Automated Evidence Generation For Regulatory-Grade Real-World Data

Recent regulatory developments have accelerated the integration of Real-World Evidence (RWE) into drug evaluation frameworks. The FDA’s July 2024 guidance on electronic health records and claims data introduces stringent criteria for data provenance and quality, while the EMA’s DARWIN EU network reported a 47.5% increase in RWD studies, reflecting growing institutional reliance on observational data. Japan’s PMDA is similarly advancing the use of RWE, particularly in rare disease contexts where randomized controlled trials are impractical. These shifts underscore a broader regulatory pivot toward structured, traceable, and reproducible evidence generation.

However, legacy methods — such as manual chart abstraction and fragmented point solutions — fail to meet these evolving standards, introducing variability and limiting scalability. Emerging automated platforms, underpinned by artificial intelligence and governed by human oversight, offer a scientifically robust alternative.

This paper explores the methodologies required to produce regulatory-grade RWE and the implications for global drug development, safety monitoring, and health technology assessment