EDUCATIONAL INTERVENTIONS TO REDUCE PAEDIATRIC PRESCRIBING ERRORS
Notice bibliographique
Résumé
Background Although prescribing errors affect all age groups, they particularly affect paediatric patients, due to the challenges involved in the prescribing process. The complexity of the dose calculations, the variation of doses according to weight and the higher usage of unlicensed and off-label medications are all factors which may increase the number of prescribing errors. Education is an important intervention which may reduce the risk. Objective To review the literature to identify educational interventions which have been used to try to reduce prescribing errors in neonatal and paediatric patients. Method Systematic search of: International Pharmaceutical Abstracts (1970 to April 2014), Medline (1946 to April 2014), Embase (1974 to April 2014), PubMed (1970 to April 2014) and Cochrane (1970 to April 2014). All types of original research studies reporting educational interventions aimed at reducing prescribing errors in neonatal and paediatric patients were selected. The search included all languages. Studies were categorised according to the number and type of interventions used. The quality of the studies was assessed using the medical education research study quality instrument (MERSQI). Results Nineteen studies met the inclusion criteria. The majority had a before-after design. These trials were classified to studies which used single-educational (8), multi-educational (5) and multi-educational and non-educational interventions (6 studies). They utilised various educational strategies (e.g. tutorial, e-learning courses and posters) which aimed to improve prescribing practices and reduce prescribing errors. Seven studies were conducted in the United Kingdom, four in Spain, three in the United States, two in Australia and the remaining three studies in Canada, Argentina and Egypt. There was only one multi-centre study while the others were single-centre. Various methods were used to assess the effectiveness of these interventions including chart review, incident reports and prescribing competency assessment. 16 out of the 19 studies demonstrated that the educational interventions were effective in reducing paediatric prescribing errors. There were eight studies in which the doctors were assessed directly after the interventions while the assessment in other studies took place between 2 weeks to 4 years after implementation of the strategies. The results showed that educational interventions can have both long and short-term effects on reduction of prescribing errors. The three studies which showed insignificant results used single short (less than one hour) educational interventions. Conclusion There are only a few studies that assess educational interventions used to improve the prescribing process in paediatric wards. However, the studies did demonstrate that training and educating physicians about Good Prescribing Practice, and increasing their awareness about prescribing errors, can reduce such errors in neonatal and paediatric patients.
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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 | 0,006 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».