The Evolution Of SDV, SDR, And RBQM In Driving Clinical Trial Data Quality

Explore the transformation of source data verification (SDV) and source data review (SDR) in clinical trials, driven by the increasing adoption of risk-based quality management (RBQM). Traditional approaches, such as 100% SDV, are resource-intensive, costly, and contribute minimally to overall data quality. Regulatory agencies and industry leaders have encouraged the adoption of risk-based monitoring (RBM) and RBQM methodologies to optimize trial processes and improve outcomes.

RBQM incorporates risk assessments, centralized monitoring, reduced SDV/SDR, and remote SDR to address the complexities of modern clinical trials, including decentralized and hybrid models. Key benefits of this approach include lower costs, enhanced data oversight, and higher sustainability through reduced travel and remote capabilities. However, barriers such as concerns over data quality, disconnected processes, and lack of organizational infrastructure have slowed adoption.

The white paper emphasizes the importance of leveraging advanced technologies, such as AI-enabled platforms, to support RBQM practices. These tools enable real-time data analysis, dynamic risk adjustments, and streamlined monitoring processes, reducing manual errors and improving trial efficiency. Case studies demonstrate significant cost savings, enhanced data integrity, and improved adaptability in trial operations.

As the clinical trial landscape evolves, RBQM stands as a critical framework for ensuring high-quality outcomes while addressing operational challenges.