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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".