A QI Partnership to Decrease CT Use for Pediatric Appendicitis in the Community Hospital Setting
Bibliographic record
Abstract
The primary aim of this quality improvement initiative was to decrease the use of computerized tomography (CT) in the evaluation of pediatric appendicitis in a community general emergency department (GED) system by 50% (from 32% to 16%) in 1 year. METHODS: Colleagues within a State Emergency Medical Service for Children (EMSC) community of practice formed the quality improvement team, representing multiple stakeholders across 3 independent institutions. The team generated project aims by reviewing baseline practice trends and implemented changes using the Model for Improvement. Ultrasound (US) use and nondiagnostic US rates served as process measures. Transfer and "over-transfer" rates served as balancing measures. Interventions included a GED pediatric appendicitis clinical pathway, US report templates, and case audit and feedback. Statistical process control tracked the main outcomes. Additionally, frontline GED providers shared perceptions of knowledge gains, practice changes, and teamwork. RESULTS: The 12-month baseline revealed a GED CT scan rate of 32%, a US rate of 63%, a nondiagnostic US rate of 77%, a transfer to a children's hospital rate of 23.5%, and an "over-transfer" rate of 0%. Project interventions achieved and sustained the primary aim by decreasing the CT scan rate to 4.5%. Frontline GED providers reported positive perceptions of knowledge gains and standardization of practice. CONCLUSIONS: Engaging regional colleagues in a pediatric-specific quality improvement initiative significantly decreased CT scan use in children cared for in a community GED system. The emphasis on the community of practice facilitated by Emergency Medical Service for Children may guide future improvement work in the state and beyond.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".