Cited by 338Telemedicine and Telehealth ImplementationDental Research and COVID-19Mobile Health and mHealth Applications

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index; none of the 64 sources its claims cite is checked for support yet.

The role of eHealth, telehealth, and telemedicine for chronic disease patients during COVID-19 pandemic: A rapid systematic review

Open access
Bitar, Hind, Alismail, Sarah·2021·DIGITAL HEALTH

Abstract

Objective: To summarize the current status of, and the current expert opinions, recommendation and evidence associated with the use and implementation of electronic health (eHealth), telemedicine, and/or telehealth to provide healthcare services for chronic disease patients during the COVID-19 pandemic. Materials and methods: We searched four electronic databases (PubMed, Google Scholar, Science Direct, and Web of Science Core Collection) to identify relevant articles published between 2019 and 2020. Searches were restricted to English language articles only. Two independent reviewers screened the titles, abstracts, and keywords for relevance. The potential eligible articles, papers with no abstract, and those that fall into the uncertain category were read in full text independently. The reviewers met and discussed which articles to include in the final review and reached a consensus. Results: We identified 51 articles of which 25 articles met the inclusion criteria. All included articles indicated the promising potential of eHealth, telehealth, and/or telemedicine solutions in delivering healthcare services to patients living with chronic diseases/conditions during the COVID-19 pandemic. We synthesized the main findings into ten usages and eight recommendations concerning the different activities for delivering healthcare services remotely for those living with chronic diseases/conditions in the era of COVID-19. Discussion and conclusions: There is limited evidence available about the effectiveness of such solutions. Further research is required during this pandemic to improve the credibility of evidence on telemedicine, telehealth, and/or eHealth-related outcomes for those living with chronic diseases.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This rapid systematic review synthesized 25 included articles on eHealth, telehealth, and/or telemedicine for chronic disease patients during the COVID-19 pandemic. All included articles indicated promising potential for remote care, but the authors report limited evidence on effectiveness and call for further rigorous research.

Key findings

  • Of 51 retrieved articles, 25 met inclusion criteria; all included articles indicated the promising potential of eHealth, telehealth, and/or telemedicine for delivering healthcare services to chronic disease patients during COVID-19.
  • Most articles used the term telemedicine (n = 17; 68%), followed by telehealth (n = 15; 53.8%), while two used eHealth (7.7%); some articles used more than one term.
  • The review synthesized main findings into eight recommendations for remote healthcare delivery; most included articles concentrated on continuity of care, ten on remote outpatient care, four on system integration, three on standardized transition, two on cost-effectiveness, two on privacy/data sharing, one on billing reimbursement, and one on simplicity.
  • The abstract reports ten usages and eight recommendations, while the results section describes six usages: follow-up visits, training, consultations, medications, communication, and caregiver support.

Methods

  • Rapid systematic review searching PubMed, Google Scholar, Science Direct, and Web of Science Core Collection on April 20, 2020, and again on May 15, 2020; searches were restricted to English-language articles using keywords “eHealth,” OR “telemedicine,” OR “telehealth,” AND “COVID-19,” OR “coronavirus,” AND “chronic disease.”
  • Inclusion criteria required focus on any chronic disease/condition, relation to COVID-19, use of eHealth/telemedicine/telehealth, and completion between December 1, 2019, and May 15, 2020; all study designs were included, such as preprints, commentary, and opinion papers.
  • Two independent reviewers screened titles, abstracts, and keywords; eligible, no-abstract, and uncertain articles were read in full text independently, then reviewers reached consensus; data extracted included author, year, paper type, country, population/chronic disease, technology type, current status, and outcomes.

