The final methodology included an offset known as the Fee-For-Service (FFS) Adjuster, which was intended to account for the different documentation standard used to develop the MA risk-adjustment model. CMS would calculate the amount of the FFS Adjuster based on a review of medical records submitted to support FFS claims. Although the amount of the FFS Adjuster had not been released by CMS, an internal CMS study estimated that diagnosis coding errors in the FFS claims data had a negative payment on the MA risk adjustment model between 4.8% and 8.1%.

In a recently proposed rule, CMS announced that it intends to revise its RADV methodology to exclude the FFS Adjuster in its payment recoupment calculations. CMS cites a new internal study finding that errors in reporting diagnosis codes in FFS claims data do not have a meaningful impact on risk-adjustment model estimation and, further, do not bias MA plan payment.

New analysis from Avalere suggests that certain key assumptions embedded in CMS’ analysis do not appropriately capture the full variation in the data and minimized the impact of documentation error. Avalere tested 3 alternatives to the assumptions made by CMS, comparing the results. Avalere’s analysis finds that audit miscalibration bias yields underpayments of nearly 8%. Avalere’s analysis suggests that a root cause of the difference in the Avalere and CMS estimates of the audit miscalibration impact may be CMS’ assumption of the average number of claims in calculating HCC person-level error rates, which underestimates the error rate.

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Funding for this research was provided by Cigna. Avalere Health retained full editorial control.

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