In today’s highly competitive healthcare landscape, the generation of robust evidence throughout the entire product lifecycle is critical for enhancing market access and achieving effective engagement with key stakeholders. This presentation will provide an in-depth exploration of the strategic frameworks and advanced methodologies employed in evidence generation and dissemination. The speakers will examine various types of evidence that can be developed, including systematic reviews, meta-analyses, network meta-analyses (NMAs), and economic modeling, all from a Health Economics and Outcomes Research (HEOR) perspective. The session will emphasize the increasing significance of Real-World Evidence (RWE) and the transition from traditional evidence generation models to more dynamic, real-world data-driven approaches. In addition, the evolving role of Artificial Intelligence (AI) in evidence synthesis will be explored, highlighting how AI technologies are enhancing the efficiency and precision of evidence generation processes. Attendees will gain comprehensive insights into the evidence generation cycle, from the initial strategic planning and production phases to the effective communication of findings, ensuring that the right information reaches the appropriate stakeholders. The speakers will also underscore the importance of conducting thorough evidence landscape assessments and tailoring evidence communication to meet the specific needs of various audiences, including payers, healthcare providers, and patients. This presentation aims to equip participants with advanced strategies for generating impactful evidence and promoting meaningful stakeholder engagement in an increasingly data-driven environment.

In this presentation, you will learn:

• Strategic frameworks and advanced methodologies for generating evidence throughout the product lifecycle, focusing on enhancing market access and stakeholder engagement.
• The different types of evidence that can be developed, including systematic reviews, meta-analyses, network meta-analyses (NMAs), and economic modeling, from a Health Economics and Outcomes Research (HEOR) perspective.
• The growing importance of Real-World Evidence (RWE) and the shift from traditional evidence generation paradigms to more innovative, real-world data-driven approaches.
• The evolving role of Artificial Intelligence (AI) in evidence synthesis and how AI technologies are improving the efficiency and accuracy of evidence generation processes.
• Techniques for conducting comprehensive evidence landscape assessments and strategies for effectively communicating evidence tailored to diverse audiences, including payers, healthcare providers, and patients.