@inproceedings{buns-etal-2026-wispermed,
    title = "{W}is{P}er{M}ed at {A}rch{EHR}-{QA} 2026: Retrieval-Augmented Prompting for Grounded {EHR} Question Answering",
    author = {B{\"u}ns, Jan-Henning  and
      Pakull, Tabea Margareta Grace  and
      Damm, Hendrik  and
      Chu, Bohao  and
      Friedrich, Christoph M.  and
      Nensa, Felix  and
      Livingstone, Elisabeth  and
      Horn, Peter A.  and
      Fuhr, Norbert},
    editor = "Gupta, Deepak  and
      Thompson, Paul  and
      Ananiadou, Sophia  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.cl4health-1.42/",
    doi = "10.63317/5bb4gnhkbqjq",
    pages = "455--468",
    abstract = "ArchEHR-QA is a grounded question-answering (QA) task for electronic health records (EHRs) comprising four subtasks: (1) question rewriting, (2) evidence identification, (3) grounded answer generation, and (4) answer-evidence alignment. In this work, we present a modular pipeline centered on retrieval-augmented generation (RAG). For Subtask 1, RAG few-shot prompting outperformed both PEFT and prompt-only baselines on the development set; however, Claude few-shot proved substantially more robust on the test set, ranking 6th out of 13 participating teams (score: 26.94). For Subtask 2, a union ensemble of open-weight LLMs (GPT-OSS-120B and Qwen3-30B-A3B) achieved a 56.7 micro-F1, rivaling the proprietary Claude Opus 4.6 while demonstrating higher recall (53.6). For Subtask 3, our RAG few-shot approach using Claude Opus 4.5 achieved the 1st place out of 13 participating teams (score: 36.33). Finally, for Subtask 4, a zero-shot Claude Opus 4.6 configuration ranked 2nd out of 16 participating teams (score: 81.3)."
}