Pharmacovigilance has undergone a profound transformation over the last century, evolving from a reactive system focused on post marketing reporting of adverse drug reactions into a complex, proactive, and digitally mediated ecosystem designed to protect public health in an increasingly data rich therapeutic environment. This article presents a comprehensive and theoretically grounded examination of next generation pharmacovigilance by synthesizing historical foundations, regulatory frameworks, methodological innovations, and the growing influence of real world evidence and digital health infrastructures. Drawing exclusively on the provided body of scholarly and regulatory references, the paper develops an integrated conceptual and operational model of modern pharmacovigilance that aligns safety surveillance with contemporary health information technologies, clinical research practices, and regulatory science.

The study begins by situating pharmacovigilance within its historical and institutional context, demonstrating how early spontaneous reporting systems, driven largely by catastrophic drug safety failures, gradually matured into structured global networks of surveillance and risk management. Building on this foundation, the article explores how advances in data science, Bayesian modeling, and standardized reporting systems have redefined the way safety signals are detected, validated, and communicated. Special attention is given to quantitative signal detection methodologies, particularly Bayesian confidence propagation neural networks, which represent a paradigm shift from simple disproportionality analyses to probabilistic learning systems capable of handling uncertainty and massive data heterogeneity.

A central contribution of this paper is its detailed analysis of the integration of real world data and real world evidence into pharmacovigilance workflows. Drawing from regulatory frameworks and methodological scholarship, the paper explains how administrative databases, electronic health records, registries, and routinely collected clinical data are reshaping both pre and post marketing safety evaluation. Unlike traditional clinical trials, these data sources reflect the complexity of real patient populations, including comorbidities, polypharmacy, and long term exposure, thereby enabling a more ecologically valid assessment of drug safety. However, the paper also critically examines the epistemological and methodological challenges associated with real world evidence, including issues of data quality, bias, measurement uncertainty, and regulatory acceptability.

The article further investigates the role of digital health technologies, health information management systems, and advanced analytics in enabling next generation pharmacovigilance. Health IT workflows, as conceptualized in complex adaptive systems theory, are shown to be both an enabler and a source of new vulnerabilities for safety surveillance. The paper demonstrates how fragmented data architectures, inconsistent coding standards, and misaligned organizational incentives can undermine even the most sophisticated analytical tools. Conversely, standardized global reporting systems and interoperable data platforms are argued to be essential infrastructures for achieving reliable and scalable pharmacovigilance in a globalized pharmaceutical market.