Development and implementation of a clinician report to reduce unnecessary urine drug screen testing in the ED: a quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Unnecessary testing is a problem-facing healthcare systems around the world striving to achieve sustainable care. Despite knowing this problem exists, clinicians continue to order tests that do not contribute to patient care. Using behavioural and implementation science can help address this problem. Locally, audit and feedback are used to provide information to clinicians about their performance on relevant metrics. However, this is often done without evidence-based methods to optimise uptake. Our objective was to improve the appropriate use of laboratory tests in the ED using evidence-based audit and feedback and behaviour change techniques. METHODS: Using the behaviour change wheel, we implemented an audit and feedback tool that provided information to ED physicians about their use of laboratory tests; specifically, we focused on education and review of the appropriate use of urine drug screen tests. The report was designed in collaboration with end users to help maximise engagement. Following development of the report, audit and feedback sessions were delivered over an 18-month period. RESULTS: Data on urine drug screen testing were collected continually throughout the intervention period and showed a sustained decrease among ED physicians. Test use dropped from a monthly departmental average of 26 urine drug screen tests per 1000 patient visits to only eight tests per 1000 patient visits following the initiation of the audit and feedback intervention. CONCLUSION: Audit and feedback reduced unnecessary urine drug screen testing in the ED. Regular feedback sessions continuously engaged physicians in the audit and feedback intervention and allowed the implementation team to react to changing priorities and feedback from the clinical group. It was important to include the end users in the design of audit and feedback tools to maximise physician engagement. Inclusion in this process can help ensure physicians adopt a sense of ownership regarding which metrics to review and provides a key component for the motivation aspect of behaviour change. Departmental leadership is also critical to the process of implementing a successful audit and feedback initiative and achieving sustained behaviour change.
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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 | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 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".