P235: Risk-Based Quality Management in Rare Disease Trials: An Observational Perspective on Data Integrity
Poster Presenter
Julien Nunes Goncalves
Head of Growth
Cyntegrity South Africa
Objectives
To qualitatively evaluate how risk-based quality management (RBQM) strategies enhance data integrity and patient safety in rare disease clinical trials by sharing observational insights derived from an RBQM system.
Method
A review was done on data collected via a dedicated RBQM system that tracks protocol deviations, data inconsistencies, and patient safety risks. Risk factors were identified along with key strategies to improve data quality, leveraging real-time risk monitoring and predictive analytics capabilities.
Results
Data gathered through the RBQM system revealed that rare disease trials, with inherently small patient cohorts and complex protocols, face significant challenges related to protocol deviations and data inconsistencies. Real-time risk monitoring via Key Risk Indicators (KRIs) and trend identification through machine learning driven predictive analytics functions enabled early identification of potential issues, such as missed safety assessments, duplicate or fraudulent data entries, and screening errors. Analysis of audit trails and risk logs demonstrated that automated alerts allowed clinical teams to promptly address deviations, reducing the frequency and severity of critical protocol violations. Additionally, descriptive trend analyses underscored the value of real-time monitoring for optimizing resource allocation and ensuring high-quality data collection. These observational insights align with regulatory recommendations and previous research on risk-based monitoring.
Conclusion
This observational study demonstrates that RBQM significantly enhances data integrity and patient safety in rare disease clinical trials by enabling the early detection and mitigation of key risks. The shared insights underscore the effectiveness of targeted RBQM strategies, such as automated alerts for protocol deviations and appropriate selection of risk indicators, in addressing challenges like missed assessments and data inconsistencies. These findings provide actionable guidance for sponsors and clinical teams to optimize quality management practices and ensure reliable trial outcomes and patient safety for rare diseases, while meeting regulatory expectations.
