Predict, Prevent, Perform: BI-Enhanced Patient Readmission Forecasting for Smarter Healthcare

Hospital readmissions are a significant concern for healthcare providers, resulting in increased operational costs, resource strain, and potential penalties under healthcare policies. Effective management of patient readmissions requires not only accurate forecasting but also actionable insights derived from comprehensive data analysis. This paper presents an integrated approach combining predictive analytics and Business Intelligence (BI) to address patient readmission challenges. Leveraging machine learning models, the study predicts the likelihood of patient readmissions based on historical healthcare data, identifying key factors contributing to repeated admissions. Furthermore, the implementation of interactive BI dashboards enables healthcare administrators to visualize trends, high-risk patient profiles, and department-wise readmission patterns, facilitating data-driven decision-making. The proposed system demonstrates how predictive modeling and BI visualization synergize to optimize healthcare delivery, improve patient outcomes, and support strategic planning. Experimental results utilizing publicly available datasets highlight the efficacy of the approach, with measurable improvements in prediction accuracy and operational insights. Future extensions include the integration of real-time Electronic Health Record (EHR) systems and advanced analytics for proactive intervention strategies.