Sustainability and cost-avoidance of reduced inappropriate red cell transfusion at community hospitals in niagara region: A follow-up analysis on a quality improvement initiative.
Bibliographic record
Abstract
Abstract Introduction: Red blood cell transfusions (RBC) are a vital component of medical treatment in Canada. Yet, RBC units are expensive resources with clinical risks that are often inappropriately used, incurring unnecessary costs to the healthcare system. There is paucity of scientific information surrounding the economic implications and potential cost savings from implementing quality improvement programs aimed at reducing the inappropriate RBC utilization. We aim to share the sustainability of a previous quality improvement project promoting appropriate RBC utilization, and review the cost avoidance/saving gained from this initiative. Methods: An initial Quality Improvement project aimed at reducing inappropriate RBC transfusions was conducted through various interventions (i.e., technologist-led screening). We will be providing an overview of the original QIP as well as updated sustainability data until Sept 2024.This study applied an activity-based costing model to estimate cost avoidance based on the Choosing Wisely Canada guidelines for RBC utilization in three acute care facilities within the Niagara Health System. A secondary data analysis was conducted on RBC utilization from May 2018 to September 2024. The unit cost of an RBC transfusion was derived from a previous study and corroborated with Canadian Blood Services, factoring in all direct costs and overhead functions (approximately $1500 per transfusion). Descriptive and forecasting statistics were used to estimate cost avoidance and analyze time trends in RBC inappropriate utilization, comparing pre- and post-intervention periods. Results: Following intervention, adherence to guidelines of pre-transfusion hemoglobin (Hb) of 80 g/L or less and single unit rose from 85% and 54% to 90% and 71%, respectively. Appropriate rates of RBC transfusion were sustained for over 2 years since the intervention was implemented. We observed a significant decrease in total RBC utilization when comparing the pre-intervention period (2018-2021) to the post-intervention period (2022-2024). The total financial spending was $11,481,000 in the pre-intervention, which dropped to $6,429,000, resulting in a $5,052,000 cost avoidance in the post-intervention. This represents a 56% reduction in RBC utilization and 44% cost avoidance, suggesting substantial savings after adjusting for expected RBC units. Some variation was noted between the three sites, indicating the differential effectiveness of the quality improvement program across sites. Interestingly, local data on transfusion adverse reactions did not change with the implementation of this QIP. Discussion: Previously, there have been few validated interventions yielding significant improvement in appropriate RBC usage. This study demonstrates a reduction in inappropriate RBC utilization aligning with national accreditation benchmarks, translating into substantial savings for the healthcare system. Moreover, our intervention demonstrates that appropriate RBC usage and cost avoidance can be sustained, likely related to continued technologist-led screening. We suspect that the lack of reduction in transfusion adverse reactions concurrent with this project is related to the underreporting of these events. Further research is needed to investigate site-specific variations, focusing on facilitators and barriers to implementation across hospitals within the Niagara region.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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".