InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care (2025.emnlp-demos)
Copied to clipboard
| Challenge: | Existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triages rates. |
| Approach: | They propose to use an AI-driven multilingual TT system to provide decision support for triage. |
| Outcome: | The proposed system achieves word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction. |
Similar Papers
TriageAgent: Towards Better Multi-Agents Collaborations for Large Language Model-Based Clinical Triage (2024.findings-emnlp)
Copied to clipboard
| Challenge: | escalation in emergency department patient visits poses challenges to efficient clinical management . Currently, hospitals rely on human experts to review clinical notes and determine case urgency . |
| Approach: | a team of researchers develop a multi-agent framework to enhance collaborative decision-making in clinical triage. |
| Outcome: | The proposed framework outperforms state-of-the-art LLM-based methods on three clinical triage test sets. |
OccuTriage: An AI Agent Orchestration Framework for Occupational Health Triage Prediction (2025.acl-industry)
Copied to clipboard
| Challenge: | Experimental evaluation on 2,589 occupational health cases demonstrates that OccuTriage outperforms single-agent approaches with a 20.16% average discordance rate compared to baseline rates of 43.05% . |
| Approach: | They propose to use specialized LLM agents, retrieval augmentation, and a bidirectional decision architecture to simulate healthcare professionals’ reasoning. |
| Outcome: | The proposed framework outperforms single-agent approaches with a 20.16% average discordance rate while matching or exceeding human expert performance. |
SOAPTriage: SOAP-Guided Multi-View Clinical Text Modeling Framework for Automated ESI Prediction (2026.acl-long)
Copied to clipboard
Enming Wang, Jianlei Wang, Xueping Peng, Hongjiao Guan, Yinglong Wang, Sibo Wei, Jianbin Guo, Ruifeng Xu, Wenpeng Lu
| Challenge: | Emergency departments rely on the Emergency Severity Index (ESI) to assess patient acuity and prioritize care. |
| Approach: | They propose a SOAP-guided multi-view clinical text modeling framework for automated ESI prediction based on the SOAP paradigm . |
| Outcome: | The proposed framework outperforms prompting-based, multi-agent, and encoder-based baselines. |
Detecting Urgency in Multilingual Medical SMS in Kenya (2022.aacl-srw)
Copied to clipboard
| Challenge: | Access to mobile phones has increased exponentially over the last 20 years, providing an opportunity to connect patients with healthcare interventions through mobile phones. |
| Approach: | They propose to use natural language processing to improve nurses' management of messages from pregnant and postpartum women in Kenya. |
| Outcome: | The proposed model did not reach the clinical usefulness threshold but could improve nurse workflow and responsiveness to urgent messages. |
CLARITY: Clinical Assistant for Routing, Inference, and Triage (2025.emnlp-industry)
Copied to clipboard
Vladimir Shaposhnikov, Alexandr Nesterov, Ilia Kopanichuk, Ivan Bakulin, Zhelvakov Egor, Ruslan Abramov, Tsapieva Ekaterina Olegovna, Iaroslav Radionovich Bespalov, Dmitry V. Dylov, Ivan Oseledets
| Challenge: | Medical dialogue systems are still flawed for real-world adoption in healthcare. |
| Approach: | They propose to integrate CLARITY (Clinical Assistant for Routing, Inference and Triage) it combines a Finite State Machine (FSM) and collaborative agents that employ Large Language Model (LLM) they report that it surpasses human-level performance in terms of first-attempt routing precision . |
| Outcome: | The proposed platform surpasses human-level performance in terms of first-attempt routing precision. |
LLMs in the Real World: Evaluating “AI” in Emergency Contexts (2026.findings-acl)
Copied to clipboard
| Challenge: | Despite considerable overlap between academic and industry-based developers of Large Language Models (LLMs), it seems Natural Language Processing researchers have a science outreach problem. |
| Approach: | They propose a set of concrete recommendations for stakeholders at every stage of the development and deployment pipeline. |
| Outcome: | The proposed model performs worse with lower-resourced languages or worse with higher-resource languages. |
Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting (P19-1)
Copied to clipboard
| Challenge: | a study aims to automate a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. |
| Approach: | They examine a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. |
| Outcome: | The proposed model can accelerate response time by several orders of magnitude. |
Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies? (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing research has focused on the earlier stages of emergency response . lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation is limiting current research . |
| Approach: | They propose a first real-world emergency decision-making dataset EDM-Bench . they propose 'rule-enhanced reasoning framework' that integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability. |
| Outcome: | The proposed framework improves decision safety and interpretability by integrating regulatory knowledge with constrained inference mechanisms. |
ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models lack convincing, human-understandable explanations, making them difficult for physicians to trust and use in practice. |
| Approach: | They propose a framework that aims to automatically assign ICD codes to clinical notes while providing explicit justifications for each assignment. |
| Outcome: | The proposed framework achieves effective ICD coding with accurate explanations using two collaborative LLM agents: a coding agent and a critical agent. |
Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic? (2022.coling-1)
Copied to clipboard
| Challenge: | Language Technology (LT) has been used in the COVID-19 pandemic, but only in a handful of languages. |
| Approach: | They propose to use conversational agents for information dissemination and basic diagnosis in 15 Asian and African languages with varying resource-availability to test their knowledge of LT. |
| Outcome: | The proposed research confirms the pitiful state of LT even for languages with large speaker bases, such as Sinhala and Hausa, and identifies the gaps that could help prioritize research and investment strategies in LT for healthcare. |