Event

Apply large language models (LLMs) in HEOR and real-world evidence

This course introduces large language models (LLMs) and their applications in real-world evidence (RWE) and HEOR, with a focus on practical use and responsible implementation.

Technical topics include:

• LLM architecture, embeddings, and context windows
• Hallucinations, risk frameworks, and model evaluation

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

• Hands-on prompt engineering techniques
• Use cases such as literature retrieval, PICO extraction, and data analysis

Participants will gain the skills to effectively and responsibly apply LLMs in HEOR contexts. To participate in practical exercises, attendees are required to bring a personal laptop and have access to a personal or corporate LLM account with file upload functionality.

PREREQUISITE: General knowledge of chat-based LLMs (GPT, Claude, etc) is important. This is an intermediate course, and students should have prior knowledge of AI and have used chat based LLMs in a professional/work setting.

LEVEL: Intermediate 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.