@inproceedings{soni-etal-2026-reading,
    title = "Reading Between the Lines: Toward Translating Verbose Patient-authored Messages into Clinician-Formulated Questions",
    author = "Soni, Sarvesh  and
      Bittner, Madeline  and
      Demner-Fushman, Dina",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.38/",
    pages = "481--489",
    ISBN = "979-8-89176-434-7",
    abstract = "Patient portal messages often embed clinical questions inside long, emotionally nuanced narratives, requiring clinicians to infer the underlying information need. We study the task of rewriting verbose patient-authored narratives into concise, clinician-interpreted questions framed as if querying an electronic health record (EHR) system. We evaluate a lightweight LLM-based rewrite pipeline that constrains outputs to 10-15 words and uses rule-based validation with regeneration. We test the approach on 140 distinct patient questions drawn from the ArchEHR-QA dataset and shared task. Each system output is double-annotated by two annotators for quality (Good/Ok/Bad) and error types (Generic, Malformed, Tangential, Hallucination). Results show that while models follow output constraints, they often produce overly generic or tangential questions, and occasional hallucinations introduce unsupported clinical details. Across both clinician-question and patient-narrative comparison settings, automatic metrics show substantial overlap across human quality labels; in pairwise meta-evaluation, BERTScore is the strongest proxy for human preferences. We release our code and annotations to support future work."
}