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Leveraging Data Analytics to Drive Compliance and Quality in a Risk-Based Monitoring Environment
Session Chair(s)
Matthew Krumrai
Director, Clinical Quality Assurance, AbbVie, United States
The abundance of data collected in clinical research offers an increasing opportunity for insights into risks, quality, and compliance.To realize the full value, the data must be harnessed in a manner that is useful and meaningful, which can be a challenge. The key lies in developing analytics and models that provide insights. Leveraging data is particularly valuable in an environment where Risk Based Monitoring (RBM) is now the norm and the models and dashboards derived from analytics can proactively indicate risks and issues. Interpretation can drive compliance and quality decision making and a focus on what matters most. Advanced analytics and machine learning can also be leveraged to detect irregularities that may indicate falsification and misconduct.
This session will explore the potential of data analytics and modeling as applied to risk-based monitoring activities as well as quality assurance oversight of risk-base monitoring.
Learning Objective : Describe how measured risk changes over time Compare change in risk relative to operational attributes Judge the effects of RBM on risk mitigation
Speaker(s)
Statistical Models of Risk Flux Reveal Dynamics of Risk-Based Monitoring Managed Clinical Trials
Senior Director of Customer Success, Castor, United States
Study on Designing a Method of Sampling SDV in a Clinical Trial Using Audit Trail Data
Student, Waseda University, Japan
Subject Data Analysis and Potential Fraud and Misconduct Analysis for Data Quality in Clinical Trials
Centralized Monitoring Manager, IQVIA, United States
Maintaining Quality Assurance in a Risk-Based Monitoring Environment
Director, Clinical Quality Assurance, AbbVie, United States
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