Barriers and Enablers to Routine Clinical Implementation of Cardiac Implantable Electronic Device Remote Monitoring in Australia Among Cardiologists, Cardiac Physiologists, Nurses, and Patients: Interview Study
Notice bibliographique
Résumé
Background: Remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) has demonstrated many patient and health care system benefits. Consequently, the use of RM technology for patients with CIEDs is the standard of care as highlighted by international guidelines. However, RM has not yet been integrated into universal, routine clinical practice. Objective: We aimed to establish key stakeholder perspectives on the barriers and enablers of CIED RM implementation and to apply the theoretical domain framework to highlight the most effective approaches to facilitate routine adoption of CIED RM. Methods: This was a qualitative study, using semistructured interviews to explore the barriers and enablers encountered when incorporating RM into CIED management. Participants included cardiologists, cardiac clinicians or physiologists, nurses, and patients. Interviews were transcribed verbatim and analyzed through inductive thematic analysis and deductive approaches using the NVivo (version 14; QRS International Pty Ltd) software. The theoretical domains framework was used to understand barriers and enablers. In the inductive phase, we did not assess trustworthiness, as our thematic analysis approach views data as interpretations rather than objective truths. In the deductive phase, we conferred to ensure consistency in theme alignment with existing frameworks. Results: Interviews were conducted among 35 participants (16 patients, 10 cardiologists, and 9 cardiac physiologists and nurses). We identified 5 main themes and their associated subthemes, with 1 representing an enabler and 4 representing barriers. They were: (1) patient benefits from RM, such as improved CIED and cardiovascular management, and improved patient-centered care; (2) insufficient allocation of CIED RM resources, which included insufficient RM clinic funding and staffing, insufficient RM service reimbursement, and RM infrastructure and access inequity; (3) suboptimal management of data, which includes inconsistent RM alert interpretation and management, lack of guidance for clinic staff on RM data management, and an increased alert burden for clinics; (4) insufficient patient education post-CIED implant, this was attributed to limited health care worker availability and resulted in inadequate patient CIED and RM knowledge postimplant and patient anxiety associated with RM; and (5) patient engagement with CIED management, which included the need for increased patient interaction with RM alerts and the ability to share data with patients. These subthemes were mapped to 6 specific domains of the theoretical domains framework: "Beliefs About Capabilities," "Environmental Context and Resources," "Beliefs About Consequences," "Knowledge," "Emotions," and "Goals." Conclusions: Patient engagement was identified in 3 of the 5 themes describing barriers and enablers to RM. These highlight the importance of addressing patient engagement with RM to better implement and integrate the use of RM into routine clinical practice. Barriers and enablers extend across multiple domains and suggest that a multipronged approach is required to translate the gold standard care of RM to routine clinical practice.
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) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».