Infection control and hospital epidemiology · 2026
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.
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Enriquez Kyle T, Dulanto Chiang Augusto, Smiley Casey, Staub Milner
Аннотация
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 CopilotTM could facilitate healthcare economics analyses for ASP programs without dedicated healthcare economic supports.
DESIGN: AI (Microsoft CopilotTM) was prompted to develop a cytomegalovirus prophylaxis protocol for lung transplant recipients using only PubMed-indexed articles. CopilotTM 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AI-generated prophylaxis protocol was evidence-based without hallucination, but this conservative protocol relied on outdated evidence and was associated with significant increases in expected per-patient cost (mean +$4740, P 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, CoPilotTM provided a thorough cost-effectiveness analysis comparing all potential and existing protocols. With appropriate oversight, AI and Microsoft CopilotTM can conduct healthcare economic analyses suitable for ASP strategic planning and implementation.
DESIGN: AI (Microsoft CopilotTM) was prompted to develop a cytomegalovirus prophylaxis protocol for lung transplant recipients using only PubMed-indexed articles. CopilotTM 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, CoPilotTM provided a thorough cost-effectiveness analysis comparing all potential and existing protocols. With appropriate oversight, AI and Microsoft CopilotTM can conduct healthcare economic analyses suitable for ASP strategic planning and implementation.