Developing electronic health records as a source of real-world data for veterinary pharmacoepidemiology.

Spontaneous reporting of adverse events (AEs) by veterinary professionals and the public is the cornerstone of post-marketing safety surveillance for veterinary medicinal products (VMPs). However, studies suggest that most veterinary AEs remain unreported. Veterinary medicine regulators, including the United Kingdom Veterinary Medicines Directorate and the European Medicines Agency, have included the exploration of big data utilization to support pharmacovigilance efforts in their regulatory strategies. In this study, we describe the application of veterinary electronic healthcare records (EHRs) from the SAVSNET veterinary first opinion informatics system to conduct pharmacoepidemiological analyses. Five VMP-AE pairs were selected for investigation in a proof-of-concept study, where drug exposure was identified from semi-structured treatment data and AEs from the unstructured free-text clinical narrative. Dictionaries were developed to identify AEs based on standard terminology. The precision of these dictionaries improved when they were expanded using word vectorization and expert opinion. A key strength of first-opinion EHR datasets is their ability to enable cohort studies and facilitate calculations of absolute incidence and relative risk. Thus, we demonstrate that unstructured free-text clinical narratives can be used to identify outcomes for veterinary pharmacoepidemiological studies and, consequently, support and expand pharmacovigilance efforts based on spontaneous AE reports.