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

arXiv: https://arxiv.org/abs/2601.10020 · HTML: https://mlsanity.com/paper/2601.10020/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 29 of 29)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL](https://mlsanity.com/paper/2312.11242/) (2023)
- [EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records](https://mlsanity.com/paper/2401.07128/) (2024)
- [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019)
- [EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images](https://mlsanity.com/paper/2310.18652/) (2023)
- [EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records](https://mlsanity.com/paper/2301.07695/) (2023)
- [EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries](https://mlsanity.com/paper/2402.16040/) (2024)
- [Knowledge Graph-based Question Answering with Electronic Health Records](https://mlsanity.com/paper/2010.09394/) (2020)
- [DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries](https://mlsanity.com/paper/2205.01290/) (2022)
- [BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models](https://mlsanity.com/paper/2402.11573/) (2024)
- [Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction](https://mlsanity.com/paper/2501.18724/) (2025)
- [Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records](https://mlsanity.com/paper/2405.06673/) (2024)
- [Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture](https://mlsanity.com/paper/2111.14703/) (2021)
- [Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records](https://mlsanity.com/paper/2403.09226/) (2024)
- [Question Answering for Electronic Health Records: A Scoping Review of datasets and models](https://mlsanity.com/paper/2310.08759/) (2023)
- [EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records](https://mlsanity.com/paper/2406.00019/) (2024)
- [Evaluating Search Engines and Large Language Models for Answering Health Questions](https://mlsanity.com/paper/2407.12468/) (2024)
- [MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation](https://mlsanity.com/paper/2503.17900/) (2025)
- [Dynamic Q&A of Clinical Documents with Large Language Models](https://mlsanity.com/paper/2401.10733/) (2024)
- [A semi-autonomous approach to connecting proprietary EHR standards to FHIR](https://mlsanity.com/paper/1911.12254/) (2019)
- [Self-supervised Answer Retrieval on Clinical Notes](https://mlsanity.com/paper/2108.00775/) (2021)

## Built on by (most cited first, 1 of 1)

- [ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning](https://mlsanity.com/paper/2605.20176/) (2026)

## Abstract (arXiv)

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.