An NLP Approach to Generating Patient Record Summaries

： 这项提案的长期目标是加强医生接触、处理和 通过为患者记录中显示的信息提供自动生成的、全面的、最新的摘要来组织医疗信息。在病人护理方面，医生通常必须迅速处理与病人有关的潜在的大量信息。如果不能有效地做到这一点，可能会导致提供不太理想的护理。一些电子健康记录系统提供自动生成的“封面”，以帮助医生对给定患者有广泛的概述，但信息是从患者记录中的结构化数据字段中导出的，忽略了临床医生随着时间的推移输入的有价值的叙述文本。我们正在总结和自然语言处理方面的先前工作的基础上，并利用我们在认知研究、研究信息需求和临床医生决策方面的专业知识来构建一个患者记录摘要，它收集记录中的信息叙述(非结构化)以及结构化部分。我们专注于为肾脏疾病患者制作摘要，因为他们通常有复杂的病史，有许多情况、程序和药物。提供他们的图表的全面、最新的摘要将被证明对一般内科医生，特别是肾病学家是有价值的。将实现以下三个目标：(1)进行一项形成性研究，以确定医生在审查病历时如何区分相关信息的优先顺序和如何在头脑中呈现相关信息；(2)创建一套自动方法来选择患者记录中的重要信息片段，并将其组织成连贯的摘要；以及(3)评估与使用摘要工具相关的有效性、效率和医生用户满意度。这项提议的一个主要优势是，我们正在解决信息过载的问题，这是电子健康记录使用中的一个瓶颈，并评估我们的解决方案对临床医生的行动和患者的健康结果的影响。此外，我们建议使用新的自然语言处理、基于知识和数据挖掘的方法来提取和组织显著信息。最后，我们通过扩展电子健康记录的功能来为信息学研究做出贡献，超越了简单的文件输入系统，成为医生有用的参考和决策工具

: The long-term goal of this proposal is to enhance the manner in which physicians access, process and marshal medical information by providing them with an automatically generated, comprehensive, and up-to date summary of the information appearing in a patient record. At the point of patient care, physicians must often rapidly process a potentially overwhelming quantity of information pertaining to a patient. Failure to do so effectively may lead to provision of suboptimal care. Some electronic health record systems provide an automatically produced “cover sheet” geared to help physicians with a broad overview of a given patient, but the information is derived from the structured data fields in the patient record, ignoring the valuable narrative text entered by clinicians over time. We are building upon our prior work in summarization and natural language processing and leveraging our expertise in cognitive research studying information needs and decision making of clinicians to build a patient record summarizer that gathers information narrative (unstructured) as well as structured parts in the record. We focus on producing a summary for patients with kidney disease, as they often have a complex medical history with numerous conditions, procedures and medications. Providing a holistic, up-to-date summary of their chart would prove valuable to physicians in general and nephrologists in particular. The following three aims will be carried out: (1) conduct a formative study to determine how physicians prioritize and mentally represent relevant information when reviewing a patient chart; (2) create a set of automated methods to select salient pieces of information in the patient record and organize them into a coherent summary; and (3) evaluate the efficacy, efficiency and physician-user satisfaction associated with the use of the summarizer. A primary strength of this proposal is that we are addressing the problem of information overload, a bottleneck in the use of electronic health records, and evaluate the impact of our solution on clinicians’ actions and patients’ health outcomes. Furthermore, we propose to use novel natural language processing, knowledge-based and data mining methods to extract and organize salient information. Finally, we contribute to informatics research by extending the electronic health record functionalities to go beyond a simple documentation-entry system towards a useful reference and decision-making tool for physicians