Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Abstract

Background: Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods: We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under 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: We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion: Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of 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: electronic health records; health equity; natural language processing; social determinants of health; social risk factors.

Figure 1.

Data-to-Knowledge-to-Action Workflow for Translating SDoH into Clinical Care

Figure 2.

Five SDoH categories describing the data workflow from data capture efforts to interventions.

Figure 3.

PRISMA 2020 Flow Diagram.

Figure 4.

UpSet Plot of NLP for SDoH Algorithms ~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, SVM, logistic regression, random forest, etc), excluding neural networks and pre-trained approaches.