Integration of Screening and Referral Tools for Social Determinants of Health and Modifiable Lifestyle Factors in the Epic Electronic Health Record System: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Recent health behavior interventions combine social determinants of health (SDOH) and biosocial perspectives, refocusing from the individual to broader societal contexts under the SDOH approach. Targeting modifiable health behaviors can significantly reduce disease risk and save up to 30% of health care costs. Screening tools individual and societal factors are being increasingly integrated into electronic health record (EHR) systems. Epic Systems is a leading, most adopted EHRs worldwide, with modules on SDOH and modifiable risk factors. Literature on integration and use of screening tools for SDOH and modifiable risk factors is lacking. OBJECTIVE: This review aimed to (1) summarize evidence integrating screening and referral tools for SDOH and modifiable risk factors including tobacco/alcohol use and physical inactivity in the Epic EHR; (2) synthesize findings on implementation methods, processes, clinical workflow modifications, and outcomes from integrating SDOH screening and referral tools in EHR systems; and (3) capture the major barriers, facilitators, and lessons learned across the included implementation studies. METHODS: We followed Joanna Briggs Institute's guidelines, Arksey and O'Malley's framework, and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We included 3 peer-reviewed databases, 2 gray literature sources, and citation chaining from related reviews and articles. RESULTS: All included studies (n=43) were from 24 US states; 26 reported quantitative methods, 12 reported mixed methods, and 6 were qualitative studies across various health settings. Most studies focused on adults, with the top 3 SDOH domains being housing, food and transportation, while physical activity, alcohol and tobacco were the most common modifiable risk factors. The top 3 SDOH domains were housing, food, and transportation, while physical activity, alcohol, and tobacco use were the most common risk factors targeted. Various screening tools were used, with the Protocol for Responding to & Assessing Patients' Assets, Risks, and Experiences (PRAPARE) being used the most across 6 studies. Most integrations used enhanced support or optimized workflows, with MyChart and Best Practice Advisories being the most used Epic modules and functions. MyChart was the most patient-accepted module. Screening and referral patient outcomes varied, with many studies presenting a significant impact. The most important integration facilitators included leadership support, dedicated clinical champions, and well-defined roles; barriers included clinician time, inefficient workflows, and the availability of devices and staff to ensure integrated tools' usage. CONCLUSIONS: Integration of SDOH and modifiable risk factors in the Epic EHR is being increasingly adopted to capture and target equitable health services. While Epic is among the most globally adopted EHRs, studies are primarily from the United States. Epic's SDOH wheel module is insufficient in capturing context-based SDOH and behavioral domains. Need for contextual standardization of SDOH and modifiable risk factor domains and EHR tools is being increasingly felt. Future research is needed for enhanced learning, improvement and use of built-in and customized tools, standardization, and processes for integrating targeted patient-centered interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".