Egészség27 február, 2023

How CMS-HCC Version 28 will impact risk adjustment factor (RAF) scores

The proposed Medicare Advantage 2024 Advance Notice makes significant changes to HCC codes, disease mappings and disease coefficient values, which will impact RAF scores for Medical Advantage Organizations.

Recently, we took a first look at the Medicare Advantage 2024 Advance Notice, released by the Centers for Medicare and Medicaid Services (CMS). These proposed changes, which are slated for implementation in 2024, will have a significant impact on risk adjustment factor (RAF) scores for Medicare Advantage beneficiaries. Before we jump into an analysis of that impact, let’s review RAF scores.

What is a RAF score?

Risk Adjustment Factor (RAF) scores are part of the model used by CMS to estimate the associated cost of Medicare Advantage beneficiaries. The RAF score determines the amount paid by CMS to the health plan per beneficiary during the corresponding payment year. Medicare Advantage Organizations (MAOs) are paid at a higher rate for patients that have multiple conditions and conditions with greater levels of severity, as their anticipated costs of care will be higher.

How RAF scores are calculated

The RAF score is based on both the demographics and the disease risk scores for the beneficiary. The demographic score includes age, sex, residence (whether in the community, skilled nursing facility, or other institution), and disability status. The disease risk score is based on the reported diagnoses from patient encounters and their corresponding Hierarchical Condition Category (HCC) codes.

Generally, a higher RAF score indicates sicker patients and lower RAF scores indicate healthier patients. However, a low RAF score could also be an indicator of inaccurate coding due to lack of information on the patient record or a gap in care.

How RAF scores may be impacted by CMS-HCC Version 28

CMS made significant changes to the structure of the HCC model in Version 28 (V28), which will impact RAF scores for a large percentage of Medicare Advantage beneficiaries. These changes include:

  • How the V28 HCC codes are named and numbered
  • An expanded number of HCCs
  • Changes to ICD-10-CM code to HCC mappings
  • Changes to the HCC coefficient values
  • Removal of 2,294 diagnosis codes that no longer map to a payment HCC
  • Addition of 268 diagnosis codes that did not map to a payment CMS-HCC in V24

Reflecting fee-for-service updates

CMS used updated fee-for-service data years (including 2018 diagnoses and 2019 expenditures) to calibrate the V28 model. They stated the proposed model “results in more appropriate relative weights for the HCCs in the model because they reflect more recent utilization, coding and expenditure patterns in FFS Medicare.” CMS decided not to include HCCs (and diagnoses) in the V28 model if:

  • The conditions did not accurately predict costs
  • Coefficients were small
  • The conditions they represent are uncommon
  • Conditions that did not have “well-specified” diagnostic coding criteria

Fewer ICD-10-CM to HCC mappings

CMS has released a file titled “PY 2024 Proposed Clinical Revision Part C Model ICD-10-CM Mappings”, which provides detailed insight at the level of specific ICD-10-CM codes and HCC codes. It includes the proposed ICD-10-CM mappings to V28 HCC codes as well as current ICD-10-CM mappings to V24 HCC codes. Based on this file 2,294 ICD-10-CM codes that mapped to a payment HCC in V24 no longer map to a payment HCC in V28. Selected groups of related conditions impacted by the proposed changes are shown in our full analysis.

A relatively small number of diagnosis codes that did not previously map to a payment HCC will map to a payment HCC in V28 as proposed. Notable examples are available in tables included in our full analysis. Of the 268 “new” ICD-10-CM diagnosis codes that map to a CMS-HCC in V28, over 40% represent conditions not encountered in the majority of patients in the Medicare Advantage population.

Constraining will likely lower RAF scores

CMS acknowledged that the changes in the proposed rule could change beneficiary risk scores with or without a change in the patient’s health status. They stated the proposed model “results in more appropriate relative weights for the HCCs in the model because they reflect more recent utilization, coding and expenditure patterns in FFS Medicare.”

CMS used a process referred to as constraining, where related HCCs are given the same coefficients. A significant example of constraining in the V28 model affects the Diabetes diagnosis category. The contribution to the RAF score from diabetic disorders will not change regardless of whether the patient has uncomplicated diabetes or diabetes with complications. However, type 2 diabetes mellitus without complications (E11.9), for example, will receive a slightly higher coefficient in V28 than it currently does in V24 (i.e., from 0.105 to 0.166). Overall, this will result in a significant reduction in the RAF score for patients with acute or chronic complications from diabetes. The financial impact on MAOs and other stakeholders will depend on case mix.

