DISSEMINATION, IMPLEMENTATION, AND IMPACT
Notice bibliographique
Résumé
Research Objective Effective health innovations are rarely scaled‐up—missing countless opportunities to improve health systems. Yet, little evidence exists on strategies to support large scale implementation of proven innovations. eConsult is an asynchronous online platform connecting primary care providers to specialists to discuss patients’ care, that improves access, patient experience, provider satisfaction, and reduces costs. eConsult services have been implemented in 16 countries, most widely in the United States and Canada. In Canada, each province has a single payer health system with universal coverage and efforts to scale‐up eConsult are underway in several provinces, with over 75 000 eConsult cases completed across the country thus far. This provides a unique opportunity to understand the strategies used to support scale‐up of this type of innovation across a range of contexts. Our objective is to present the strategies used to scale‐up eConsult in multiple jurisdictions. Study Design We conducted a multiple case study, where the cases were four Canadian provinces at varying stages of scaling‐up eConsult (Ontario, Quebec, Newfoundland and Labrador and Manitoba). Our data sources include document review (e.g., meeting minutes, reports, websites, presentations), meeting observations where provincial scale‐up of eConsult is discussed ( n = 26), semi‐structured interviews ( n = 25) and focus groups ( n = 2). We conducted deductive and inductive thematic analysis using Milat et al.’s scaling‐up conceptual framework (2016) to identify strategies and emerging themes. Population Studied Our study was conducted with key stakeholders (e.g., patients, family physicians, specialists, policy makers, decision makers) involved in the scaling‐up of eConsult in four Canadian provinces. Principal Findings We identified 6 common strategies used to support scaling‐up of eConsult in Canada : (a) Linking eConsult to research has helped develop and improve the innovation (e.g., technological infrastructure), establishing rigorous evidence to support the rationale for scaling‐up provincially, and maintaining eConsult activities during the scaling‐up process through funding and other resources; (b) building on a “coalition of the willing” by engaging partners who believe in the innovation and are willing to support it; (c) having physicians (both primary care and specialists) lead and adapt the innovation to their practice contexts, and relying on physician champions to help spread and scale the innovation; (d) engaging patient partners to strengthen the case to scale‐up eConsult through compelling patient stories and help drive scaling‐up efforts; (e) creating a “buzz” by engaging stakeholders across Canada, getting endorsements from organizations with influence, holding an annual national eConsult policy forum, presenting at various events, and building on other provinces’ experiences and expertise to help catalyze scale‐up efforts; (f) keeping the innovation simple to avoid becoming side‐tracked by peripheral challenges thus facilitating widespread adoption by physicians. Conclusions These strategies have been used in a range of contexts across Canada and are perceived by stakeholders as instrumental in scaling‐up efforts to date. The next step of this study is to foster cross‐level and cross‐jurisdictional knowledge exchange between key stakeholders. Implications for Policy or Practice This provides a new real‐world understanding of how stakeholders have tackled the challenges of scaling‐up eConsult and may be useful to inform future scaling‐up efforts for eConsult and similar innovations. Primary Funding Source Canadian Institutes of Health Research.
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) | 0,000 | 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 ».