"The Largest Lean Transformation in the World": The Implementation and Evaluation of Lean in Saskatchewan Healthcare
Bibliographic record
Abstract
The Saskatchewan Ministry of Health has committed to a multi-million dollar investment toward the implementation of Lean methodology across the province's healthcare system. Originating as a production line discipline (the Toyota Production System), Lean has evolved to encompass process improvements including inventory management, waste reduction and quality improvement techniques. With an initial focus on leadership, strategic alignment, training and the creation of a supportive infrastructure (Lean promotion offices), the goal in Saskatchewan is a whole health system transformation that produces "better health, better value, better care, and better teams." Given the scope and scale of the initiative and the commitment of resources, it is vital that a comprehensive, longitudinal evaluation plan be implemented to support ongoing decision-making and program design. The nature of the initiative also offers a unique opportunity to contribute to health quality improvement science by advancing our understanding of the implementation and evaluation of complex, large-scale healthcare interventions. The purpose of this article is to summarize the background to Lean in Saskatchewan and the proposed evaluation methods.
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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 | 0.045 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".