使用自然语言处理从住院电子病历中提取健康的社会决定因素
Elliot A Martin1, Adam G D'Souza1, Vineet Saini2
1Centre for Health Informatics, University of Calgary, Calgary, Canada; Health Research Methods and Analytics, Alberta Health Services, Calgary, Canada.
Journal of epidemiology and population health
|November 15, 2024
概括
从电子医疗记录中提取健康的社会决定因素对于了解患者的结果至关重要. 新的方法从临床笔记中准确地捕捉语言障碍,就业,教育和生活情况,揭示结构化数据中的差距.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 临床研究 临床研究
背景情况:
- 健康的社会决定因素 (SDOH) 显著影响患者的健康结果.
- 电子医疗记录 (EMR) 系统为SDOH信息提供了一个潜在的数据源.
- 当前的EMR系统在捕获全面的SDOH数据方面可能存在局限性.
研究的目的:
- 开发和验证从住院EMR数据中提取健康的关键社会决定因素的方法.
- 为了比较从非结构化临床笔记中提取的SDOH与结构化数据字段.
- 评估EMR中SDOH文档的准确性和完整性.
主要方法:
- 针对四个SDOH:语言障碍,就业状态,教育和独自生活.
- 开发了使用卡尔加里住院EMR数据的提取算法.
- 在医院入院的单独队列上验证了算法.
- 从自由文本注释中提取的SDOH与结构化数据进行比较.
主要成果:
- 在自由文本注释中发现的语言障碍比结构化数据更多.
- 提取的SDOH元素获得了高正预测值 (PPV) (例如,语言障碍99%,单身生活98%).
- 在结构化数据和自由文本注释之间发现了显著的差异,结构化数据有时会误导.
结论:
- 从EMR自由文本注释中提取SDOH的方法显示出高精度.
- 结构化EMR数据往往缺乏全面的SDOH信息,并且可能具有误导性.
- 改进的EMR数据提取方法对于在患者护理中完全理解SDOH至关重要.
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