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    <abstract>The application of large language models (LLMs) to personalized medical assistants has garnered growing interest. However, existing medical benchmarks largely rely on static question answering with pre-selected evidence, leaving unclear whether LLMs can make reliable clinical decisions from real longitudinal electronic health records (EHRs). To bridge this gap, we introduce ObGynLongBench, a rule-grounded long-context EHR benchmark for obstetric and gynecologic decision-making, comprising 1,500 clinical decision-point cases from 976 real pregnancy EHR histories and traceable rules. Each case is anchored to a patient, a pregnancy-timeline point, and a pre-decision information boundary, enabling Evidence-only, Visit-level EHR, and History-level EHR evaluation. Evaluating 17 LLMs reveals a substantial Evidence-to-EHR Gap: models perform well when evidence is directly provided, but accuracy drops when evidence must be extracted from same-day records or full pre-decision EHR histories. Further analyses identify evidence utilization as a key bottleneck: performance decreases with longer EHR contexts and more complex evidence requirements, and earlier failures often predict later failures within the same patient history. Finally, active-search agents perform best among EHR access strategies, highlighting patient-specific evidence utilization as a central challenge for reliable personalized medical assistants.</abstract>
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%0 Conference Proceedings
%T ObGynLongBench: Revealing the Evidence-to-EHR Gap in Longitudinal EHR Decision-Making
%A Xiang, Jun
%A Bao, Zhijie
%A Rong, Hu
%A Qin, Kaizhou
%A Chen, Wei
%A Wei, Zhongyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xiang-etal-2026-obgynlongbench
%X The application of large language models (LLMs) to personalized medical assistants has garnered growing interest. However, existing medical benchmarks largely rely on static question answering with pre-selected evidence, leaving unclear whether LLMs can make reliable clinical decisions from real longitudinal electronic health records (EHRs). To bridge this gap, we introduce ObGynLongBench, a rule-grounded long-context EHR benchmark for obstetric and gynecologic decision-making, comprising 1,500 clinical decision-point cases from 976 real pregnancy EHR histories and traceable rules. Each case is anchored to a patient, a pregnancy-timeline point, and a pre-decision information boundary, enabling Evidence-only, Visit-level EHR, and History-level EHR evaluation. Evaluating 17 LLMs reveals a substantial Evidence-to-EHR Gap: models perform well when evidence is directly provided, but accuracy drops when evidence must be extracted from same-day records or full pre-decision EHR histories. Further analyses identify evidence utilization as a key bottleneck: performance decreases with longer EHR contexts and more complex evidence requirements, and earlier failures often predict later failures within the same patient history. Finally, active-search agents perform best among EHR access strategies, highlighting patient-specific evidence utilization as a central challenge for reliable personalized medical assistants.
%U https://aclanthology.org/2026.emnlp-main.685/
%P 15764-15788

Markdown (Informal)

[ObGynLongBench: Revealing the Evidence-to-EHR Gap in Longitudinal EHR Decision-Making](https://aclanthology.org/2026.emnlp-main.685/) (Xiang et al., EMNLP 2026)

• ObGynLongBench: Revealing the Evidence-to-EHR Gap in Longitudinal EHR Decision-Making (Xiang et al., EMNLP 2026)

ACL

• Jun Xiang, Zhijie Bao, Hu Rong, Kaizhou Qin, Wei Chen, and Zhongyu Wei. 2026. ObGynLongBench: Revealing the Evidence-to-EHR Gap in Longitudinal EHR Decision-Making. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15764–15788, Budapest, Hungary. Association for Computational Linguistics.