Establishing performance management objectives and measurements of red blood cell inventory planning in a large tertiary care hospital in British Columbia, Canada
Bibliographic record
Abstract
Background Traditional key performance indicators (KPIs) for red blood cell (RBC) inventory management such as blood shortage rate (BSR) and outdate rate (ODR) alone are observed to be insensitive for large hospitals, often due to fluctuating demands and rapid turnover. We hypothesized that improvement in complementary KPIs for RBC supply chains, including age of blood as a surrogate for the pre‐hospital and in‐hospital supply chains, may further improve efficiency. Methods Red blood cell supply, inventory and disposition data from a large tertiary care hospital blood bank were retrospectively assessed from June 2014 to 2015 as the baseline period. From June 2015 to 2016, (1) collaboration with the blood supplier to improve logistics and (2) a ‘demand‐driven inventory planning policy’ (DDIP) to determine better inventory levels aided by discrete‐event simulation modelling were instituted. Age of blood transfused (ABT) was chosen as the main KPI for the efficiency of the entire RBC supply chain. Results Improvements in age of blood received (ABR) led to the greatest efficiency gains. Reduction in ABT (28.7 ± 8.8 days vs. 22.1 ± 9.5 days, P < 0.01), ABR (19.4 ± 8.8 days vs. 13.4 ± 7.0 days, P < 0.01), inventory‐to‐transfusion ratio ( P < 0.01), O‐negative RBC utilization (7.4 vs. 6.4 units/day, P < 0.01) and ODR ( P < 0.01) compared to the baseline period was observed without a significant increase in BSR. Conclusion Collaboration with the blood supplier to improve logistics, implementation of DDIP to determine better inventory levels and use of KPIs other than BSR and ODR led to inventory efficiency gains in a large tertiary care hospital blood bank.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".