@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Operation-Mechanism Alignment for Reliable Clinical Reasoning over Electronic Health Records
%A Tao, Guanyu
%A Wang, Siyao
%A Xue, Yong
%A Tanwar, Ashwani
%A Ji, Yuting
%A Sun, Kai
%A Mok, Monica
%A Chowdhury, Marzana
%A Gupta, Deepa
%A Gupta, Ashok
%A Zhang, Jingqing
%A Gupta, Vibhor
%A Guo, Yike
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S BioNLP 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-434-7
%F tao-etal-2026-operation
%X 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.
%U https://aclanthology.org/2026.bionlp-1.48/
%P 605-616
Markdown (Informal)
[Operation-Mechanism Alignment for Reliable Clinical Reasoning over Electronic Health Records](https://aclanthology.org/2026.bionlp-1.48/) (Tao et al., BioNLP 2026)
ACL
- Guanyu Tao, Siyao Wang, Yong Xue, Ashwani Tanwar, Yuting Ji, Kai Sun, Monica Mok, Marzana Chowdhury, Deepa Gupta, Ashok Gupta, Jingqing Zhang, Vibhor Gupta, and Yike Guo. 2026. Operation-Mechanism Alignment for Reliable Clinical Reasoning over Electronic Health Records. In BioNLP 2026, pages 605–616, San Diego, California. Association for Computational Linguistics.