Aligning AI Payment Policy With Desired Outcomes Rather Than Inputs May Require Customized Pathways

Artificial intelligence (AI) has the potential to create health care value independent of traditional inputs, such as clinicians' time, skill, and resources. However, Medicare's current structuring of reimbursement around human inputs has the potential to miscalculate the value of AI in clinical practice. We examine the tension between input-based prices and outcome-based care by comparing and contrasting payment for AI with the approach for prescription drug pricing. We propose a classification system to distinguish between the types of AI that differ in their implications for clinician time and cost. By aligning AI reimbursement policy with desired outcomes rather than inputs, policy makers can ensure that innovators, clinicians, and patients alike benefit from novel AI technologies.