在复杂的护理中改进个人口译员的利用:由步骤边缘集群随机试验的综合人工智能随机试验的发现
Amelia Barwise1,2, Inna Strechen3, Targ Eltalhi4
1Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA. barwise.amelia@mayo.edu.
Journal of general internal medicine
|January 6, 2026
概括
一个集成机器学习的算法,旨在增加对英语能力有限的患者的面对面翻译使用. 虽然干预显示了越来越多地使用口译员的趋势,但结果在统计学上并不显著,这表明需要进一步研究.
科学领域:
- 医疗信息学 医疗信息学
- 临床操作 临床操作
- 健康 公平 卫生 公平
背景情况:
- 人口口译员的不足利用导致了医疗复杂的住院患者的健康差异,而英语水平有限.
- 解决语言障碍对于公平的医疗保健服务至关重要.
研究的目的:
- 实施机器学习和信息学算法,以提高对非英语语言偏好 (NELP) 复杂住院患者的面对面口译员的利用率.
- 将该算法集成到现有的临床和语言服务工作流程中.
主要方法:
- 进行了一项涉及35个住院病房的双臂阶梯集群随机试验.
- 该研究包括在急性护理环境中患有NELP的成年住院患者 (≥18岁).
- 一个算法识别了需要口译员的复杂患者,语言服务随后启动了有针对性的推广.
主要成果:
- 这项研究包括749名对照患者和672名干预患者.
- 在对照组和干预组之间,在接受个人口译员的时间内没有观察到统计学上显著的差异 (HR=1.02,95% CI [0.81,1.29],p=0.87).
结论:
- 干预表明,在NELP和复杂需求的住院患者中,人际口译使用增加的趋势并不显著.
- 这些发现支持需要一个更大,更强大的多中心试验,以进一步评估算法的有效性.
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