Reduction of urea test ordering in the emergency department: multicomponent intervention including education, electronic ordering, and data feedback
Bibliographic record
Abstract
INTRODUCTION: In the emergency department (ED), laboratory testing accounts for a significant portion of the medical assessment. Although excess laboratory test ordering has been proven to be prevalent, different types of interventions have been used to encourage a behavioural change in how physicians order tests. In one western Canadian hospital medicine program, a quality improvement project aimed to reduce the total monthly blood urea nitrogen (BUN) test ordered by physicians was found to be successful. The objective of this project was to evaluate a similar multicomponent intervention aimed at ED physician ordering, with the primary goal of reducing the number of monthly BUN tests ordered per ED visit. METHODS: A pre post intervention design was conducted over 12-months. The first intervention component was an educational presentation conducted by physician leaders. Second, a regularly used order panel within the ED electronic order system was modified, removing the BUN test. The third component involved audit and feedback; the total monthly BUN test ordered for the ED department post intervention start was shared with all ED physicians twice (at 5 and 12 months).An interrupted time series analysis was completed to evaluate the multicomponent intervention effect. RESULTS: The total monthly ordered BUN test declined from an average of 1905 pre-intervention to 448 post-intervention, and the total monthly BUN test to total ED visit ratio declined from 0.46 to 0.1. These results were a statistically significant reduction in physician BUN test ordering. CONCLUSIONS: Targeted education, order panel design and data feedback interventions can impact physician ordering behaviour in the emergent healthcare context, where diagnostic tests are often over used.
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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 | 0.003 |
| 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 | 0.001 |
| 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 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".