Ai-Augmented Healthcare Systems: Exploring The Potential Of Ai To Transform Healthcare Delivery And Improve Patient Outcomes

Abstract

Hospital readmissions place a significant burden on healthcare systems, contributing to increased costs and adverse patient outcomes [1]. This study proposes an AI-driven predictive framework designed to identify patients at high risk of 30-day hospital readmission, utilizing electronic health records, demographic data, and laboratory results. The framework integrates a gradient boosting machine learning model with explainability techniques to enhance clinical trust and adoption. We evaluated the model on a retrospective dataset of 12,500 patient records from a tertiary hospital. Results demonstrated an AUC of 0.86, outperforming traditional logistic regression models (AUC 0.71) [2], and achieving a 23% reduction in false positives. Simulated cost-benefit analysis suggests potential annual savings of $1.2 million for mid-sized hospitals through targeted interventions. These findings indicate that AI-augmented systems can significantly improve early detection of readmission risks, optimizing resource allocation and enhancing patient outcomes while addressing ethical and operational considerations [3].