Identifying and Overcoming Policy‐Level Barriers to the Implementation of Digital Health Innovation in Ontario: Qualitative Study
Notice bibliographique
Résumé
Research Objective Digital health innovation is the cornerstone of health care modernization internationally, yet innovations often fail to become part of routine practice. Translating evidence into practice remains a challenge despite the accumulating body of evidence regarding factors underlying success and failure. Innovations that establish proof of concept often lack a strategic plan for diffusion, which complicates adoption; however, high‐level policy barriers impede widespread adoption more broadly for even the most well‐positioned innovations. The aim of this study was to explore the challenges and opportunities in the implementation of digital health innovation to identify policy‐level actions to support uptake at scale. Study Design A qualitative study was conducted using a constructivist paradigm. Semi‐structured interviews were conducted with senior leadership members from organizations engaged in digital health planning and/or implementation activities in Ontario, Canada. Interviews were transcribed and analyzed inductively to the point of thematic saturation (point at which no new themes emerged). Findings are presented in terms of the key aspects of policy that require attention to best promote the adoption of digital health innovations at a system level. Population Studied The research team generated a list of key organizations involved in digital health activities in Ontario, Canada, which was circulated to a broader advisory group for further recommendations. Participants were required to hold a senior position within their respective organizations to ensure they could speak to system‐level barriers. Principal Findings Ten participants participated across nine interviews. Participants were from the Ontario Ministry of Health, Ontario Telemedicine Network, Canada Health Infoway, Ontario MD, and the MaRS Excellence in Clinical Innovation program and represented key organizations engaged in the governance, implementation, and adoption of digital health activities. The importance of strong leadership at an organizational and system level was viewed as critical to the successful implementation of digital health innovation, with an emphasis on establishing a culture of innovation. Participants described six key priorities requiring action at the policy level to catalyze digital health innovation, including: 1) a system‐level definition of innovation; 2) a clear overarching mission for digital health innovation; and 3) clearly defined organizational roles. Operationally, there is a need to 4) provide guidance on standardized processes; 5) shift the emphasis to change management; and 6) align funding structures. Conclusions Governments and decision makers play a central role in charting the digital course by developing a vision and creating the foundation upon which (currently fragmented) innovation activities will be modeled. Health care systems around the world and their stakeholders can reflect on these findings and recommendations to consider their utility in advancing local health innovation agendas. Implications for Policy or Practice These findings emphasize the critical role of the government in developing a shared vision and creating a foundation upon which digital health innovation activities can be implemented and adopted in health care systems. Achieving change may rely more on the thoughtful and efficient reconfiguration of existing practices as opposed to the addition of new resources. Primary Funding Source Ontario Ministry of Health Canada.
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 | 0,019 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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; 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 ».