Event

Implement and scale generative AI in HEOR practice

This intermediate course explores how to move from experimentation to real-world implementation of generative AI (GenAI) in health economics and outcomes research (HEOR). It focuses on validating, deploying, and governing AI solutions within regulated, evidence-driven environments.

Technical topics include:

• GenAI implementation lifecycle from pilot to production
• Workflow orchestration, modularization, and scaling strategies
• Retrieval-Augmented Generation (RAG) and integration of external data sources
• Agentic AI and multi-step automation of HEOR processes
• Evaluation frameworks for reliability, reproducibility, and regulatory alignment

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

• Hands-on implementation of a simple RAG pipeline (data chunking, embeddings, prompt augmentation)
• Demonstration of agentic workflows for task orchestration and monitoring
• Frameworks for evaluating and validating GenAI tools (eg, ELEVATE-GenAI, NICE, FDA guidance)
• Practical strategies for integrating GenAI into HEOR workflows with transparency and control

Participants will gain the knowledge and skills to evaluate, implement, and scale GenAI solutions that enhance productivity, scientific rigor, and decision-making in HEOR.

PREREQUISITES: Completion of the “Applied Generative AI for HEOR: Introduction” ISPOR course or familiarity with concepts such as prompt engineering, APIs, and LLM workflows. A basic understanding of Python or other similar scripting languages is recommended to get the most benefit from the guided practical sessions.

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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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