Background: Antibiotic resistance (ABR) remains a global health threat that requires urgent action. Antibiotic use is a key driver of ABR and is particularly problematic in the outpatient setting. General practitioners (GPs), the public, and pharmacists therefore play an important role in safeguarding antibiotics. In this study, we aimed to gain a better understanding of the antibiotic prescribing-use-dispensation dynamic in Malta from the perspective of GPs, pharmacists, and parents; Methods: we conducted 8 focus groups with 8 GPs, 24 pharmacists, and 18 parents between 2014 and 2016. Data were analysed using inductive and deductive content analysis; Results: Awareness on antibiotic overuse and ABR was generally high among interviewees although antibiotic use was thought to be improving. Despite this, some believed that antibiotic demand, non-compliance, and over-the-counter dispensing are still a problem. Nevertheless, interviewees believed that the public is more accepting of alternative strategies, such as delayed antibiotic prescription. Both GPs and pharmacists were enthusiastic about their roles as patient educators in raising knowledge and awareness in this context; Conclusions: While antibiotic use and misuse, and knowledge and awareness, were perceived to have improved in Malta, our study suggests that even though stakeholders indicated willingness to drive change, there is still much room for improvement.

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Background: Pharmacovigilance (PV) is the activity to identify comprehensive information on the safety characteristics of the drug after its marketing. The PV data sources are dynamic, large, structured, and unstructured; therefore, the automation of data processing is essential. Purpose: This review aims to identify the machine learning applications in PV activities. Methods: Nine (9) studies that were published within the period from 2016 to 2020 were reviewed. The studies were extracted from two databases; PubMed and web of science. The review and analysis were done in December 2020. Results: The supervised and semi-supervised learning techniques are applied in the main three PV group activities; adverse drug reactions (ADRs) and signal detection, individual case safety reports (ICSRs) identification, and ADRs prediction. Future research is needed to identify the applicability of unsupervised learning in PV and to formulate the legal framework of the false positive predicted data.