Author(s) :

Selva Raj.

Volume/Issue :

Abstract :

Artificial Intelligence (AI) has emerged as a transformative tool for predicting hospital readmission rates, a critical challenge in healthcare that impacts patient outcomes and costs. By leveraging machine learning algorithms and integrating diverse data sources—such as electronic health records, patient demographics, clinical histories, and social determinants—AI models can accurately identify patients at high risk for readmission. These models enable healthcare providers to implement targeted interventions, improving care quality and reducing avoidable hospitalizations. Despite challenges including data quality issues, model interpretability, integration barriers, and ethical considerations, ongoing advancements in AI techniques and collaborative efforts among clinicians, data scientists, and policymakers hold promise for more effective, equitable, and scalable predictive solutions. This article reviews the fundamentals of AI in healthcare, data preparation strategies, model development processes, real-world applications, and future directions for enhancing hospital readmission prediction, emphasizing the critical role of AI in transforming patient care and healthcare management.

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