Electronic health records contain abundant unstructured text, but long and noisy patient context can hinder effective document retrieval. This work evaluates a retrieval-augmented generation approach that integrates medical knowledge graphs with large language models for query reformulation. Experiments on two TREC benchmark datasets indicate that knowledge-graph grounding can improve the reliability and domain relevance of medical query refinement.