Empiric treatment and probability estimates before and after a decision support system intervention in a sore throat setting: a scenario-based survey study.

ABSTRACT:

Background

Antimicrobial resistance poses challenges for physicians, who must balance individual patient care and public health when prescribing antibiotics. Machine learning-based computerized decision support systems (ML-CDSS) are increasingly proposed to aid in this challenge. We aimed to assess physicians' decision-making in a common bacterial vs. viral infection scenario, and the impact of an ML-CDSS on it.

Methods

We administered an online scenario-based survey to physicians (N = 211), mainly pediatricians (67.8%). The estimated response rate was 33-40%. Each participant encountered four sore throat scenarios, corresponding to one of four McIsaac scores. Participants estimated the probabilities of bacterial infections and determined treatment strategies. This sequence

SUBMITTER: Baruch S 

PROVIDER: S-EPMC12821950 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature