Revolutionizing Healthcare with Artificial Intelligence: Predictive Models for Readmission Reduction in U.S. Hospitals

Abstract

The integration of Artificial Intelligence (AI) into healthcare systems holds transformative potential for improving patient outcomes, reducing costs, and enhancing operational efficiency. One of the critical areas where AI can be leveraged is in predicting hospital readmissions, a growing concern for healthcare providers, especially in the United States. This paper explores the use of predictive models powered by AI and machine learning (ML) algorithms to forecast hospital readmissions. The study applies a range of predictive techniques, including Logistic Regression, Random Forest, Gradient Boosting, and Artificial Neural Networks (ANN), to a dataset of patient records from U.S. hospitals. The performance of these models is evaluated based on several key metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The results show that AI-driven models, particularly ANN and Gradient Boosting, outperform traditional approaches in predicting readmission risks, demonstrating high levels of accuracy and precision. The findings underscore the potential of AI to significantly reduce hospital readmissions, optimize patient care, and improve resource allocation. This research paves the way for integrating AI-powered predictive tools into healthcare systems, ultimately contributing to more efficient healthcare delivery and better patient outcomes.