Evidence map›Paper›PMID 33779566›Full record

ArticleJMIR human factors2021

Identifying Barriers to and Opportunities for Telehealth Implementation Amidst the COVID-19 Pandemic by Using a Human Factors Approach: A Leap Into the Future of Health Care Delivery?

Tianyi Zhang, Jarrod Mosier, Vignesh Subbian

Abstract read
In one paragraph

Article in JMIR human factors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
–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

33 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  15. A step-by-step evaluation framework for telehealth programs in respiratory care.Canadian journal of respiratory therapy : CJRT = Revue canadienne de la therapie respiratoire : RCTR · 2025
    Article
  16. Article
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  18. Telehealth Utilization for Opioid Use Disorder: A Nationwide Analysis Before and After the COVID-19 Public Health Emergency Declaration.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2024
    Article
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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

3 authors.

Tianyi Zhang *Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, AZ, United States.ORCID https://orcid.org/0000-0003-0291-4917
Jarrod Mosier *Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, United States.ORCID https://orcid.org/0000-0002-5371-0845
Vignesh Subbian *Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, AZ, United States.ORCID https://orcid.org/0000-0001-9974-8382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The extensive uptake of telehealth has considerably transformed health care delivery since the beginning of the COVID-19 pandemic and has imposed tremendous challenges to its large-scale implementation and adaptation. Given the shift in paradigm from telehealth as an alternative mechanism of care delivery to telehealth as an integral part of the health system, it is imperative to take a systematic approach to identifying barriers to, opportunities for, and the overall impact of telehealth implementation amidst the current pandemic. In this work, we apply a human factors framework, the Systems Engineering Initiative for Patient Safety model, to guide our holistic analysis and discussion of telehealth implementation, encompassing the health care work system, care processes, and outcomes.

Indexed as

COVID-19healthcare systemhuman factorsimplementationSEIPStelehealth

Identifiers

PMID33779566
PMCPMC8041052

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.