环境AI对临床文档的语言影响:一项匹配的前后研究
medRxiv : the preprint server for health sciences
|February 27, 2026
概括
环境人工智能文档系统创建了更多标准化的临床笔记,特别是在当前疾病史部分. 这些系统增强了语法复杂性和话语连贯性,但效果因供应商和注释部分而异.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 环境情报系统越来越多地被用于临床文档.
- 量化这些人工智能系统产生的笔记中的语言变化至关重要.
研究的目的:
- 在实施环境AI文档系统之前和之后分析临床笔记中的语言差异.
- 为了比较不同环境AI供应商 (Nuance DAX和Abridge) 对笔记结构和语言的影响.
主要方法:
- 对6026个门诊临床笔记进行了匹配的前后分析.
- 语言特征包括语法复杂性,词汇模两可,连贯性和可读性被评估.
- 自动化分析通过手动审查50张配对的笔记来验证.
主要成果:
- 环境文档导致了更长的"当前疾病史" (HPI) 笔记,语法复杂性和话语连贯性增加.
- 词汇含糊性减少,语言模型困惑性在环境HPI笔记中较低.
- 评估和计划 (A&P) 部分显示较小,更异质的语言变化.
- 影响依赖于供应商和特定于部门,HPI显示了更显著的标准化.
结论:
- 环境人工智能文档系统系统地改变临床笔记语言学,导致更详细和可预测的叙述.
- 影响因笔记部分 (HPI与A&P) 和AI供应商而异.
- 未来的评估应考虑效率以外的更广泛的影响,例如沟通和认知负载.
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