Ineffective and inefficient monitoring of data quality in traditional data management delays risk identification by months. Current methods are resource intensive, and manual processes only identify risks that clinical teams know to look for:

• Up to ~80% of edit checks never fire
• Many edit checks that DO fire are closed without the site fixing the problem
• Many errors are never detected and add risk to drug submissions

With the advancement of machine learning, there is a more effective way. We’ll present a modern approach to data quality monitoring that uses machine learning to identify anomalies and help you comply with ICH E6 (R2).

In this webinar, you’ll learn:

• Analysis on how companies waste time and money on edit checks
• What the new ICH E6 (R2) guidelines mean for you
• A novel approach to improve data quality, reduce risk, and improve productivity
• About the convergence of centralized monitoring and data management

P.S. Schedule conflict? Sign up anyway and get a copy of the slides and access to the on-demand presentation.