Novel web-based real-time dashboard to optimize recycling and use of red cell units at a large multi-site transfusion service
Bibliographic record
Abstract
BACKGROUND: Effective blood inventory management reduces outdates of blood products. Multiple strategies have been employed to reduce the rate of red blood cell (RBC) unit outdate. We designed an automated real-time web-based dashboard interfaced with our laboratory information system to effectively recycle red cell units. The objective of our approach is to decrease RBC outdate rates within our transfusion service. METHODS: The dashboard was deployed in August 2011 and is accessed by a shortcut that was placed on the desktops of all blood transfusion services computers in the Capital District Health Authority region. It was designed to refresh automatically every 10 min. The dashboard provides all vital information on RBC units, and implemented a color coding scheme to indicate an RBC unit's proximity to expiration. RESULTS: The overall RBC unit outdate rate in the 7 months period following implementation of the dashboard (September 2011-March 2012) was 1.24% (123 units outdated/9763 units received), compared to similar periods in 2010-2011 and 2009-2010: 2.03% (188/9395) and 2.81% (261/9220), respectively. The odds ratio of a RBC unit outdate postdashboard (2011-2012) compared with 2010-2011 was 0.625 (95% confidence interval: 0.497-0.786; P < 0.0001). CONCLUSION: Our dashboard system is an inexpensive and novel blood inventory management system which was associated with a significant reduction in RBC unit outdate rates at our institution over a period of 7 months. This system, or components of it, could be a useful addition to existing RBC management systems at other institutions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".