Less plasma, less drama: A quality improvement initiative to reduce inappropriate plasmatransfusions across community hospitals in the niagara region.
Bibliographic record
Abstract
Abstract Background Plasma transfusion has limited clinical indications, primarily for patients with coagulopathy and active bleeding or those undergoing major surgery. Appropriate use is defined by an international normalized ratio (INR) >1.7 and a transfusion dose of 3–4 units in adults. However, audits in tertiary care settings have consistently revealed high rates of inappropriate plasma use. Local Problem At Niagara Health, recent audits across three community hospitals identified that 42% of plasma transfusions in hospitalized adults were inappropriate. This misuse contributes to resource wastage, increased healthcare costs, and unnecessary patient exposure to transfusion-related risks. Aim This study aimed to improve the rates of appropriate plasma transfusions (as set by appropriate metrics of INR and dose) amongst hospitalized adult patients of three major community hospitals in the Niagara Region by 25% by June 30, 2025. This will be achieved through awareness campaign, enhanced audit and feedback, as well as incorporation of electronic Transfusion Medicine order set in hospitals EMR. Methods This is a nonrandomized, interrupted time series quality improvement project (QIP) following the Model for Improvement framework (MFI). The QIP is designed to develop, test, and implement change ideas following sequential Play-Do-Study-Act cycles starting July 2024, monitoring rates of plasma transfusions monthly. Results Over the study period, 253 plasma units were transfused. Median monthly rates of appropriate transfusions were 90% (INR >1.7), 89% (dose >2 units), and 78% (meeting both criteria). A positive trend in appropriateness was observed, particularly in dosing. Additionally, the proportion of out-of-guideline plasma requests screened by transfusion medicine technologists decreased by 15%. Importantly, there were no significant changes in the overall use of plasma or red cell products. Conclusions The implementation of an electronic order set, supported by technologist screening, targeted education, and audit-feedback loops, led to improved adherence to plasma transfusion guidelines without increasing laboratory workload or affecting the use of other blood products. These interventions demonstrate potential for broader application to other blood components. Ongoing evaluation will assess the sustainability of these improvements.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".