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CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 253-263
DOI: 10.1055/s-0043-1768732
Section 11: Public Health and Epidemiology Informatics
Survey

Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities

Zhe He
1   School of Information, Florida State University, United States
2   Department of Behavioral Sciences and Social Medicine, College of Medicine, Florida State University, United States
,
Emily Pfaff
3   Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, United States
,
Serena Jingchuan Guo
4   Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, United States
,
Yi Guo
5   Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, United States
,
Yonghui Wu
5   Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, United States
,
Cui Tao
6   School of Biomedical Informatics, University of Texas Health Science Center at Houston, United States
,
Gregor Stiglic
7   Faculty of Health Science, University of Maribor, Slovenia
8   Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia
9   Usher Institute, University of Edinburgh, UK
,
Jiang Bian
5   Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, United States
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Summary

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.

Keywords

Social determinants of health - public health informatics - electronic health records - exposome


Publication History

Article published online:
26 December 2023

© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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