EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic
  Health Record Analysis
Authors: name: Yusheng Liao; name: Chaoyi Wu; name: Junwei Liu; name: Shuyang Jiang; name: Pengcheng Qiu; name: Haowen Wang; name: Yun Yue; name: Shuai Zhen; name: Jian Wang; name: Qianrui Fan; name: Jinjie Gu; name: Ya Zhang; name: Yanfeng Wang; name: Yu Wang; name: Weidi Xie
Published: 2025-10-29 15:32:47

Abstract: Electronic Health Records (EHRs) contain rich yet complex information, and
their automated analysis is critical for clinical decision-making. Despite
recent advances of large language models (LLMs) in clinical workflows, their
ability to analyze EHRs remains limited due to narrow task coverage and lack of
EHR-oriented reasoning capabilities. This paper aims to bridge the gap,
specifically, we present EHR-Ins, a large-scale, comprehensive EHR reasoning
instruction dataset, comprising 300k high-quality reasoning cases and 4M
non-reasoning cases across 42 distinct EHR tasks. Its core innovation is a
thinking-graph-driven framework that enables to generate high-quality reasoning
data at scale. Based on it, we develop EHR-R1, a series of reasoning-enhanced
LLMs with up to 72B parameters tailored for EHR analysis. Through a multi-stage
training paradigm, including domain adaptation, reasoning enhancement, and
reinforcement learning, EHR-R1 systematically acquires domain knowledge and
diverse reasoning capabilities, enabling accurate and robust EHR analysis.
Lastly, we introduce EHR-Bench, a new benchmark curated from MIMIC-IV, spanning
42 tasks, to comprehensively assess reasoning and prediction across EHR
scenarios. In experiments, we show that the resulting EHR-R1 consistently
outperforms state-of-the-art commercial and open-source LLMs (including
DeepSeek-V3 and GPT-4o), surpassing GPT-4o by over 30 points on MIMIC-Bench and
achieving a 10\% higher zero-shot AUROC on EHRSHOT. Collectively, EHR-Ins,
EHR-R1, and EHR-Bench have significantly advanced the development for more
reliable and clinically relevant EHR analysis.