Explainable Risk Stratification and Resource Coordination for Hospital Readmission Management through Integrated Prediction-Intervention-Evaluation Framework

Abstract

Hospital readmissions impose substantial financial penalties and strain healthcare resources, necessitating intelligent management strategies. This paper presents an integrated prediction-intervention-evaluation framework that couples explainable risk stratification with coordinated resource allocation to reduce 30/90-day readmission rates. The methodology employs quantile-based risk binning with isotonic calibration to achieve interpretable patient stratification, followed by constraint-satisfaction optimization for bed and nursing time allocation. Prospective simulation demonstrates a 1.9 percentage-point reduction in 30-day readmissions while improving bed utilization efficiency by 18.7%. Cross-seasonal validation confirms robustness across temporal variations, with threshold sensitivity analysis revealing stable operating points. The framework achieves an AUROC of 0.847 and maintains an overall calibration error of 0.032. By emphasizing variable availability and uncertainty quantification over complex engineering implementations, this approach facilitates multi-site deployment and regulatory compliance while addressing Medicare penalty structures and workforce sustainability challenges.