Electronic health records (EHRs) are central to clinical prediction, but existing methods either rely on correlation-driven deep models or use single large language models (LLMs), making it difficult to support multidisciplinary clinical reasoning. Recent multi-agent systems (MAS) provide a promising alternative, yet current EHR-grounded MAS methods still suffer from weak evidence differentiation across agents and redundant multi-round interaction. We propose D2MDT, a Department-aware MultiDisciplinary Team Consultation with Deliberation for Efficient clinical prediction. D2MDT first constructs structured EHR evidence and consultation-ready semantic evidence for multi-agent consultation. It then assigns patient-specific department perspectives to doctor agents and retrieves complementary evidence for collaborative consultation. To improve efficiency, D2MDT further introduces residual deliberation, which updates only unresolved consensus rather than replaying the full discussion history. Finally, D2MDT fuses the refined consensus report with structured EHR representations for prediction. Experiments on mortality prediction show that D2MDT improves both predictive performance and consultation efficiency. We release the code online to ease the reproducibility of this paper.

翻译：电子健康记录（EHR）是临床预测的核心，但现有方法要么依赖相关性驱动的深度模型，要么使用单一大型语言模型（LLM），难以支持多学科临床推理。近期多智能体系统（MAS）提供了有前景的替代方案，但当前基于EHR的MAS方法仍存在智能体间证据区分度弱、多轮交互冗余等问题。我们提出D2MDT——一种面向高效临床预测的科室感知多学科团队讨论式会诊方法。D2MDT首先构建结构化EHR证据和可用于会诊的语义证据以支持多智能体会诊，随后为医生智能体分配患者特定科室视角，并检索互补性证据用于协作会诊。为提升效率，D2MDT进一步引入残差讨论机制，仅更新未解决共识而非重放完整讨论历史。最终，D2MDT将精炼后的共识报告与结构化EHR表示融合以完成预测。在死亡率预测任务上的实验表明，D2MDT同时提升了预测性能与会诊效率。我们已开源代码以促进本研究的可复现性。