Explainable AI Models for Risk Prediction and Quality Improvement in Public and Private Health Service Systems

Abstract: This study investigates explainable artificial intelligence (XAI) models for predicting 30-day adverse events and enabling quality improvement across public and private health service systems. The framework integrates clinical records (case-mix, comorbidity, vital signs, laboratory markers, and process indicators) with patient-generated sensor summaries (activity, sleep, and heart-rate variability) to estimate risk and provide governance-ready explanations. Empirical evaluation on a de