@inproceedings{reason-etal-2025-utsamuel,
    title = "{UTS}amuel at {A}rch{EHR}-{QA} 2025: A Clinical Question Answering System for Responding to Patient Portal Messages Using Generative {AI}",
    author = "Reason, Samuel  and
      Wang, Liwei  and
      Liu, Hongfang  and
      Huang, Ming",
    editor = "Soni, Sarvesh  and
      Demner-Fushman, Dina",
    booktitle = "BioNLP 2025 Shared Tasks",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-share.11/",
    pages = "91--95",
    ISBN = "979-8-89176-276-3",
    abstract = "Responding to patient portal messages places a substantial burden on clinicians. To mitigate this, automatically generating answers to patient questions by considering their medical records is a critical solution. In this study, we proposed a clinical question answering system for the BioNLP 2025 Shared Task on Grounded Electronic Health Record Question Answering. The system processed each patient message case by selecting relevant sentences as evidences from the associated clinical notes and generating a concise, medically accurate answer to the patient{'}s question. A generative AI model from OpenAI (GPT-4o) was leveraged to assist with sentence selection and answer generation. Each response is grounded in source text, limited to 75 words, and includes sentence-level citations. The system was evaluated on 100 test cases using alignment, citation, and summarization metrics. Our results indicate the significant potential of the clinical question answering system based on generative AI models to streamline communication between patients and healthcare providers by automatically generating responses to patient messages."
}