使用人工智能优化临床决策支持系统产生的药物警报:一个范围审查
Jetske Graafsma1, Rachel M Murphy2,3, Ewoudt M W van de Garde4,5
1Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, 9713GZ, The Netherlands.
概括
人工智能 (AI) 可以优化医院的药物警报,减少警报疲劳,提高处方准确性. 建议对AI模型开发,验证和现实世界的实施进行进一步的研究.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 目前的临床决策支持系统 (CDSS) 产生具有有限临床价值的药物警报,导致警报疲劳.
- 人工智能 (AI) 为优化这些药物警报提供了一个潜在的解决方案.
研究的目的:
- 对使用人工智能来优化医院环境中的药物警报进行范围审查.
- 确定应用的人工智能方法,其性能指标和主要结果指标.
主要方法:
- 在Medline,Embase和Cochrane图书馆数据库中进行了全面的搜索.
- 包括任何研究AI用于优化CDSS医院药物警报的定量设计的研究.
- ASReview软件支持了选过程.
主要成果:
- 包括10项研究,其中30%报告了统计性能和临床结果.
- 人工智能优化的警报减少了警报负担,改善了不适当处方的识别,并预测了用户的反应.
- 只有两项研究在实践中实施了基于AI的警报,没有一项研究进行了外部验证.
结论:
- 基于人工智能的方法显示出优化医院药物警报的前景.
- 需要改进人工智能模型开发和验证的报告.
- 鼓励外部验证和在临床实践中实施至关重要.
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