National-Scale Predictive Analytics for Medicare and Medicaid: An AI-Driven Approach to Identifying High-Risk Populations and Reducing Healthcare Costs

Abstract

Rising expenditures in Medicare and Medicaid continue to challenge the long-term sustainability of public healthcare financing in the United
States. A relatively small proportion of beneficiaries accounts for a disproportionate share of annual spending due to chronic disease burden,
repeated hospitalizations, fragmented care pathways, and unmet social needs. Early identification of these high-risk and high-cost populations is
therefore essential for improving outcomes while controlling avoidable expenditure. This study examines the application of artificial intelligencedriven
predictive analytics at national scale to strengthen population health management across Medicare and Medicaid programs. Using
integrated claims records, electronic health records, demographic indicators, utilization histories, and selected social determinants of health
variables, multiple machine learning models were developed to estimate future hospitalization risk, readmission probability, and annual cost
escalation. Comparative evaluation indicates that ensemble and deep learning approaches outperform conventional regression-based methods
in risk stratification accuracy, sensitivity, and cost forecasting performance. Prior utilization, multimorbidity burden, medication complexity,
emergency department use, and socioeconomic vulnerability emerged as the most influential predictors. Simulated deployment results suggest
that earlier targeting of case management, preventive outreach, and transitional care programs could reduce unnecessary admissions and moderate
total program spending. The findings demonstrate that scalable predictive systems can support more proactive and efficient allocation of limited
healthcare resources. From a policy perspective, national implementation of AI-enabled analytics may improve care coordination, strengthen
value-based purchasing strategies, and enhance equity by identifying underserved beneficiaries with elevated risk profiles.