The increasing importance of digital health and the need for better health literacy require effective methods to access and understand Electronic Health Records (EHRs). While Large Language Models (LLMs) show promise in this domain, traditional Retrieval-Augmented Generation (RAG) struggles to handle the complex, interconnected nature of clinical data. GraphRAG emerges as a powerful alternative, leveraging knowledge graphs (KGs) to capture semantic relationships within EHRs. This research evaluates the effectiveness of graph expansion in a GraphRAG to enhance information retrieval from FHIR-formatted medical data. We propose a 1-hop expansion approach built upon a lexical search baseline which, while inheriting some limitations of traditional keyword-based retrieval, significantly enhances LLMs’ access to comprehensive and diverse contextual information. Our evaluation, using synthetic patient data and a targeted set of questions across five models, reveals that the 1-hop expansion strategy consistently outperforms the baseline in subjective metrics like comprehensiveness and diversity, and frequently in quantitative metrics such as answer and contextual relevancy. These results highlight the potential of our proposal to enhance LLM response when querying intricate medical data.