A Knowledge Graph Enhancement Technique for HIPAA Compliant Health Question Answering in Personal Health Libraries

Large Language Models (LLMs) show promise for answering general health questions, but their utility for personalized health queries is limited by their lack of access to complete individual health records and general non-compliance with HIPAA requirements. Additionally, the opacity of LLMs and their tendency to generate hallucinated responses further reduce their reliability for personalized health Question Answering (QA). In contrast, Knowledge Graphs (KGs) have proven effective for QA tasks, especially in extracting structured insights from text, but transforming free text into KGs often leads to information or context loss that can compromise answer accuracy. To overcome this challenge, we present a novel iterative and monotonic KG refinement technique that enriches knowledge representation without sacrificing contextual integrity. We formalize this approach within a mathematical framework, demonstrating that each refinement step preserves or improves answer quality. Using electronic health record data, we validate the practical feasibility of this method and introduce PerHL , a personalized health QA system built on this refinement process. Our empirical results show that the approach significantly improves answer quality for personalized health queries, marking an important step toward trustworthy, context-aware health information systems.