How does the Content and Dissemination of Communications on the Risks of Medicines Affect Prescriber Awareness, Knowledge, and Behaviour: A Systematic Review
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".