Tackling overutilization of hospital tests and treatments: Lessons learned from a grassroots approach
Bibliographic record
Abstract
Recent data suggest unnecessary medical testing and treatment is relatively common in Canada. A number of harms to patients can arise as a result of unnecessary tests and treatments. In addition to patient harm, unnecessary tests and treatments add to the cost of medical care. Inspired by the Choosing Wisely campaign, St. Michael's Hospital in Toronto, Ontario, developed a hospital-wide program to address many different forms of overutilization at our hospital. The program prioritizes harm reduction over cost-containment and aims to create sustainable change through grassroots clinician engagement. This article will review important lessons learned from the St. Michael's experience.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.041 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 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".