Decreasing Misdiagnoses of Urinary Tract Infections in a Pediatric Emergency Department
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Urinary tract infection (UTI) is a common diagnosis in the emergency department (ED), often resulting in empirical antibiotic treatment before culture results. Diagnosis of a UTI, particularly in children, can be challenging and misdiagnosis is common. The aim of this initiative was to decrease the misdiagnosis of uncomplicated pediatric UTIs by 50% while improving antimicrobial stewardship in the ED over 4 years. METHODS: By using the Model for Improvement, 3 interventions were developed: (1) an electronic UTI diagnostic algorithm, (2) a callback system, and (3) a standardized discharge antibiotic prescription. Outcome measures included the percentage of patients with UTI misdiagnosis (prescribed antibiotics, but urine culture results negative) and antibiotic days saved. As a balancing measure, positive urine culture results without a UTI diagnosis were reviewed for ED return visits or hospitalization. Statistical process control and run charts were used for analysis. RESULTS: From 2017 to 2021, the mean UTI misdiagnosis decreased from 54.6% to 26.4%. The adherence to the standardized antibiotic duration improved from 45.1% to 84.6%. With the callback system, 2128 antibiotic days were saved with a median of 89% of patients with negative culture results contacted to discontinue antibiotics. Of 186 patients with positive urine culture results with an unremarkable urinalysis, 14 returned to the ED, and 2 were hospitalized for multiresistant organism UTI treatment. CONCLUSIONS: A UTI diagnostic algorithm coupled with a callback system safely reduced UTI misdiagnoses and antibiotic usage. Embedding these interventions electronically as a decision support tool, targeted audit and feedback, reminders, and education all supported long-term sustainability.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".