@inproceedings{park-etal-2026-response,
    title = "Response Content Units: Evaluating Completeness and Proactiveness in Medical Open-Response Question Answering",
    author = "Park, Yongsin  and
      Yim, Wen-wai  and
      McKibbin, Emma  and
      Ben Abacha, Asma  and
      Xia, Fei",
    editor = "Mille, Simon  and
      Gehrmann, Sebastian  and
      Schmidtov{\'a}, Patr{\'i}cia  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Fadaee, Marzieh  and
      Lo, Kyle  and
      Santus, Enrico  and
      Stanovsky, Gabriel",
    booktitle = "Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics ({GEM})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.gem-main.25/",
    pages = "231--252",
    ISBN = "979-8-89176-423-1",
    abstract = "Remote clinical care has significantly increased the workload for healthcare professionals managing digital inquiries. While automated systems aim to alleviate this burden, consumer health questions present unique challenges due to their linguistic complexity and the need for proactive clinical guidance, which traditional question-answering models often overlook. We introduce the medical Response Content Units (RCU) schema, a framework that facilitates automatic analysis to identify question-answer completeness and critical answer subparts, which can then be used as tools for supporting clinician response or for automatic metric evaluation. Our analysis using this schema reveals a 16.4{\%} gap in response completeness in professional replies and demonstrates that essential medical directives are provided 2.4 to 12.1 times as frequently as direct answers. We provide baseline results and publicly release our annotations and source code to offer an evaluation framework that is more closely aligned with real-world clinical requirements."
}