Evidence map›Paper›PMID 35717159›Full record

ArticleBMC primary care2022

Examining telehealth use among primary care patients, providers, and clinics during the COVID-19 pandemic.

Claudia Der-Martirosian, Karen Chu, W Neil Steers, Tamar Wyte-Lake, Michelle D Balut, Aram Dobalian, Leonie Heyworth, Neil M Paige, Lucinda Leung

Abstract read
In one paragraph

Article in BMC primary care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Pandemic Telehealth Utilization Among Ambulatory Care Diabetes Patients Managed at an Urban Teaching Hospital in Washington, DC.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2025
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  4. Quality of asynchronous webchatsThe Lancet regional health. Western Pacific · 2024
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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

9 authors.

Claudia Der-MartirosianVeterans Emergency Management Evaluation Center (VEMEC), US Department of Veterans Affairs, North Hills, CA, USA. claudia.der-martirosian@va.gov.ORCID 0000-0001-6841-198X
Karen ChuVeterans Emergency Management Evaluation Center (VEMEC), US Department of Veterans Affairs, North Hills, CA, USA.
W Neil SteersVeterans Emergency Management Evaluation Center (VEMEC), US Department of Veterans Affairs, North Hills, CA, USA.
Tamar Wyte-LakeVeterans Emergency Management Evaluation Center (VEMEC), US Department of Veterans Affairs, North Hills, CA, USA.
Michelle D BalutVeterans Emergency Management Evaluation Center (VEMEC), US Department of Veterans Affairs, North Hills, CA, USA.
Aram DobalianVeterans Emergency Management Evaluation Center (VEMEC), US Department of Veterans Affairs, North Hills, CA, USA.
Leonie HeyworthOffice of Connected Care, Veterans Health Administration, US Department of Veterans Affairs, Washington DC, USA.
Neil M PaigeUS Department of Veterans Affairs, Greater Los Angeles Healthcare System (VAGLAHCS), Los Angeles, CA, USA.
Lucinda LeungCenter for the Study of Healthcare Innovation, Implementation & Policy (CSHIIP), US Department of Veterans Affairs, Greater Los Angeles Healthcare System (VAGLAHCS), Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAt the onset of COVID-19, there was a rapid expansion of telehealth (video/telephone) visits to maintain delivery of primary care (PC) services at the Veterans Health Administration (VA). This study examines patient, provider, and site-level characteristics of any virtual and video-based care in PC.

methodsInterrupted time series (ITS) design was conducted using VA administrative/clinical, electronic healthcare data, 12-months before and 12-months after COVID-19 onset (set at March 2020) at the VA Greater Los Angeles Healthcare System (GLA), between 2019 and 2021. Patients with at least one visit to a VA PC clinic at GLA (n = 547,730 visits) were included in the analysis. The two main outcomes for this study were 1) any telehealth (versus in-person), as well as 2) video-based care (versus telephone). For the ITS analysis, segmented logistic regression on repeated monthly observations of any telehealth and video-based care was used.

resultsPercent telehealth and video use increased from 13.9 to 63.1%, and 0.3 to 11.3%, respectively, before to after COVID-19 onset. According to adjusted percentages, GLA community-based clinics (37.7%, versus 29.8% in hospital-based clinics, p < .001), social workers/pharmacists/dietitians (53.7%, versus 34.0% for PC clinicians, p < .001), and minority groups, non-Hispanic African Americans (36.3%) and Hispanics (34.4%, versus 35.3% for Whites, p < .001) were more likely to use telephone than video. Conversely, mental health providers (43.3%) compared to PC clinicians (15.3%), and women (for all age groups, except 75+) compared to men, were more likely to use video than telephone (all p's < .001).

conclusionsSince telehealth care provision is likely to continue after COVID-19, additional research is needed to identify which PC outpatient services are better suited for telephone (e.g., case management) versus video-based care (e.g., integrated mental health visits). Additionally, it is important to understand how all clinics can systematically increase access to both telephone- and video-based PC services, while ensuring equitable care for all patient populations.

Indexed as

COVID-19TelemedicineDelivery of Health CareFemaleHumansMalePandemicsPrimary Health CarePrimary CareTelehealthUS Veterans Health AdmininstrationVeteransVideo-Based Care

Identifiers

PMID35717159
PMCPMC9206131

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.