We present a structured analysis of how contemporary clinical AI systems integrate electronic health record (EHR) data and the extent to which they support longitudinal clinical reasoning. Drawing on a curated corpus of clinical natural language processing (NLP) and EHR-integrated systems, we develop a coding framework that captures both technical integration strategies and reasoning-relevant representational features, such as trajectory modeling, cross-encounter synthesis, longitudinal analysis, and absence reasoning. We also elicited the experiences of three physicians in their EHR use, including what strengths and weaknesses they found with their institution's current EHR system(s). Our analysis shows that while many systems incorporate EHR data, they predominantly operate on encounter-level or aggregated representations, with limited support for explicit temporal reasoning across patient histories. Reasoning-relevant structures are inconsistently represented, and evaluation paradigms remain largely focused on predictive performance instead of longitudinal interpretability. We argue that current approaches treat EHR data as a static input rather than a substrate for ongoing clinical reasoning, and we outline a framework for understanding how future systems might more effectively align with the temporal and interpretive structure of clinical practice.

翻译：我们提出了一项结构化分析，探讨当代临床AI系统如何整合电子健康记录（EHR）数据，以及它们在多大程度上支持纵向临床推理。基于精选的临床自然语言处理（NLP）和EHR整合系统语料库，我们开发了一个编码框架，涵盖技术整合策略与推理相关的表示特征，例如轨迹建模、跨就诊综合、纵向分析及缺失推理。我们还收集了三位医生在使用EHR时的体验，包括他们对其所在机构当前EHR系统的优缺点的看法。分析表明，虽然许多系统整合了EHR数据，但它们主要基于就诊级或聚合表示运行，对跨患者历史的显式时间推理支持有限。推理相关的结构表示不一致，评估范式仍主要侧重于预测性能而非纵向可解释性。我们认为，当前方法将EHR数据视为静态输入而非持续临床推理的基质，并概述了一个框架，以理解未来系统如何更有效地与临床实践的时间性和解释性结构对齐。