社会风险因素与家庭医疗保健住院的风险有关:自然语言处理研究
Mollie Hobensack1, Jiyoun Song1, Sungho Oh2
1Columbia University School of Nursing, New York City, NY, USA.
Journal of the American Medical Directors Association
|August 8, 2023
概括
一个自然语言处理系统在家庭医疗保健笔记中确定了社会风险因素,发现大多数与住院治疗或急诊室访问的增加有关.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 公共卫生 公共卫生
背景情况:
- 社会风险因素显著影响患者的结果,包括住院和急诊室 (ED) 访问.
- 在临床笔记中准确识别这些因素对于家庭医疗保健 (HHC) 的有效干预至关重要.
研究的目的:
- 开发一种自然语言处理 (NLP) 系统,用于在HHC临床笔记中识别社会风险因素.
- 分析已识别的社会风险因素与随后的住院或急救诊所访问之间的关联.
主要方法:
- 一项回顾性队列研究利用了来自美国东北部HHC机构的临床笔记 (86,866次护理).
- 开发了一个NLP系统,使用专家定义的社会风险因素词汇.
- 调整后勤回归检查了NLP识别因素与住院/ED访问之间的联系.
主要成果:
- 该NLP系统实现了高性能 (F分数为0.91).
- 在33%的HHC发作中记录了社会风险因素.
- 物理和社会环境因素是最常见的;大多数确定的因素 (除住房/经济因素外) 与住院/ED访问率的增加相关.
结论:
- 在临床笔记中,HHC临床医生的文档捕捉了与不良健康事件相关的社会风险因素.
- NLP提供了一种可行的方法来识别这些因素,潜在地改善医疗保健沟通和风险预测.
- 进一步的研究可以利用NLP识别的社会风险来提高患者护理,减少住院/ED访问.
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