From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department
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 d...
| Authors | Dipankar Srirag, Aditya Joshi, Salil Kanhere et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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