Improving the Uptake of Transcatheter Aortic Valve Replacement in Ontario
Notice bibliographique
Résumé
Background The rising costs of healthcare delivery globally and the increasing research production rate create immense opportunities for implementing novel and more effective medical interventions that significantly benefit patient outcomes. However, the successful uptake of medical innovations is complex and often extremely contextual based on many sociopolitical and economic factors. These barriers to implementation can delay or derail new practices, procedures, products, and pharmaceuticals. Understanding the barriers to the successful implementation of medical innovations and the best practices and strategies to mitigate them is an extremely important area for translational research in health sciences. This study examines the barriers and potential challenges in implementing medical innovations and the possible preemptive measures that can be addressed early to increase the use of life-saving medical innovations. We consider the importance of appropriate, timely, and user-defined implementation techniques as a critical component of the successful uptake of medical innovations and use the uptake of transcatheter valve replacement therapy (TAVR), which is an alternative life-saving intervention for patients at risk for surgical complications, in Ontario, Canada as the practical case study of the challenges and potential instructive opportunities to establish best practices for systematic and effective innovation uptake. Methodology In addition to contextual and informal investigations, a small pilot survey of decision-makers across the University of Toronto-affiliated teaching hospitals helped compare and contrast the barriers to medical innovation uptake (in the literature) with the suggested barriers to the successful implementation of TAVR. This study looks primarily at the role of funding, physician preference, clinical guidelines, and patient comorbidities as decision-making factors contributing to TAVR uptake. The study also explores how the differences and similarities of TAVR uptake related to the decision-making factors above can help develop recommended strategies to address future implementation barriers. Results We observed that the decision-makers across the surveyed institutions refer patients with intermediate to high risk for surgery for TAVR. Funding and physician preference were identified as possible barriers to TAVR uptake, with underlying comorbidities of patients being a primary decision determinant for TAVR referral. Physician preferences were based on multiple factors such as clinical judgment, patient comorbidities, clinical guidelines, knowledge, TAVR, and surgical valve replacement skills. Conclusions To the best of our knowledge, this study is one of the first to use the Toronto Translational Thinking Framework to assess an innovative treatment uptake in the Ontario healthcare system. Although the study sample size was 11 and did not reflect the views of all decision-makers regarding TAVR use in Ontario, the survey reflected participants who directly make decisions regarding TAVR use, strengthening the credibility of the survey results. The insights from this study are intended to inform both the continued implementation of TAVR and to contribute to a broader field of investigation that aims to identify and operationalize the principles and best practices of translational research that may contribute to the efficacy of implementing other medical innovations in Ontario hospitals and beyond.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».