Making hospitals innovative: Macro-level policy to sustain micro-innovations in healthcare
Bibliographic record
Abstract
Successful innovation clusters are notoriously difficult to establish, and many attempts fail. How can we go about designing such systems reliably? We describe how ecosystems can be strengthened through grassroots bottom-up efforts that empower user and community innovation, as opposed to economic policies that dictate innovation. Specifically focusing on the healthcare industry, we advocate that community hospitals which constitute 90% of all hospitals in Canada are the ideal setting for such community innovation efforts. We investigated the distribution of innovation output from hospitals over the past 13 years and found a decrease in predominance of major teaching hospitals, supporting the potential role for community hospitals in this space. We categorize different types of innovations and recommend institutional policies that can sustain bottom-up, micro-level efforts. Such policies could improve and enhance the development of micro-innovations and the creation of health innovation clusters.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".