Source: Kaiser Family Foundation (KFF)

KFF examines the growing use of artificial intelligence across the claims review cycle, highlighting that the Trump administration’s AI Framework advocates national AI standards that could preempt many state consumer-protection laws governing prior authorization and claims review. The brief explains the difference between prior authorization (coverage determinations before care) and claims review (payment determinations after care), and documents AI adoption by insurers, TPAs, providers, and patient-directed tools across prior authorization, retrospective/post-claim review, and concurrent review processes.

The brief cites a National Association of Insurance Commissioners survey finding that 84% of 93 responding insurers in 16 states use AI or machine learning for tasks including utilization management and prior authorization; it describes provider uses (RCM and generative AI for encounter summaries) and notes forthcoming federal regulations requiring APIs that may increase data sharing. KFF flags consumer risks: inaccurate or biased AI outputs, reduced individualized clinical review when human judgment is absent, privacy and security gaps because HIPAA does not cover many technology firms, and litigation trends—patients have filed class actions alleging improper denials tied to algorithmic decision-making and lack of transparency. The brief also references a study finding algorithms that use health care costs as a proxy can underestimate Black patients’ needs, and it documents 2025 federal actions cited in the dossier—insurers’ voluntary 2025 pledge to have clinicians review clinical denials, Executive Orders and an AI Action Plan—and warns Congress to watch federal-state tensions as it considers AI legislation that could reshape consumer protections in health coverage.