Reducing Hospital Readmission Risk Using Predictive Analytics

Abstract

This study highlights the development and application of a predictive analytics system in a Midwestern hospital to assess and manage the risk of patient readmissions within 30 days of discharge. By integrating advanced analytical modeling with electronic health records, the system enables the creation of personalized care plans by accurately predicting patients' readmission risks and the optimal timing for interventions. The results suggest that such models can significantly improve resource allocation and the personalization of care plans, thereby reducing unnecessary readmissions and aligning with value-based, patient-centered healthcare goals.