The overall impact of the proposed changes on beneficiary RAF scores will depend on several factors, however RAF score in general will likely decline. CMS projects that the CY 2024 impact on MA risk scores of the proposed Part C CMS-HCC model is projected to be -3.12%. This projects to $11.0 billion in net savings to the Medicare Trust fund in 2024.  Actual PMPM payment amounts are based on multiple addition factors.

Examples of the proposed changes on RAF scores

The following examples demonstrate the potential impact of the proposed changes on RAF scores (based on disease coefficients only) in 2023 vs. 2024.  

In our first example, there is a significant negative impact on risk score based on disease coefficients in a Community, NonDual, Aged 73-year-old female beneficiary with multiple conditions. Only the disease coefficients for V24 and V28 are shown in the tables below.

Disease coefficients risk score using V24

V24 HCC Coefficients     
Community, Non Dual, Aged Beneficiary Age 70-74 years
HCC21  Protein Calorie Malnutrition   0.455 
HCC96  Atrial Fibrillation  0.268 
HCC18/HCC108  Diabetes with PVD  0.302 + 0.288 
HCC85  Chronic Systolic CHF  0.331 
HCC189  Toe Amputation 
0.519 
  Dx interaction DM + CHF 
0.121 
  Dx interaction CHF+ AFIB 
0.085 
  6 HCCS (Condition Count Factor) 
0.077 
 
Total V24 Disease Coefficient Risk Score: 2.446

Disease coefficients risk score using V28

V28 HC Coefficients   
 
Community, NonDual, Aged Beneficiary Age 70-74 years  
  Protein Calorie Malnutrition   n/a 
HCC238  Atrial Fibrillation  0.299 
HCC37  Diabetes with PVD  0.166 
HCC226  Chronic Systolic CHF  0.36 
  Toe Amputation  n/a 
  Dx interaction DM + CHF  0.112 
  Dx interaction CHF+ AFIB  0.077 
  4 HCCs (Condition Count Factor)  n/a 
    Total  V28 Disease Coefficient Risk Score: 1.014 

In our second example, although this patient’s record also includes a diagnosis that “newly” maps to a payment CMS-HCC in V28, the disease component of the risk store is still lowered for the same multiple chronic conditions.

Disease coefficients risk score using V24

V24 HCC Coefficients     
Community, NonDual, Aged Beneficiary Age 70-74 years
No HCC  Alcoholic hepatitis without ascites (K70.10)  N/A 
HCC48  Other nonthrombocytopenic purpura (D69.2)  0.192 
HCC59  Major depressive disorder, single episode, in full remission (F32.5)  0.309 
HCC88  Refractory angina pectoris (I20.2)  0.135 
  3 HCCs (Condition Count Factor) 
    Total V24 Disease Coefficient Risk Score: 0.636

Disease coefficients risk score using V28

V28 HCC Coefficients     
Community, NonDual Aged Beneficiary Age 70-74 years
HCC65  Alcoholic hepatitis without ascites (K70.10)  0.185 
N/A  Other nonthrombocytopenic purpura (D69.2)  N/A 
N/A  Major depressive disorder, single episode, in full remission (F32.5)  N/A 
N/A  Refractory angina pectoris (I20.2)  N/A 
  1 HCC (Condition Count Factor) 
    Total V28 Disease Coefficient Risk Score: 0.185

In summary, a large number of relatively common conditions will not map to a payment HCC in V28, indicating that RAF scores will decrease for many beneficiaries. CMS’s use of constraining, where related HCCs like those for diabetes have the same coefficients are also likely to impact RAF scores for a large percentage of patients. Once the changes are finalized, stakeholders will need to determine the overall impact based on their case mix, including changes that positively and negatively impact RAF scores.

Improving the accuracy and efficiency of risk adjustment through expert solutions

Medicare Advantage 2024 Advance Notice and the recently published RADV final rule are likely to create additional challenges for MAOs and other stakeholders. There needs to be continued benchmarking of current member health statuses and analysis of how these changes will impact their organization.

Investing in technologies that allow for the accurate and efficient coding of large volumes of clinical documents will be key in how MAO’s and other stakeholders can effectively manage their risk adjustment program. Is your organization prepared for the changes? Contact us to talk about improving the accuracy and efficiency of your risk adjustment coding.

Download Our Full Analysis
Learn More About Health Language Risk Adjustment
Back To Top