Machine Learning for Medicare Hospitalization Risk and Cost Prediction: A Conceptual Framework
Abstract
Accurate prediction of hospitalization risk and healthcare expenditure is foundational to effective care management programme design in Medicare Advantage and managed care settings, yet existing prediction approaches inadequately address the clinical complexity of ageing populations and the governance requirements of regulated payers. The proposed Medicare Risk and Cost Intelligence augmented framework — a modular prediction and governance architecture that integrates a validated four-module prediction pipeline with a structured compliance and equity monitoring system for population-level hospitalization risk stratification and cost forecasting among older adults. The MRCI augmentation taxonomy extends standard HCC risk adjustment with geriatric-specific features encompassing frailty indices, functional status trajectories, anticholinergic medication burden, social determinants, and longitudinal utilization patterns. A five-segment actionable risk classification scheme — stratifying beneficiaries from Critical High Cost to Emerging Risk — is proposed to translate continuous risk scores into operationally distinct care management programme enrolment pathways. The governance architecture specifies a compliance controls matrix addressing five primary regulatory and ethical risks, with a model operations dashboard tracking nine performance metrics across discrimination, calibration, and equity monitoring domains. Anti-gaming controls are specified for the high-stakes Medicare Advantage Star Ratings context. The paper identifies an actionable evidence-to-implementation pathway for health plans seeking to improve care management efficiency, reduce avoidable hospitalization, and close performance disparities by race, ethnicity, and area deprivation. Evaluation using difference-in-differences methodology with propensity- score matched comparison groups is recommended for programme impact assessment. Index Terms — Medicare; hospitalization risk prediction; cost forecasting; HCC risk adjustment; two-part model; machine learning; value-based care; compliance governance.
References
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