糖尿病患者结构化和非结构化临床数据中的健康文档的社会决定因素:比较分析
Shivani Mehta1, Courtney R Lyles1,2,3,4, Anna D Rubinsky5
1Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States.
JMIR medical informatics
|August 25, 2023
概括
电子健康记录 (EHR) 往往错过了健康的社会决定因素 (SDOH). 用EMERSE等NLP工具分析的非结构化临床笔记,揭示了更全面的患者SDOH见解,改善了护理.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 公共卫生 公共卫生
背景情况:
- 由于数据限制和临床医生负担,电子健康记录 (EHR) 难以捕捉健康的全面社会决定因素 (SDOH).
- 患者临床笔记 (非结构化数据) 提供了潜在的更丰富的SDOH信息来源.
研究的目的:
- 评估EHR数据的准确性,反映患者对SDOH的实践经验.
- 探索高通量工具,PatientExploreR和EMERSE,用于将SDOH映射到结构化和非结构化患者数据.
主要方法:
- 利用专家评级者和SDOH的汇编来开发NLP语料库,用于分析UCSF (2018-2019) 糖尿病患者的临床笔记.
- 应用先进的EMERSE NLP查询解析器,在非结构化临床文本中识别与SDOH相关的字符串相似性.
主要成果:
- 对4283名糖尿病患者的分析表明,SDOH比最初在结构化EHR数据中捕获的更为重要.
- 埃默斯NLP确定了与社会联系,就业,财务和住房不安全,粮食不安全,教育和压力相关的患者SDOH信息.
结论:
- 非结构化临床笔记包含比结构化EHR数据更广泛的SDOH信息.
- 开发用户友好的工具,从非结构化数据中提取SDOH,可以提高对影响患者健康的社会因素的理解,并为临床决策提供信息.
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