Event

Implement and scale generative AI in HEOR practice

This intermediate course explores how to design and build robust, purpose-built generative AI (GenAI) solutions for health economics and outcomes research (HEOR) and market access. Moving beyond general-purpose chatbots and one-off pilots, the course focuses on how architectural choices can support AI outputs that are reliable, traceable, and defensible in regulatory, HTA, and payer environments.

Technical topics include:

• Context engineering and the integration of external knowledge into GenAI workflows
• Retrieval-Augmented Generation (RAG), tool use, and other approaches for improving factual accuracy, traceability, and domain fit
• Agentic AI for coordinating multi-step HEOR processes with appropriate boundaries, monitoring, and human oversight
• Design principles for developing rigorous, HTA-ready AI tools
• Evaluation and validation of GenAI systems for reliability, reproducibility, and regulatory alignment
• Professional development environments, including integrated development environments (IDEs), AI-assisted development, and reusable components

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

• Applied examples demonstrating context engineering, agentic AI, and rigorous AI design for HEOR and market access
• Design patterns for building purpose-built GenAI solutions that incorporate external evidence and domain-specific information
• Strategies for orchestrating multi-step AI workflows while maintaining transparency, control, and accountability
• Frameworks for evaluating and validating GenAI tools (eg, ELEVATE-GenAI, NICE and FDA guidance)
• Full, runnable code for a worked example that participants can run and adapt after the course

Participants will gain the knowledge and skills to design AI architectures that meet the evidentiary standards of HEOR and market access. They will leave with concrete design patterns, runnable reference implementations, and validation frameworks for developing GenAI tools whose outputs can withstand scrutiny in HTA, regulatory, and payer settings.

PREREQUISITES: Attendance at “Applied Generative AI for HEOR: Introduction” or familiarity with concepts such as prompt engineering, APIs, and LLM workflows is necessary. A basic understanding of Python or another similar scripting language is recommended to get the most benefit from the provided worked examples.

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*Conference attendance is not required to attend an ISPOR Short Course. Separate registration is required for conference attendees.

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