Evidence map›Paper›PMID 35446680›Full record

Observational studyJCO oncology practice2022

Factors Influencing Telemedicine Use at a Northern New England Cancer Center During the COVID-19 Pandemic.

Matthew B Mackwood, Tor D Tosteson, Jennifer A Alford-Teaster, Kevin M Curtis, Mary L Lowry, Jennifer A Snide, Wenyan Zhao, Anna N A Tosteson

Abstract readObservational Study
In one paragraph

Observational study in JCO oncology practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Trial
  2. Article
  3. Transforming patient-centered cancer care using telehealth: the MATCHES Center.Journal of the National Cancer Institute. Monographs · 2024
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

8 authors.

Matthew B MackwoodDepartment of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH.ORCID 0000-0002-4508-8267
Tor D TostesonDepartment of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH.ORCID 0000-0001-9302-4762
Jennifer A Alford-TeasterDartmouth Cancer Center, Dartmouth-Hitchcock Health, Lebanon, NH.ORCID 0000-0002-2600-446X
Kevin M CurtisConnected Care, Dartmouth-Hitchcock Health, Lebanon, NH.
Mary L LowryConnected Care, Dartmouth-Hitchcock Health, Lebanon, NH.
Jennifer A SnideDartmouth Cancer Center, Dartmouth-Hitchcock Health, Lebanon, NH.ORCID 0000-0001-6862-3050
Wenyan ZhaoDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH.ORCID 0000-0002-2222-377X
Anna N A TostesonDepartment of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH.ORCID 0000-0001-7718-8943

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Steven D Leach · 1985 to 2026
$91.3M
NCI NIH HHS P30 CA023108
6 · The paper itself

Abstract

purposeTo characterize the use of telemedicine for oncology care over the course of the COVID-19 pandemic in Northern New England with a focus on factors affecting trends.

methodsWe performed a retrospective observational study using patient visit data from electronic health records from hematology-oncology and radiation-oncology service lines spanning the local onset of the pandemic from March 18, 2020, through March 31, 2021. This period was subdivided into four phases designated as lockdown, transition, stabilization, and second wave. Generalized linear mixed regression models were used to estimate the effects of patient characteristics on trends for rates of telemedicine use across phases and the effects of visit type on patient satisfaction and postvisit ER or hospital admissions within 2 weeks.

resultsA total of 19,280 patients with 102,349 visits (13.1% audio-only and 1.4% video) were studied. Patient age (increased use in age < 45 and 85 years and older) and urban residence were associated with higher use of telemedicine, especially after initial lockdown. Recent cancer therapy, ER use, and hospital admissions in the past year were all associated with lower telemedicine utilization across pandemic phases. Provider clinical department corresponded to the largest differences in telemedicine use across all phases. ER and hospital admission rates in the 2 weeks after a telehealth visit were lower than those in in-person visits (0.7%

conclusionTelemedicine use in oncology during the COVID-19 pandemic varied according to the phase and patient, medical, and health system factors, suggesting opportunities for standardization of care and need for attention to equitable telemedicine access.

Indexed as

COVID-19NeoplasmsTelemedicineAgedAged, 80 and overCommunicable Disease ControlHumansMiddle AgedPandemicsPatient Satisfaction

Identifiers

PMID35446680
PMCPMC9287286

What OpenQuestion holds

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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.