Reducing blood product wastage through the inter‐hospital redistribution of near‐outdate inventory
Bibliographic record
Abstract
BACKGROUND: Hospital transfusion services order blood products to satisfy orders and maintain inventory levels during unexpected periods of increased blood demand. Surplus inventory may outdate before being allocated to a recipient. Blood product outdating is the largest contributor to blood wastage. STUDY DESIGN: A province-wide redistribution program was designed and implemented to redistribute near-outdate plasma protein and related blood products from low-usage to high-usage hospitals. Program operations and details are described in this paper. Two transport container configurations were designed and validated for transport of all blood products. A cost-analysis was performed to determine the effectiveness of this redistribution program. RESULTS: A total of 130 hospital transfusion services contributed at least one near-outdate blood product for redistribution between January 2012 and March 2020. These services redistributed 15,499 products through 3412 shipments, preventing the outdating of $17,570,700 CAD worth of product. Program costs were $14,900 for shipping and $30,000 for staffing. Failed time limits or non-compliance with packing configurations resulted in $388,200 worth of blood products (97 shipments containing 816 products) being discarded. Courier transport delays was the most common reason (42/97; 43%) for transport failure. CONCLUSION: Redistributing near-outdate blood products between hospitals is a feasible solution to minimize outdating. Despite heterogeneity of Canadian blood product inventory, all products (each with unique storage and transport requirements) were successfully redistributed in one of two validated and simple containers. Total operation costs of this program were small in comparison to the $17.6 million in savings associated with preventing the discard of outdated products.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".