Master the Next Generation of Clinical Research:
5 Strategies to Dramatically Improve Data Quality
May 8th - 11am (ET)
Advances in clinical research over the last decade have given rise to an intricate patient data ecosystem, presenting enormous opportunities for clinical research professionals to create a more complete view of the patient. One key challenge, however, lies in integrating and managing those sources to deliver high-quality data, faster. How do we overcome this?
In this session, we will unravel the complexities of fragmented data systems, emphasizing the urgent need to adopt technology and processes that harmonize diverse data sources and ensure quality throughout the clinical data lifecycle. You’ll discover how to unify data management and risk-based quality management approaches, and augment processes with advanced technologies like AI to rectify manual data entry errors more efficiently and ensure overall quality end-to-end.
Join this webinar to learn five strategies to help you adopt a clinical data quality framework that addresses the complexity of the modern clinical data landscape.
This webinar explores key solutions to overcome data quality challenges in clinical research:
The top challenges organizations face when adopting an end-to-end clinical data quality strategy
Recommendations to overcome roadblocks, including technology and implementation best practices to make sure you avoid those challenges
Best practices on how to ensure successful adoption by end users
Key Learning Objectives for Enhancing Clinical Data Quality:
Move from manual, reactive data review and cleaning to proactive, risk-based approaches based on integrated data
Provide clinical data management and monitoring teams with workflows and analytics that support their day-to-day functions
Leverage technologies that incorporate AI to automate manual tasks and identify potential issues sooner
