Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We identify three critical limitations in current approaches: they generate treatment plans in a single pass rather than following the sequential reasoning process used by clinicians; they rarely incorporate patient-specific historical context; and they fail to effectively distinguish between subjective and objective clinical information. Motivated by the SOAP methodology (Subjective, Objective, Assessment, Plan), we introduce \ours{}, a novel framework that structures LLM reasoning to align with real-life clinician workflows. Our approach employs a two-stage architecture that first generates a clinical assessment based on patient symptoms and objective data, then formulates a structured treatment plan informed by this assessment and enriched with patient-specific information through retrieval-augmented generation. Comprehensive evaluation demonstrates that our method significantly outperforms baseline approaches in both assessment accuracy and treatment plan quality.

翻译：尽管近期在将大型语言模型应用于电子健康记录方面取得了成功，但现有系统大多侧重于评估而非治疗规划。我们指出了当前方法的三个关键局限：它们以单次生成方式制定治疗方案，而非遵循临床医生使用的顺序推理流程；它们很少纳入患者特定的历史背景；且未能有效区分主观与客观临床信息。受SOAP方法（主观、客观、评估、计划）启发，我们提出了\ours{}这一创新框架，通过结构化LLM推理使其与真实临床工作流程保持一致。该方法采用两阶段架构：首先生成基于患者症状和客观数据的临床评估，随后利用检索增强生成技术，结合该评估结果及患者特定信息，制定结构化治疗方案。综合评估表明，本方法在评估准确性和治疗方案质量上均显著优于基线方法。