This course delves into the transformative role of Generative AI, particularly large language models (LLMs), in enhancing key areas of HEOR such as systematic literature reviews (SLR) and real-world evidence synthesis, economic modeling, regulatory decision-making, and the development of external control arms.

Participants will learn to apply Generative AI technologies to conduct comprehensive and efficient systematic literature reviews, synthesize evidence at scale, and construct robust economic models that support healthcare policy and market strategies. The course also covers the strategic use of AI in developing external control arms, essential for clinical trials and regulatory submissions, thereby improving the quality and speed of healthcare decisions.

This program is ideal for HEOR data analysts, outcome researchers, epidemiologist, health economists, regulatory affairs professionals, and anyone in all substance matter fields involved in the planning and implementation of healthcare strategies. Through a combination of expert lectures, hands-on exercises, and case study analyses, attendees will gain practical skills and insights into leveraging AI to streamline research processes, enhance data analysis, and forge ahead in the dynamic field of healthcare research and policy. Participants are required to bring a laptop with the capability to connect to Wi-Fi and sufficient processing power to handle basic analytical tasks. For conducting SLR, participants may need access to databases such as PubMed, Cochrane Library, or Embase.

PREREQUISITE: Students are expected to have a basic knowledge of HEOR concepts, health data analysis, research methods, and interest in artificial intelligence.