Evaluating chain-of-thought prompting in a GPT chatbot for BCID2 interpretation and stewardship: how does AI compare...

Rapid molecular diagnostics, such as the BIOFIRE® Blood Culture Identification 2 (BCID2) panel, have improved the time to pathogen identification in bloodstream infections. However, accurate interpretation and antimicrobial optimization require Infectious Disease (ID) expertise, which may not always be readily available. GPT-powered chatbots could support antimicrobial stewardship programs (ASPs) by assisting non-specialist providers in BCID2 result interpretation and treatment recommendations. This study evaluates the performance of a GPT-4 chatbot compared to ASP prospective audit and feedback interventions.

This prospective observational study assessed 43 consecutive real-world cases of bacteremia at a 399-bed VA Medical Center from January to May 2024. The GPT-chatbot utilized "chain-of-thought" prompting and external knowledge integration to generate recommendations. Two independent ID physicians evaluated chatbot and ASP recommendations across four domains: BCID2 interpretation, source control, antibiotic therapy, and additional diagnostic workup. The primary endpoint was the combined rate of harmful or inadequate recommendations. Secondary endpoints assessed the rate of harmful or inadequate responses for each domain.

The chatbot had a significantly higher rate of harmful or inadequate recommendations (13%) compared to ASP (4%, p = 0.047). The most significant discrepancy was observed in the domain of antibiotic therapy, where harmful recommendations occurred in up to 10% (p <0.05) of chatbot evaluations. The chatbot performed well in BCID2 interpretation (100% accuracy) but provided more inadequate responses in source control consideration (10% vs. 2% for ASP, p = 0.022).

GPT-powered chatbots show potential for supporting antimicrobial stewardship but should only complement, not replace, human expertise in infectious disease management.