Evidence map›Paper›PMID 36053531›Full record

ArticleJAMA network open2022

Assessment of Clinician Diagnostic Concordance With Video Telemedicine in the Integrated Multispecialty Practice at Mayo Clinic During the Beginning of COVID-19 Pandemic From March to June 2020.

Bart M Demaerschalk, Andrew Pines, Richard Butterfield, Jack M Haglin, Tufia C Haddad, James Yiannias, Christopher E Colby, Sarvam P TerKonda, Steve R Ommen, Matthew S Bushman and 7 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 2 pooled it
9.1field-weighted citation impact, top 1% 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

32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Telehealth Research and Innovation for Veterans with Cancer: the THRIVE Center.Journal of the National Cancer Institute. Monographs · 2024
    Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 4 institutions in 1 country.

Bart M DemaerschalkDepartment of Neurology and Center for Digital Health, Mayo Clinic College of Medicine and Science, Phoenix, Arizona.
Andrew PinesMayo Clinic Alix School of Medicine, Phoenix, Arizona.
Richard ButterfieldDivision of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Phoenix, Arizona.
Jack M HaglinMayo Clinic Alix School of Medicine, Phoenix, Arizona.
Tufia C HaddadDepartment of Medical Oncology and Center for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
James YianniasDepartment of Dermatology and Center for Digital Health, Mayo Clinic College of Medicine and Science, Phoenix, Arizona.
Christopher E ColbyDepartment of Pediatric and Adolescent Medicine, Neonatology, Critical Care, and Center for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Sarvam P TerKondaDepartment of Surgery and Plastic and Reconstructive Surgery, Center for Digital Health, Mayo Clinic College of Medicine and Science, Jacksonville, Florida.
Steve R OmmenDepartment of Cardiovascular Medicine and Center for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Matthew S BushmanCenter for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Troy G LokkenCenter for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Rebecca N BlegenCenter for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Mekenzie D HoffCenter for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Jordan D CoffeyCenter for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Greg S AnthonyCenter for Digital Health, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.
Nan ZhangDivision of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Phoenix, Arizona.
Diagnostic Accuracy of Telemedicine Utilized at Mayo Clinic Alix School of Medicine Study Group Investigators
Mayo Clinic · USMayo Clinic in Arizona · USJacksonville College · USMayo Clinic in Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: There was a shift in patient volume from in-person to video telemedicine visits during the COVID-19 pandemic. Objective: To determine the concordance of provisional diagnoses established at a video telemedicine visit with diagnoses established at an in-person visit for patients presenting with a new clinical problem. Design, Setting, and Participants: This is a diagnostic study of patients who underwent a video telemedicine consultation followed by an in-person outpatient visit for the same clinical problem in the same specialty within a 90-day window. The provisional diagnosis made during the video telemedicine visit was compared with the reference standard diagnosis by 2 blinded, independent medical reviewers. A multivariate logistic regression model was used to determine factors significantly related to diagnostic concordance. The study was conducted at a large academic integrated multispecialty health care institution (Mayo Clinic locations in Rochester, Minnesota; Scottsdale and Phoenix, Arizona; and Jacksonville, Florida; and Mayo Clinic Health System locations in Iowa, Wisconsin, and Minnesota) between March 24 and June 24, 2020. Participants included Mayo Clinic patients residing in the US without age restriction. Data analysis was performed from December 2020 to June 2021. Exposures: New clinical problem assessed via video telemedicine visit to home using Zoom Care Anyplace integrated into Epic. Main Outcomes and Measures: Concordance of provisional diagnoses established over video telemedicine visits compared against a reference standard diagnosis. Results: There were 2393 participants in the analysis. The median (IQR) age of patients was 53 (37-64) years; 1381 (57.7%) identified as female, and 1012 (42.3%) identified as male. Overall, the provisional diagnosis established over video telemedicine visit was concordant with the in-person reference standard diagnosis in 2080 of 2393 cases (86.9%; 95% CI, 85.6%-88.3%). Diagnostic concordance by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision chapter ranged from 64.7% (95% CI, 42.0%-87.4%) for diseases of the ear and mastoid process to 96.8% (95% CI, 94.7%-98.8%) for neoplasms. Diagnostic concordance by medical specialty ranged from 77.3% (95% CI, 64.9%-89.7%) for otorhinolaryngology to 96.0% (92.1%-99.8%) for psychiatry. Specialty care was found to be significantly more likely than primary care to result in video telemedicine diagnoses concordant with a subsequent in-person visit (odds ratio, 1.69; 95% CI, 1.24-2.30; P < .001). Conclusions and Relevance: This diagnostic study of video telemedicine visits yielded a high degree of diagnostic concordance compared with in-person visits for most new clinical concerns. Some specific clinical circumstances over video telemedicine were associated with a lower diagnostic concordance, and these patients may benefit from timely in-person follow-up.

Indexed as

COVID-19TelemedicineAmbulatory Care FacilitiesCOVID-19 TestingFemaleHumansMaleMiddle AgedPandemicsReferral and Consultation

Identifiers

PMID36053531
PMCPMC9440401
OpenAlexW4294266276

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.