Evidence map›Paper›PMID 41670883›Full record

ArticleJournal of general internal medicine2026

Temporal Trends and Sociodemographic Differences in Telemedicine Utilization, 2019-2024.

Bingyu Zhang, Lu Li, Yiwen Lu, Jingchuan Guo, Jiang Bian, John B Salmon, Robert L Stetson, Michael A Horst, Srinivas K Sridhara, Mitchell D Schnall and 3 more

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

13 authors.

Bingyu ZhangCenter for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA.
Lu LiCenter for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA.
Yiwen LuCenter for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA.
Jingchuan GuoDepartment of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL, USA.
Jiang BianDepartment of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA.
John B SalmonUniversity of Pennsylvania Health System, Philadelphia, PA, USA.
Robert L StetsonUniversity of Pennsylvania Health System, Philadelphia, PA, USA.
Michael A HorstUniversity of Pennsylvania Health System, Philadelphia, PA, USA.
Srinivas K SridharaUniversity of Pennsylvania Health System, Philadelphia, PA, USA.
Mitchell D SchnallDepartment of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
Kevin B MahoneyOffice of the Chief Executive Officer, University of Pennsylvania Health System, Philadelphia, PA, USA.
David A AschDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Yong ChenCenter for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA. ychen123@pennmedicine.upenn.edu.ORCID 0000-0003-0835-0788

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTelemedicine usage surged during the COVID-19 pandemic, shaping how patients access healthcare services. Its sustained role in post-pandemic healthcare may uncover long-term trends and variations in utilization.

objectiveTo characterize telemedicine utilization from 2019 to 2024 and identify patient characteristics associated with telemedicine use. DESIGN AND

participantsThis retrospective cohort study analyzed outpatient visits across five hospitals within the University of Pennsylvania Health System (Penn Medicine) from January 1, 2019, to September 30, 2024. MAIN MEASURES: The primary outcome was whether each outpatient encounter was conducted via telemedicine (vs in-person). We used multivariable logistic regression clustering on patients to assess associations between telemedicine use and patient- and encounter-level characteristics, including demographics, insurance, patient portal use, income, clinical comorbidity, distance from care, provider specialty, encounter type, hospital index, and visit year. KEY

resultsThe study included 46,149,734 visits among 2,248,341 patients. Telemedicine surged from 1% of visits pre-pandemic to 17% in April 2020, stabilized at 8-13% through late 2020, and remained 4-6% from 2022 to 2024. Telemedicine use was lower among older adults (aOR 0.67 for ages 40-64; 0.47 for ≥ 65 vs. < 40 years), males (aOR 0.90), and new visits (aOR 0.46). Higher use was observed among unmarried (aOR 1.10), patient portal users (aOR 1.44), patients with fewer comorbidities, those living ≥ 15 miles from care (aOR 1.42 vs. < 5 miles), lower-income (< $50,000 aOR 1.06 vs. $50,000-$100,000), and primary care (aOR 1.23 vs. specialty care). Telemedicine use was lower among Non-Hispanic Black (aOR 0.88), Hispanic (aOR 0.94), and Asian (aOR 0.82) patients compared to Non-Hispanic White patients. Patterns differed by clinical condition, with disproportionately higher use among White patients with mental health disorders.

conclusionsTelemedicine use persists post-pandemic but reflects differences in access by age, race/ethnicity, socioeconomic status, and prior engagement with the patient portal. Targeted policies are needed to ensure equitable telemedicine adoption and accessibility for all patients.

Indexed as

COVID-19Patient Acceptance of Health CareTelemedicineAdultAgedDigital HealthFemaleHumansMaleMiddle AgedPennsylvaniaRetrospective StudiesSociodemographic FactorsSocioeconomic Disparities in HealthSocioeconomic FactorsYoung Adultelectronic health recordspatient characteristicstelemedicinetemporal trends

Identifiers

PMID41670883
PMCPMC13304034

What Socratic 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 Socratic graph.