Emergency departments (ED) rely on the Emergency Severity Index (ESI) to assess patient acuity and prioritize care, a process that is largely driven by clinical triage text. Despite recent progress in automated ESI prediction, two fundamental challenges remain: the scarcity of high-quality triage text data due to privacy and regulatory constraints and the lack of a clinically grounded triage framework capable of explicitly capturing the multidimensional structure of triage reasoning. To address these challenges, we draw inspiration from the clinically grounded SOAP paradigm, in which SOAP refers to Subjective, Objective, Assessment, and Plan and captures four complementary aspects of clinical reasoning. Building on this paradigm, we propose SOAPTriage, a SOAP-guided multi-view clinical text modeling framework for automated ESI prediction. To mitigate data scarcity, SOAPTriage introduces a Clinical Note Augmentation (CNA) module that generates natural-language triage notes from structured ED records, resulting in 15,393 augmented clinical notes derived from a real-world dataset. To incorporate clinical structure, SOAPTriage employs a SOAP-Guided Encoding (SGE) module that models patient conditions from four complementary SOAP perspectives, together with an adaptive SOAP-Aware Aggregation and Inference (SAAI) module that performs multi-view reasoning to infer ESI levels. Extensive experiments show that SOAPTriage consistently outperforms strong prompting-based, multi-agent, and encoder-based baselines, demonstrating the effectiveness of SOAP-guided multi-view clinical text modeling for automated emergency triage.

Automated ICD coding is a critical task for standardizing clinical information from electronic health records (EHRs) and supporting downstream healthcare administration.However, existing automated ICD coding systems face several fundamental challenges. First, the majority of existing research focuses on English ICD tasks, with limited attention to Chinese-language clinical contexts due to the scarcity of publicly available Chinese ICD datasets. Second, most approaches primarily target disease coding, overlooking procedure coding as well as the multi-stage workflows followed in real-world clinical practice. Moreover, many recent methods rely heavily on closed-source large language models or substantial computational resources, which limits their scalability and deployability in clinical environments.To address these gaps, this paper proposes JointCoder, which includes a real-world Chinese ICD coding dataset and a multi-agent framework that reformulates automated ICD coding as a joint disease-procedure coding task. JointCoder explicitly models real-world clinical coding workflows through stage-wise agent collaboration.All agents are instantiated using locally deployed 1.7B-parameter models, enabling scalable and privacy-preserving deployment.Extensive experiments on real-world Chinese ICD coding datasets demonstrate JointCoder’s superiority over state-of-the-art baselines across all evaluation metrics.