Sustainability and cost avoidance of reduced inappropriate red blood cell transfusion at community hospitals in Niagara Region: a follow-up analysis on a quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Inappropriate packed red blood cell (pRBC) transfusions increase patient risk and healthcare costs. Initial audits at Niagara Health (Ontario, Canada) revealed only 85% and 54% compliance with Choosing Wisely Canada guidelines for pre-transfusion hemoglobin (≤80 g/L) and single-unit transfusion, respectively. METHODS: We conducted a nonrandomized, interrupted time-series Quality Improvement Project (QIP) using the Model for Improvement. Interventions included technologist-led prospective screening of pRBC orders, policy updates, and educational campaigns. Outcome measures were rates of inappropriate transfusions based on hemoglobin and single-unit criteria; balancing measures included transfusion-related adverse events. Sustainability was assessed using Statistical Process Control charts. Cost analysis estimated savings using an activity-based cost of $C1500 per pRBC unit. RESULTS: Initial implementation improved compliance to 90% (pre-transfusion hemoglobin) and 71% (single-unit) within three months. Extended analysis (2021-2024) demonstrated sustained rates of 90% and 77%, respectively. At the St. Catharines Site, monthly median transfusions decreased from 273 to 173 units, yielding a 56% reduction in RBC utilization and 44% cost savings amounting to $C5052000. CONCLUSIONS: Technologist-led screening achieved sustained improvements in transfusion appropriateness, leading to substantial cost savings. Variability across sites underscores the need for further research on contextual factors influencing future QIP success.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".