Reducing two-unit red cell transfusions on the oncology ward: a choosing wisely initiative
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background/context: Despite Choosing Wisely recommendations for single unit red blood cell transfusion orders, ~50% of orders on the oncology ward at London Health Sciences Centre (LHSC) were for two units. The oncology ward at LHSC is a 60 bed tertiary care unit. In mid 2016, LHSC was 18 months into its implementation of computerised provider order entry (CPOE). Aim/objectives: By December 2017, increase the proportion of one-unit red cell transfusion orders on the oncology ward from 50% to 80. Measures: Outcome: % one-unit red cell transfusion orders (aggregated monthly). Improvement/innovation/change ideas: Our initial theory was that unawareness of the guidelines (established in 2014) and subscription to the obsolete doctrine of two-unit transfusions were the primary behavioural drivers. Initial change ideas included an educational/awareness blitz including rounds presentations, memos and posters. Failure led us to revisit our hypothesis and carry out a real-time audit, where our team was notified on each two-unit transfusion. This revealed the true root cause: the overwhelming majority of two-unit transfusions could be traced back to standing orders that were entered on an admission order set. After provider engagement, we proceeded to remove all admission order sets containing two-unit transfusions. Impact/lessons learned/results: After order set removal, our one-unit transfusion rate rose to 86% and was sustained for 17 months. We learnt two primary lessons. First that CPOE and poor order set design combined to perpetuate poor ordering practices. Second that revisiting our hypothesis and engaging in thoughtful root cause analysis that included direct observation ultimately led to an effective, sustainable solution. Discussion/spread: Our study underscores the importance of executing root cause analysis on a microsystem level. We would expect the factors driving poor performance to be completely different on a service such as general internal medicine. Our study also highlights the potential pitfalls of CPOE and the importance of regular order set review to ensure adherence to current evidence.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 it