Real-world evidence (RWE) has emerged as an essential complement to randomized controlled trial data, providing insights into the effectiveness, safety, and tolerability of healthcare interventions in routine practice. The increasing digitalization of healthcare systems has enabled the generation of vast amounts of real-world data (RWD) from electronic medical records, claims databases, and digital health technologies. When appropriately analyzed, these data can inform clinical decision-making, regulatory evaluations, payer assessments, and healthcare resource optimization. Despite its growing importance, the generation of high-quality RWE presents significant methodological and operational challenges. Common challenges such as misclassification bias, confounding, incomplete longitudinal follow-up, poor data linkage, and risks of selective reporting. Addressing these challenges requires rigorous study designs, transparent methodologies, pre-specified and publicly registered protocols, and adherence to standard reporting guidelines. Advances in artificial intelligence (AI) and natural language processing hold promise for improving data accuracy, addressing time-varying confounding, scalability, efficiency, and enhancing study reproducibility. However, AI-driven approaches currently serve as supportive tools rather than autonomous solutions and require robust scientific oversight to ensure appropriate study design, causal reasoning, and clinical contextualization. This article highlights key principles, challenges, and emerging trends in RWE generation and dissemination, with a focus on the role of methodological rigor and expert scientific communication. Agencies and medical communication experts play a crucial role in ensuring that RWE generation aligns with stakeholder needs, regulatory frameworks, and scientific integrity. By leveraging innovative methodologies, robust protocols, transparent reporting, and stakeholder-focused dissemination strategies, RWE can reach its full potential in transforming patient care and driving evidence-based decision-making across healthcare systems.
D’Souza et al. (Tue,) studied this question.