用大模型统一临床笔记格式，提升可读性和数据可用性。
Efficient Standardization of Clinical Notes using Large Language Models
- 用大模型自动修正语法、拼写、缩写和术语，标准化非结构化笔记。
- 每篇笔记平均修正4.9个语法错误、3.1个非标准术语，展开15.8个缩写。
- 保留原始信息无丢失，适合医疗数据清洗与系统互操作应用。
临床笔记包含丰富的患者信息，但因写作风格多样、使用俚语、缩写、医学术语、语法错误和非标准格式，存在诸多不一致，阻碍电子健康记录（EHR）中有效数据提取，影响质量改进、人群健康、精准医疗、决策支持和研究。本文提出一种基于大语言模型的方法，对1,618篇临床笔记进行标准化。处理后平均每篇笔记修正4.9±1.8个语法错误、3.3±5.2个拼写错误，将3.1±3.0个非标准术语转为标准术语，并展开15.8±9.1个缩写和首字母词。同时，笔记被重排至规范章节并采用标准标题。该过程使笔记更易用于关键概念提取、映射医学本体，以及转换为如FHIR等互操作数据格式。专家随机抽样评审显示标准化后无显著数据损失。本概念验证研究证明，通过大模型标准化临床笔记可显著提升其可读性、一致性和可用性，同时促进向互操作格式的转换。
原文摘要 · Abstract (English)
Clinician notes are a rich source of patient information but often contain inconsistencies due to varied writing styles, colloquialisms, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder the extraction of meaningful data from electronic health records (EHRs), posing challenges for quality improvement, population health, precision medicine, decision support, and research. We present a large language model approach to standardizing a corpus of 1,618 clinical notes. Standardization corrected an average of $4.9 +/- 1.8$ grammatical errors, $3.3 +/- 5.2$ spelling errors, converted $3.1 +/- 3.0$ non-standard terms to standard terminology, and expanded $15.8 +/- 9.1$ abbreviations and acronyms per note. Additionally, notes were re-organized into canonical sections with standardized headings. This process prepared notes for key concept extraction, mapping to medical ontologies, and conversion to interoperable data formats such as FHIR. Expert review of randomly sampled notes found no significant data loss after standardization. This proof-of-concept study demonstrates that standardization of clinical notes can improve their readability, consistency, and usability, while also facilitating their conversion into interoperable data formats.
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