Li Chenyu, Mowery Danielle L, Ma Xiaomeng, Yang Rui, Vurgun Ugurcan, Hwang Sy, Donnelly Hayoung K, Bandhey Harsh, Senathirajah Yalini, Visweswaran Shyam, Sadhu Eugene M, Akhtar Zohaib, Getzen Emily, Freda Philip J, Long Qi, Becich Michael J
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
J Clin Transl Sci. 2024 Oct 10;8(1):e147. doi: 10.1017/cts.2024.571. eCollection 2024.
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.
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.
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.
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.
健康的社会决定因素(SDoH),如社会经济状况和社区环境,对健康结果有强烈影响。然而,电子健康记录(EHR)中标准化SDoH数据的现状尚不完善,这是研究和医疗质量提升的重大障碍。
我们使用医学主题词“SDOH”和“EHR”在PubMed上进行检索,分析纳入的文章在五个领域的情况: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数据与健康结果;5)以SDoH为驱动的干预措施。
在检索到的685篇文章中,324篇进行了全面审查。主要发现包括实施定制化筛查工具、将人口普查和理赔数据相链接以构建情境化SDoH概况、利用NLP系统从病历中提取SDoH、SDoH与医疗保健利用及慢性病控制之间的关联,以及综合护理管理项目。然而,数据源、工具和结果的差异凸显了标准化的必要性。
尽管在识别患者社会需求方面取得了进展,但进一步制定标准、预测模型以及协调干预措施对于SDoH与EHR的整合至关重要。额外的数据库检索可以加强这项范围综述。最终,广泛收集、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。