Failure Mode, Effect, and Criticality Analysis of the Parenteral Nutrition Process in a Mother–Child Hospital: The AMELIORE Study
Bibliographic record
Abstract
BACKGROUND: The parenteral nutrition (PN) process is complex and involves multiple steps and substeps, especially in pediatrics and neonatology, given the particular needs of these patients. The objective of this study was to perform a critical analysis of the PN process at the Centre Hospitalier Universitaire Sainte-Justine to determine which potential pitfalls are related to this process and which should be prioritized when implementing corrective measures. METHODS: This is a Failure Mode, Effect, and Criticality Analysis (FMECA) study. A multidisciplinary team assessed each step of the PN process and identified associated failure modes. Adapted rating scales were used to determine severity, frequency, and detectability of the failure modes. Ratings were established through multidisplinary consensus, and a criticality index (CI) was calculated for each failure mode. RESULTS: A total of 265 failure modes were identified in the 5 major steps of the PN process. The failure mode with the highest CI was the inscription of an inaccurate weight at prescription, with a CI of 800. The step with the highest cumulative CIs was administration to patients, with a CI sum of 7691. Various recommendations aimed at minimizing the risks associated with the PN process were made following this FMECA. Additional interventions are expected to emanate from this project because data will be presented throughout the departments involved. CONCLUSION: This study is a successful example for other hospitals interested in carrying out the same kind of healthcare improvement initiative.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".