支持通过自然语言处理捕捉社会需求
Lewis J Frey1, Chanita Hughes Halbert2, Christopher D Blasy2
1From the Ralph H. Johnson Veterans Affairs Medical Center (VAMC), Charleston, SC (LJF, CDB); University of Southern California, Los Angeles, CA (CHH) lewis.frey@va.gov.
概括
医生在临床笔记中记录社会需求,但非结构化数据阻碍了护理. 自然语言处理可以从电子健康记录中提取健康的社会决定因素,提高文档一致性.
科学领域:
- 临床信息学 临床信息学
- 公共卫生 公共卫生
- 医疗保健服务研究 医疗服务研究
背景情况:
- 社会因素显著影响患者的发病率和死亡率.
- 家庭医生通常在临床笔记中记录社会需求.
- 电子健康记录 (EHR) 中关于社会因素的非结构化数据阻碍了临床行动.
研究的目的:
- 提出和评估自然语言处理 (NLP) 以在电子健康系统中识别社会需求.
- 为了使健康的社会决定因素 (SDOH) 的结构化和可重复的文档.
主要方法:
- 使用NLP技术来分析非结构化的临床笔记.
- 开发算法来提取预定义的社会需求类别.
- 评估提取信息的一致性和可重复性.
主要成果:
- 从非结构化的临床文本中,NLP成功地识别了社会需求.
- 拟议的方法有助于在不增加医生工作量的情况下获取结构化数据.
- 有潜力更好地识别和解决患者的社会需求.
结论:
- 在记录社会需求方面,NLP提供了一种可行的解决方案,以克服非结构化数据的挑战.
- 这种方法可以通过提供结构化的SDOH数据来增强临床决策.
- 自动化社会需求提取可以简化文档,改善患者护理.
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