Reichert Daniel, Kaufman David, Bloxham Benjamin, Chase Herbert, Elhadad Noémie
Department of Biomedical Informatics, Columbia University, New York, NY.
AMIA Annu Symp Proc. 2010 Nov 13;2010:667-71.
Electronic health records contain an abundance of valuable information that can be used to guide patient care. However, the large volume of information embodied in these records also renders access to relevant information a time-consuming and inefficient process. Our ultimate objective is to develop an automated summarizer that succinctly captures all relevant information in the patient record. In this paper, we present a cognitive study of 8 clinicians who were asked to create summaries based on data contained in the patients' electronic health record. The study characterized the primary sources of information that were prioritized by clinicians, the temporal strategies used to develop a summary and the cognitive operations used to guide the summarization process. Although we would not expect the automated summarizer to emulate human performance, we anticipate that this study will inform its development in instrumental ways.
电子健康记录包含大量可用于指导患者护理的宝贵信息。然而，这些记录中所包含的大量信息也使得获取相关信息成为一个耗时且低效的过程。我们的最终目标是开发一种自动摘要器，能够简洁地提取患者记录中的所有相关信息。在本文中，我们展示了一项针对8名临床医生的认知研究，要求他们根据患者电子健康记录中的数据创建摘要。该研究描述了临床医生优先考虑的主要信息来源、用于生成摘要的时间策略以及用于指导摘要过程的认知操作。虽然我们并不期望自动摘要器能模仿人类的表现，但我们预计这项研究将在工具层面为其开发提供参考。
