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评估从电子健康记录中提取的患者层面社会风险数据的可比性:一项系统性综述。

Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review.

作者信息

Linfield Gaia H, Patel Shyam, Ko Hee Joo, Lacar Benjamin, Gottlieb Laura M, Adler-Milstein Julia, Singh Nina V, Pantell Matthew S, De Marchis Emilia H

机构信息

School of Medicine, University of California, San Francisco, CA, USA.

Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Berkeley Institute for Data Science, University of California, Berkeley.

出版信息

Health Informatics J. 2023 Jul-Sep;29(3):14604582231200300. doi: 10.1177/14604582231200300.

Abstract

To evaluate how and from where social risk data are extracted from EHRs for research purposes, and how observed differences may impact study generalizability. Systematic scoping review of peer-reviewed literature that used patient-level EHR data to assess 1 ± 6 social risk domains: housing, transportation, food, utilities, safety, social support/isolation. 111/9022 identified articles met inclusion criteria. By domain, social support/isolation was most often included ( = 68/111), predominantly defined by marital/partner status ( = 48/68) and extracted from structured sociodemographic data ( = 45/48). Housing risk was defined primarily by homelessness ( = 39/49). Structured housing data was extracted most from billing codes and screening tools ( = 15/30, 13/30, respectively). Across domains, data were predominantly sourced from structured fields ( = 89/111) versus unstructured free text ( = 32/111). We identified wide variability in how social domains are defined and extracted from EHRs for research. More consistency, particularly in how domains are operationalized, would enable greater insights across studies.

摘要

为评估如何以及从何处从电子健康记录(EHRs)中提取社会风险数据用于研究目的,以及观察到的差异可能如何影响研究的可推广性。对同行评审文献进行系统的范围综述,这些文献使用患者层面的EHR数据来评估1±6个社会风险领域:住房、交通、食品、公用事业、安全、社会支持/孤立。111/9022篇已识别文章符合纳入标准。按领域划分,社会支持/孤立最常被纳入(=68/111),主要由婚姻/伴侣状况定义(=48/68),并从结构化的社会人口数据中提取(=45/48)。住房风险主要由无家可归定义(=39/49)。结构化住房数据大多从计费代码和筛查工具中提取(分别为=15/30,13/30)。在各个领域,数据主要来自结构化字段(=89/111),而非非结构化的自由文本(=32/111)。我们发现,在如何从EHRs中定义和提取社会领域用于研究方面存在很大差异。更高的一致性,特别是在领域如何操作化方面,将有助于在各项研究中获得更深入的见解。

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