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在喉疼痛的决策支持系统干预之前和之后的经验治疗和概率估计:基于场景的调查研究
Shoham Baruch1, Maya Diamant1,2, Yoav Ganzach2
1Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical and Health Sciences, Tel Aviv University, Haim Levanon Street, Tel Aviv, 6139001, Israel.
BMC infectious diseases
|December 13, 2025
概括
医生经常高估细菌感染和过度处方抗生素,特别是诊断不确定性. 基于机器学习的计算机决策支持系统 (ML-CDSS) 可以通过提高诊断准确性和减少不必要的抗生素处方来帮助缓解这些问题.
科学领域:
- 医疗信息学 医疗信息学
- 传染性疾病 传染性疾病
- 临床决策支持 临床决策支持
背景情况:
- 抗菌素耐药性在平衡患者护理和公共卫生方面构成了重大挑战.
- 基于机器学习的计算机决策支持系统 (ML-CDSS) 是帮助医生处方抗生素的新兴工具.
- 在常见的细菌和病毒感染场景中,医生的决策需要评估.
研究的目的:
- 用一个常见的情景来评估医生在诊断细菌和病毒感染方面的决策.
- 评估假设ML-CDSS对医生诊断和治疗决策的影响.
- 了解临床不确定性如何影响抗生素处方行为.
主要方法:
- 对211名医生进行了基于场景的在线调查,主要是儿科医生.
- 参与者根据麦克艾萨克分数评估了四种喉疼痛情景,估计了细菌感染概率和治疗策略.
- 这些评估是在模拟ML-CDSS干预之前和之后进行的,提供了细菌感染概率输出.
主要成果:
- 医生始终高估了细菌感染的概率,并在所有场景中过度处方经验性抗生素.
- ML-CDSS干预显著影响了概率估计,将其降低了高达14个百分点.
- 通过ML-CDSS干预,抗生素处方的几率降低了0.42.
结论:
- 过度估计细菌感染和随后的抗生素过度处方是普遍存在的,特别是在不确定的临床情况下.
- 在模拟环境中,ML-CDSS干预表明能够减轻这些处方趋势.
- 这些发现支持ML-CDSS的发展和整合到初级保健中,等待临床验证和政策考虑.
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