Self-Reported Medical Errors in 7 Countries: Implications for Pharmacy
Notice bibliographique
Résumé
Purpose The purpose of this study was to determine the rate of self-reported errors in Canada compared with other countries, and to identify risk factors for medical error. Methods In 2007, the Commonwealth Fund surveyed a sample of adults in 7 industrialized nations: Australia, Canada, Germany, the Netherlands, New Zealand, the United Kingdom and the United States. The surveys were conducted by telephone by Harris Surveys and country affiliates, with an average interview time of 17 minutes. Data from this source was used to perform a bivariate analysis comparing those individuals who reported having experienced a medical error to those who had not, followed by a logistic regression model in order to delineate the relationship between medical error and several explanatory variables. The goodness of fit of the final model was assessed, as was the possible presence of multicollinearity. All data analysis was performed using SPSS Version 16.0. Results Overall, 11,910 respondents from 7 countries were included in the analysis. The rate of self-reported medical error ranged from 12%– 20% in the 7 nations. Approximately 1 in 6 (17%) Canadians reported having experienced at least 1 error in the previous 2 years, which translates to 4.2 million adult Canadians. Several variables were found to have a statistically significant relationship to self-reported medical errors in the final regression model, including high prescription drug use (4 or more medications), presence of a chronic condition, lack of physician time with the patient, age under 65, lack of patient's involvement in care, perceived inadequate nursing staffing and absence of a regular doctor. Conclusions This study has demonstrated that medical error is a commonly occurring problem from the perspective of patients in 7 industrialized countries. The risk factors for self-reported medical error that have been identified in this study should aid clinicians, including pharmacists, in the design and implementation of targeted strategies to address this issue. By proactively identifying patient, provider and system-related risk factors for medical error, the opportunity for improving the safety of Canada's health care system could be greatly enhanced. While not all of the errors identified in this study are related to medications, there are implications for pharmacy practice. More specifically, our results suggest that pharmacists should be extra vigilant with patients with high prescription drug use and chronic conditions and ensure that patients are given the opportunity to be involved in their care.
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 | 0,004 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».