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.
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 | 0.055 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".