Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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NEJM catalyst innovations in care delivery
Thinking about the Impact of Artificial Intelligence on U.S. Health Care Costs and Spending Growth
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.
16 July 2026
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Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study
BACKGROUND: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of medical futures studies requires methodologies that bridge qualitative strategic foresight with quantitative operational modeling. These system-level dynamics directly influence timely diagnosis, treatment delays, and overall health system resilience. OBJECTIVE: This study aimed to develop a proof-of-concept framework coupling qualitative scenario planning with computational discrete-event simulation to stress-test oncology AI adoption strategies. METHODS: We defined a strategic state space using 2 orthogonal axes, AI automation intensity and data interoperability, resulting in 4 distinct futures scenarios. We translated these qualitative narratives into a quantitative discrete-event simulation model of a 3-year operational horizon. The model quantified system performance (referral-to-treatment interval [RTTI] and throughput), volatility, and resource constraints across different adoption trajectories. RESULTS: The scenario-planning phase yielded 4 operational archetypes (analog oncology, automation islands, interconnected clinicians, and AI-orchestrated care) with distinct constraints, risks, and failure modes. In the simulation, the fully integrated scenario maximized capacity (1244, SD 21.4 patients per year) and halved the mean RTTI to 14.9 (SD 0.3) days, a magnitude comparable to major pathway redesign interventions. Isolated automation without data infrastructure led to reduced system performance, increasing RTTI by 26% (37.1, SD 1.3 days) and reducing throughput to 647 (SD 10.1) patients per year due to administrative governance saturation. The model illustrated a structural bottleneck migration: successful upstream AI adoption shifted binding constraints from diagnostic scanners to downstream chemotherapy infusion units, whereas missing data interoperability resulted in governance constraints. Pathway optimization analysis indicated that a coordinated strategy prioritizing early improvements in data interoperability reduced transition volatility compared to an automation-first approach. CONCLUSIONS: Integrating qualitative scenario planning with quantitative simulations enabled a systematic evaluation of oncology AI adoption strategies. As a proof of concept, it offers a replicable framework for health leaders to model future scenarios of digital transformation in times of high uncertainty. Subsequent work should expand this methodology to incorporate financial and health equity dimensions, establishing simulation-based scenario planning as an important tool in medical futures studies.
26 May 2026
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