Application of Failure Mode, Effects, and Criticality Analysis to the Medication-Use Process for Temperature-Sensitive Drugs in a University Hospital
Bibliographic record
Abstract
Background: In the hospital setting, the medication-use system for temperature-sensitive drugs is a high-risk process. Objectives: To analyze the risks associated with the hospital-based medication-use process and to propose corrective and preventive actions for the most critical failure modes. Methods: A multidisciplinary team was trained to analyze the medication-use process for temperature-sensitive drugs and to identify potential failures using a risk analysis method known as failure mode, effects, and criticality analysis (FMECA). The medication-use process, from initial supply to administration to patients, was investigated using “the 5 Ws and How” method (Who? What? Where? When? Why? How?), and the causes of the failure modes were analyzed using Ishikawa diagrams. The most critical failure modes were selected using the Pareto law, and relevant improvement actions were proposed. Results: This analysis identified 41 failure modes for the 9 stages of the medication-use process, of which only 36 were deemed assessable by the participants. Eighteen (50%) of these failure modes were critical, according to the Pareto law, with criticality indices between 12 and 60. The stage of tidying up and storage in patient care units had the highest number of critical failures (n = 5). A total of 48 corrective actions were proposed. Conclusion: The proposed action plan prioritized 3 areas for improvement: the documentation system, staff training, and equipment acquisition. A second FMECA should be carried out to reassess the medication-use process after implementation of these improvement actions. The second FMECA, allowing detection of residual risks and identification of new risks, will be part of a continuous improvement process. RÉSUMÉ Contexte : Le circuit des médicaments thermosensibles en milieu hospitalier fait partie des processus à risque. Objectifs : Analyser a priori les risques liés à ce circuit et proposer des actions correctives et préventives contre les modes de défaillances les plus critiques. Méthodes : Une équipe pluridisciplinaire a été formée pour analyser le circuit des médicaments thermosensibles et identifier les défaillances par la méthode de l’analyse des modes de défaillances, de leurs effets et de leurs criticités (AMDEC). Le processus, qui va de l’approvisionnement jusqu’à l’administration des médicaments aux patients, a été décortiqué en utilisant la méthode « Qui? Quoi? Où? Quand? Comment? Pourquoi? » (QQOQCP) et les causes des défaillances ont été analysées à l’aide de diagrammes d’Ishikawa. Les défaillances les plus critiques ont été sélectionnées par la loi de Pareto, et des mesures d’amélioration pertinentes ont été proposées. Résultats : Cette analyse a mis en évidence 41 modes de défaillances pour les 9 étapes du circuit, dont uniquement 36 sont jugés évaluables par les membres de l’équipe pluridisciplinaire. Dix-huit modes sur 36 (soit 50 %) sont critiques selon la loi de Pareto, avec des indices de criticité répartis entre 12 et 60. L’étape comprenant le nombre le plus élevé de défaillances critiques est celle du rangement et du stockage dans les services de soins, avec 5 défaillances. Au total, 48 mesures correctives ont été proposées. Conclusions : Le plan d’action proposé priorisait 3 domaines d’amélioration : le système de documentation, la formation du personnel et l’acquisition d’équipements. Une deuxième AMDEC devrait être réalisée pour réévaluer le circuit après la mise en œuvre des mesures d’amélioration. La deuxième AMDEC permettra la détection des risques résiduels et l’identification de nouveaux risques et s’inscrira dans une démarche d’amélioration continue.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".