What does this research mean for the field?

With appropriate oversight, generative AI platforms like Microsoft Copilot can successfully conduct healthcare economic analyses and support evidence-based protocol development for antimicrobial stewardship programs. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to evaluate the capability of AI in conducting healthcare economic analyses for antimicrobial stewardship programs.

June 10, 2026Open Access

Updating CMV protocols in lung transplant patients: a single-center case study modeling use of generative AI for antimicrobial stewardship protocol development and economic impact analysis

KEKyle T. EnriquezOffice of Naval Research ACAugusto Dulanto ChiangVanderbilt University Medical Center CSCasey SmileyVanderbilt University Medical Center

Key Points

This study aims to evaluate the capability of AI in conducting healthcare economic analyses for antimicrobial stewardship programs.
AI (Microsoft Copilot TM) developed a cytomegalovirus prophylaxis protocol using PubMed-indexed articles.
Probabilistic samples of simulated patients were created from a cohort statistics of 165 patients.
Cost-effectiveness was assessed across four cytomegalovirus prophylaxis protocols.
The AI-generated protocol had a mean increase of $4740 per patient (P < .01) compared to recent guidelines.
AI independently performed sensitivity analyses highlighting the significant cost impact of letermovir.
The AI protocol was less cost-effective than existing protocols but provided valuable literature for ASP development.

Abstract

Abstract Objective: Antimicrobial Stewardship Programs (ASPs) need healthcare economic analyses to support and inform ASP strategies. This work aimed to determine whether widely available artificial intelligence (AI) platforms like Microsoft Copilot TM could facilitate healthcare economics analyses for ASP programs without dedicated healthcare economic supports. Design: AI (Microsoft Copilot TM) was prompted to develop a cytomegalovirus prophylaxis protocol for lung transplant recipients using only PubMed-indexed articles. Copilot TM was then prompted to produce probabilistic samples of simulated patients from aggregate statistics of a 165-patient cohort from Vanderbilt University Medical Center and to analyze cost-effectiveness across four distinct cytomegalovirus prophylaxis protocols, including its own. Setting: Tertiary care academic medical center, including outpatient and inpatient environments. Patients or participants: Simulated patient data was developed via random, single-blind, probabilistic selection from pre-defined aggregate cohort statistics. Results: The AI-generated prophylaxis protocol was evidence-based without hallucination, but this conservative protocol relied on outdated evidence and was associated with significant increases in expected per-patient cost (mean +4740, P <. 01) compared to recent guideline-based and institutional protocols. AI independently identified and executed sensitivity analyses, which revealed that in this simplified model, letermovir use had a large impact on expected per-patient cost. Conclusions: The AI-proposed protocol was less cost-effective, but data suggest that careful prompting can provide appropriate PubMed-indexed literature to support ASP protocol development. Additionally, CoPilot TM provided a thorough cost-effectiveness analysis comparing all potential and existing protocols. With appropriate oversight, AI and Microsoft Copilot TM can conduct healthcare economic analyses suitable for ASP strategic planning and implementation.

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