Assessing the Cost-Containment Effectiveness of AI-Based Predictive Models in Reducing Avoidable Readmissions and Overtreatment in U.S. Medicare Hospitals

Abstract

The U.S. Medicare system, serving over 60 million beneficiaries, faces escalating cost-containment challenges, with expenditures reaching $944 billion in 2022 and projected to hit $1.8 trillion by 2030. Avoidable hospital readmissions, costing an estimated $17 billion annually, and overtreatment, accounting for up to 30% of wasteful spending, are significant contributors to this financial burden. This study assesses the cost-containment effectiveness of AI-based predictive models in reducing avoidable readmissions and overtreatment in U.S. Medicare hospitals. Through a systematic literature review of 28 studies and case reports from PubMed, Scopus, Web of Science, and CMS Innovation Center reports (2015–2024), the research evaluates AI-driven interventions leveraging machine learning and predictive analytics. Findings indicate that AI models, by analyzing electronic health records, claims data, and social determinants, achieved a 12–15% reduction in 30-day readmission rates, up to 16% decreases in unnecessary procedures, and annual cost savings ranging from $1.3 million to $2.3 million per hospital. These outcomes align with the Hospital Readmissions Reduction Program (HRRP) goals, reducing CMS penalties and optimizing resource use. However, barriers such as data integration challenges, high implementation costs, and clinician resistance hinder widespread adoption. The study recommends CMS incentivize AI integration within value-based care frameworks, hospitals invest in interoperable EHR systems and staff training, and future research focus on longitudinal and national-level impact assessments. By providing empirical evidence on AI’s financial and operational benefits, this research informs strategies to enhance cost-efficiency and care quality in Medicare hospitals.