Unlocking electronic health records: a hybrid graph RAG approach to safe clinical AI for patient QA

TL;DR

MediGRAF is a new system that combines structured and unstructured data to improve clinical information retrieval from electronic health records, reducing errors and improving accuracy.

Contribution

Introduces MediGRAF, a hybrid graph RAG system that integrates structured and unstructured data for safer and more accurate clinical AI.

Findings

Achieved 100% recall for factual queries, ensuring all relevant information was retrieved.

Complex inference tasks scored 4.25/5 in expert evaluation with zero safety violations.

Demonstrated hybrid graph-grounding as a superior approach for clinical information retrieval.

Abstract

Electronic health record (EHR) systems present clinicians with vast repositories of clinical information, creating a significant cognitive burden where critical details are easily overlooked. While Large Language Models (LLMs) offer transformative potential for data processing, they face significant limitations in clinical settings, particularly regarding context grounding and hallucinations. Current solutions typically isolate retrieval methods, focusing either on structured data (SQL/Cypher) or unstructured semantic search, but fail to integrate both simultaneously.

This work presents MediGRAF (Medical Graph Retrieval Augmented Framework), a novel hybrid Graph RAG system that bridges this gap. By uniquely combining Neo4j Text2Cypher capabilities for structured relationship traversal with vector embeddings for unstructured narrative retrieval, MediGRAF enables natural language querying of the complete patient journey. The system was evaluated using data from 10 patients from the MIMIC-IV dataset, generating 5,973 nodes and 5,963 relationships, across varying query complexities using both deterministic retrieval metrics and a structured clinical expert evaluation protocol.

The system demonstrated 100% recall for factual queries, meaning all relevant information was retrieved and included in the output. Complex inference tasks achieved a mean expert quality score of 4.25/5 with zero safety violations across all evaluated cases.

These results demonstrate that hybrid graph-grounding significantly advances clinical information retrieval, offering a safer and more comprehensive alternative to standard LLM deployments. By combining structured graph traversal with semantic vector search, MediGRAF addresses the critical limitations of isolated retrieval approaches, establishing a foundation for responsible AI deployment in clinical settings.

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