SummaryIn 2018, CMS proposed to revise its Risk Adjustment Data Validation (RADV) methodology to exclude the FFS Adjuster in its payment recoupment calculations. New analysis from Avalere finds that the payment impact associated with fee-for-service (FFS) Medicare coding discrepancies would be greater for certain subgroups of beneficiaries (e.g., dual-eligible, those with certain common and potentially serious health conditions) enrolled in the MA program.
In the proposed rule, CMS cites an internal study that found errors in reporting diagnosis codes in FFS claims data do not have a meaningful impact on risk-adjustment model estimation. Further, CMS concluded that the discrepancies do not bias MA plan payment.
This study builds upon a March 2019 Avalere paper (“Eliminating the FFS Adjuster from the RADV Methodology May Affect Plan Payment”), which found that CMS may have significantly underestimated the impact that FFS and Medicare coding discrepancies have on MA plan payments. Specifically, Avalere tested alternatives to the assumptions made by CMS and found that audit miscalibration bias may yield underpayments to MA plans of nearly 8%.
In this study, Avalere examined the differential payment impact associated with FFS coding discrepancies based on beneficiary eligibility status and health conditions:
- Dual-Eligible Beneficiaries: Avalere evaluated the impact of FFS coding discrepancies on MA plan payment for plans based on the number of beneficiaries eligible for both Medicare and Medicaid enrolled in the plan. Avalere estimates the mean underpayment would be approximately 9.1% for a plan with 100% duals, compared with a mean underpayment of 7.3% for a plan with no duals. A plan that exclusively enrolls dual-eligible beneficiaries, such as a Dual-Eligible Special Needs Plan, would be expected to experience a 24% greater payment impact than a plan with no dual-eligible enrollees.
- Beneficiaries with Certain Common and Significant Health Conditions (e.g., diabetes): Avalere examined beneficiaries with a condition in 10 disease severity hierarchies and 3 conditions that are not in a hierarchy (13 comparison groups) and compared beneficiaries in the comparison group to those who did not have that disease. For 2 of the diseases included in the study, the mean underpayment could be lower than overall average (e.g., the mean underpayment for cancer is estimated to be 6.6%). However, for several hierarchical condition categories (HCCs), it was substantially higher: 13.5% for kidney disease, 11.5% for congestive heart failure (CHF), and 10.3% for diabetes.
Overall, Avalere’s analysis suggests that there is significant variation in the audit miscalibration estimates based on the types of enrollees a plan serves. This means that plans with higher concentrations of dual-eligible beneficiaries or with beneficiaries with certain conditions could experience a higher rate of underpayment and highlights methodological concerns with CMS’ study, which does not consider any variations in the impact of FFS coding discrepancies across different MA plans.
Funding for this research was provided by Cigna. Avalere Health retained full editorial control.
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