The AMÉLIE project: failure mode, effects and criticality analysis: a model to evaluate the nurse medication administration process on the floor
Bibliographic record
Abstract
OBJECTIVE: The objective of this article was to critically evaluate the causes of adverse drug events during the nurse medication administration process in paediatric care units in order to identify and prioritize interventions that need to be implemented. METHODOLOGY: This is a failure mode, effects and criticality analysis (FMECA) study. A multidisciplinary committee composed of nurses, pharmacists, physicians and risk managers evaluated through consensus the process of administering medications at the Centre hospitalier universitaire de Sainte-Justine. By mapping the process, all the failure modes were identified and associated with at least one cause each. Using a summary grid, each failure mode was evaluated by rating frequency (from 1 to 9), likelihood of failure detection (from 0 to 100%) and severity (from 1 to 9) using adapted versions of already published scales. RESULTS: A 10-member committee was set up, and it met eight times between January and April 2010. In the two specialized paediatric units selected (n = 38 beds), an average number of approximately 20 000 drug doses was administered monthly from about 400 non-proprietary names. Through consensus, the committee identified 16 processes and 53 failure modes. While frequency and severity were based on perceptions that could be objectivized with local data and scientific documentation, the likelihood of detection was mainly based on individual perception. CONCLUSION: FMECA is a useful approach to improve the medication process.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".