Evidence map›Paper›PMID 36858112›Full record

ArticleApplied clinical informatics2023

Telemedicine Use across Medical Specialties and Diagnoses.

Vivian Hsiao, Thevaa Chandereng, Jeffrey A Huebner, David T Kunstman, Grace E Flood, Amye J Tevaarwerk, David F Schneider

Open access · bronzeAbstract read
In one paragraph

Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
7.8field-weighted citation impact, top 2% 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

18 citing papers in PubMed, 27 citations in OpenAlex.

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  18. Impact of Transportation Barriers on Telehealth Use During the COVID-19 Pandemic.Inquiry : a journal of medical care organization, provision and financing
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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

7 authors at 2 institutions in 1 country.

Vivian HsiaoSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States.
Thevaa ChanderengUW Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, United States.
Jeffrey A HuebnerSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States.
David T KunstmanSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States.
Grace E FloodSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States.
Amye J TevaarwerkDepartment of Oncology, Mayo Clinic, Rochester, Minnesota, United States.
David F SchneiderSchool of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States.
University of Wisconsin–Madison · USMayo Clinic · US

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Christian Capitini · 1985 to 2026
$142.6M
University of Wisconsin Institute for Clinical and Translational ResearchUL1TR002373 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI ELIZABETH S BURNSIDE, Allan R. Brasier · 2017 to 2026
$75.9M
Institutional Clinical and Translational Science AwardUL1TR000427 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI DREZNER, MARC KENNETH · 2012 to 2016
$32.2M
University of Wisconsin Voice Research Training Program (T32)T32DC009401 · NIDCD · UNIVERSITY OF WISCONSIN-MADISON · PI Susan Lynn Thibeault · 2009 to 2026
$6.3M
NCATS NIH HHS UL1 TR000427NCATS NIH HHS UL1 TR002373NCI NIH HHS P30 CA014520NIDCD NIH HHS T32 DC009401
6 · The paper itself

Abstract

backgroundThe COVID-19 (coronavirus disease 2019) pandemic rapidly expanded telemedicine scale and scope. As telemedicine becomes routine, understanding how specialty and diagnosis combine with demographics to impact telemedicine use will aid in addressing its current limitations.

objectivesTo analyze the relationship between medical specialty, diagnosis, and telemedicine use, and their interplay with patient demographics in determining telemedicine usage patterns.

methodsWe extracted encounter and patient data of all adults who scheduled outpatient visits from June 1, 2020 to June 30, 2021 from the electronic health record of an integrated academic health system encompassing a broad range of subspecialties. Extracted variables included medical specialty, primary visit diagnosis, visit modality (video, audio, or in-person), and patient age, sex, self-reported race/ethnicity and 2013 rural-urban continuum code. Six specialties (General Surgery, Family Medicine, Gastroenterology, Oncology, General Internal Medicine, and Psychiatry) ranging from the lowest to the highest quartile of telemedicine use (video and audio) were chosen for analysis. Relative proportions of video, audio, and in-person modalities were compared. We examined diagnoses associated with the most and least frequent telemedicine use within each specialty. Finally, we analyzed associations between patient characteristics and telemedicine modality (video vs. audio/in-person, and video/audio vs. in-person) using a mixed-effects logistic regression model.

resultsA total of 2,494,296 encounters occurred during the study period, representing 420,876 unique patients (mean age: 44 years, standard deviation: 24 years, 54% female). Medical diagnoses requiring physical examination or minor procedures were more likely to be conducted in-person. Rural patients were more likely than urban patients to use video telemedicine in General Surgery and Gastroenterology and less likely to use video for all other specialties. Within most specialties, male patients and patients of nonwhite race were overall less likely to use video modality and video/audio telemedicine. In Psychiatry, members of several demographic groups used video telemedicine more commonly than expected, while in other specialties, members of these groups tended to use less telemedicine overall.

conclusionMedical diagnoses requiring physical examination or minor procedures are more likely to be conducted in-person. Patient characteristics (age, sex, rural vs. urban, race/ethnicity) affect video and video/audio telemedicine use differently depending on medical specialty. These factors contribute to a unique clinical scenario which impacts perceived usefulness and accessibility of telemedicine to providers and patients, and are likely to impact rates of telemedicine adoption.

Indexed as

COVID-19GastroenterologyTelemedicineAdultElectronic Health RecordsFemaleHumansInternal MedicineMale

Identifiers

PMID36858112
PMCPMC9977562
OpenAlexW4322722693

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.