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New method filters clinician edits to improve AI clinical documentation models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to improve the capabilities of large language models (LLMs) used in clinical documentation by filtering user edit data. A significant portion of clinician edits on AI-generated outputs are not directly supported by the input, leading to potential hallucinations if naively used for training. The proposed approach involves creating a taxonomy of editing behaviors, generating semi-synthetic edit data for a discharge-summary task using the MIMIC-III dataset, and implementing filtering methods to identify actionable edits. This filtering process aims to enhance clinician alignment and faithfulness to input context, avoiding the generation of ungrounded content. AI

IMPACT This research could lead to more reliable AI tools for clinical documentation by reducing hallucinations and improving faithfulness to source material.

RANK_REASON Academic paper detailing a new method for improving LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method filters clinician edits to improve AI clinical documentation models

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Academic paper detailing a new method for improving LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Daniel P. Jeong, Yewon Byun, John Giorgi, Peng Xu, Zachary C. Lipton, Michael Oberst ·

    Actionable Feedback: Improving Model Capabilities from User Edit Data on Clinical Documentation

    arXiv:2611.12904v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly being deployed in clinical documentation workflows, where clinicians review and edit their outputs before use. In principle, these clinician edits represent a scalable source of superv…

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