Health Care Professionals’ Use of Digital Technology in the Secondary Prevention of Cardiovascular Disease in Austria: Online Survey Study
Notice bibliographique
Résumé
Background: Advances in digital technology, such as health apps and telerehabilitation systems, offer promising treatment modalities in the secondary prevention of cardiovascular disease. However, the successful adoption of digital technology in clinical practice depends on a variety of factors. A comprehensive understanding of the influencing factors on digital technology usage in health care can support the complex implementation process of digital technology in clinical practice. Objective: The aim of this study was to identify barriers and facilitators of digital technology usage in cardiovascular disease secondary prevention from the perspective of health care professionals, and to explore whether certain characteristics of health care professionals are related to the current usage of digital technology in clinical practice. Methods: We conducted an exploratory online survey, inquiring about the perspectives and uses of digital technologies in cardiovascular disease secondary prevention. We developed an original questionnaire to address the study aim. The survey invitation was distributed among health care professionals from November 2021 to February 2022, via all cardiac rehabilitation centers, all community-based disease management services for patients with chronic heart failure, and all relevant national health care professional associations in Austria. Qualitative survey data were analyzed using thematic content analysis. Quantitative survey data were analyzed using descriptive statistics, group comparison tests, and association statistics. Results: Overall, 125 health care professionals (mean age 41, SD 11 y; n=80, 64% females) across different professions and settings, including cardiac rehabilitation phases I through IV, were recruited. General readiness for using digital technologies in the care of cardiac patients was high, but only 65 (52%) respondents reported doing so. The top 3 rated barriers to digital technology use were poor user-experience of devices and apps, lack of cost coverage, and low digital competence of patients. The top 3 rated potential application areas for digital technology were organization and appointment planning, documenting treatments, and creating personalized treatment plans. The top 3 rated facilitators for digital technology use were assurance of patient safety, assurance of patients' privacy, and availability of technical support. Greater personal use of digital technology, younger age, and higher technology affinity of health care professionals was associated with higher readiness to use digital technology with cardiac patients. Conclusions: While there is interest in digital technology for the secondary prevention of cardiovascular disease in Austria, barriers to uptake need to be addressed. Our findings may inform the design and implementation of future digitalization projects.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».