Using the Theoretical Domains Framework to Identify Barriers and Enablers to Implementing a Virtual Tertiary–Regional Telemedicine Rounding and Consultation for Kids (TRaC-K) Model: Qualitative Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND Inequities in access to health services are a global concern and a concern for Canadian populations living in rural areas. Rural children hospitalized at tertiary children’s hospitals have higher rates of medical complexity and experience more expensive hospitalizations and more frequent readmissions. The 2 tertiary pediatric hospitals in Alberta, Canada, have already been operating above capacity, but the pediatric beds at regional hospitals are underused. Such imbalance could lead to poor patient safety and increased readmission risk at tertiary pediatric hospitals and diminish the clinical exposure of regional pediatric health care providers, erode their confidence, and compel health systems to further reduce the capacity at regional sites. A Telemedicine Rounding and Consultation for Kids (TRaC-K) model was proposed to enable health care providers at Alberta Children’s Hospital to partner with their counterparts at Medicine Hat Regional Hospital to provide inpatient clinical care for pediatric patients who would otherwise have to travel or be transferred to the tertiary site. OBJECTIVE The aim of this study is to identify perceived barriers and enablers to implementing the TRaC-K model. METHODS This study was guided by the Theoretical Domains Framework (TDF) and used qualitative methods. We collected qualitative data from 42 participants from tertiary and regional hospitals through 31 semistructured interviews and 2 focus groups. These data were thematically analyzed to identify major subthemes within each TDF domain. These subthemes were further aggregated and categorized into barriers or enablers to implementing the TRaC-K model and were tabulated separately. RESULTS Our study identified 31 subthemes in 14 TDF domains, ranging from administrative issues to specific clinical conditions. We were able to merge these subthemes into larger themes and categorize them into 4 barriers and 4 enablers. Our findings showed that the barriers were lack of awareness of telemedicine, skills to provide virtual clinical care, unclear processes and resources to support TRaC-K, and concerns about clear roles and responsibilities. The enablers were health care providers’ motivation to provide care closer to home, supporting system resource stewardship, site and practice compatibility, and motivation to strengthen tertiary–regional relationships. CONCLUSIONS This systematic inquiry into the perceived barriers and enablers to the implementation of TRaC-K helped us to gain insights from various health care providers’ and family members’ perspectives. We will use these findings to design interventions to overcome the identified barriers and harness the enablers to encourage successful implementation of TRaC-K. These findings will inform the implementation of telemedicine-based interventions in pediatric settings in other parts of Canada and beyond. INTERNATIONAL REGISTERED REPORT RR2-10.1186/s12913-018-3859-2
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,010 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».