Assessments for Hierarchical Condition 
Categories at High Risk for Improper Payments  
When conducting audits of a sample of risk-adjustment data submitted by 
MAOs, CMS determines  whether the  diagnoses that resulted in risk-adjusted 
payments can be validated by medical record documentation.  When audits 
cannot validate diagnoses, CMS uses this information to recover overpayments 
from MAOs and calculate a payment error rate.  The 2014 payment year is the 
most recent payment year that CMS identified HCCs at high risk for payment 
errors (including both overpayments and underpayments).49  The 10 HCCs that 
CMS identified as having the highest payment error rates for 2014 accounted 
for $152.3 million of the estimated payments solely from all HRAs for 2017.50   
Exhibit C-1 outlines the estimated amount of 2017 risk-adjusted payments 
attributed to each of these high-risk HCCs. 
Exhibit C-1: For HCCs that CMS previously identified as at high risk for improper payments, 
estimated risk-adjusted payments from HRAs totaled $152 million for 2017 
HCC Identified   Risk-Adjusted  Risk-Adjusted  Risk-Adjusted 
by CMS as  Payments From  Payments From  Payments From 
High-Risk  HCC Description   In-Home HRAs  Facility-Based HRAs  All HRAs  
HCC75  Myasthenia Gravis/Myoneural  $102,878,201  $8,805,499   $111,683,700   
Disorders and Guillain-Barre 
Syndrome/Inflammatory and Toxic 
Neuropathy  
HCC106  Atherosclerosis of the  Extremities With   $13,944,640  $1,930,652   $15,875,292  
Ulceration or Gangrene  
HCC9   Lung  and Other Severe Cancers   $6,145,930   $3,479,056   $9,624,986  
HCC100  Ischemic or Unspecified Stroke  $316,551  $5,477,506   $5,794,057  
HCC27   End-Stage Liver Disease   $1,984,807  $1,286,525   $3,271,332   
HCC87   Unstable Angina and Other Acute  $1,661,437  $1,005,786   $2,667,223  
Ischemic Heart Disease 
HCC136   Chronic Kidney Disease, Stage 5   $1,071,463   $596,758   $1,668,221  
HCC99   Cerebral Hemorrhage  $40,799   $769,949   $810,748  
HCC54  Drug/Alcohol Psychosis   $456,813   $180,761  $637,574  
HCC114  Aspiration and Specified Bacterial  $107,586  $198,491   $306,077  
Pneumonias 
TOTAL  $128,608,227  $23,730,983   $152,339,210  
Source: OIG estimation of 2017  payment amounts using 2016 encounter data from CMSs IDR and CMSs list of HCCs at a high-risk for payment 
errors for 2014  
 
49 CMS, High-Risk Hierarchal Condition Categories, November 2017. 
50 We compared our list of HCCs from the 2017 CMS-HCC model that were added by HRAs to the HCCs that CMS identified as at 
high risk for payment errors from the 2014 CMS-HCC model.  Across the 2014 and 2017 models, there may be differences in the 
relative factor assigned to each HCC. 
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APPENDIX D:  Agency Comments 
 
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Billions in Estimated Medicare Advantage Payments From  Diagnoses Reported Only on HRAs Raise Concerns  40 
OEI-03-17-00471   
ACKNOWLEDGMENTS 
Jacqualine Reid served as the team leader for this  study.  Others in the 
Office of Evaluation and Inspections who conducted the study include San 
Le and Rachel Seitz.  Office of Evaluation and Inspections staff who provided 
support include Joe Chiarenzelli. 
We would also like to acknowledge the contributions of other  Office of 
Inspector General staff, including Julie Brown, Rodney Brown, Logan 
Kingma, Berivan Demir Neubert, and Jessica Swanstrom. 
This report was prepared under the  direction of Linda Ragone, Regional 
Inspector General for Evaluation and Inspections in the Philadelphia regional 
office, and Joanna Bisgaier, Deputy Regional Inspector General. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
To obtain additional information concerning this report or to obtain copies, 
contact the Office of Public Affairs at Public.Affairs@oig.hhs.gov.  
Billions in Estimated Medicare Advantage Payments From  Diagnoses Reported Only on HRAs Raise Concerns  41 
OEI-03-17-00471   
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