Randomized controlled trials (RCTs) remain the gold standard for evaluating medical interventions, but they often face challenges related to patient recruitment, cost, and efficiency. Real-world data (RWD) has emerged as a valuable tool to enhance trial design, improve patient identification, and support regulatory decision-making. However, integrating RWD into RCTs presents methodological, regulatory, and operational challenges. To address these issues, a think tank was convened in May 2024 at the Duke Clinical Research Institute, bringing together experts from academia, industry, healthcare systems, regulatory agencies, and patient advocacy groups. Discussions focused on three key areas: optimizing patient identification and outcome assessment, leveraging RWD for safety assessments, and using RWD in RCTs supporting regulatory approval. RWD has the potential to simplify eligibility criteria, enhance recruitment through artificial intelligence, and provide practical endpoints for evaluating treatment effects. The think tank underscored the need for collaboration across stakeholders to address challenges, such as data inconsistencies, privacy concerns, and infrastructure limitations. The event concluded with actionable recommendations, including the following: (1) standardizing RWD sources to ensure consistency and improve interoperability across healthcare systems, (2) developing regulatory frameworks that define acceptable use cases for RWD in clinical trials, (3) enhancing data quality through robust validation methodologies and real-time monitoring, (4) investing in artificial intelligence-driven patient identification tools to streamline recruitment, and (5) fostering multi-stakeholder collaboration to align expectations and share best practices. Moving forward, implementing these strategies will be critical to fully harness the potential of RWD in clinical research and improve trial efficiency.
Nouhravesh et al. (2026) studied this question.