Evidence map›Paper›PMID 39478779›Full record

ArticleJournal of clinical and translational science2024

Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.

Chenyu Li, Danielle L Mowery, Xiaomeng Ma, Rui Yang, Ugurcan Vurgun, Sy Hwang, Hayoung K Donnelly, Harsh Bandhey, Yalini Senathirajah, Shyam Visweswaran and 6 more

Abstract readScoping Review
In one paragraph

Article in Journal of clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

33 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Guideline
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  15. Review
  16. Article
  17. Evaluating Redundancy and Biases in EHR Social Determinants of Health Data Screening.medRxiv : the preprint server for health sciences · 2026
    Article
  18. Article
  19. Using reasoning LLMs to extract SDOH events from clinical notes.Proceedings. IEEE International Conference on Healthcare Informatics · 2026
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Chenyu LiDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0001-7434-6571
Danielle L MoweryInstitute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0003-3802-4457
Xiaomeng MaInstitute of Health Policy Management and Evaluations, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-4165-3062
Rui YangCentre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.ORCID https://orcid.org/0009-0006-0597-7197
Ugurcan VurgunInstitute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0001-6021-4316
Sy HwangInstitute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0003-3851-9521
Hayoung K DonnellyDepartment of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0002-5633-1488
Harsh BandheyDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0002-4113-0616
Yalini SenathirajahDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Shyam VisweswaranDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Eugene M SadhuDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Zohaib AkhtarKellogg School of Management, Northwestern University, Evanston, IL, USA.ORCID https://orcid.org/0000-0002-2136-3754
Emily GetzenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Philip J FredaDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Qi LongInstitute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0003-0660-5230
Michael J BecichDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0001-5998-8074

Funding

University of Pittsburgh Clinical and Translational Science InstituteUL1TR001857 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2016 to 2025
$129.3M
Signature ProjectP50MH127511 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BROWN, GREGORY K · 2021 to 2025
$14.4M
Precision Approaches to Reduce Asthma Disparities with Electronic Health Record DataR01HL162354 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI HUBBARD, REBECCA, WEISSMAN, GARY · 2022 to 2025
$3.1M
MENTORING IN PATIENT-ORIENTED RESEARCH IN DEEP PHENOTYPING IN CARDIO-ONCOLOGYK24HL167127 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI Bonnie Ky · 2023 to 2026
$496k
NCATS NIH HHS UL1 TR001857NHLBI NIH HHS K24 HL167127NIMH NIH HHS P50 MH127511
6 · The paper itself

Abstract

Background: Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality. Methods: We conducted a PubMed search using "SDOH" and "EHR" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results: Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization. Discussion: Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Indexed as

electronic health recordshealth equitynatural language processingSocial determinants of healthsocial risk factors

Identifiers

PMID39478779
PMCPMC11523026

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.