Barriers and facilitators of digital health intervention uptake among healthcare providers in cardiovascular disease prevention: qualitative results from a systematic review
Notice bibliographique
Résumé
Abstract Background Evidence-based digital health interventions (DHIs) can aide cardiovascular disease (CVD) prevention (e.g., though telemonitoring, mHealth apps, etc). Despite this, the global uptake of DHIs in CVD prevention remains slow [1]. Healthcare providers (HCPs) often fulfil the role of gatekeepers and implementers of DHIs for patient care and their perspectives are valuable to understand barriers and facilitators of DHI uptake. Purpose The purpose of this study was to synthesise barriers and facilitators to DHI uptake in CVD (primary and secondary) prevention reported in the international scientific literature from the perspectives of HCPs. Methods We conducted a systematic review of qualitative and quantitative primary studies published January 2020 to May 2024 which explored HCPs’ perspectives of DHIs in CVD prevention. Excluded were non-peer reviewed articles, review papers, studies with non-generalisable feedback on a specific product, and studies with non-patient-facing digital tools. We retrieved records from Ovid MEDLINE, EMBASE, CINAHL, ACM Digital Library, Web of Science, Google scholar, IEEE Xplore, and Scopus. We used the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields to assess the quality of included studies [2]. For this abstract, we present partial findings of the systematic review pertaining to the qualitative data extracted from included studies. We extracted and coded reported barriers and facilitators according to an inductively created codebook and categorized each to one of four roadblocks described by the World Heart Federation roadmap on digital health in cardiology: ‘health system’, ‘health workforce’, ‘patient’ and ‘technological’ [1]. We used vote counting to describe which barriers and facilitators are most prevalent across studies. Results A total of 7,638 search results from the databases was retrieved. Following de-duplication and abstract/full-text screening, 110 studies reporting qualitative findings were included. Our findings represent a total of 2,594 HCP perspectives with their geographic distribution shown in figure 1. Study quality was median 80% (range 40-100%) out of a possible 100%. Across all categories ‘health system’, ‘health workforce’, ‘patient’ and ‘technological’, we extracted 82 barriers and 84 facilitators from the perspectives of HCPs. Table 1 shows all barriers and facilitators reported in ≥11 (10%) of included studies. Conclusion Our findings provide a global perspective on the current factors encouraging and hindering HCPs from adopting DHIs into their practice. Implementation scientists can use these findings to plan best approaches for efficient and long-term uptake of DHIs in CVD prevention. Overall, our findings align with the World Health Organization (WHO) Global Strategy on Digital Health 2020-2025 strategic objective 4: advocating for people-centred health systems that are enabled by digital health.Geographic distribution of respondents Barriers and facilitators
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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 | 0,031 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,008 |
| Bibliométrie | 0,001 | 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 | 0,001 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».