Pharmacovigilance involves detecting, assessing, understanding, and preventing adverse drug effects. The field aims to systematically monitor medicines and promote their safe, effective use. However, pharmacovigilance faces challenges such as fragmented systems and passive reporting. As the discipline advances, priorities now include centralizing data analysis, applying technologies like AI for safety signals, leveraging real-world data, implementing proactive risk planning, and increasing patient engagement. Recent advancements have emphasized the role of machine learning in identifying adverse drug reactions (ADRs), predicting drug-drug interactions, and enhancing personalized care. Big Data and cloud computing also play critical roles in facilitating the efficient analysis of massive healthcare datasets, contributing to earlier safety signal detection. Moreover, the emerging application of blockchain technology promises to revolutionize drug traceability, data security, and real-time reporting, thus improving drug safety monitoring. The COVID-19 pandemic revealed the need for robust systems that can enable rapid responses to emerging safety issues around epidemic treatments and vaccines. The next era of pharmacovigilance seeks to transform monitoring to improve patient outcomes worldwide. However, thoughtful implementation, focused on ethics, validity, and equity, is critical. The aim of this chapter is to provide perspectives on optimizing pharmacovigilance practices in an evolving landscape, integrating cutting-edge technologies to better safeguard patients.
