Event

Unlock the transformative potential of generative AI (GenAI) in health economics and outcomes research (HEOR). This course provides a foundational understanding of generative AI models, with a focus on large language models (LLMs) and their applications in HEOR. Participants will explore the best practices for accessing and utilizing LLMs, moving beyond chatbot interactions to AI-driven scientific research, systematic literature reviews (SLR), and economic evaluation.

Technical Topics Include:

• Overview of generative AI models, with a focus on LLMs in HEOR
• Beyond chatbots: Best practices for accessing and utilizing LLMs
• Prompt engineering: Techniques to optimize AI outputs for research
• Retrieval-Augmented Generation (RAG) and AI agents: Enhancing research efficiency
• Privacy and security considerations: Ethical and responsible AI use in HEOR
• Practical applications: Using AI for systematic literature reviews (SLR) and economic evaluations

This Course Includes Tools and Concepts That Can Be Immediately Applied, Including:

• Exploration of Python and relevant AI frameworks for HEOR research
• Practical exercises demonstrating GenAI techniques such as RAG and AI-driven analysis
• Guidance on AI-enhanced approaches for conducting systematic literature reviews and economic modeling
• Strategies for integrating AI into HEOR workflows to improve efficiency and accuracy

Participants will leave this course with the knowledge and practical insights needed to apply generative AI techniques in HEOR, enhancing efficiency, accuracy, and innovation in the field.

PREREQUISITES: Students should have a general understanding of common HEOR concepts such as SLRs and cost-effectiveness models. Knowledge of Python or similar programming languages such as R is considered a benefit but not required.

Schedule:

LENGTH: 4 Hours | Course runs 1 day

Sunday, 13 May 2025 | Course runs 1 Day
8:00am-12:00pm Eastern Daylight Time (EDT)