PROJECTS

This project introduces HealthQ, a framework that evaluates how effectively large language model (LLM) chains ask questions in digital healthcare settings. Going beyond simple answering, HealthQ assesses the ability of advanced LLM chains — including Retrieval-Augmented Generation (RAG), Chain of Thought (CoT), and reflective chains — to elicit specific, relevant, and useful patient information. Using an LLM judge and metrics like ROUGE and NER-based comparisons, HealthQ is validated on medical dialogue datasets (ChatDoctor, MTS-Dialog) and tested across multiple judge models. The framework establishes a systematic, model-agnostic method for measuring questioning quality and demonstrates how stronger inquiry skills directly improve patient information gathering, leading to safer and more effective healthcare conversations.