Artificial Intelligence as a Catalyst for Value-Based Health Insurance in the United States: Narrative Review and Policy Perspective

Abstract

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. This article explores how artificial intelligence (AI) can serve as the digital backbone to accelerate the transition from FFS to VBC. 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 associated challenges and future considerations using an illustrative case. Bundled payment models, such as the Comprehensive Care for Joint Replacement program, have shown average savings of approximately $1012 per episode; whereas, the ACO REACH model achieved average savings of roughly $930 per beneficiary, relative to FFS benchmarks. AI applications provide scalable solutions for forecasting costs, optimizing care coordination, and supporting preventive interventions. A case vignette in congestive heart failure illustrates how AI-enabled VBC can reduce episode costs by approximately 20% under favorable implementation conditions. AI has the potential to accelerate the scaling of VBC by enhancing its efficiency, equity, and sustainability. 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.

Ähnliche Arbeiten