@inproceedings{tao-etal-2026-operation,
    title = "Operation-Mechanism Alignment for Reliable Clinical Reasoning over Electronic Health Records",
    author = "Tao, Guanyu  and
      Wang, Siyao  and
      Xue, Yong  and
      Tanwar, Ashwani  and
      Ji, Yuting  and
      Sun, Kai  and
      Mok, Monica  and
      Chowdhury, Marzana  and
      Gupta, Deepa  and
      Gupta, Ashok  and
      Zhang, Jingqing  and
      Gupta, Vibhor  and
      Guo, Yike",
    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-1.48/",
    pages = "605--616",
    ISBN = "979-8-89176-434-7",
    abstract = "Clinical reasoning over electronic health records (EHRs) involves heterogeneous operations, including text interpretation, numerical computation, temporal filtering, and guideline-based aggregation. However, many existing LLM-based approaches still cast these heterogeneous operations as a single end-to-end generation process, obscuring their different reliability requirements and making intermediate failures difficult to inspect. We therefore propose a framework based on operation-mechanism alignment that represents clinical reasoning as a directed acyclic graph of typed operations, where each node is assigned to the execution mechanism best suited to its reliability requirements. The framework also preserves structured evidence provenance for intermediate results. Across six clinician-annotated binary decision tasks, the framework outperforms direct prompting, single-step retrieval-augmented prompting, and chain-of-thought baselines, supporting operation-mechanism alignment as a practical design principle for reliable clinical reasoning over EHRs."
}