Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities.
Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities.
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DOI：
10.1055/s-0043-1768732
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发表时间：
2023-08
影响因子：
--
通讯作者：
Bian J
中科院分区：
文献类型：
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作者：
He Z;Pfaff E;Guo SJ;Guo Y;Wu Y;Tao C;Stiglic G;Bian J
目标：总结利用现实世界数据（例如电子健康记录（EHR）和健康社会决定因素（SDoH））促进公共和人口健康和健康公平的最新方法和应用，并确定成功、挑战和可能的解决方案。 方法：在本意见综述中，基于基于社会生态模型的概念框架，我们调查了数据源和最新的信息学方法，这些方法能够利用 SDoH 以及现实世界的数据来支持公共卫生和临床健康应用，包括帮助设计公共卫生干预措施、加强风险分层以及预测未满足的社会需求。 结果：除了总结数据源之外，我们还发现了现有 EHR 系统中捕获 SDoH 数据的差距，以及利用信息学方法从结构化和非结构化 EHR 数据或通过与公共调查和环境数据链接收集 SDoH 信息的机会。我们还调查了最近开发的用于标准化 SDoH 信息的本体论和方法，其中将 SDoH 纳入疾病风险分层、公共卫生危机预测和制定定制干预措施。 结论：为了利用 SDoH 的真实世界数据实现有效的公共卫生和临床应用，有必要开发涉及激励、政策和培训的非技术解决方案以及技术解决方案，例如集成到临床工作流程中的新型社会风险管理工具。最终，SDoH 支持的社会风险管理、疾病风险预测以及开发 SDoH 定制的疾病预防和管理干预措施有可能改善人口健康、减少差距并提高健康公平。
Objective : To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions. Methods : In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs. Results : Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions. Conclusions : To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.