AI-Driven Predictive Models for Hospital Readmission: Reducing Healthcare Costs and Improving Patient Outcomes

Abstract

Hospital readmissions are a significant burden on healthcare systems. This paper investigates how artificial intelligence (AI) can predict patient readmissions and reduce their incidence through targeted interventions. We review various AI algorithms and their applications in analyzing patient data to identify those at high risk of readmission. Case studies from hospitals using these predictive models demonstrate their effectiveness in reducing readmission rates and associated costs. The paper also addresses challenges such as model accuracy, data integration, and ethical considerations, proposing strategies to enhance the adoption and impact of AI-driven predictive models in healthcare settings.