RAG Proposed to Fix AI Gaps in Health Economics and Outcomes Research

General-purpose large language models (LLMs) are poorly suited for Health Economics and Outcomes Research (HEOR) because they are trained on public web data rather than protected, real-world clinical evidence. Key challenges include GDPR restrictions on patient data, fragmented electronic health records, and the complex longitudinal reasoning required for metrics like Quality-Adjusted Life Years (QALYs). Retrieval-Augmented Generation (RAG) has been proposed as a solution, allowing AI models to analyze retrieved slices of actual real-world evidence in real time instead of relying on memorized training data. A conceptual implementation using LangChain and ChromaDB demonstrates how medical documents can be stored in a local vector database and queried securely without exposing sensitive data to external model training. Proponents argue this approach could better align AI capabilities with the rigorous, data-driven demands of European healthcare policy and budget optimization.