A pre and post intervention study to reduce unnecessary urinary catheter use on general internal medicine wards of a large academic health science center
Bibliographic record
Abstract
BACKGROUND: Urinary catheters are a common medical intervention, yet they can also be associated with harmful adverse events such as infection, urinary tract trauma, delirium and patient discomfort. The purpose of this study was to describe the use of the SafetyLEAP program to drive improvement efforts, and specifically to reduce the use of urinary catheters on general internal medicine wards. METHODS: A pre and post intervention study using the SafetyLEAP program was performed with urinary catheter prevalence as the primary outcome on two general internal medicine wards in a large academic health sciences center. RESULTS: A total of n = 534 patients (n = 283 from ward #1; and n = 252 from ward #2) were included in the initial audit and feedback portion of the study and 1601 patients (n = 824 pre-intervention and n = 777 post-intervention were included in the planned quality improvement portion of the study). A total of 379 patients during the quality improvement intervention had a urinary catheter. Overall, the adherence to the SafetyLEAP program was 97.4% on both general internal medicine wards. The daily catheter point prevalence decreased from 22 to 13%. After the implementation of the program, the urinary catheter utilization ratio (defined as urinary catheter days/patient days) declined from 0.14 to 0.12. Catheter-associated urinary tract infections (CAUTI) were unchanged. CONCLUSION: The SafetyLEAP program can help provide a systematic approach to the detection, and reduction of safety incidents. Future studies should aim at refining and implementing this intervention broadly.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".