Look-alike, sound-alike medication perioperative incidents in a regional Australian hospital: assessment using a novel medication safety culture assessment tool
Notice bibliographique
Résumé
BACKGROUND: Medication safety remains a global concern, with governments and organizations striving to mitigate preventable patient harm across healthcare systems. Look-alike, sound-alike medication incidents and the safety culture are widely acknowledged as a contributor to medication errors, particularly within the high-risk perioperative environment. The Medication Safety Culture Indicator Matrix (MedSCIM) is a novel tool developed by the Canadian Institute for Safe Medication Practices to assess the maturity of the medication safety culture. This study aims to delineate look-alike sound-alike (LASA) medication incidents reported in the pharmacy and perioperative settings of an Australian hospital and assess the maturity of the medication safety culture. METHODS: The study setting is within a large regional hospital in Australia, servicing both adult and paediatric populations. Medication incidents from 1 April 2018 to 1 April 2023 were retrospectively gathered from the Clinical Incident Management System, Riskman®. Data and statistical analyses were carried out using Microsoft Excel®. The necessary approvals were secured from the Heath Service Human Research and Ethics Committee. RESULTS: During the 5-year period, a total of 246 (4.1%) of the 6002 medication incidents within the health service were identified as meeting the inclusion criteria. Of the 246 medication incidents, 63.0% were identified from the Pharmacy Department, while 22.0% and 15.0% were from the Post Anaesthetic Care Unit and Anaesthetics Department, respectively. The most frequently reported incident classification in both the Anaesthetics Department and Post Anaesthetic Care Unit was 'incorrect dose', followed by 'incorrect medication'. Throughout the 5-year period, 46 (18.7%) of the 246 medication incidents were attributed to look-alike, sound-alike sources of error, predominantly identified in the Pharmacy Department (73.9%), followed by the Anaesthetics Department (17.4%) and the Post Anaesthetic Care Unit (8.7%). High-risk medications were most frequently reported to the Anaesthetics Department. Packaging (packaging alone, naming and packaging, and syringe swaps) was determined to be a contributing factor in 30 (65.2%) of the 46 LASA medication incidents. MedSCIM assessment revealed a reactive medication safety culture. Additionally, the medication incident report documentation was found to be mostly complete or semi-complete. CONCLUSION: Our analysis delineated medication incidents occurring across the entire medication management cycle and identified incidents related to LASA medications as a contributor to medication incidents across these clinical settings. This novel medication safety culture tool assessment highlighted opportunities for improvement with clinical incident documentation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».