Limitations

  • Stated: the review is not comprehensive because it was limited to English-language articles; the quality of included articles is not known; there is limited evidence on effectiveness, with no experimental design studies found; some chronic diseases were limited or under-represented; only “chronic disease” was used as a diagnosis-specific search keyword; 12 included articles (46.1%) had no specified country; the rapid timeframe means more articles were rapidly emerging; and the review was not registered on PROSPERO.
  • Evident from the text: the abstract reports “ten usages” while the results section reports “six usages,” indicating an internal inconsistency in the synthesis.
  • Evident from the text: most included articles were reviews and expert opinions, and quality of evidence was not assessed due to the absence of included trials, limiting the strength of conclusions about effectiveness.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1177/20552076211009396

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 64 cited sources checked · 0 supported · 0 flagged

Show all 25 claims

  • Probably not a substantial claim. 31
  • It may state two separate claims as one. 12
36 of 36
  • The paper relies on this without support.

    Social distancing is a key factor to slow the spread of COVID-19.

    “In response to the existential health threat of a global pandemic, social distancing is a key factor to slow the spread of COVID-19, with the result that medical visits have been changed from in-person to remote medical appointments.”
  • The authors state this as an assumption.

    The spread of the COVID-19 pandemic creates the need to rapidly assess and synthesize the evidence that has been generated.

    “The justification of the present paper is that the spread of the pandemic creates the need to rapidly assess and synthesize the evidence that has been generated.”
2 of 2

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Abstract reports ten usages, but Results report six

    Minor

    The abstract says the review synthesized ten usages, while the Results section identifies and lists six usages.

  • Reference 13 is classified as an included article despite its 2012 publication date

    Minor

    The overview lists reference 13 among the included articles, although the stated inclusion period begins in December 2019 and reference 13 is dated 2012.

  • Article 38 is listed as having no defined setting despite a US setting in the table

    Minor

    The overview includes reference 38 among articles with no defined setting, but Table 1 gives its country as the United States.

  • Reference 22 has conflicting paper-type classifications

    Minor

    The overview counts reference 22 as a review paper, whereas Table 1 calls it a brief correspondence.

  • eHealth percentage does not match the included-article count

    Minor

    Of 25 included articles, two represent 8.0%, not 7.7%.

    Reported 7.7 · recomputed 8

  • Reference 31 has conflicting paper-type classifications

    Minor

    The overview calls reference 31 a position statement, while Table 1 classifies it as a quantitative study.

  • Telehealth percentage does not match the included-article count

    Minor

    The review includes 25 articles, so 15 articles represent 60%, not 53.8%.

    Reported 53.8 · recomputed 60

  • Reference 30 has conflicting paper-type classifications

    Minor

    The overview calls reference 30 a quantitative study, while Table 1 classifies it as a clinical practice statement.

8 of 8

Citations

Cites

Flagged first, then by citations

Show all 38 references

Research works printed but not linked: 5

  1. Tricco AC, 2017, Rapid reviews to strengthen health policy and systems: a practical guide
  2. Płońska-Gościniak E, 2020, Kardiologia Polska
  3. Sarsak H., 2020, Int Phys Med Rehabil J, 5, 93
  4. Aziz A, 2020, Am J Perinatol
  5. Yusif S, 2014, J Health Inform Develop Countries, 8

Cited by

Sorted by citations

Items 101–200 of 338

Metadata

Authors
Bitar, H., & Alismail, S.
Affiliations
  • King Abdulaziz University (SA)
  • Claremont Graduate University (US)
Corresponding author
Bitar, Hind
Venue
DIGITAL HEALTH
Published
2021-01
Volume
7
Pages
20552076211009396-20552076211009396
ISSN
2055-2076 (ISSN-L)
OpenAlex
W3152586041
Cited by
325
References
38
Citations by year
2026: 26 · 2025: 38 · 2024: 33 · 2023: 44 · 2022: 171 · 2021: 12
FWCI
11.58
Type
journal article
Language
en
Open access
Locations
  • Digital Health (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
  • Health Sciences › Dentistry › General Dentistry › Dental Research and COVID-19
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
e-health, telemedicine, chronic disease, COVID-19 pandemic, remote healthcare delivery, telehealth implementation, healthcare access during pandemic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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