Thinking about the Impact of Artificial Intelligence on U.S. Health Care Costs and Spending Growth

Clinical Snapshot

Key Findings

• P Value: Not applicable — no hypothesis testing conducted

• Effect Size: Not quantified — analysis is qualitative and directional; no numerical effect estimates are reported

• Primary Outcome: Under the prevailing U.S. fee-for-service payment model and in highly consolidated hospital and insurance markets, AI adoption is more likely to increase total health care costs and accelerate spending growth in the short-to-medium term, even as it delivers improvements in patient access and clinical quality

• Nnt Or Sensitivity: Not applicable — this is a policy analysis, not a clinical trial or diagnostic study; no NNT, sensitivity, specificity, or hazard ratio is reported

• Confidence Interval: Not applicable — no statistical analysis performed

Clinical Application

The article's policy recommendations — introducing regulatory and reimbursement levers to align AI adoption with cost containment — are conceptually feasible but operationally complex. Implementation would require coordinated action across federal regulators (CMS, FDA), private payers, and health system leadership. No implementation roadmap or cost-effectiveness threshold is provided. Australia's health system differs structurally from the U.S. in ways that are directly relevant to this analysis. The MBS (Medicare Benefits Schedule) and PBS (Pharmaceutical Benefits Scheme) provide universal coverage with centralised price-setting mechanisms that may attenuate some of the cost-amplifying dynamics described for U.S. fee-for-service. However, Australia's growing private health insurance sector, increasing direct-to-consumer digital health offerings, and MBS item-number-based remuneration for telehealth and chronic disease management create analogous incentive structures. The TGA's evolving regulatory framework for Software as a Medical Device (SaMD) and AI-enabled diagnostics is directly relevant. RACGP guidelines on digital health and chronic care management would need to incorporate the payment-model-contingent cost implications identified in this article. Australian policymakers considering AI reimbursement through MBS should heed the authors' warning that fee-for-service reimbursement of AI-enabled services may increase, rather than reduce, aggregate health expenditure. Health system administrators, policymakers, payers, and clinical leaders in any health system considering large-scale AI adoption; most directly applicable to U.S. stakeholders operating under fee-for-service Medicare/Medicaid structures

Abstract

This article critically examines the potential impacts of artificial intelligence (AI) on prices, total spending, and the rate of spending growth in the areas of prescription drug innovation and expanded patient access to care - including developments in remote patient monitoring, chronic care management, direct-to-consumer health care, clinical decision support, and nonclinical administrative labor. Based on observed industry practices, economics, and public policy, the article presents a thought exercise on the most likely effects for patients, providers, payers, and the broader health care system. The authors argue that under the still-dominant fee-for-service payment model, as well as the highly consolidated hospital and insurance markets, AI is more likely to increase total costs and spending growth in the short to medium term rather than slow them, even as it delivers substantial access and clinical quality improvements for patients. The cost-bending potential of AI varies distinctly by fee-for-service versus value-based payment. Regulators need to introduce policy and reimbursement levers for AI to slow cost growth. The authors conclude that, without significant changes in the health care system's financial incentives and market structures, AI will not slow cost growth.

References

1. 1.Kocher, B., Zhao, B., & Duffy, E. (2026). Thinking about the impact of artificial intelligence on U.S. health care costs and spending growth. NEJM Catalyst Innovations in Care Delivery. https://doi.org/10.1056/CAT.25.0509