Decreasing Invasive Urinary Tract Infection Screening in a Pediatric Emergency Department to Improve Quality of Care
Bibliographic record
Abstract
OBJECTIVES: Obtaining urine samples in younger children undergoing urinary tract infection (UTI) screening can be challenging in busy emergency departments (EDs), and sterile techniques, like catheterization, are invasive, traumatizing, and time consuming to complete. Noninvasive techniques have been shown to reduce catheterization rates but are variably implemented. Our aim was to implement a standardized urine bag UTI screening approach in febrile children aged 6 to 24 months to decrease the number of unnecessary catheterizations by 50% without impacting ED length of stay (LOS) or return visits (RVs). METHODS: After forming an interprofessional study team and engaging key stakeholders, a multipronged intervention strategy was developed using the Model for Improvement. A urine bag screening pathway was created and implemented using Plan, Do, Study Act (PDSA) cycles for children aged 6 to 24 months being evaluated for UTIs. A urine bag sample with point-of-care (POC) urinalysis (UA) was integrated as a screening approach. The outcome measure was the rate of ED urine catheterizations, and balancing measures included ED LOS and RVs. Statistical process control methods were used for analysis. RESULTS: During the 3-year study period from January 2019 to June 2022, the ED catheterization rate successfully decreased from a baseline of 73.3% to 37.7% and was sustained for approximately 2 years. Unnecessary urine cultures requiring microbiology processing decreased from 79.8% to 40.7%. The ED LOS initially decreased; however, it increased by 17 minutes during the last 8 months of the study. There was no change in RVs. CONCLUSION: A urine bag screening pathway was successfully implemented to decrease unnecessary, invasive catheterizations for UTI screening in children with only a slight increase in ED LOS. In addition to the urine bag pathway, an ED nursing champion, strategic alignment, and broad provider engagement were all instrumental in the initiative's success.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".