November 8, 2026
Examine large language models (LLMs) for real-world evidence and HEOR
This course explores large language models (LLMs) from leading organizations such as OpenAI, Anthropic, Google, and Meta, with a focus on their application in real-world evidence (RWE) generation and HEOR. It introduces key technical concepts and considerations for responsible use in regulated environments.
Technical topics include:
- LLM architecture, processing layers, and attention mechanisms
- Embeddings, context windows, and hallucinations
- Risk-based frameworks and model evaluation benchmarks
- Practical considerations for applying LLMs in HEOR and RWE
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 extraction
- Summarizing tables and figures, automating captions, and generating code
- Practical exercises using commercially available LLM tools
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.
*Conference attendance is not required to attend an ISPOR Short Course. Separate registration is required for conference attendees.
LEVEL: Intermediate
TRACK: Methodological & Statistical Research
FACULTY MEMBERS
Schedule:
LENGTH: 4 Hours | Course runs 1 day
Sunday, 8 November 2026 | Course runs 1 Day
1:00pm-5:00pm Central European Time (CET)
*Conference attendance is not required to attend an ISPOR Short Course. Separate registration is required for conference attendees.
Visit the ISPOR Europe 2026 Program page to view all short courses offered.