使用自然语言处理技术分析医生对数据录入模板所做的修改。
Using natural language processing to analyze physician modifications to data entry templates.
作者信息
Wilcox Adam B, Narus Scott P, Bowes Watson A
机构信息
Department of Medical Informatics, Intermountain Health Care, Salt Lake City, UT, USA.
出版信息
Proc AMIA Symp. 2002:899-903.
Efficient data entry by clinicians remains a significant challenge for electronic medical records. Current approaches have largely focused on either structured data entry, which can be limiting in expressive power, or free-text entry, which restricts the use of the data for automated decision support. Text-based templates are a semi-structured data entry method that has been used to assist physicians in manually entering clinical notes, by allowing them to edit predefined example notes. We analyzed changes made to 18,726 sentences from text templates, using a natural language processor. The most common changes were addition or deletion of normal observations, or changes in certainty. We identified common modifications that could be captured in structured form by a graphical user interface.
临床医生进行高效的数据录入仍然是电子病历面临的一项重大挑战。目前的方法主要集中在结构化数据录入（其表达能力可能有限）或自由文本录入（这限制了数据在自动化决策支持中的使用）。基于文本的模板是一种半结构化数据录入方法，通过允许医生编辑预定义的示例记录来辅助他们手动录入临床记录。我们使用自然语言处理器分析了文本模板中18726个句子的变化。最常见的变化是添加或删除正常观察结果，或确定性的变化。我们识别出了可以通过图形用户界面以结构化形式捕获的常见修改。
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