Reviewing Pharmacovigilance Strategies Using Real-World Data for Drug Safety Monitoring and Management

Abstract

Pharmacovigilance plays a critical role in ensuring drug safety and protecting public health by identifying, assessing, and mitigating adverse drug reactions (ADRs). The integration of real-world data (RWD) from sources such as electronic health records, patient registries, and social media has significantly enhanced pharmacovigilance strategies, addressing limitations of traditional methods. This paper reviews the framework of pharmacovigilance systems, emphasizing the integration of innovative tools like artificial intelligence (AI), machine learning (ML), and predictive analytics for improved signal detection and proactive risk management. Additionally, advancements in real-time monitoring systems and global collaborations have strengthened drug safety efforts, although challenges related to data quality, privacy, and resource disparities remain significant. Recommendations for improving pharmacovigilance strategies include standardizing data, enhancing stakeholder capacity, and addressing ethical concerns to fully harness the potential of RWD. Future research and policy directions focus on advancing technologies and fostering international cooperation to create a robust and equitable pharmacovigilance ecosystem capable of adapting to evolving healthcare needs.