@inproceedings{sahu-etal-2025-occutriage,
    title = "{O}ccu{T}riage: An {AI} Agent Orchestration Framework for Occupational Health Triage Prediction",
    author = "Sahu, Alok Kumar  and
      Sun, Yi  and
      Swanton, Eamonn  and
      Amirabdollahian, Farshid  and
      Wren, Abi",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-industry.84/",
    pages = "1217--1226",
    ISBN = "979-8-89176-288-6",
    abstract = "Occupational Health (OH) triage is a systematic process for evaluating and prioritising workplace health concerns to determine appropriate care and interventions. This research addresses critical triage challenges through our novel AI agent orchestration framework, OccuTriage, developed in collaboration with Healthcare Provider. Our framework simulates healthcare professionals' reasoning using specialized LLM agents, retrieval augmentation with domain-specific knowledge, and a bidirectional decision architecture. Experimental evaluation on 2,589 OH cases demonstrates OccuTriage outperforms single-agent approaches with a 20.16{\%} average discordance rate compared to baseline rates of 43.05{\%}, while matching or exceeding human expert performance (25.11{\%}). The system excels in reducing under-triage rates, achieving 9.84{\%} and 3.1{\%} for appointment and assessor type decisions respectively. These results establish OccuTriage{'}s efficacy in performing complex OH triage while maintaining safety and optimizing resource allocation."
}