Artificial Intelligence as a Catalyst for Value-Based Care in the U.S. Health Insurance (Preprint)

BACKGROUND The United States health insurance system is at a critical crossroads. Inflating costs, fragmented care, and administrative inefficiencies have revealed the limitations of the fee-for-service (FFS) model. This long-standing structure, while once effective in expanding access, now struggles to deliver efficiency and value. Value-based care (VBC) aims to realign incentives toward outcomes, quality, and efficiency. OBJECTIVE This article explores how artificial intelligence (AI) can serve as the digital backbone to accelerate the transition from FFS to VBC. METHODS The article reviews evidence from bundled payment programs and Accountable Care Organizations (ACOs), examines AI-driven frameworks for cost prediction, outcome measurement, and risk adjustment, and discusses challenges and future considerations with the aid of an illustrative case and example. RESULTS Bundled payment models, such as the Comprehensive Care for Joint Replacement program, have shown average savings of ≈1, 012 per episode, while the ACO REACH model achieved average savings of ≈930 per beneficiary compared with FFS benchmarks. AI applications provide scalable solutions for forecasting costs, optimizing care coordination, and supporting preventive interventions. An illustrative case vignette in congestive heart failure illustrates how AI-enabled VBC can reduce and lower episode costs by approximately 20%. CONCLUSIONS AI has the potential to accelerate the scaling of VBC by making it more efficient, equitable, and sustainable. However, realizing this promise requires safeguards for data quality, interoperability, fairness, and transparency. In the AI era, the defining measure of health insurance will shift from the number of claims processed to the number of lives improved. CLINICALTRIAL N/A