Signage as an intervention on a general medicine ward to reduce unnecessary testing
Bibliographic record
Abstract
BACKGROUND: Up to 30% of medical spending in developed countries is unnecessary. Unnecessary testing is not only wasteful economically, but can be injurious to patients. Studies have shown that interventions such as education, auditing, and restrictive ordering can reduce unnecessary testing. However, these interventions are time- and resource-intensive. We conducted a study to determine if the passive intervention of placing signs on clinicians' computers was effective in reducing unnecessary testing. AIMS: To determine the effectiveness of signage on physicians' computers to limit unnecessary testing. METHODS: We identified two acute medicine wards on which all orders are placed via computer. On one ward (Ward A), we placed signs outlining recommendations regarding responsible test-ordering. Ward B acted as a control. Data was collected during a 6-month study period to determine whether test-ordering practices differed. RESULTS: A total of 1645 patients accounting for 17 786 patient-days were included in the study. Fewer tests were ordered on Ward A than Ward B (7.38 vs 8.20 tests/patient-day; P < 0.01). Additionally, significantly fewer patients on Ward B received ≥1 complete blood count/day (36.1% vs 42.5%, P = 0.04). This effect was most pronounced among patients admitted for 7-30 days. CONCLUSION: The passive intervention of placing signs on clinicians' computers significantly reduced unnecessary testing.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".