Acceptance of Telemedicine by Specialists and General Practitioners in Cardiology Care: Cross-Sectional Survey Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».