Improving the Uptake of Transcatheter Aortic Valve Replacement in Ontario
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".