Frequency and Type of Medication Discrepancies in One Tertiary Care Hospital
Notice bibliographique
Résumé
Background/Objective: Discrepancies in records used within the medication use system have been identified as a contributing factor of medication errors.The objective of this study was to determine the frequency and type of discrepancies in the medication use system in one tertiary care hospital.Methods: Using a sample of patients (convenience sampling technique), the physician's orders, the nursing medication administration record and the pharmacy profile were compared in an attempt to identify discrepancies among them.A discrepancy was defined as a deviation from the physician's order as written in the chart.Each discrepancy was categorized according to seven components of the medication order, its location in the medication use process and its mode of delivery.Results: One thousand, four hundred twenty-four orders representing 197 patients from 13 nursing units were sampled for this study.Thirteen percent of the orders were discrepant and 61% of patients had at least one discrepancy.The most frequent types of discrepancies were drug omissions and unordered drugs.Discussion: The discrepancies identified in this study suggest that either orders are not reaching pharmacy or orders are not being processed appropriately in pharmacy.The location of discrepancies also suggests that there are deficiencies in communication between healthcare professionals. Background/ObjectiveThe safety of the medication use system (MUS) is an issue which is a concern in many healthcare organizations today.This problem was clearly identified in the Institute of Medicine's report To Err Is Human (Kohn et al. 1999).The Canadian Adverse Events Study (Baker et al. 2004) helped to quantify the magnitude of this problem in the Canadian inpatient environment.In that study, almost one-quarter of all the adverse events identified were drug-or fluid-related.The annual cost of preventable drug-related morbidity and mortality in Canada has been estimated to be $11 billion per year in older adults alone (Kidney and MacKinnon 2001).Problems related to documentation and communication in the MUS are commonly cited in studies.Of 134 patients in the intervention arm of a study (Nickerson et al. 2005) at the Moncton Hospital, NB, 96.3% (129) patients had at least one drug-therapy problem for monitoring, while 39.6% (53) patients had a drug-therapy inconsistency or omission.All of these problems were identified just prior to discharge from hospital.In another study that focused on unintended discrepancies on admission, 53.6% of patients had at least one such discrepancy (Cornish et al. 2005).Current efforts by the Canadian Council on Health Services Accreditation (2006) and the Safer Healthcare Now! (2006) campaign directed toward Frequency and Type of Medication Discrepancies in One Tertiary Care Hospital
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 | 0,002 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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) | 0,003 | 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 ».