How does the Content and Dissemination of Communications on the Risks of Medicines Affect Prescriber Awareness, Knowledge, and Behaviour: A Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Medicines have important and sometimes lifesaving health benefits. They can also be the cause of harm and injury due to adverse drug reactions (ADRs). Effective communication of medicine risks is crucial to informed prescribing decisions and the protection of patient health. Clinicians must receive, interpret, and then implement these communications to achieve desired outcomes; however, this has not always been successful. Therefore, it is important to understand how the content of risk communication about medicines and the methods of dissemination may affect prescribers' awareness, knowledge, and behaviours. AIMS: This systematic review provides an overview of the effect of content and dissemination of risk communications about medicines on prescribers' awareness, knowledge, and behaviour and ultimately on patient health. METHODS: A systematic review was conducted. Studies were included if they were randomised controlled trials investigating the effect of the content or dissemination of risk communications about medicines on prescribers' knowledge, awareness, and behaviour. MEDLINE, Embase, and PsycINFO via Ovid, Scopus, and Web of Science databases were searched up to December 2024. Data on intervention type, study design, prescriber type, and outcomes were extracted. Outcomes were synthesised, and meta-analysis was undertaken where results allowed for this. RESULTS: Twenty-three studies met the inclusion criteria: ten investigated the content of risk communication, ten investigated dissemination methods, and three investigated both. Twenty-one studies assessed prescribing behaviours, and one study each assessed clinicians' awareness and knowledge, respectively. Two studies evaluated how risk communication content and its delivery to clinicians affected patient health outcomes. Interventions included computerised clinical systems, risk assessment tools, alerting systems, targeted messaging, and education. Visual risk assessment tools and targeted education reduced ADR rates, improving patient health. Alerts to change clinical monitoring and assessment behaviour were modestly effective (relative risk [RR] 1.03; 95% confidence interval [CI] 1.01-1.05). Multicomponent approaches also positively affected prescribing behaviours. Targeted messages, such as audit and feedback, improved clinicians' awareness of risk communications. Computer alerts and risk assessments that were interruptive and easily accessed in workflows or provided actions or information to avoid or minimise risk to patients did not significantly change prescribing (RR 1.50; 95% CI 0.87-2.60 and RR 1.41; 95% CI 0.89-2.24). However, study heterogeneity and small sample sizes limited the power to detect differences. CONCLUSION: There is limited evidence from randomised controlled trials comparing the effectiveness of drug risk communication strategies targeting prescribers. No one content or dissemination intervention was wholly effective; however, key aspects of risk communication content and its dissemination to clinicians were identified, including multi-modal approaches. Further investigation is warranted.
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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 | 0,029 | 0,134 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,012 |
| Bibliométrie | 0,009 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| 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 ».