Innovation in Health Care Delivery: Commentary on an Evolutionary Approach
Bibliographic record
Abstract
Zwarenstein (2015) proposes a novel approach to healthcare innovation that parallels biological evolution, based on stimulation and reward of multiple small competing innovation projects conducted in the field by decentralized teams. Projects would be designed with explicit outcome targets and results would be widely disseminated and publicly available. More successful projects would be grown and spread. Critical to the model is accepting and reporting failure as well as success, for the benefit of future project design. Examining biological evolution for lessons for healthcare delivery innovation illuminates the need for diversity among healthcare systems to achieve optimum application of best practice interventions across jurisdictions with differing population, provider and facility characteristics. However, careful coordination will be needed to achieve the balance between diversity and harmony across jurisdictions necessary for effective governance and interaction. There are important methodological issues to be addressed to reduce the uncertainty inherent in comparisons of results among discrete innovation projects, especially when observed improvements over the baseline are modest. As well as evolutionary improvement in healthcare outcomes, the model should progressively increase decentralized capacity and expertise in innovation processes.
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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 | 0.036 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.081 | 0.075 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".