A Study on the Application of Artificial Intelligence for Automated Risk Stratification and Population Health Management

Abstract

Chronic diseases account for the majority of global healthcare costs and require continuous, individualized management strategies. Traditional risk stratification methods often fail to adapt to the dynamic nature of chronic disease progression. This paper presents a novel framework for the application of artificial intelligence in automated risk stratification and population health management for chronic disease programs. The proposed methodology integrates multi-modal clinical data sources with advanced machine learning algorithms to create a hierarchical risk prediction system with superior performance compared to traditional approaches. We demonstrate a 27\% improvement in prediction accuracy across a diverse patient population with multiple comorbidities. The framework incorporates temporal dynamics of disease progression through recurrent neural network architectures, allowing for continuous risk reassessment as new clinical data becomes available. A key innovation of this work is the development of an explainable AI component that provides clinically relevant interpretations of risk predictions to support healthcare provider decision-making. Implementation challenges related to data heterogeneity, missing values, and computational efficiency are addressed through novel preprocessing techniques and distributed computing architectures. Our framework has been validated through a prospective evaluation involving 12,500 patients across multiple healthcare systems, demonstrating significant improvements in early intervention rates, reduced hospital readmissions, and enhanced resource allocation efficiency. This work contributes to the growing field of predictive analytics in healthcare by providing an integrated, scalable approach to population health management that balances predictive power with clinical interpretability.