Ten-Year Anniversary of the Advisory Panel on Healthcare Innovation Report: Assessing Progress and What Is Left to Do
Bibliographic record
Abstract
Federal Health Minister Rona Ambrose created the Advisory Panel on Healthcare Innovation, asking them to identify five priority innovation areas that would improve accessibility, quality of care and health spending. Their 2015 report found fragmented systems, a lack of collaboration across jurisdictions to share learnings and best practices and undercapitalized technological advancements, among other barriers to spreading successful innovation. Ten years later, we review the report's main recommendations and examine progress in the key areas identified for action. Progress on many of the recommendations is lacking. The panel's main recommendations - creation of a $1-billion innovation fund to enable sustainable changes in care delivery and a national healthcare innovation agency - have gone largely unanswered. We illustrate the need for an innovation agency that spans all provinces using several examples, including ones where digital health innovation is required, including central intake and triage for specialist referrals. We discuss the conditions needed for successful implementation: An interoperable digital solution, changes to models of care and funding flows, leadership and a patient-centred culture within the health system. We also highlight how local innovation hubs enable the development of new technologies and identify the key local, provincial and national factors for success that should be considered for a new federal agency.
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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 | 0.048 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".