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Record W3160819010 · doi:10.2196/29251

Physicians’ Attitudes Toward Telemedicine Consultations During the COVID-19 Pandemic: Cross-sectional Study

2021· article· en· W3160819010 on OpenAlexvenueno aff
Noora Alhajri, Mecit Can Emre Simsekler, Buthaina Alfalasi, Mohamed Alhashmi, Majd AlGhatrif, Nahed Balalaa, Maryam Al Ali, Raghda Almaashari, Shammah Al Memari, Farida Al Hosani, Yousif Al Zaabi, Shereena Almazroui, Hamed Alhashemi, Ovidiu Constantin Baltatu

Bibliographic record

VenueJMIR Medical Informatics · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersKhalifa University of Science, Technology and Research
KeywordsTelemedicineMedicineLikert scaleSpecialtyPandemicDescriptive statisticsHealth careFamily medicineCross-sectional studyTeleradiologyMedical emergencyCoronavirus disease 2019 (COVID-19)PsychologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: To mitigate the effect of the COVID-19 pandemic, health care systems worldwide have implemented telemedicine technologies to respond to the growing need for health care services during these unprecedented times. In the United Arab Emirates, video and audio consultations have been implemented to deliver health services during the pandemic. OBJECTIVE: This study aimed to evaluate whether differences exist in physicians' attitudes and perceptions of video and audio consultations when delivering telemedicine services during the COVID-19 pandemic. METHODS: This survey was conducted on a cohort of 880 physicians from outpatient facilities in Abu Dhabi, which delivered telemedicine services during the COVID-19 pandemic between November and December 2020. In total, 623 physicians responded (response rate=70.8%). The survey included a 5-point Likert scale to measure physician's attitudes and perceptions of video and audio consultations with reference to the quality of the clinical consultation and the professional productivity. Descriptive statistics were used to describe physicians' sociodemographic characteristics (age, sex, designation, clinical specialty, duration of practice, and previous experience with telemedicine) and telemedicine modality (video vs audio consultations). Regression models were used to assess the association between telemedicine modality and physicians' characteristics with the perceived outcomes of the web-based consultation. RESULTS: Compared to audio consultations, video consultations were significantly associated with physicians' confidence toward managing acute consultations (odds ratio [OR] 1.62, 95% CI 1.2-2.21; P=.002) and an increased ability to provide patient education during the web-based consultation (OR 2.21, 95% CI 1.04-4.33; P=.04). There was no significant difference in physicians' confidence toward managing long-term and follow-up consultations through video or audio consultations (OR 1.35, 95% CI 0.88-2.08; P=.17). Video consultations were less likely to be associated with a reduced overall consultation time (OR 0.69, 95% CI 0.51-0.93; P=.02) and reduced time for patient note-taking compared to face-to-face visits (OR 0.48, 95% CI 0.36-0.65; P<.001). Previous experience with telemedicine was significantly associated with a lower perceived risk of misdiagnosis (OR 0.46, 95% CI 0.3-0.71; P<.001) and an enhanced physician-patient rapport (OR 2.49, 95% CI 1.26-4.9; P=.008). CONCLUSIONS: These results indicate that video consultations should be adopted frequently in the new remote clinical consultations. Previous experience with telemedicine was associated with a 2-fold confidence in treating acute conditions, less than a half of the perceived risk of misdiagnosis, and an increased ability to provide patients with health education and enhance the physician-patient rapport. Additionally, these results show that audio consultations are equivalent to video consultations in providing remote follow-up care to patients with chronic conditions. These findings may be beneficial to policymakers of e-health programs in low- and middle-income countries, where audio consultations may significantly increase access to geographically remote health services.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.088
GPT teacher head0.448
Teacher spread0.360 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations59
Published2021
Admission routes1
Has abstractyes

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