P‐PB‐17 | Using Blood Wisely: A National Campaign to Engage Hospitals in Appropriate RBC Transfusion Practice
Bibliographic record
Abstract
In September 2020, a national campaign called “Using Blood Wisely” was launched. The aim was to engage hospitals nationally to audit their red blood cell (RBC) use and participate in an effort to decrease inappropriate use. Using Blood Wisely met with stakeholders to develop a national benchmark for appropriate transfusion, a measurement strategy, effective change interventions based on the best available evidence and a mechanism to recognize success. Resources to support implementation included: educational videos; a planning survey; templates for guidelines, order sets, and transfusion order screening standard operating procedures; and a webinar series. Benchmarks were defined as having at least 65% of RBC transfusion episodes as single unit transfusions and at least 80% of RBC transfusions with a pre-transfusion hemoglobin 8 g/dL or less. Engagement in the initiative was measured by the number of organizations signing up to participate, entering audits, meeting benchmarks, and being designated as a Using Blood Wisely hospital when benchmarks were sustained for 4 months. Secondary outcomes were the types of interventions employed by designated organizations. Nationally, 659 hospital sites receive blood for transfusion. Up to December 31, 2022, 169 organizations (239 hospital sites) signed up to participate in Using Blood Wisely; 154 organizations (91%) submitted a baseline audit: 98 (58%) met the single unit transfusion benchmark; 112 (66%) met the pre-transfusion hemoglobin benchmark; and 81 (48%) met both benchmarks (Figure A). After sustaining the benchmarks for at least 4 months, 68 organizations received the Using Blood Wisely designation; of these, 15 (22%) had not met the benchmarks at baseline. Designated organizations employed the following interventions: guidelines (82%), education (74%), transfusion order screening (66%), order sets (65%), audit and feedback (62%) and alternatives to blood initiatives (34%). Using Blood Wisely was successful in engaging hospitals to participate in a national campaign to measure appropriate RBC transfusion practice and be recognized for their efforts. Although most designated organizations met the benchmarks at baseline, 22% achieved the benchmark during the campaign. The next phase of the initiative will focus on understanding if there are key interventions necessary for success and supporting hospitals who are actively entering audit data but have not yet met the benchmarks.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.063 | 0.014 |
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".