Harnessing system-focused simulation, debriefing and FMEA to inform healthcare blood transfusion safety and policy
Bibliographic record
Abstract
Introduction Healthcare systems improvement using simulation and debriefing is an increasingly employed, yet underutilized quality improvement tool to enable user-centred design. This approach allows users to experience real-life systems and processes through simulation and then provide feedback on how a system supports them within their role. Understanding this interaction of people and their systems is critical to safe, quality, reliable and efficient care and bridges the gap between how we think a system is working and how it is working. Methods This novel project was collaboratively developed and led by simulation, human factors and patient safety experts and used existing organizational safety data to target further high-risk safety threats surrounding administering, cross-checking and labelling blood products for transfusion. A system-focused simulation-based approach was used to identify system issues for a large healthcare organization’s transfusion policy redesign. A Failure Mode and Effects Analysis (FMEA) was then used to apply a risk score to the findings from the simulation user feedback to inform a large high-risk policy redesign. Results Multiple recommendations were provided to the participating units and policy and procedure redesign teams surrounding environmental issues, standards, interpretation and usability of the policy. Discussion Our collaborative patient safety, simulation and human factors project was successful in proactively identifying both active and latent factors contributing to adverse events and identifying recommendations using FMEA methodology to improve patient safety, including revisions to the physical space within the lab, and the provincial blood transfusion policy and procedure.
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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 | 0.037 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".