Leveraging Real-World Evidence (RWE) and Advanced Analytics to Improve Drug Utilization in U.S Health Systems

The rising cost of pharmaceuticals, increasing complexity of therapeutic regimens, expansion of specialty drugs, and persistent variability in prescribing patterns have intensified the need for data-driven optimization of drug utilization in U.S. health systems. Real-world evidence (RWE), derived from electronic health records (EHRs), claims databases, registries, and patient-reported outcomes, has emerged as a transformative tool for evaluating treatment effectiveness, safety, adherence, and value in routine clinical practice. Concurrently, advances in analytics including machine learning, predictive modeling, natural language processing, and causal inference methods have enhanced the ability to extract actionable insights from large, heterogeneous healthcare datasets. This paper synthesizes contemporary literature to examine how RWE and advanced analytics can be leveraged to improve drug utilization within U.S. health systems. It reviews methodological foundations, data infrastructure, regulatory perspectives, real-world applications in formulary management and medication safety, and the integration of analytics into clinical decision support systems. The study further explores implementation challenges, including data quality, bias, governance, interoperability, and ethical considerations. By consolidating developments in RWE methodologies and advanced analytics, the paper proposes a structured perspective on optimizing medication use, enhancing patient outcomes, and improving cost-effectiveness in complex health system environments.