Reducing Blood Bank workload through effective Remote Electronic Blood Issue
Bibliographic record
Abstract
Hospital blood banks are facing growing pressures due to increasing demand and a shortage of qualified blood bank techs. One response to this has been the use of Electronic Cross Matching of blood for patients, which allows blood to be directly issued for patients based on the patient’s blood tests and history. This has helped reduce workload, but the practice of ‘pre-allocating’ blood units prior to surgery means that most hospital blood banks crossmatch an average of two blood units for each unit actually transfused. Electronic release of blood admits the possibility of Remote Electronic Blood Issue, in which unallocated blood stocks are kept near the point of use and released “just in time”. We have combined Remote Electronic Blood Issue with computer controlled multi-compartment refrigerators to provide safe and effective “Blood Vending Machines”. This approach has reduced blood bank workloads by up to 52%, has reduced the amount of blood required in inventory, and has reduced the time required to provide blood from 20 minutes or more to about one minute. This system is now in use in several hospitals in the UK, US and Canada, and similar results have been seen in each case.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".