分析医生如何将智能语音笔记中的口语化表达转为专业术语。
Consumer-to-Clinical Language Shifts in Ambient AI Draft Notes and Clinician-Finalized Documentation: A Multi-level Analysis
- 用词典匹配法量化医患对话转写稿到正式病历的术语转换。
- 59.3%的术语修改发生在诊断与计划部分，共发现7576次转换。
- 不同医生转换强度差异显著，提示需考虑个体差异的智能辅助设计。
环境人工智能从医患对话中生成临床笔记草稿，常使用通俗易懂的消费者语言以促进患者理解，但医生如何将其修订为符合专业规范的正式文档尚不明确。本研究采用词典确认的转换框架，量化了从消费语言到临床术语的规范化编辑过程。分析了来自34,726次就诊的71,173对AI草稿与终版记录。确认的转换定义为在同一章节内，将消费者表达替换为其词典映射的临床对应词。编辑显著降低了各章节的术语密度（p < 0.001）。诊断与计划部分的转换量最大，占总数的59.3%。共识别出4,114个笔记段落中的7,576次转换事件，占消费者术语删除总量的1.2%。个体医生间的转换强度存在显著差异（p < 0.001）。总体表明，医生后编辑一致地将非正式表达转向标准化、章节适配的临床术语，支持面向章节感知的环境人工智能设计。
原文摘要 · Abstract (English)
Ambient AI generates draft clinical notes from patient-clinician conversations, often using lay or consumer-oriented phrasing to support patient understanding instead of standardized clinical terminology. How clinicians revise these drafts for professional documentation conventions remains unclear. We quantified clinician editing for consumer-to- clinical normalization using a dictionary-confirmed transformation framework. We analyzed 71,173 AI-draft and finalized-note section pairs from 34,726 encounters. Confirmed transformations were defined as replacing a consumer expression with its dictionary-mapped clinical equivalent in the same section. Editing significantly reduced terminology density across all sections (p < 0.001). The Assessment and Plan accounted for the largest transformation volume (59.3%). Our analysis identified 7,576 transformation events across 4,114 note sections (5.8%), representing 1.2% consumer-term deletions. Transformation intensity varied across individual clinicians (p < 0.001). Overall, clinician post-editing demonstrates consistent shifts from conversational phrasing toward standardized, section- appropriate clinical terminology, supporting section-aware ambient AI design.
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