An order set and checklist improve physician transfusion ordering practices to mitigate the risk of transfusion‐associated circulatory overload
Bibliographic record
Abstract
OBJECTIVES AND BACKGROUND: There are few studies of quality interventions to mitigate the risk of transfusion-associated circulatory overload (TACO). Our aim was to reduce TACO risk in patients admitted to internal medicine at our hospital, by addressing gaps in transfusion practice. MATERIALS AND METHODS: A 3-month baseline audit of red blood cell (RBC) transfusion orders was conducted. An intervention consisting of a transfusion order set and physician checklist was developed and implemented based on identified gaps, followed by a 3-month post-intervention audit. Compliance with appropriateness criteria for RBC transfusion was ascertained, along with documentation of transfusion rate, diuretic usage and consent. RESULTS: A total of 97 transfusion orders from 68 inpatients and 95 orders from 62 inpatients were audited in the baseline and post-intervention groups, respectively. Compliance with appropriateness criteria was similar pre- and post-intervention (87 versus 85%, P = 0·81). Specification of transfusion rate improved (84 versus 98%, P < 0·01), and diuretics were appropriately ordered more frequently for patients with TACO risk factors (37 versus 64%, P < 0·01). Timing of diuretics shifted from between or post-transfusion to pre-transfusion (35 versus 86%, P < 0·01), without increases in hypokalemia or acute kidney injury. No case of TACO was observed during the study. Documentation of specific risks discussed during consent discussion improved (4 versus 23%, P < 0·01). CONCLUSION: A checklist and order set are tools that can improve the quality of transfusion orders by increasing the judicious use of pre-transfusion diuretics and augmenting the specification of transfusion rate. These interventions could be adapted to electronic order formats to improve transfusion safety.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".