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Record W4289550043 · doi:10.1186/s12911-022-01952-0

Challenges of Telemedicine during the COVID-19 pandemic: a systematic review

2022· review· en· W4289550043 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBMC Medical Informatics and Decision Making · 2022
Typereview
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelemedicineCINAHLSystematic reviewReimbursementHealth careConfidentialityMEDLINEMedicineCritical appraisalScopusMedical educationFamily medicineNursingAlternative medicineComputer sciencePolitical sciencePsychological interventionComputer security

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic has prompted the decrease of in-person visits to reduce the risk of virus transmission. Telemedicine is an efficient communication tool employed between healthcare providers and patients that prevents the risk of exposure to infected persons. However, telemedicine use is not infallible; its users reported multiple issues that complicated the expansion of this technology. So, this systematic review aimed to explore the barriers and challenges of telemedicine use during the pandemic and to propose solutions for improving future use. METHODS: A systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. PubMed, Scopus, Web of Science, Academic Search Complete, CINAHL, Embase, and Science Direct were used to look for articles addressing barriers and challenges, in addition to articles proposing solutions. Studies were screened by title and abstract, followed by a full-text review. Risk of bias assessment was done using Critical Appraisal Skills Program for qualitative studies, Newcastle-Ottawa Scale for cross-sectional studies, and A MeaSurement Tool to Assess Systematic Reviews for systematic reviews. After the extraction of data, a narrative synthesis and analysis of the outcomes were performed. RESULTS: Among 1194 papers identified, only 27 studies were included. Barriers and challenges were assembled under 7 categories: technical aspects, privacy, data confidentiality and reimbursement, physical examination and diagnostics, special populations, training of healthcare providers and patients, doctor-patient relationship, and acceptability. Poor internet connection and lack of universal access to technology were among the technical barriers. Concerns about patient privacy and reimbursement hindered the use of telemedicine too. Physical examination and certain procedures were impossible to perform via telemedicine. Training both healthcare providers and patients was deficient. The doctor-patient relationship was troubled by telemedicine, and both healthcare providers and patients were reluctant to use telemedicine. CONCLUSION: Widespread use of telemedicine is still hampered by various barriers and challenges. Healthcare providers should work with various stakeholders to implement the proposed solutions. More research and policy changes are essential to optimize telemedicine utilization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.255
GPT teacher head0.488
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it