Session 24 - Cut Through the Data Clutter: Mitigate Risk BEFORE it Becomes an Issue

  • #explore
Wednesday, September 16, 2020: 12:30 PM - 1:30 PM
/ CEU Credits:0.2

SESSION CHAIRS & SPEAKERS

Speaker
Chandeep Kaur
Senior Clinical Data Analyst
PAREXEL International
Session Chair
Jennifer Aulsebrook
Senior Engagement Consultant
Medidata Solutions , a Dassault Systemes Company
Speaker
Lisa Ensign
Senior Director of Statistics
corn AI by Medidata, a Dassault Systèmes company
Speaker
Michelle Cartland
Manager, CDS Strategic Technology Advancement Team
Syneos Health

SESSION DESCRIPTION 

With the exponential growth in clinical research data from disparate sources, data quality issues will persist. More concerning is the difficulty to detect these issues with the increased trial complexity.

To date, data quality efforts are often the final step to secure database lock and feel confident for regulatory submission. What if you could proactively detect issues and remediate quickly? How valuable would it be to process real-time data from disparate sources? How would overall trial execution improve with resource efficiencies realized?

Learn from our expert panel how technology that incorporates machine learning has the power to: 

  • Collect data from multiple sources.
  • Identify issues as they arise. 
  • Generate insights that help operational staff mitigate those risks.

Machine learning searches that data for trends, patterns and anomalies that not only help mitigate risk in the existing trial but also help better plan for future risk.


TOPIC


LEARNING OBJECTIVES

1. Overview of Machine-Learning, Data Clutter, Risk Mitigation
2. Collecting data from multiple sources, Identifying issues as they arise, Generating insights to mitigate risk
3. Technology Adoption: End User Considerations and Tool Examples
4. Insights from a Real-World Example

Handout(s)


SESSION INFORMATION

CDM Certification Competencies::
Processing External Data,Data Base Quality Control Audits,Data Base Lock Procedures

Target Audience:
Statistician,Data Management,Technology Adoption