The Effective Strategies to Avoid Medication Errors and Improving Reporting Systems
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Notice bibliographique
Résumé
BACKGROUND: Population-based studies from several countries have constantly shown excessively high rates of medication errors and avoidable deaths. An efficient medication error reporting system is the backbone of reliable practice and a measure of progress towards achieving safety. Improvement efforts and system changes of medication error reporting systems should be targeted towards reductions in the likelihood of injury to future patients. However, the aim of this review is to provide a summary of medication errors reporting culture, incidence reporting systems, creating effective reporting methods, analysis of medication error reports, and recommendations to improve medication errors reporting systems. METHODS: Electronic databases (PubMed, Ovid, EBSCOhost, EMBASE, and ProQuest) were examined from 1 January 1998 to 30 June 2020. 180 articles were found and 60 papers were ultimately included in the review. Data were mined by two reviewers and verified by two other reviewers. The search yielded 684 articles, which were then reduced to 60 after the deletion of duplicates via vetting of titles, abstracts, and full-text papers. RESULTS: Studies were principally from the United States of America and the United Kingdom. Limited studies were from Canada, Australia, New Zealand, Korea, Japan, Greece, France, Saudi Arabia, and Egypt. Detection, measurement, and analysis of medication errors require an active rather than a passive approach. Efforts are needed to encourage medication error reporting, including involving staff in opportunities for improvement and the determination of root cause(s). The National Coordinating Council for Medication Error Reporting and Prevention taxonomy is a classification system to describe and analyze the details around individual medication error events. CONCLUSION: A successful medication error reporting program should be safe for the reporter, result in constructive and useful recommendations and effective changes while being inclusive of everyone and supported with required resources. Health organizations need to adopt an effectual reporting environment for the medication use process in order to advance into a sounder practice.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle