Enabling decision-making and innovation in learning health systems through simulation modelling
Bibliographic record
Abstract
Canadian healthcare systems require profound transformations to enhance patient experience, improve population health, reduce costs, and improve the work life of healthcare providers. Learning Health Systems (LHSs) are an approach for undertaking this transformation in an effective, efficient, and sustainable manner with digital technologies as a key enabler for change. However, the successful implementation of a LHS brings with it challenging and potentially risky changes to clinical practices and operations. Simulation modelling is an advanced analytics technique particularly well-suited for informing decision-making and planning prior to and during the transformation of complex systems such as LHSs. Yet, despite the use and demonstrated benefits of simulation modelling in many different industries including healthcare, its application in the context of LHSs has received limited attention. In this article, we discuss how simulation modelling can be leveraged to support better-informed, lower-risk decisions and innovation in LHSs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".