基于机器学习的临床决策支持系统的能力,以减少警觉疲劳,错误药物错误,并提醒用户看起来相似,声音相似的药物
Chun-You Chen1, Ya-Lin Chen2, Jeremiah Scholl3
1College of Medical Science and Technology, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan; Department of Radiation Oncology, Taipei Municipal Wan Fang Hospital, Taipei 110, Taiwan; Information Technology Office in Taipei Municipal Wan Fang Hospital, Taipei Medical University, Taipei 110, Taiwan; Artificial Intelligence Research and Development Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
这项研究表明,MedGuard机器学习临床决策支持系统 (CDSS) 有效地触发了临床相关的警报,提高了患者的安全性并减少了药物错误. 高警报接受率也有助于减轻临床医生的倦怠.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 患者安全 患者安全
背景情况:
- 临床决策支持系统 (CDSS) 可能会受到不相关警告的"警报疲劳"的阻碍.
- 不适当的药物错误,相似/听起来相似 (LASA) 错误以及问题列表文档问题构成风险.
- 基于机器学习的CDSS为这些挑战提供了潜在的解决方案.
研究的目的:
- 为了评估 MedGuard 基于机器学习的 CDSS 的性能.
- 评估MedGuard能够触发临床相关警报的能力及其接受率.
- 确定了系统在拦截不合适药物和LASA药物错误方面的有效性.
主要方法:
- 追溯性研究分析了从2019年7月到2021年6月在门诊机构的MedGuard警报.
- 一位专家药剂师审查了警报的适用性,接受率,错误的药物错误和混的药物对.
- 数据包括处方订单,触发警报和处方修改.
主要成果:
- 超过120万份处方导致28536个警报 (2.36%的警报率).
- 医生接受了48.88%的警报,导致了28.08%的处方变化.
- 该系统截获的药物错误率为1.64% (16.4每1000个订单).
结论:
- 基于机器学习的CDSSMedGuard通过生成临床有效的警报来提高患者的安全性.
- 该系统有助于更好的问题列表文档,并拦截药物和LASA错误,提高药物安全性.
- 高警报接受率有助于减少临床医生倦怠和不良事件.
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