Incidents et accidents médicamenteux en établissement de santé : une analyse descriptive au sein d’un CHU mère-enfant de 2018 à 2022
Bibliographic record
Abstract
Contexte : La sécurité des soins prodigués repose notamment sur une analyse des incidents et accidents médicamenteux (IAM). Objectif : L’objectif principal est de décrire les IAM au sein d’un centre hospitalier universitaire (CHU). Méthodologie : Cette étude descriptive rétrospective utilisait les déclarations provenant d’un CHU mère-enfant de 500 lits. Tous les incidents et accidents (IA) déclarés entre le 1er avril 2018 et le 31 mars 2022 ont été considérés. Ont été inclus tous les IAM survenus durant une admission ou en ambulatoire. Certaines variables ont été recodées manuellement. Des statistiques descriptives ont été effectuées. Résultats : Un total de 23 284 IA ont été considérés, incluant 7578 IAM. Il y avait une moyenne de 15,9 ± 14,0 IA déclarés par jour et 5,2 ± 0,3 IAM par jour. Il y avait 22,4 IAM/1000 jours-présence. La majorité des IAM sont survenus en chirurgie (20 %, 1530/7578), en oncologie (19 %, 1405/7578) et en pédiatrie (16 %, 1200/7578). La majorité était associée à une mauvaise posologie (21 %, 1575/7578), à des infiltrations/extravasations/voies retirées (19 %, 1405/7578) et à des omissions (16 %, 1205/7578). Des conséquences physiques ont été déclarées dans 15 % (1158/7578) des IAM. En revanche, des conséquences psychologiques ont été déclarées pour moins de 1 % (44/7578) des IAM. Conclusions : L’étude offre un profil descriptif complet sur quatre ans. La majorité des IA rapportés n’ont pas mené à des conséquences pour les patients. Le partage de ratios favorise l’analyse comparative avec d’autres établissements et peut contribuer aux échanges entourant la réduction des risques. Une culture de déclaration des événements est présente au sein de l’établissement. Mots-clés : gestion des risques, incidents, accidents, médicaments, prévention ABSTRACT Background: The safety of care provided is based on an analysis of medication incidents and accidents.Objective: The primary objective was to describe medication-related incidents and accidents (I&A) within a university-affiliated hospital. Methods: This retrospective descriptive study was based on data from a 500-bed mother-child university-affiliated hospital. All I&As declared between April 1, 2018, and March 31, 2022, were considered. The analysis included all medication-related I&As that occurred during an admission or in an outpatient setting. Some variables were recoded manually. Descriptive statistical analyses were performed.Results: A total of 23 284 I&As were considered, including 7578 medication-related I&As. Daily averages of 15.9 ± 14.0 I&As and 5.2 ±0.3 medication-related I&As were reported. There were 22.4 medication- related I&As/1000 inpatient days. The majority of medication-related I&As occurred in surgery (20%, 1530/7578), oncology (19%, 1405/7578), and pediatrics (16%, 1200/7578). Most were associated with incorrect dosing (21%, 1575/7578); infiltration, extravasation, or removed lines (19%, 1405/7578); and omissions (16%, 1205/7578). Physical consequences were reported in 15% (1158/7578) of the medication-related I&As. Conversely, psychological consequences were reported in less than 1% (44/7578) of medication-related I&As. Conclusions: This study provides a comprehensive descriptive profile over a 4-year period. Most of the reported I&As did not lead to consequences for patients. The sharing of ratios promotes comparative analysis with other facilities and can contribute to discussions about risk reduction. A culture of reporting events is present within this health care facility. Keywords: risk management, incidents, accidents, medications, prevention
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".