Mispricing in the Medicare advantage risk adjustment model
Ellis, Randall P.
Toro, Katherine H.
Ash, Arlene S.
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Citation (published version)Jing Chen, Randall P Ellis, Katherine H Toro, Arlene S Ash. 2015. "Mispricing in the Medicare Advantage Risk Adjustment Model." INQUIRY: The Journal of Health Care Organization, Provision, and Financing, Volume 52, pp. ? - ? (7). https://doi.org/10.1177/0046958015583089
The Centers for Medicare and Medicaid Services (CMS) implemented hierarchical condition category (HCC) models in 2004 to adjust payments to Medicare Advantage (MA) plans to reflect enrollees’ expected health care costs. We use Verisk Health’s diagnostic cost group (DxCG) Medicare models, refined “descendants” of the same HCC framework with 189 comprehensive clinical categories available to CMS in 2004, to reveal 2 mispricing errors resulting from CMS’ implementation. One comes from ignoring all diagnostic information for “new enrollees” (those with less than 12 months of prior claims). Another comes from continuing to use the simplified models that were originally adopted in response to assertions from some capitated health plans that submitting the claims-like data that facilitate richer models was too burdensome. Even the main CMS model being used in 2014 recognizes only 79 condition categories, excluding many diagnoses and merging conditions with somewhat heterogeneous costs. Omitted conditions are typically lower cost or “vague” and not easily audited from simplified data submissions. In contrast, DxCG Medicare models use a comprehensive, 394-HCC classification system. Applying both models to Medicare’s 2010-2011 fee-for-service 5% sample, we find mispricing and lower predictive accuracy for the CMS implementation. For example, in 2010, 13% of beneficiaries had at least 1 higher cost DxCG-recognized condition but no CMS-recognized condition; their 2011 actual costs averaged US$6628, almost one-third more than the CMS model prediction. As MA plans must now supply encounter data, CMS should consider using more refined and comprehensive (DxCG-like) models.
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