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创建一个映射的、机器可读的分类法,以促进从电子健康记录中提取健康数据的社会决定因素。

Creation of a Mapped, Machine-Readable Taxonomy to Facilitate Extraction of Social Determinants of Health Data from Electronic Health Records.

作者信息

Patel Svati B, Nguyen Nam T

机构信息

Veradigm, Chicago, IL.

Veradigm, San Francisco, CA.

出版信息

AMIA Annu Symp Proc. 2022 Feb 21;2021:959-968. eCollection 2021.

Abstract

A comprehensive, mapped social determinants of health (SDH) taxonomy in machine readable format was developed. The framework is intended to facilitate the extraction of social risk factors (SRFs) out of electronic health record (EHR) data and categorize them by domain and determinant to facilitate interpretation. Where other SDH frameworks have been focused on data input, this framework is designed from a data extraction point of view using EHR data in conjunction with published literature, public health policy documents, and official crosswalk maps. Frameworks developed by leading public health organizations were reviewed and synthesized to create an SDH framework comprising of 97 distinct SRFs organized under 16 domains. 2,329 medical codes across three standardized medical vocabularies, 10,896 free-text diagnosis descriptors, and 25 health insurance keywords were mapped to individual SRFs in the SDH framework. The framework is available as an open-source resource in Python dictionary or JSON format.

摘要

开发了一种机器可读格式的全面的、映射的健康社会决定因素(SDH)分类法。该框架旨在促进从电子健康记录(EHR)数据中提取社会风险因素(SRF),并按领域和决定因素对其进行分类,以方便解读。其他SDH框架侧重于数据输入,而本框架是从数据提取的角度设计的,使用EHR数据并结合已发表的文献、公共卫生政策文件和官方交叉映射图。对领先公共卫生组织开发的框架进行了审查和综合,以创建一个SDH框架,该框架由16个领域下组织的97个不同的SRF组成。来自三种标准化医学词汇表的2329个医学代码、10896个自由文本诊断描述符和25个健康保险关键词被映射到SDH框架中的各个SRF。该框架以Python字典或JSON格式作为开源资源提供。

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