在追溯图表审查研究中对微软Excel宏的准确性的比较分析
Justin Bauzon1, Gustavo Romero-Velez1, Ludovico Sehnem1
1Department of Endocrine Surgery, Cleveland Clinic Foundation, Cleveland, Ohio.
The Journal of surgical research
|May 24, 2025
概括
微软Excel宏显著加速电子健康记录数据抽象用于研究. 虽然手动检查的错误略有减少,但宏是270倍更快,高度准确,对于大数据集来说是有效的.
科学领域:
- 临床研究方法论临床研究方法论
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 回顾性图表审查对临床研究至关重要,但在电子健康记录 (EHR) 数据抽象方面面临挑战.
- 从EHR中手动提取非结构化文本数据是耗时的,容易出现错误,容易产生偏见.
- 开发自动化工具对于提高EHR数据抽象的效率和准确性至关重要.
研究的目的:
- 评估微软Excel宏的准确性和效率,以促进从电子健康记录中抽象数据.
- 为了比较宏观辅助数据提取与医生审核员手动抽象.
- 评估宏的潜力,以改善追溯图表审查研究的数据收集.
主要方法:
- 一百份甲状腺癌病理报告被追溯分析了20个预定义的变量.
- 开发了一个自定义的Excel宏 (ThyMAC) 来自动提取这些变量.
- 医生手动抽象与宏观辅助提取 (macro-assisted extraction) 进行了比较,对错率和时间使用对联t-test进行了比较.
主要成果:
- 手动抽象的错误率略低于初始宏观 (3.8%) (5.3%),但宏观辅助的数据收集速度快270倍 (每报告0.24s vs. 65s).
- 对于大多数变量,ThyMAC显示了高准确度 (87-100%) 和中等到完美的一致性.
- 在纠正错误后,宏观错误率显著下降 (0.5%),表现优于医生抽象器.
结论:
- 埃克塞尔宏提供了一个高度准确和显著更快的替代手动从非结构化的EHR文本抽象数据.
- 基于宏观的数据收集是高效和多功能,特别是在大规模的回顾性图表审查.
- 可预防的宏错误可以通过优化来解决,提高整体数据提取可靠性.
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