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Background <p>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.</p> Methods <p>We administered an online scenario-based survey to physicians (<i>N</i> = 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 occurred both before and after simulated hypothetical ML-CDSS interventions, in the form of a probability of bacterial infection output.</p> Results <p>The average probability estimates of bacterial infection under the four McIsaac scenarios were monotonically increasing: (1) 25.6% (95% CI 22.8–28.4%), (2) 43.8% (40.6–46.7%), (3) 65.1% (62.2–67.0%), and (4) 69.1% (66.3–71.8%). Furthermore, empiric treatment was generally overprescribed: (1) 11.4% (2) 38.4% (3) 65.8% (4) 73.0%. These estimates and treatment percentages are higher than expected given the relevant scientific literature. The interventions had substantial effects on probability estimates and empiric prescription; e.g. reducing average estimates by up to 14% points and lowering odds of antibiotic prescription by a factor 0.42.</p> Conclusions <p>Overestimation of bacterial infections and subsequent antibiotic overprescription are common, particularly under conditions of clinical uncertainty. These tendencies can be mitigated through ML-CDSS interventions, as demonstrated in a scenario-based survey setting. Our findings provide initial support for the design of ML-CDSS tools and their integration into primary care, pending further validation in clinical trials. Additionally, they support policy initiatives aimed at clarifying default clinical actions in situations of diagnostic uncertainty.</p> Clinical trial <p>Not applicable.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月13日

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  • Shoham Baruch,
  • Maya Diamant,
  • Yoav Ganzach,
  • Zachi Grossman,
  • Michal Stein,
  • Uri Obolski

摘要

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 occurred both before and after simulated hypothetical ML-CDSS interventions, in the form of a probability of bacterial infection output.

Results

The average probability estimates of bacterial infection under the four McIsaac scenarios were monotonically increasing: (1) 25.6% (95% CI 22.8–28.4%), (2) 43.8% (40.6–46.7%), (3) 65.1% (62.2–67.0%), and (4) 69.1% (66.3–71.8%). Furthermore, empiric treatment was generally overprescribed: (1) 11.4% (2) 38.4% (3) 65.8% (4) 73.0%. These estimates and treatment percentages are higher than expected given the relevant scientific literature. The interventions had substantial effects on probability estimates and empiric prescription; e.g. reducing average estimates by up to 14% points and lowering odds of antibiotic prescription by a factor 0.42.

Conclusions

Overestimation of bacterial infections and subsequent antibiotic overprescription are common, particularly under conditions of clinical uncertainty. These tendencies can be mitigated through ML-CDSS interventions, as demonstrated in a scenario-based survey setting. Our findings provide initial support for the design of ML-CDSS tools and their integration into primary care, pending further validation in clinical trials. Additionally, they support policy initiatives aimed at clarifying default clinical actions in situations of diagnostic uncertainty.

Clinical trial

Not applicable.