EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records

TL;DR

EHRNavigator is a multi-agent system designed to answer patient-specific questions from heterogeneous EHR data, demonstrating high accuracy and clinical relevance in real-world hospital settings.

Contribution

It introduces a novel multi-agent framework that effectively handles multimodal, heterogeneous EHR data for patient-level question answering in practical clinical environments.

Findings

Achieved 86% accuracy on real-world EHR cases

Maintained clinically acceptable response times

Demonstrated strong generalization across datasets

Abstract

Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language question-answering (QA) systems are evaluated solely on benchmark datasets, limiting their practical relevance. To overcome this limitation, we introduce EHRNavigator, a multi-agent framework that harnesses AI agents to perform patient-level question answering across heterogeneous and multimodal EHR data. We assessed its performance using both public benchmark and institutional datasets under realistic hospital conditions characterized by diverse schemas, temporal reasoning demands, and multimodal evidence integration. Through quantitative evaluation and clinician-validated chart review, EHRNavigator demonstrated strong generalization, achieving 86% accuracy on real-world cases while maintaining clinically acceptable response times. Overall, these findings confirm that EHRNavigator effectively bridges the gap between benchmark evaluation and clinical deployment, offering a robust, adaptive, and efficient solution for real-world EHR question answering.