Data Review And Tracking – The Evolution From Data Integrity To Data Quality

By Divya Ravishankar, Director Product Strategy, ThoughtSphere

In the not-so-distant past, clinical trials relied on a few data sources known for having simple integrations guided by ALCOA principles. Since then, however, times have changed. Today, with over 60% of clinical data coming from various digital sources, the traditional data-cleaning approach falls short. Life Science organizations must now embrace data quality measures across the clinical delivery life-cycle and leverage automated processes to ensure data validity amidst the rapid flow of information.

To support this shift, industry guidance from FDA and EMA emphasizes building quality systematically and operationally into clinical studies. ICH E6(R2) and the E6(R3) draft outline data quality and governance requirements, ensuring data security, traceability, and transformation. ICH E8(R1) complements this by designing quality into trials and safeguarding Critical to Quality (CtQ) Factors.

Adopting an outcome-driven, risk-based, central monitoring strategy is crucial to assess data integrity, honesty, and reliability in today's digital landscape with complex trial designs. Consider these three factors when transitioning to a more robust and quality-driven data cleaning and central monitoring strategy.