PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong
For Hong Kong citizens and healthcare providers, PriHA addresses the problem of accessing fragmented clinical guidelines, but the improvement is incremental over existing RAG methods.
PriHA is a Retrieval-Augmented Generation (RAG) system designed to assist primary healthcare in Hong Kong by retrieving fragmented clinical guidelines. It outperforms baselines in accuracy and clarity, providing a reliable dialogue framework for high-risk localized applications.
To address the unsustainable rise in public health expenditures, the Hong Kong SAR Government is shifting its strategic focus to primary healthcare and encouraging citizens to use community resources to self-manage their health. However, official clinical guidelines are fragmented across disparate departments and formats, creating significant access barriers. While general-purpose Large Language Models (LLMs) such as ChatGPT and DeepSeek offer potential solutions for information accessibility, they are prone to generating factually inaccurate content due to a lack of localized and domain-specific knowledge. To this end, we propose a Retrieval-Augmented Generation-Enhanced LLM system as Primary Healthcare Assistant (PriHA) in Hong Kong. Specifically, a tri-stage pipeline is proposed that leverages a query optimizer to generalize user intent-oriented sub-queries, followed by a novel Dual Retrieval Augmented Generation (DRAG) architecture for mixed-source retrieval and context-reorganized generation. Comprehensive experiments and a detailed case study demonstrate that our proposed method can outperform both ablations and baseline in terms of accuracy and clarity. Our research provides a reliable and traceable dialogue retrieval framework for exploring other high-risk, localized application scenarios.