Real World Evidence (RWE) Analytics for Drug Monitoring

Abstract

Real World Evidence (RWE) is increasingly recognized as a pivotal component in drug monitoring, regulatory decision-making, and post-marketing surveillance. Derived from Real World Data (RWD), which encompasses electronic health records, claims databases, patient registries, and wearable device information, RWE provides insights beyond controlled clinical trials. This review focuses on the applications of RWE analytics in drug safety, efficacy monitoring, adverse event detection, and treatment optimization. Additionally, it explores the methodological frameworks, analytical tools, and challenges in harnessing RWE for informed decision-making. Recent advances in machine learning, artificial intelligence, and predictive modeling have enhanced the robustness of RWE studies, enabling timely interventions in pharmacovigilance. The paper also highlights case studies demonstrating successful RWE integration in drug monitoring programs. Overall, RWE analytics represents a transformative approach in bridging the gap between clinical research and real-world therapeutic outcomes.

KEYWORDS: Real World Evidence, Real World Data, Drug Monitoring, Pharmacovigilance, Machine Learning, Post-Marketing Surveillance, Safety Analytics, Healthcare Data, Adverse Event Detection.