AI-Driven Functional Independence Prediction and Assistive Technology Optimization to Reduce Medicare Expenditures Among Older Adults in the United States

Abstract

The accelerating growth of the older adult population in the United States presents a structural challenge to Medicare sustainability, particularly as functional decline drives preventable hospitalization, post-acute care utilization, and long-term institutional placement. This study develops and evaluates an Artificial Intelligence–enhanced Functional Independence Support Technology and Analytics (FISTA) framework designed to predict functional decline, optimize assistive technology deployment, and quantify Medicare cost avoidance. A longitudinal quasi-experimental design was implemented using nationally representative aging datasets, Medicare claims data, and multi-site pilot deployments. The AI-based Functional Risk Score (FRS-AI), constructed using supervised machine learning models, demonstrated superior predictive performance compared to traditional screening approaches (AUC = 0.87 vs. 0.68), improving early identification of individuals at risk for ADL/IADL decline. The Technology Suitability Index (TSI-AI), supported by multi-criteria optimization and reinforcement learning, significantly increased sustained assistive technology adoption and reduced device abandonment. Longitudinal analyses revealed measurable improvements in ADL trajectories, reduced fall incidence, improved medication management, and delayed institutionalization. Medicare utilization outcomes showed a 22% reduction in inpatient admissions, 26% reduction in emergency department visits, and significant reductions in post-acute care spending, generating positive perbeneficiary cost avoidance exceeding implementation costs. The Functional Independence Gain (FIG) metric provided a structured mechanism linking device utilization, care integration, and hospitalization risk reduction to fiscal outcomes. These findings position AI-driven functional independence optimization as a scalable system-level economic intervention aligned with value-based care and national aging-in-place strategies.