Every Tube Counts: reducing extra tubes drawn in the emergency department
Bibliographic record
Abstract
A common practice exists in hospitals where extra tubes of blood are collected for possible add-on testing, this practice contributes to wastage of consumables. Baseline estimates from a 5-month local lab information system audit revealed that ~65 extra tubes per day were being collected, with an additional 2-week manual audit of all extra tubes received in the laboratory confirming the practice. The audits showed that the majority of the tubes (~99%) were being drawn from the adult emergency department (ED). Furthermore, only 5% of the extra tubes were being used for add-on testing, whereas the remaining tubes had no testing performed on them and were discarded at the end of the day. This translates to over 23 000 extra tubes being wasted annually.After initial discussion with ED leadership, the practice was identified as primarily nurse driven. An educational intervention was created and entitled 'Every Tube Counts', with the aim to reduce extra tube collections in the adult ED by 50% within the first month of intervention. First, a memo with initial findings and a request to stop the practice of extra tube collection was sent out to all ED staff. After 2 weeks of additional data collection, it was noticed that extra tubes were still being collected. A second intervention, which consisted of another communication and utilisation of nurse educators to disseminate the information to nursing staff, saw a remarkable ~80% reduction in collection of extra tubes in the following few months after the second intervention. The practice was followed for an additional 15 months, which saw a slight increase of extra tube collections over time with a levelling off towards the latter period of the study. However, the target goal was maintained over the entire study period.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".