Learn How You Can Automate the Manual Data Review and Resolution Process for Anomalies Using IQVIA’s CDAS Data Discrepancy Detection

Clinical trial data will always have inconsistencies because of various reasons: incorrect data entry, data integration issues, etc. Currently you must manually look through all data to identify potential discrepancies potentially resulting in valid discrepancies go unnoticed or unidentified.

CDAS’ Data Discrepancy Detection uses Generative AI to automate that process. This “always learning” feature, uses historical data to identify “patterns of discrepancy” and applies them to active trial data to generate “discrepancy candidates” that the reviewer can focus on, rather than scanning the complete data set.