Evidence map›Paper›PMID 34549294›Full record

ReviewJournal of the American Medical Informatics Association : JAMIA2021

Documentation and review of social determinants of health data in the EHR: measures and associated insights.

Michael Wang, Matthew S Pantell, Laura M Gottlieb, Julia Adler-Milstein

Open access · greenAbstract readReview
In one paragraph

Review in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 1 pooled it
19.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

73 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 citations in OpenAlex.

  1. Guideline
  2. Article
  3. Article
  4. Article
  5. What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Evaluating Redundancy and Biases in EHR Social Determinants of Health Data Screening.medRxiv : the preprint server for health sciences · 2026
    Article
  12. Article
  13. SDoH-GPT: using large language models to extract social determinants of health.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

13 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Michael WangCenter for Clinical Informatics & Improvement Research, University of California, San Francisco, San Francisco, California, USA.
Matthew S PantellDepartment of Pediatrics, University of California, San Francisco, San Francisco, California, USA.
Laura M GottliebCenter for Health and Community, University of California, San Francisco, San Francisco, California, USA.
Julia Adler-MilsteinCenter for Clinical Informatics & Improvement Research, University of California, San Francisco, San Francisco, California, USA.
University of California, San Francisco · US

Funding

Institutional Career Development Core SupplementKL2TR001870 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KANAYA, ALKA M., SARKAR, URMIMALA · 2016 to 2025
$19.7M
UCSF Learning Health System K12 Career Development ProgramK12HS026383 · AHRQ · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GONZALES, RALPH, SARKAR, URMIMALA · 2018 to 2022
$3.6M
AHRQ HHS K12 HS026383AHRQ HHS K12HS026383NCATS NIH HHS KL2 TR001870NCATS NIH HHS KL2TR001870
6 · The paper itself

Abstract

objectiveElectronic Health Records (EHRs) increasingly include designated fields to capture social determinants of health (SDOH). We developed measures to characterize their use, and use of other SDOH data types, to optimize SDOH data integration. MATERIALS AND

methodsWe developed 3 measures that accommodate different EHR data types on an encounter or patient-year basis. We implemented these measures-documented during encounter (DDE) captures documentation occurring during the encounter; documented by discharge (DBD) includes DDE plus documentation occurring any time prior to admission; and reviewed during encounter (RDE) captures whether anyone reviewed documented data-for the newly available structured SDOH fields and 4 other comparator SDOH data types (problem list, inpatient nursing question, social history free text, and social work notes) on a hospital encounter basis (with patient-year metrics in the Supplementary Appendix). Our sample included all patients (n = 27 127) with at least one hospitalization at UCSF Health (a large, urban, tertiary medical center) over a 1-year period.

resultsWe observed substantial variation in the use of different SDOH EHR data types. Notably, social history question fields (newly added at study period start) were rarely used (DDE: 0.03% of encounters, DBD: 0.26%, RDE: 0.03%). Free-text patient social history fields had higher use (DDE: 12.1%, DBD: 49.0%, RDE: 14.4%). DISCUSSION: Our measures of real-world SDOH data use can guide current efforts to capture and leverage these data. For our institution, measures revealed substantial variation across data types, suggesting the need to engage in efforts such as EHR-user education and targeted workflow integration.

conclusionMeasures revealed opportunities to optimize SDOH data documentation and review.

Indexed as

DocumentationSocial Determinants of HealthElectronic Health RecordsHumansSurveys and QuestionnairesWorkflowEHRSDOHsocial informatics

Identifiers

PMID34549294
PMCPMC8633631
OpenAlexW3200519641

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.