Table 3 -.

SDoH-driven Translational Research: Deriving and Translating Actionable Knowledge into Clinical Care

SDoH Screening Tools	SDoH Data Collection and Documentation	NLP in SDoH	SDoH and Health outcomes	SDoH Interventions
SDoH Domains Assessed  • Most common elements screened were housing instability/insecurity, food insecurity, transportation needs, utility needs, financial resource strain, interpersonal safety issues.  • Other factors included social isolation, health literacy, education level, employment status.  • Tools targeted a range of personal and structural determinants. Screening Approaches  • Majority utilized home-grown, customized questionnaires rather than standardized validated instruments.  • Most common existing tools referenced were PRAPARE, CMS Accountable Health Communities, and NAM recommendations.  • Both active screening by staff and passive self-report methods used. Settings  • Implemented across varied clinical settings - primary care, EDs, inpatient units, community health centers.  • Some population-based screening at schools or by telephone. Effectiveness  • Broad feasibility shown across populations and settings to identify unmet social needs.  • More evidence needed regarding interventions to address identified needs. Key Next Steps  • Expanding regular screening with validated tools tied to follow-up resources.  • Increasing structural screening and community-clinical linkages.  • Integrating social needs data with EHRs and longitudinal outcomes.	Data Sources  • The most common external SDoH data sources linked to EHRs were census and community survey data (at both patient/individual and area/neighborhood levels), administrative data like claims records, and disease registries.  • Other sources included geospatial data, crime statistics, built environment data, education data, and proprietary population health databases.  • Some studies used qualitative interviews or surveys to gather additional patient SDoH information not found in the EHR. SDoH Elements  • External data provided various socioeconomic factors (income, education, employment, poverty level), neighborhood variables (segregation, safety, walkability), and health behaviors (diet, exercise, smoking).  • These complemented and expanded the individual-level SDoH data (food/housing security, transportation, interpersonal violence, etc.) captured directly in EHRs. Linkage Approaches  • Technical approaches to integrate external SDoH data with EHRs included geocoding patient addresses, aggregating community variables to patients, and direct linkage using unique identifiers.  • Integration enabled richer patient- and population-level SDoH data for risk stratification, outcomes research, social care coordination, and addressing health disparities. Gaps & Challenges  • More work is still needed to systematically collect, link, analyze, and act upon SDoH data from external sources together with EHR data.	Methods Used  • Most common NLP approaches were rule-based systems using regular expressions or lexicons and supervised machine learning models like CNNs, LSTMs, SVMs, and ensembles.  • Recent studies utilized pretrained contextual models like BERT which showed promising performance.  • Both generic NLP libraries (spaCy) and custom systems tailored to SDoH were tested. Performance  • Accuracy ranged widely based on model type and SDoH category but precision and recall generally over 80% for housing, occupation, and some social risks.  • Simple models had high precision but lower sensitivity in identifying key SDoH entities. Advanced neural networks improved recall.  • Overall, NLP could identify more SDoH data than structured EHR fields alone. Applications  • Inferring patients’ social risks, socioeconomic status, and exposures to guide interventions.  • Predicting outcomes like hospital readmissions, suicide risk, and future healthcare utilization.  • Enriching datasets for disparities research and population health surveillance. Limitations & Next Steps  • Better standardized corpora for model development and testing are needed.  • Methods to efficiently integrate NLP pipelines into clinical workflows rather than one-off analyses.  • Domain ontologies and shareable custom systems for SDoH extraction from notes.	Outcomes Assessed  • Most common outcomes examined were healthcare utilization (ED visits, hospitalizations, readmissions), chronic disease control (diabetes, hypertension, CVD), and COVID-19 severity.  • Other outcomes included cancer screening/treatment, obesity/BMI, mortality, mental health, substance use disorders, and patient-reported metrics. SDoH Factors  • Frequently measured SDoH elements were neighborhood disadvantage, food/housing insecurity, access to care/insurance, education, income, and social support.  • Race/ethnicity, immigrant status, and geographic factors were also analyzed as social determinants. Analytical Approaches  • Regression models evaluated associations between SDoH factors and outcomes. Some prediction models incorporated both clinical and social variables.  • Studies linked area-level SDoH data from census and surveys to individual-level EHR data. Key Findings  • Multiple studies found socioeconomic deprivation, insecure housing, lack of social support, and similar factors increased risk for adverse outcomes.  • But overall evidence was mixed, highlighting context-specific impacts. More research is needed on mechanisms.	Types of Interventions  • Most common interventions were social programs like community health initiatives, resource referrals/patient navigation services, integrated care management, and group education sessions.  • Some studies allocated additional resources like medical staff or equipment.  • A few tested policy changes or system-level practice transformations. Implementation Levels  • Interventions operated at the community, hospital/clinic, or health system level.  • Community programs enabled broader reach and incorporation of public health principles.  • Clinic-based initiatives allowed better integration with healthcare delivery. Components  • Roughly half emphasized family/peer support and social connections as part of the intervention. Outcomes  • Knowledge, self-efficacy, and resource utilization were commonly measured process outcomes.  • Clinical outcomes like chronic disease control, medication adherence, and health behaviors were assessed in some studies.  • Cost savings and healthcare utilization were less frequently examined. Effectiveness  • Most interventions showed some benefits but had limited generalizability due to small samples or single health systems.  • Overall evidence was mixed and highlighted implementation barriers regarding sustainability, adoption, and cost.