Using natural language processing to analyze physician modifications to data entry templates.

Abstract

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.