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.
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.
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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.
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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.
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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.
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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.
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.
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