Resource stewardship and Choosing Wisely in a children’s hospital
Bibliographic record
Abstract
Objectives: Evidence suggests that approximately 30% of the tests and treatments currently prescribed in healthcare are potentially unnecessary, may not add value, and in some cases cause harm. We describe the evolution of our hospital's Choosing Wisely (CW) program over the first 5 years of existence, highlighting the enablers, challenges, and overall lessons learned with the goal of informing other healthcare providers about implementing resource stewardship initiatives in paediatric healthcare settings. Methods: We describe the development of de novo "top 5" CW lists of recommendations using anonymous surveys and Likert scale scoring. Composition and role of the steering committee, measurement of data and outcomes, and implementation strategies are outlined. Results: Many projects have resulted in a successful decrease in inappropriate utilization while simultaneously monitoring for unintended consequences. Examples include respiratory viral testing in the emergency department (ED) decreased by greater than 80%; ankle radiographs for children with ankle injuries decreased from 88% to 54%; and use of IVIG for treatment of typical ITP cases decreased from 88% to 55%. Early involvement focused within General Paediatrics and the ED, but later expanded to include perioperative services and paediatric subspecialties. Conclusions: An internally developed CW program in a children's hospital can reduce targeted areas of potentially unnecessary tests and treatments. Enablers include credible clinician champions, organizational leadership support, reliable measurement strategies, and dedicated resource stewardship education. The lessons learned may be generalizable to other paediatric healthcare settings and providers looking to introduce a similar approach to target unnecessary care in their own organizations.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| 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".