A CHILDREN’S HOSPITAL’S EXPERIENCE PROMOTING VALUE AT THE BEDSIDE - A CHOOSING WISELY INITIATIVE
Bibliographic record
Abstract
BACKGROUND: It is estimated that 20-30% of the annual Canadian healthcare budget may be wasted on unnecessary tests and treatments. Choosing Wisely is an initiative dedicated to addressing this problem. In Canada it has focused primarily on adult healthcare. OBJECTIVES: To describe the development, implementation and initial impact of a departmental Choosing Wisely top 5 list on paediatric care at a Canadian childrens hospital. DESIGN/METHODS: After key stakeholder input and review of current specialty society lists, an inventory of potential paediatric recommendations relevant to hospital care was generated. A survey was developed and broadly administered to rank items. Two hospitalist leaders independently scored top ranking items based on ease of implementation, measurement, alignment, and value. Five final items were chosen. Baseline measurement was achieved through various hospital databases, chart review or audit where appropriate. After appointing a physician lead and developing an implementation strategy for each recommendation, the Choosing Wisely top 5 list was launched in January 2016. Recommendations were implemented using various improvement methodologies. RESULTS: Early results nine months into the initiative show large improvements in reducing unnecessary care. For example: by not automatically giving IVIG as first-line treatment for children with typical newly diagnosed ITP, usage of IVIG has decreased from an initial baseline of 85% to 20%. In the emergency department, nasopharyngeal testing for respiratory viruses has decreased by more than 80% and routine radiography for children with low risk acute ankle injuries has decreased from 86% to 57%. CONCLUSION: Developing and implementing a Choosing Wisely top 5 list at a childrens hospital aims to promote a culture of quality, evidence-based and high-value care. We have plans for further improvements in the current project, sustaining the gains already achieved, and to expand the initiative to other areas in our institution. This model, along with lessons learned, is being shared with paediatric healthcare providers across the country through presentation at meetings and rounds, as well as publication in various media and high impact journals.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".