From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department

2026-08-23

AI Summary

本文分析了急诊科中利用自然语言处理技术解决分诊、诊断和处置阶段任务的问题，采用预训练语言模型等方法，并指出了当前面临的挑战。

Abstract

Emergency departments (EDs) operate under time pressure, generating multimodal data such as clinical conversations, triage notes, and discharge documents. Recent advances in natural language processing (NLP), particularly pretrained transformers and large language models, have created new opportunities to support language and time-intensive stages of emergency care. Yet existing surveys map clinical NLP across the broader hospital workflow or focus on specific tasks. This survey analyses 46 papers spanning the three phases of ED: triage, diagnosis, and disposition, covering tasks such as triage classification, clinical summarisation, automatic diagnosis, report generation, and discharge documentation. We examine modelling paradigms, evaluation practices, and emerging benchmarks and shared tasks. Across tasks, we identify common trends, including a shift from task-specific neural architectures to pretrained language models, growing interest in interactive clinical systems, and increasing attention to clinically grounded evaluation. Finally, we detail open challenges such as limited generalisability, noisy clinical inputs, and workflow constraints that inform future ED-NLP research.

Problem

• Emergency Department
• Natural Language Processing
• Triage
• Discharge
• Clinical Data

Innovation

• pretrained language models
• interactive clinical systems
• clinically grounded evaluation