Alternative Predictive Models for Medicare Patient Cost

Abstract

As health care expenditures increase, patient cost mitigation becomes more essential. Cost mitigation through intervention programs such as accountable care organizations relies on the ability to accurately predict patient risk, which is notoriously difficult because of highly skewed data. We examine the Medicare Limited dataset (a 5% sample of Medicare claims) that includes demographics, costs, and health conditions. We first consider the Centers for Medicare and Medicaid Services (CMS) currently used Hierarchical Condition Category (HCC) linear model and then implement more complex two-part generalized additive and random forest models to predict patient costs in a future year based on current-year data. We find that the latter models more accurately predict the entire distribution of Medicare patient costs and can better support the existing cost mitigation frameworks. The two-part lognormal generalized additive model is chosen as the optimal model for its robust performance and reasonable interpretability when the data have extreme values.