Cited by 252Electronic Health Records SystemsMedical Coding and Health InformationPrimary Care and Health Outcomes

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index; none of the 25 sources its claims cite is checked for support yet.

Adoption of Social Determinants of Health EHR Tools by Community Health Centers

Open access

Abstract

PURPOSE: This pilot study assessed the feasibility of implementing electronic health record (EHR) tools for collecting, reviewing, and acting on patient-reported social determinants of health (SDH) data in community health centers (CHCs). We believe it is the first such US study. METHODS: We implemented a suite of SDH data tools in 3 Pacific Northwest CHCs in June 2016, and used mixed methods to assess their adoption through July 2017. We modified the tools at clinic request; for example, we added questions that ask if the patient wanted assistance with SDH needs. RESULTS: Social determinants of health data were collected on 1,130 patients during the study period; 97% to 99% of screened patients (n = 1,098) had ≥1 SDH need documented in the EHR, of whom 211 (19%) had an EHR-documented SDH referral. Only 15% to 21% of patients with a documented SDH need indicated wanting help. Examples of lessons learned on adoption of EHR SDH tools indicate that clinics should: consider how to best integrate tools into existing workflow processes; ensure that staff tasked with SDH efforts receive adequate tool training and access; and consider that timing of data entry impacts how and when SDH data can be used. CONCLUSIONS: Our results indicate that adoption of systematic EHR-based SDH documentation may be feasible, but substantial barriers to adoption exist. Lessons from this study may inform primary care providers seeking to implement SDH-related efforts, and related health policies. Far more research is needed to address implementation barriers related to SDH documentation in EHRs.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This pilot implemented EHR tools for collecting, reviewing, and acting on patient-reported social determinants of health (SDH) data in 3 Pacific Northwest community health centers. It found systematic EHR-based SDH documentation feasible but identified substantial adoption barriers, with high positive screening rates but low referral and patient-requested-help rates.

Key findings

  • SDH data were collected on 1,130 patients, representing 4% to 18% of adult patients with an ambulatory visit at the pilot clinics; 97% to 99% of screened patients had ≥1 SDH need documented, and 211 (19%) had an EHR-documented SDH referral.
  • After questions about wanting help were added, 15% of Clinic A patients and 21 of Clinic B patients with a documented SDH need requested help.
  • Facilitators included a trusted, EHR-savvy clinic champion, ability to customize EHR views, and reporting requirements; barriers included fragmented patient data, added workflow difficulty, paper-based data entry, time-consuming referrals, and unmanageable follow-up workload before desire-for-help questions were added.
  • The clinics successfully documented SDH in the EHR and continued post-study, but referral documentation was especially challenging.

Methods

  • Concurrent mixed-methods pilot at 3 OCHIN member community health centers in the Pacific Northwest; Epic-based SDH tools built on PRAPARE were deployed in June 2016 and assessed through July 2017, extended from 12 to 13 months.
  • Quantitative EHR data described adult patients with documented SDH data or ≥1 ambulatory visit, including demographics, SDH needs, referrals, and problem list diagnoses.
  • Qualitative data included 6 days of workflow observation, 24 interviews with care team members, and observations of clinic decision-maker discussions; analysis used deductive and inductive coding and the constant comparative method.

Limitations

  • The paper states it could not calculate SDH screening documentation rates among targeted patients because target populations changed, change dates were unclear, and target criteria were not easily extracted from the EHR; screened vs unscreened differences therefore cannot be interpreted as related to EHR-based SDH screening.
  • The paper states screened-patient counts may be low and SDH need prevalence may be higher than in the overall population because some paper surveys were likely not entered into the EHR; referral rates are likely underestimated because referrals were often documented in chart notes rather than discrete fields.
  • The paper states it could not assess changes in SDH status, care quality, or biomarkers; could not quantify how often the SDH summary was accessed or by whom; the SDH screening measures have not been validated as predictive of specific outcomes; and the single EHR system limits transferability. Evident from the text: this was a small 3-clinic pilot with clinic-selected pragmatic target groups and no control or comparison group.

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Integrity signals

Registry

Crossref · 10.1370/afm.2275

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 25 cited sources checked · 0 supported · 0 flagged

Show all 30 claims

  • It may state two separate claims as one. 26
  • Probably not a substantial claim. 23
36 of 36
  • The authors state this as an assumption.

    The study is the first US study on the collection of SDH data using EHR-based tools in community health centers.

    “We believe it is the first such US study.”
    “We believe this is the first US study on the collection of SDH data using EHR-based tools in CHCs.”
  • The paper relies on this without support.

    Most health care systems document and track most patient information in EHRs.

    “Since most health care systems document and track most patient information in EHRs, SDH data should also be in EHRs.”
2 of 2

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Citations

Cites

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Research works printed but not linked: 1

  1. U. S. Department of Health & Human Services (DHHS). Healthy People 2010, 2nd Edition. Washington, DC: US Government Printing Office; 2000.

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Metadata

Authors
Gold, R., Bunce, A., Cowburn, S., Dambrun, K., Dearing, M., Middendorf, M., Mossman, N., Hollombe, C., Mahr, P., Melgar, G., Davis, J., Gottlieb, L., & Cottrell, E.
Affiliations
  • Kaiser Permanente Center for Health Research (US)
  • Ochin (US)
  • Multnomah County Health Department (US)
  • University of California, San Francisco (US)
Venue
The Annals of Family Medicine
Published
2018-09
Volume
16
Issue
5
Pages
399-407
ISSN
  • 1544-1709 (ISSN-L)
  • 1544-1717
OpenAlex
W2891941547
Cited by
252
References
55
Citations by year
2026: 21 · 2025: 32 · 2024: 25 · 2023: 38 · 2022: 38 · 2021: 42 · 2020: 31 · 2019: 23 · 2018: 2
FWCI
31.11
Type
journal article
Language
en
Open access
Locations
  • The Annals of Family Medicine (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
National Institute of Diabetes and Digestive and Kidney Diseases (R18DK105463)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Health Sciences › Medicine › Family Practice › Primary Care and Health Outcomes
Keywords
social determinants of health, electronic health records, community health centers, social needs screening, referral documentation, implementation barriers, primary care, health policy
MeSH terms
Community Health Centers, Documentation, Feasibility Studies, Health Plan Implementation, Humans, Pilot Projects, Referral and Consultation, Electronic Health Records, Social Determinants of Health

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

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