Episode 6: Data Managers: Driving the Future of Clinical Research

This video directly addresses the evolving landscape of clinical data management within the pharmaceutical industry, the strategic importance of data, and the increasing role of advanced technologies like AI/ML.

This video explores the transformative journey of clinical data management, highlighting its shift from a back-office function to a strategic imperative driving the future of clinical research. Richard Young and Mayank Anand discuss how data professionals' roles are evolving in an era of decentralized data, emphasizing the critical need for data to optimize trial protocols, operations, and patient access to treatments. The conversation delves into GSK's innovative approach to data strategy and management, which integrates various data functions and prioritizes an enterprise mindset, stakeholder collaboration, and patient-centricity. A key theme is the operationalization of AI/ML in clinical research, moving beyond buzzwords to real-world applications, while also considering ethical implications and the need for continuous professional evolution.

Key Takeaways:

• Strategic Elevation of Data Management: Clinical data management is no longer a secondary function but is now "front and center" in optimizing clinical trials, requiring professionals to drive the future of research rather than just support it.
• AI/ML as an Operational Reality: AI and Machine Learning are no longer just buzzwords but are becoming integral to clinical data management, offering significant opportunities for efficiency and transformation, necessitating discussions around ethical AI and regulatory alignment.
• Integrated Data Strategy for End-to-End Oversight: Leading pharmaceutical companies like GSK are adopting a holistic "Data Strategy and Management" approach, consolidating functions such as data acquisition, medical coding, core data management, centralized monitoring, and data analytics to ensure comprehensive and strategic data oversight.
• Enterprise Mindset and Collaborative Innovation: Successful data transformation requires an "enterprise mindset" that considers all stakeholders (clinical operations, biostats, pharmacovigilance, etc.) and fosters collective ownership, utilizing agile methodologies to accelerate delivery and manage complex interdependencies.
• "Total Experience" for Adoption and Impact: Designing new technologies and processes with "Total Experience" (TX) in mind—encompassing change management, communication, and the impact on all end-users, including patients and investigators—is crucial for successful adoption and achieving desired outcomes.
• Evolving Role of Data Professionals: The traditional "data manager" role is transforming into future-oriented positions like "data scientist" or "data engineer," emphasizing continuous learning and adaptation to new technologies, rather than fearing job displacement by AI.
• Challenges in Infrastructure and Data Democratization: Significant industry challenges persist in modernizing legacy infrastructure and achieving broader data democratization, highlighting areas for future innovation and collaborative efforts to unlock the full potential of clinical data.