In its response to a recent CMS request for information on modernizing the Medicare Advantage (MA) program, Applied Policy encouraged the agency to explore new approaches to responsibly combining data across federal agencies to improve risk identification, payment accuracy, and care targeting.<\/p>\n
Applied Policy President and CEO Jim Scott notes that current MA and CMS Innovation Center models rely largely on retrospective claims and clinical data, which often identify risk only after disease progression or an acute medical event has occurred. In the comment letter, he suggests that incorporating selected social and economic risk indicators\u2014many of which are already collected by other federal agencies\u2014could support more predictive, proactive models of care.<\/p>\n
\u201cCMS has access to unparalleled healthcare utilization, quality, and cost data, but many of the strongest predictors of avoidable utilization sit outside traditional claims,\u201d Scott writes. \u201cUsing those signals in a privacy-preserving way could help identify risk earlier and better align benefits and interventions with patient needs.\u201d<\/p>\n
The letter highlights limitations of the current Hierarchical Condition Categories (HCC) risk adjustment model, noting that while it captures costs associated with established disease, it does not reflect upstream factors that influence beneficiaries\u2019 ability to manage chronic conditions. As a result, care management efforts are often deployed only after avoidable deterioration has already occurred.<\/p>\n
In alignment with broader federal efforts to improve outcomes, accountability, and value in Medicare Advantage, Applied Policy urged CMS\u2019s Innovation Center to test alternative data use and modeling approaches that rely on de-identified, geographically based risk indicators rather than individual-level non-CMS data.<\/p>\n
\u201cModernizing risk identification doesn\u2019t require reinventing Medicare Advantage,\u201d said Brittany La Couture, Vice President for Health Policy at Applied Policy. \u201cIt can be achieved by using existing data more effectively to understand variation in outcomes and target resources where they can have the greatest impact.\u201d<\/p>\n
Applied Policy also emphasized the importance of transparency and independent evaluation, noting that existing legal authorities provide a foundation for responsible cross-agency data use and testing.<\/p>\n