Bolton AI antimicrobial prescribing

The Impact of Artificial Intelligence-Driven Decision Support on Uncertain Antimicrobial Prescribing: A Randomised, Multimethod Study

Patient / Population Intervention / Exposition Vergleich Endpunkt

In 42 UK clinicians from 23 hospitals who regularly prescribe antibiotics, randomised per case vignette, an AI-driven decision support system with explanations for intravenous-to-oral antibiotic switching did not differ from standard-of-care information alone for most switching decisions and completion times across 12 vignettes, but pushed decisions towards not switching where it did differ.

N
42
Design
Randomised multimethod vignette study, clinicians randomised, UK
Endpunkt
Difference in clinicians' intravenous-to-oral switching decisions with vs without the AI CDSS, per vignette and overall; usability (SUS) and acceptance (TAM)
Relevanz
2
ErgebnisMost decisions and completion times equivalent; where a difference appeared the CDSS steered clinicians towards not switching (OR 0.13, 95% CI 0.03–0.50; P=0.0031). Explanations used only 9% of the time; SUS 72.3/100.
Bolton WJ, et al. Lancet Digit Health. 2025;7(11):100912. 10.1016/j.landig.2025.100912
Diskussion & Kritik

A rare randomised look at how an AI CDSS actually shapes prescribing behaviour: clinicians could spot and ignore wrong advice, were swayed mainly towards continuing intravenous therapy, and almost never consulted the explanations — a sobering finding for explainable-AI design. Vignettes rather than patients, 42 self-selected clinicians recruited through personal networks, and no safety or outcome data.