Enhancing Pharmacovigilance with Data Analytics

Abstract

Pharmacovigilance is a critical aspect of healthcare that involves the detection,
assessment, understanding, and prevention of adverse drug reactions (ADRs). Traditionally
reliant on voluntary reporting systems and labor-intensive analysis, pharmacovigilance has
faced challenges in ensuring timely and accurate detection of ADRs across diverse patient
populations. This article explores how data analytics, particularly machine learning (ML),
big data, predictive analytics, and real-time monitoring, can enhance pharmacovigilance
strategies. By leveraging vast and varied datasets—ranging from clinical trials to electronic
health records (EHRs), social media, and real-world evidence (RWE)—data analytics can
significantly improve ADR detection, signal identification, risk assessment, and decision-
making in pharmacovigilance. The paper also discusses the challenges and ethical
considerations surrounding the use of advanced data analytics in pharmacovigilance,
providing insights into future trends and the evolution of this field.