PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong

• URL: http://arxiv.org/abs/2604.14215v1
• Date: Fri, 10 Apr 2026 06:47:43 GMT
• Title: PriHA: A RAG-Enhanced LLM Framework for Primary Healthcare Assistant in Hong Kong
• Abstract summary: We propose a Retrieval-Augmented Generation-Enhanced LLM system as Primary Healthcare Assistant (PriHA) in Hong Kong.<n>Our proposed method can outperform both ablations and baseline in terms of accuracy and clarity.
• Score: 17.280741707627122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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