Proactive Risk Assessment of Blood Transfusion Process, in Pediatric Emergency, Using the Health Care Failure Mode and Effects Analysis (HFMEA)
Bibliographic record
Abstract
INTRODUCTION: Pediatric emergency has been considered as a high risk area, and blood transfusion is known as a unique clinical measure, therefore this study was conducted with the purpose of assessing the proactive risk assessment of blood transfusion process in Pediatric Emergency of Qaem education- treatment center in Mashhad, by the Healthcare Failure Mode and Effects Analysis (HFMEA) methodology. METHODOLOGY: This cross-sectional study analyzed the failure mode and effects of blood transfusion process by a mixture of quantitative-qualitative method. The proactive HFMEA was used to identify and analyze the potential failures of the process. The information of the items in HFMEA forms was collected after obtaining a consensus of experts' panel views via the interview and focus group discussion sessions. RESULTS: The Number of 77 failure modes were identified for 24 sub-processes enlisted in 8 processes of blood transfusion. Totally 13 failure modes were identified as non-acceptable risk (a hazard score above 8) in the blood transfusion process and were transferred to the decision tree. Root causes of high risk modes were discussed in cause-effect meetings and were classified based on the UK national health system (NHS) approved classifications model. Action types were classified in the form of acceptance (11.6%), control (74.2%) and elimination (14.2%). Recommendations were placed in 7 categories using TRIZ ("Theory of Inventive Problem Solving.") CONCLUSION: The re-engineering process for the required changes, standardizing and updating the blood transfusion procedure, root cause analysis of blood transfusion catastrophic events, patient identification bracelet, training classes and educational pamphlets for raising awareness of personnel, and monthly gathering of transfusion medicine committee have all been considered as executive strategies in work agenda in pediatric emergency.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".