Evidence map›Paper›PMID 40126552›Full record

ArticleJournal of medical Internet research2025

Differences in Telemedicine Use for Patients With Diabetes in an Academic Versus Safety Net Health System: Retrospective Cohort Study.

Jonathan J Shih, Magdalene Kuznia, Sarah Nouri, Elizabeth B Sherwin, Kathryn E Kemper, Anna D Rubinsky, Courtney R Lyles, Elaine C Khoong

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Factors Affecting Telehealth Consultation in Diabetic Patients: A Systematic Scoping Review.International journal of telemedicine and applications · 2026
    Review
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

8 authors.

Jonathan J Shih *School of Medicine, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-8651-7806
Magdalene Kuznia *School of Nursing, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-2605-4416
Sarah NouriDivision of Palliative Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0001-7163-0366
Elizabeth B SherwinDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-7513-3823
Kathryn E KemperDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-2847-0325
Anna D RubinskyAcademic Research Services, Information Technology, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-8977-4779
Courtney R LylesCenter for Healthcare Policy and Research, University of California, Davis, Davis, CA, United States.ORCID https://orcid.org/0000-0002-1111-8595
Elaine C KhoongUCSF Action Research Center for Health Equity, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-2514-3572

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
VARC CoreP30AG044281 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Alexander Keliimoeanu Smith · 2013 to 2026
$19.9M
Translational Research Core - Health Engagement & Action Translational (HEAT)P30DK092924 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI Alyce Sophia Adams, HILARY Kessler SELIGMAN · 2011 to 2026
$9.2M
Designing a Remote Monitoring Intervention for Value & Equity in Hypertension (DRIVE-HTN)K23HL157750 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KHOONG, ELAINE · 2021 to 2025
$921k
CARES: A Caregiver-Facing Digital Symptom Assessment Tool for Marginalized Older Adults with Serious IllnessK76AG088348 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Sarah Nouri · 2024 to 2026
$728k
NCATS NIH HHS UL1 TR001872NHLBI NIH HHS K23 HL157750NIA NIH HHS K76 AG088348NIA NIH HHS P30 AG044281NIDDK NIH HHS P30 DK092924
6 · The paper itself

Abstract

backgroundThe COVID-19 public health emergency catalyzed widespread adoption of both video- and audio-only telemedicine visits. This proliferation highlighted inequities in use by age, race and ethnicity, and preferred language. Few studies have investigated how differences in health system telemedicine implementation affected these inequities.

objectiveThis study aims to describe patients who used telemedicine during the public health emergency and identify predictors of telemedicine use across 2 health systems with different telemedicine implementations.

methodsThis retrospective cohort study included adults with diabetes receiving primary care between July 2020 and March 2021 at 2 independent health systems in San Francisco, California. Participant sociodemographic characteristics, health information, and telemedicine utilization were acquired from electronic health records. The primary outcome was visit type (any audio or video telemedicine vs in-person only) during the study period. We used multivariable logistic regression to assess the association between visit type and key predictors associated with digital exclusion (age, race and ethnicity, preferred language, and neighborhood socioeconomic status), adjusting for baseline health. We included an interaction term to evaluate health system impact on each predictor and then stratified by health system (academic, which prioritized video-enabled visits, vs safety net, which prioritized audio-only visits).

resultsAmong 10,201 patients, we found higher odds of telemedicine use in the safety net system compared with the academic system (adjusted odds ratio [aOR] 2.94, 95% CI 2.48-3.48). Patients with younger age (18-34 years: aOR 2.55, 95% CI 1.63-3.97; 35-49 years: aOR 1.39, 95% CI 1.12-1.73 vs 75+ years) and Chinese-language preference (aOR 2.04, 95% CI 1.66-2.5 vs English) had higher odds of having a telemedicine visit. Non-Hispanic Asian (aOR 0.67, 95% CI 0.56-0.79), non-Hispanic Black (aOR 0.83, 95% CI 0.68-1), and Hispanic or Latine (aOR 0.76, 95% CI 0.61-0.95) patients had lower odds of having a telemedicine visit than non-Hispanic White patients. We found significant interactions between health system and age, race and ethnicity, and preferred language (P<.05). After stratifying by health system, several differences persisted in the academic system: non-Hispanic Asian (aOR 0.57, 95% CI 0.46-0.70) and Latine (aOR 0.67, 95% CI 0.50-0.91) patients had lower odds of a telemedicine visit, and younger age groups had higher odds (18-34 years: aOR 3.97, 95% CI 1.99-7.93; 35-49 years: aOR 1.86, 95% CI 1.36-2.56). In the safety net system, Chinese-speaking patients had higher odds of having a telemedicine visit (aOR 2.52, 95% CI 1.85-3.42).

conclusionsWe found disparities in telemedicine utilization by age, race and ethnicity, and preferred language, primarily in the health system that used more video visits. While telemedicine expanded rapidly recently, certain populations remain at risk for digital exclusion. These findings suggest that system-level factors influence telemedicine adoption and implementation decisions impact accessibility for populations at risk for digital exclusion.

Indexed as

Diabetes MellitusSafety-net ProvidersTelemedicineAdultAgedCOVID-19FemaleHumansMaleMiddle AgedRetrospective StudiesSan FranciscoSARS-CoV-2chronic diseaseCOVID-19delivery of health carediabeteshealth care disparitieshealth care utilizationolder adultsprimary health carepublic health emergencysafety net providerstelehealthtelemedicine

Identifiers

PMID40126552
PMCPMC11976178

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.