Evaluation of a Choosing Wisely Canada Initiative to Reduce Unnecessary Testing in the Emergency Department
Bibliographic record
Abstract
Up to 30% of healthcare services in Canada are unnecessary and finding ways to reduce such care is critical for upholding quality of care and patient safety. This research consists of three overlapping studies investigating ways to reduce unnecessary care and the impacts of those interventions. Specifically, this research begins with a literature search to determine what is already known about interventions to reduce unnecessary care, using the example of pediatric imaging in the emergency department (ED). Building on these learnings, we evaluated the emergency department of hospital that was an early adopter of Choosing Wisely Canada (CWC)—a campaign to reduce unnecessary tests, treatments, and procedures. Specifically, the ED made changes and mandated adherence to medical directives. After analyzing their laboratory data using an interrupted time series analysis, we found significant reductions across most of the observed tests. We followed this quantitative study with semi-structured interviews from hospital leadership and ED clinicians to identify what was particularly impactful about the CWC intervention. We conclude by discussing what other healthcare organizations can learn from our research and how they can adapt our findings to suit their needs. We also discuss the challenges and benefits to using real hospital data extracted from the electronic medical record. Overall, reducing unnecessary care has become a priority for many healthcare organizations and our learnings could support these institutions in their endeavors to combat such services.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".