Event

Apply generative AI, RAG, and agents in HEOR research

This course introduces generative AI (GenAI), with a focus on large language models (LLMs), and their applications in health economics and outcomes research (HEOR). It explores practical approaches for accessing and using these tools beyond chat-based interfaces.

Technical topics include:

• Fundamentals of GenAI and LLMs
• Prompt engineering, retrieval-augmented generation (RAG), and agent-based workflows
• Methods for accessing and deploying LLMs
• Privacy and security considerations in HEOR applications

This course includes tools and concepts that can be immediately applied, including:

• Applications in systematic literature reviews (SLRs) and economic evaluation
• Hands-on prompt engineering, RAG, and agent-based use cases
• Practical exercises using Python and AI frameworks
• Approaches for integrating GenAI into HEOR workflows

Participants will gain the knowledge and skills to begin applying GenAI techniques to enhance HEOR research and support more efficient, data-driven decision-making.

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.

LEVEL: Introductory TRACK: Methodological & Statistical Research HEOR Competency: 10.3 Artificial Intelligence (AI) and Machine Learning (ML) HEOR Competencies: A resource for HEOR Professionals

FACULTY MEMBERS

ISPOR short courses are designed to enhance knowledge and techniques in core health economics and outcomes research (HEOR) topics as well as emerging trends in the field. Short courses offer 4 or 8 hours of premium scientific education and an electronic course book. Active attendee participation combined with our expert faculty creates an immersive and impactful learning experience. Short courses are not recorded and are only available during the live course presentation.