AI-enhanced healthcare analytics and predictive modeling for value-based care: A comprehensive analysis of implementation and outcomes in the United States healthcare system

Kunle, AKINBODE Azeez (2025) AI-enhanced healthcare analytics and predictive modeling for value-based care: A comprehensive analysis of implementation and outcomes in the United States healthcare system. World Journal of Advanced Research and Reviews, 26 (3). pp. 1433-1445. ISSN 2581-9615

The transition from fee-for-service to value-based care (VBC) models represents a fundamental shift in the US healthcare system, emphasizing patient outcomes and cost-effectiveness over volume of services. This comprehensive analysis examines the role of Artificial Intelligence (AI) and predictive modeling in enhancing healthcare analytics within VBC frameworks. Through systematic evaluation of current implementations, technological capabilities, and outcome metrics, this study demonstrates that AI-enhanced healthcare analytics significantly improve care quality, reduce costs, and optimize resource allocation. The integration of machine learning algorithms, natural language processing, and predictive analytics has shown measurable improvements in patient outcomes while reducing healthcare expenditures by an average of 15-25% across participating healthcare systems. This article presents evidence-based recommendations for healthcare organizations considering AI implementation in their VBC initiatives.