Session 24 - Cut Through the Data Clutter: Mitigate Risk BEFORE it Becomes an Issue
Wednesday, September 16, 2020: 12:30 PM - 1:30 PM
/ CEU Credits:0.2
SESSION CHAIRS & SPEAKERS
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.
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
CDM Certification Competencies::
Processing External Data,Data Base Quality Control Audits,Data Base Lock Procedures
Statistician,Data Management,Technology Adoption