发挥电子病历系统中社会决定因素数据的潜力:对筛选、链接、提取、分析和干预方法的范围审查。

IF 2.1 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Journal of Clinical and Translational Science Pub Date : 2024-10-10 eCollection Date: 2024-01-01 DOI:10.1017/cts.2024.571
Chenyu Li, Danielle L Mowery, Xiaomeng Ma, Rui Yang, Ugurcan Vurgun, Sy Hwang, Hayoung K Donnelly, Harsh Bandhey, Yalini Senathirajah, Shyam Visweswaran, Eugene M Sadhu, Zohaib Akhtar, Emily Getzen, Philip J Freda, Qi Long, Michael J Becich
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引用次数: 0

摘要

背景:健康的社会决定因素(SDoH),如社会经济和邻里关系,对健康结果有很大影响。然而,目前电子健康记录(EHR)中缺乏标准化的 SDoH 数据,这是研究和医疗质量的一大障碍:我们使用 "SDOH "和 "EHR "医学主题词进行了PubMed搜索,分析了五个领域的文章:1)SDoH 筛查和评估方法;2)SDoH 数据收集和记录;3)使用自然语言处理 (NLP) 提取 SDoH;4)SDoH 数据和健康结果;5)SDoH 驱动的干预措施:在确定的 685 篇文章中,有 324 篇进行了全面审查。主要研究结果包括:实施量身定制的筛查工具、将人口普查和理赔数据联系起来以建立 SDoH 背景档案、从笔记中提取 SDoH 的 NLP 系统、SDoH 与医疗保健利用率和慢性病控制之间的关联以及综合护理管理计划。然而,数据来源、工具和结果之间的差异凸显了标准化的必要性:尽管在确定患者社会需求方面取得了进展,但进一步制定标准、预测模型和协调干预措施对于 SDoH-EHR 整合至关重要。更多的数据库搜索可以加强此次范围界定审查。最终,广泛采集、分析多维 SDoH 数据并将其转化为临床护理对于促进健康公平至关重要。
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Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.

Background: Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.

Methods: We conducted a PubMed search using "SDOH" and "EHR" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results: Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.

Discussion: Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

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来源期刊
Journal of Clinical and Translational Science
Journal of Clinical and Translational Science MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
2.80
自引率
26.90%
发文量
437
审稿时长
18 weeks
期刊最新文献
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