AIHealthcare Analytics

Overview

The question of how clinical AI should be paid for is one of the most consequential unresolved issues in healthcare AI policy. A 2026 report from the Peterson Health Technology Institute argues that current payment models are structurally ill-suited for clinical AI and risk driving up costs rather than reducing them.

Peterson Institute Findings

The Peterson Health Technology Institute report identifies three core structural mismatches between clinical AI and existing payment frameworks: (1) AI-generated value is often diffuse and not attributable to discrete billable events; (2) fee-for-service models create volume incentives that may reward AI-assisted overutilization; and (3) the absence of outcome-linked payment mechanisms makes it impossible to distinguish high-value AI tools from low-value ones at the payment level. The report concludes that without payment reform, widespread clinical AI adoption could increase total healthcare spending.

CMS Signals

CMS has begun signaling movement toward revised payment frameworks for clinical AI, including new pathways for AI tools used in clinical decision support and reforms to prior authorization processing. However, comprehensive AI-specific reimbursement codes and value-based payment models remain underdeveloped, leaving a significant policy gap.

Implications for Adoption

The payment gap creates a structural barrier to clinical AI adoption: health systems cannot justify AI procurement costs without clear reimbursement pathways, and vendors cannot build sustainable business models on cost-savings arguments alone when those savings accrue to payers rather than providers. Resolving this tension is increasingly recognized as a policy priority alongside regulatory and safety frameworks.