AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization
Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constrained by a reliance on curated inputs and simplified retrieval tasks. To bridge the gap between idealized experimental settings and realistic clinical environments, we present AgentEHR. This benchmark challenges agents to execute complex decision-making tasks, such as diagnosis and treatment planning, requiring long-range interactive reasoning directly within raw and high-noise databases. In tackling these tasks, we identify that existing summarization methods inevitably suffer from critical information loss and fractured reasoning continuity. To address this, we propose RetroSum, a novel framework that unifies a retrospective summarization mechanism with an evolving experience strategy. By dynamically re-evaluating interaction history, the retrospective mechanism prevents long-context information loss and ensures unbroken logical coherence. Additionally, the evolving strategy bridges the domain gap by retrieving accumulated experience from a memory bank. Extensive empirical evaluations demonstrate that RetroSum achieves performance gains of up to 29.16% over competitive baselines, while significantly decreasing total interaction errors by up to 92.3%.
6971b232c1c7409747bf9654
Yusheng Liao
claimed_verified
2026-01-22T08:44:49.734Z
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Yusheng Liao
BlueZeros
user
BlueZeros
6971b232c1c7409747bf9655
Chuan Xuan
claimed_verified
2026-01-22T08:44:47.363Z
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xuan
handmaster
user
handmaster
6971b232c1c7409747bf9656
Yutong Cai
6971b232c1c7409747bf9657
Lina Yang
6971b232c1c7409747bf9658
Zhe Chen
6971b232c1c7409747bf9659
Yanfeng Wang
6971b232c1c7409747bf965a
Yu Wang
AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization
Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constrained by a reliance on curated inputs and simplified retrieval tasks. To bridge the gap between idealized experimental settings and realistic clinical environments, we present AgentEHR. This benchmark challenges agents to execute complex decision-making tasks, such as diagnosis and treatment planning, requiring long-range interactive reasoning directly within raw and high-noise databases. In tackling these tasks, we identify that existing summarization methods inevitably suffer from critical information loss and fractured reasoning continuity. To address this, we propose RetroSum, a novel framework that unifies a retrospective summarization mechanism with an evolving experience strategy. By dynamically re-evaluating interaction history, the retrospective mechanism prevents long-context information loss and ensures unbroken logical coherence. Additionally, the evolving strategy bridges the domain gap by retrieving accumulated experience from a memory bank. Extensive empirical evaluations demonstrate that RetroSum achieves performance gains of up to 29.16% over competitive baselines, while significantly decreasing total interaction errors by up to 92.3%.
AgentEHR presents a benchmark for autonomous EHR navigation requiring complex decision-making, while RetroSum framework improves performance through retrospective summarization and evolving experience strategies.
Electronic Health Records large language models decision-making retrospective summarization evolving experience strategy interaction history memory bank clinical environments autonomous navigation