97 Decreasing invasive urinary tract infection screening in a paediatric emergency department: A quality improvement initiative
Bibliographic record
Abstract
Abstract Primary Subject area Emergency Medicine - Paediatric Background Fever is a common presentation among children coming to the Emergency Department (ED) and a urinary tract infection (UTI) often needs to be excluded. Sterile techniques, like catheterization, are invasive, can be traumatizing to children, and are time consuming to complete. A two-step approach has been shown to reduce the catheterization rate in febrile, young children without unintended consequences. Objectives Our aim was to implement a two-step approach for UTI screening in febrile children 6-24 months in order to decrease unnecessary urine catheterizations by 50% without impacting ED length of stay (LOS) or return visits (RVs). Design/Methods After engaging key stakeholders and a nursing champion, we created a process map to understand the current urine collection process in our ED, and areas for targeted improvement. Using the model for improvement, we adopted a 2-step pathway for a suspected UTI in children 6-24 months as our change idea. The pathway involved identifying children who met inclusion criteria for UTI screening, followed by urine bag application and urinalysis (UA) if clinically indicated. Only if the UA was positive, a second urine sample was collected via catheterization, for repeat UA and culture. Through multiple PDSA cycles, our pathway was implemented in the ED along with concurrent staff education. The outcome measure was the rate of ED urine catheterizations. Process measures included the total number of urine cultures sent to microbiology and percent positivity. The balancing measures included ED LOS and RVs. Results Since project initiation in July 2019, the ED catheterization rate decreased from 73% to 53% (Figure 1) and the number of urine cultures sent to Microbiology decreased by 23%. The number of urine cultures sent to Microbiology decreased by 23% with a mild improvement in the positivity rate by 2% (Figure 2). There was no significant change in RVs. There was a slight 10-min increase in ED LOS, most likely confounded by the COVID pandemic. Conclusion Using improvement methodology, we successfully decreased the number of unnecessary catheterizations in children and the number of urine cultures sent to microbiology. Further refinements to our intervention are ongoing and include optimizing urine screening equipment in patient rooms, poster reminders, re-education for providers, and introducing a parent resource explaining the 2-step pathway. This improvement work is also being spread to the paediatric wards and can easily be adopted by other paediatric centres. It has also been adapted by the Choosing Wisely hospital campaign.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".