Implementation of a novel real‐time platelet inventory management system at a multi‐site transfusion service
Bibliographic record
Abstract
BACKGROUND: Blood platelets (PLTs) are a valuable commodity. Management of their inventory has implications both for patient care and for the cost of health care delivery. There are a variety of different methods of managing PLT inventory currently in practice and multiple theoretical models aimed at improving PLT inventory metrics. In this study we evaluate the ability of a novel electronic dashboard system that monitors and displays both PLT inventory and patient data to improve transfusion metrics at a quaternary health care center. STUDY DESIGN AND METHODS: The Capital District Health Authority is a quaternary health care center that transfuses approximately 2500 PLT units annually. To improve PLT discard rates a novel, low-overhead system that interfaces with the laboratory information system and displays real-time data between transfusion sites on PLT inventory and orders was implemented in November 2011. This study examines the transfusion quality metrics data from the 24 months before and after implementation. RESULTS: A significant reduction in mean monthly PLT outdate rate was observed after the implementation of the PLT dashboard suite from 24.5% (n = 24, SD ± 6.4%) to 15.1% (n = 24, SD ± 6.4%; p < 0.001). PLT age at time of transfusion was also reduced from 3.60 days (n = 4796, SD ± 0.97 days) to 3.46 days (n = 4881, SD ± 1.00 days; p < 0.001). CONCLUSIONS: This study describes the implementation of a novel PLT dashboard suite. This suite significantly reduced PLT outdate rates at our institution over the 48-month 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".