Acceptance of Telemedicine by Specialists and General Practitioners in Cardiology Care: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: In the coming years, telemedicine will play a key role in health care. Especially in rural areas with weak infrastructure, telemedicine could be crucial to providing adequate and personalized medical care. OBJECTIVE: We investigated the acceptance and preferences of telemedicine among cardiologists, internists, and general practitioners. In addition, we aimed to identify knowledge, explore factors that influence the decision to adopt or reject this technology, and create starting points for demand-oriented further research. METHODS: We conducted a web-based survey between May 2021 and February 2022. The 34-item questionnaire covered a wide range of questions regarding knowledge, acceptance, and use of telemedicine in cardiology care. Participants (cardiologists, internists, and general practitioners) were contacted through their professional email addresses, through a QR code published in a regional health journal, and through X (formerly known as Twitter). After exclusion of questionnaires with missed values, multidimensional scaling and k-means clustering were performed. Participants were divided into 3 clusters (C1, C2, and C3) based on their attitudes toward telecardiology. C1 uses telemedicine for personal health and clinical practice; C2 shows reluctance; C3 uses telemedicine mainly clinically. RESULTS: We contacted 929 physicians. Of those 12.1% (112/929) completed the questionnaires. Participants were 56% male (54/97), 29% female (28/97), and 2% (2/97) diverse (median age 50 years). About 16% (18/112) of the respondents currently use telemedicine daily, 14.3% (16/112) 3-4 times a week, and 43% (48/112) did not use telemedicine at all. Overall, 35.1% (34/97) rated their knowledge of telemedicine as very good or good. Most of the respondents replied that telemedicine could support cardiology care in monitoring of blood pressure and electrocardiograms (57/97, 58.8%, both), consultation (57/97, 58.8%), and extending follow-up time (59/97, 60.8%). Reported barriers to implementation were mostly administration (26/97, 26.8%), inadequate reimbursement (25/97, 25.8%), and the purchase of technology equipment (23/97, 23.7%). Attitudes toward telemedicine in clinical practice were closely related to the number of patients being treated per annual quarter: C3 (median 1350, IQR 1000-1500) versus C1 (median 750, IQR 300-1200) and C2 (median 500, IQR 105-825). The differences between clinical caseloads of C1-C3 members were significant: C1 versus C2 (P=.03), C1 versus C3 (P=.02), and C2 versus C3 (P<.001). Most participants (87/112, 77.7%) would like to expand telemedicine approaches in the future. In the field of cardiology, the participants reported a high suitability of telemedicine. The willingness to train in telemedicine is high to very high for > 50% of the participants. CONCLUSIONS: Our results indicate generally moderate use but positive attitudes toward telemedicine among participating physicians with a higher clinical caseload. The lack of a structural framework seems to be a barrier to the effective implementation of telecardiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".