Potential Risks Associated with Medication Administration, as Identified by Simple Tools and Observations
Notice bibliographique
Résumé
Adverse events are common in health care institutions. In a study published in 2007, the Canadian Institute for Health Information reported that “1 in 13 adult medical and surgical patients admitted to acute care hospitals in Canada in 2000 experienced an adverse event”. Medication errors are among the most frequent adverse events. In an international survey of adults with health problems, administered by The Commonwealth Fund, about 10% of Canadian respondents reported having received the wrong medication or dose from a health care provider in the previous 2 years. These errors may result in morbidity, mortality, increases in monitoring and costs of care, and delays in hospital discharge. A prospective cohort study analyzing the incidence of drug-related adverse events in 2 tertiary care hospitals showed that 34% of preventable adverse medication-related events were at the administration stage, making this category the second most frequent cause (after errors at the ordering stage, which accounted for 56% of preventable adverse medication events). Given this reality, the management of medication-related risks is a priority for hospitals. The medication-use system is complex, with a total of 54 identified phases, for which many activities, tools, equipment, and information systems are needed and for which several interfaces are typically required. Many of these phases, particularly the medication administration process, carry high risks. Typically, nurses are responsible for the critical stages of the medication-use system, with a risk of error at each stage. Importantly, there seems to be a link between the way nurses’ work is organized and the occurrence of errors during the administration of medications. According to a study on nurses’ perceptions of medication errors, “a single hospital patient can receive up to 18 medications per day, and a nurse can administer as many as 50 medications per working shift”. A study of the delivery of nursing care in acute care settings showed that nurses spent 16% of their time preparing or administering medications. In addition, 22% of interruptions occurred during the medication preparation process. A high number of interruptions can lead to medication errors. At the authors’ centre, medication errors were an important cause of incidents and accidents from 2004 to 2010. More specifically, medication errors represented 74% of incidents and accidents in 2004/2005, although this proportion was reduced to 39% in 2010/2011. Errors related to drug administration represented 66.3% of these medication errors. Various preventive strategies are used to manage risk within the medication-use system, including training and use of daily unit-dose medication distribution systems, with medication carts containing individual drawers designated for specific patients (identified by bed numbers). Nonetheless, errors still occur frequently in health care institutions.
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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 | 0,004 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».