Optimizing red blood cell transfusion practices in the intensive care unit: a multi-phased health technology reassessment
Bibliographic record
Abstract
BACKGROUND: Health technology reassessment (HTR) is a process to manage existing health technologies to ensure ongoing optimal use. A model to guide HTR was developed; however, there is limited practical experience. This paper addresses this knowledge gap through the completion of a multi-phase HTR of red blood cell (RBC) transfusion practices in the intensive care unit (ICU). OBJECTIVE: The HTR consisted of three phases and here we report on the final phase: the development, implementation, and evaluation of behavior change interventions aimed at addressing inappropriate RBC transfusions in an ICU. METHODS: The interventions, comprised of group education and audit and feedback, were co-designed and implemented with clinical leaders. The intervention was evaluated through a controlled before-and-after pilot feasibility study. The primary outcome was the proportion of potentially inappropriate RBC transfusions (i.e., with a pre-transfusion hemoglobin of 70 g/L or more). RESULTS: There was marked variability in the monthly proportion of potentially inappropriate RBC transfusions. Relative to the pre-intervention phase, there was no significant difference in the proportion of potentially inappropriate RBC transfusions post-intervention. Lessons from this work include the importance of early and meaningful engagement of clinical leaders; tailoring the intervention modalities; and, efficient access to data through an electronic clinical information system. CONCLUSIONS: It was feasible to design, implement, and evaluate a tailored, multi-modal behavior change intervention in this small-scale pilot study. However, early evaluation of the intervention revealed no change in technology use leading to reflection on the important question of how the HTR model needs to be improved.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".