在构建风险识别模型中利用患者-护士口头沟通:家庭医疗保健中缺少的关键数据流
Maryam Zolnoori1,2, Sridevi Sridharan, Ali Zolnour3
1School of Nursing, Columbia University, New York, NY 10032, United States.
Journal of the American Medical Informatics Association : JAMIA
|October 17, 2023
概括
将患者-护士口头沟通整合到风险模型中,可以显著改善家庭医疗保健患者住院和急诊室访问的预测.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 临床风险预测预测
- 自然语言处理自然语言处理.
背景情况:
- 家庭医疗机构为数百万患有慢性疾病的老年患者提供服务.
- 这些患者中的很大一部分面临住院或急诊室的访问.
- 目前使用电子健康记录 (EHR) 数据的风险识别模型存在局限性.
研究的目的:
- 评估将音频录制的患者-护士口头沟通纳入现有的风险预测模型的影响.
- 提高在家庭医疗保健机构中识别高风险患者的准确性.
主要方法:
- 一项试点研究涉及126次患者与护士接触的音频记录.
- 风险模型是使用结构化EHR数据 (OASIS),临床笔记和口头沟通特征开发的.
- 使用自然语言处理技术来分析沟通模式.
主要成果:
- 一个支持矢量机器分类器实现了高性能 (AUC-ROC=99.68,F1-score=94.12).
- 整合口头沟通使F1得分提高了26%.
- 高风险患者表现出特定的沟通模式,包括情绪暗示和长时间的沉默.
结论:
- 患者与护士的口头沟通是风险预测模型的宝贵补充.
- 整合通信数据可以显著改善入院或急救访问风险的患者的识别.
- 一个进化的临床工作流程应该包含患者护士口头沟通的例行记录.
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