Regulatory bodies like the FDA increasingly use real-world evidence for label expansions, safety monitoring and post-market surveillance of marketed therapies. At the same time, payers demand more real-world validation of therapeutic benefits. This shift requires manufacturers to demonstrate effectiveness and economic value beyond clinical trials.

AI Enhances Utility of Real-World Evidence 

Technological advances, particularly in artificial intelligence and machine learning, are enhancing the utility of real-world evidence by enabling faster insights, personalized care and adaptive modeling. As clinical trials face challenges due to more targeted drug development, real-world evidence fills critical gaps with real-world comparators and endpoints. 

Overcoming Data Fragmentation in Pursuit of Real-World Evidence

To realize meaningful real-world evidence, HEOR teams must carefully select and manage diverse real-world data while overcoming the challenges of data fragmentation. Success depends on evaluating data vendors for quality, interoperability and analytical readiness—focusing on connected, usable data that enables timely, evidence-based decision-making.

This ebook will help you understand the 6 proven best practices for making real-world evidence work, including:

1. Ensuring privacy and regulatory compliance
2. Understanding token precision and match quality
3. Assessing the breadth and depth of data sources
4. Verifying interoperability and standardization of data formats
5. Confirming analytical readiness
6. Ensuring effective data governance and stewardship

Ready to leverage data for real-world insights?

Get started by downloading the ebook, 6 Best Practices for Unlocking the Full Value of RWE in HEOR, including a convenient Data Partner Conversation Guide!