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. 2024 Oct 10;8(1):e147.
doi: 10.1017/cts.2024.571. eCollection 2024.

Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions

Chenyu Li  1 , Danielle L Mowery  2   3 , Xiaomeng Ma  4 , Rui Yang  5 , Ugurcan Vurgun  2 , Sy Hwang  2 , Hayoung K Donnelly  6 , Harsh Bandhey  7 , Yalini Senathirajah  1 , Shyam Visweswaran  1 , Eugene M Sadhu  1 , Zohaib Akhtar  8 , Emily Getzen  3 , Philip J Freda  7 , Qi Long  2   3 , Michael J Becich  1
Affiliations

Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions

Chenyu Li et al. J Clin Transl Sci. .

Abstract

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.

Keywords: Social determinants of health; electronic health records; health equity; natural language processing; social risk factors.

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Conflict of interest statement

There are no conflicts of interest to declare.

Figures

Figure 1.
Figure 1.
Data-to-knowledge-to-action workflow for translating social determinants of health (SDoH) into clinical care. EHR = electronic health record.
Figure 2.
Figure 2.
Five social determinants of health categories describing the data workflow from data capture efforts to interventions. EHR = electronic health records; NLP = natural language processing.
Figure 3.
Figure 3.
PRISMA 2020 flow diagram. EHR = electronic health records; NLP = natural language processing.
Figure 4.
Figure 4.
Heatmap showing the frequency of association between the top 10 social determinants of health (SDoH) elements and the top five data collection methods.
Figure 5.
Figure 5.
UpSet Plot of natural language processing for social determinants of health (SDoH) algorithms ∼electronic health record (EHR) integration. *Outlining the distribution of papers in which each approach/method individually and in combination was described in the study. *Supervised machine learning includes traditional machine learning methods (naive bayes, support vector machine, logistic regression, random forest, etc), excluding neural networks and pretrained approaches.
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References

    1. Braveman P. ’Introduction: What Influences Health? And What Influences the Influences?’, The Social Determinants of Health and Health Disparities. Online edn. New York: Oxford Academic; 2023. doi: 10.1093/oso/9780190624118.003.0001. - DOI
    1. Marmot M. Social determinants of health inequalities. Lancet. 2005;365(9464):1099–1104. - PubMed
    1. WHO Commission on Social Determinants of Health, World Health Organization. Closing the Gap in a Generation: Health Equity Through Action on the Social Determinants of Health : Commission on Social Determinants of Health Final Report. World Health Organization; 2008. https://www.who.int/publications/i/item/WHO-IER-CSDH-08.1.
    1. Social determinants of health. (https://health.gov/healthypeople/priority-areas/social-determinants-health) Accessed January 26, 2024.
    1. Barragan, [d-Ca-44] ND. Improving Social Determinants of Health Act of 2021, 2021. (https://www.congress.gov/bill/117th-congress/house-bill/379/text) Accessed January 26, 2024.

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