From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department
Comprehensive survey of 46 papers covering NLP applications across three ED phases: triage, diagnosis, and disposition Clear paradigm shift from task-specific neural architectures to pretrained language models and large language models in clinical NLP Growing emphasis on interactive clinical systems and clinically grounded evaluation practices over purely metric-driven benchmarks Key open challenges identified include limited generalisability, noisy clinical inputs, and real-world workflow const
Analysis
TL;DR
- Comprehensive survey of 46 papers covering NLP applications across three ED phases: triage, diagnosis, and disposition
- Clear paradigm shift from task-specific neural architectures to pretrained language models and large language models in clinical NLP
- Growing emphasis on interactive clinical systems and clinically grounded evaluation practices over purely metric-driven benchmarks
- Key open challenges identified include limited generalisability, noisy clinical inputs, and real-world workflow constraints
Why It Matters
This survey fills a critical gap by focusing specifically on emergency department workflows rather than broader hospital settings, making it directly relevant to practitioners building NLP systems for high-stakes, time-sensitive clinical environments. The identification of common trends and open challenges provides a roadmap for researchers aiming to develop deployable clinical NLP solutions that bridge the gap between academic benchmarks and real-world ED operations.
Technical Details
- The survey covers 46 papers spanning triage classification, clinical summarisation, automatic diagnosis, report generation, and discharge documentation across the three core ED phases
- Modelling paradigms examined show a transition from traditional task-specific neural architectures to pretrained transformers and large language models fine-tuned for clinical contexts
- Evaluation practices are analysed with attention to emerging benchmarks and shared tasks, highlighting a move toward clinically grounded assessment rather than purely statistical metrics
- The paper examines interactive clinical systems as an emerging direction, reflecting the need for NLP tools that integrate into real-time emergency workflows
- Open challenges are systematically detailed, including data noise in clinical conversations, limited model generalisability across diverse ED settings, and integration constraints within time-pressured clinical workflows
Industry Insight
- Organizations developing clinical NLP tools should prioritise robustness to noisy, real-world inputs over optimising for clean benchmark datasets, as ED environments present unique data quality challenges
- Investment in interactive and workflow-integrated NLP systems will likely yield higher clinical impact than standalone model improvements, given the emphasis on real-time decision support in emergency settings
- Future research and product development should address generalisability across diverse hospital systems and patient populations, as this remains a critical barrier to widespread clinical deployment of ED-NLP solutions
Disclaimer: The above content is generated by AI and is for reference only.