Using Blood Wisely: lessons learnt in establishing a national implementation programme to reduce inappropriate red blood cell transfusion
Bibliographic record
Abstract
BACKGROUND: Up to 50% of blood is transfused inappropriately despite best evidence. In 2020, Choosing Wisely Canada launched a major national programme, 'Using Blood Wisely', the aim was to engage hospitals to audit their red blood cell transfusion use against national benchmarks and participate in a programme to decrease inappropriate use. STUDY DESIGN: Using Blood Wisely is a quality improvement programme including national benchmarks, an audit tool, recommended evidence-based effective interventions and a designation to reward success. Hospital engagement was measured using the number of hospitals signing up, performing a baseline audit, submitting the planning survey, entering two or more audits and achieving hospital designation. Barriers to implementation were collected. RESULTS: From 1 September 2020 to 31 December 2022, 229 individual hospitals signed up over time to participate. Their results are reported as 159 hospitals and hospital groups. Collectively, this accounts for 72% of the blood used in Canada. Overall, 147 (92%) performed a baseline audit, 10 (6%) submitted a planning survey and 130 (82%) entered two or more audits. At baseline (time of enrolment), 75 (51%) met both benchmarks. The designation was awarded to 62 (39%) hospital groups (a total of 105 individual hospitals) that met and sustained benchmarks. Barriers to implementation included human resource shortages, lack of local expertise to advise the team, need for more education of transfusion prescribers and competing priorities. CONCLUSION: In its initial phase, Using Blood Wisely engaged a substantial number of hospitals in transfusion quality improvement work and maintained that engagement. This large-scale engagement across a big country was more successful than anticipated. Additional efforts are needed to rigorously evaluate the programme's impact on utilisation.
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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 | 0.004 | 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 | 0.000 | 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".