@inproceedings{he-etal-2025-intriage,
title = "{I}n{T}riage: Intelligent Telephone Triage in Pre-Hospital Emergency Care",
author = "He, Kai and
Lin, Qika and
Fei, Hao and
Chng, Eng Siong and
Hong, Dehan and
Ong, Marcus Eng Hock and
Feng, Mengling",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.67/",
doi = "10.18653/v1/2025.emnlp-demos.67",
pages = "873--885",
ISBN = "979-8-89176-334-0",
abstract = "Pre-hospital Emergency Care (PEC) systems are critical for managing life-threatening emergencies where rapid intervention can significantly impact patient outcomes. The rising global demand for PEC services, coupled with increased emergency calls and strained emergency departments, necessitates efficient resource utilization through Telephone Triage (TT) systems. However, existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triage rates. This study proposes InTriage, an AI-driven multilingual TT system to provide decision support for triage. InTriage enhances accuracy by transcribing emergency calls, extracting critical patient information, prompting supplementary, and providing real-time triage decisions support. We conducted an evaluation on a real-world corpus of approximately 40 hours of telephone data, achieving a word error rate of 14.57{\%} for speech recognition and an F1 score of 73.34{\%} for key information extraction.By improving communication efficiency and reducing triage errors, InTriage offers a scalable solution to potentially help address the growing demands on PEC systems globally."
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<abstract>Pre-hospital Emergency Care (PEC) systems are critical for managing life-threatening emergencies where rapid intervention can significantly impact patient outcomes. The rising global demand for PEC services, coupled with increased emergency calls and strained emergency departments, necessitates efficient resource utilization through Telephone Triage (TT) systems. However, existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triage rates. This study proposes InTriage, an AI-driven multilingual TT system to provide decision support for triage. InTriage enhances accuracy by transcribing emergency calls, extracting critical patient information, prompting supplementary, and providing real-time triage decisions support. We conducted an evaluation on a real-world corpus of approximately 40 hours of telephone data, achieving a word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction.By improving communication efficiency and reducing triage errors, InTriage offers a scalable solution to potentially help address the growing demands on PEC systems globally.</abstract>
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%0 Conference Proceedings
%T InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care
%A He, Kai
%A Lin, Qika
%A Fei, Hao
%A Chng, Eng Siong
%A Hong, Dehan
%A Ong, Marcus Eng Hock
%A Feng, Mengling
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F he-etal-2025-intriage
%X Pre-hospital Emergency Care (PEC) systems are critical for managing life-threatening emergencies where rapid intervention can significantly impact patient outcomes. The rising global demand for PEC services, coupled with increased emergency calls and strained emergency departments, necessitates efficient resource utilization through Telephone Triage (TT) systems. However, existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triage rates. This study proposes InTriage, an AI-driven multilingual TT system to provide decision support for triage. InTriage enhances accuracy by transcribing emergency calls, extracting critical patient information, prompting supplementary, and providing real-time triage decisions support. We conducted an evaluation on a real-world corpus of approximately 40 hours of telephone data, achieving a word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction.By improving communication efficiency and reducing triage errors, InTriage offers a scalable solution to potentially help address the growing demands on PEC systems globally.
%R 10.18653/v1/2025.emnlp-demos.67
%U https://aclanthology.org/2025.emnlp-demos.67/
%U https://doi.org/10.18653/v1/2025.emnlp-demos.67
%P 873-885
Markdown (Informal)
[InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care](https://aclanthology.org/2025.emnlp-demos.67/) (He et al., EMNLP 2025)
ACL
- Kai He, Qika Lin, Hao Fei, Eng Siong Chng, Dehan Hong, Marcus Eng Hock Ong, and Mengling Feng. 2025. InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 873–885, Suzhou, China. Association for Computational Linguistics.