Enabling health technology innovation in Canada: Barriers and facilitators in policy and regulatory processes
Bibliographic record
Abstract
OBJECTIVES: Health care innovation and technologies can improve patient outcomes, but policies and regulations established to protect the public interest may become barriers to improvement of health care delivery. We conducted a scoping review to identify policy and regulatory barriers to, and facilitators of, successful innovation and adoption of health technologies (excluding pharmaceutical and information technologies) in Canada. METHODS: The review followed Arksey and O'Malley's methodology to assess the breadth and depth of literature on this topic and drew upon published and grey literature from 2000-2016. Four reviewers independently screened citations for inclusion. RESULTS: Sixty- seven full- text documents were extracted to collect facilitators and barriers to health technology innovation and adoption. The extraction table was themed using content analysis, and reanalyzed, resulting in facilitators and barriers under six broad themes: development, assessment, implementation, Canadian policy context, partnerships and resources. CONCLUSION: This scoping review identified current barriers and highlights numerous facilitators to create a responsive regulatory and policy environment that encourages and supports effective co-creation of innovations to optimize patient and economic outcomes while emphasizing the importance of sustainability of health technologies.
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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 | 0.044 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.030 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".