An NLP Approach to Generating Patient Record Summaries

: 该提案的长期目标是改善医生获取、处理和 通过向他们提供出现在患者记录中的信息的自动生成的、全面的和最新的摘要来整理医疗信息。在患者护理方面，医生必须经常快速处理与患者有关的潜在的大量信息。如果不能有效地做到这一点，可能会导致提供次优护理。一些电子健康记录系统提供自动生成的“封面页”，以帮助医生对给定患者进行广泛的概述，但信息是从患者记录中的结构化数据字段中导出的，忽略了临床医生随时间输入的有价值的叙述性文本。我们在总结和自然语言处理方面的前期工作的基础上，利用我们在认知研究方面的专业知识，研究临床医生的信息需求和决策制定，构建一个患者记录总结器，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提供总结，因为他们通常有复杂的病史，包括多种疾病，程序和药物。提供一个全面的，最新的总结，他们的图表将被证明是有价值的医生一般和肾病学家特别。将实现以下三个目标：（1）进行形成性研究，以确定医生在查看患者图表时如何优先考虑和在心理上表示相关信息;（2）创建一套自动化方法，以选择患者记录中的突出信息并将其组织成连贯的摘要;以及（3）评估与使用摘要器相关的功效、效率和医生-用户满意度。该提案的主要优势在于，我们正在解决信息过载问题，这是使用电子健康记录的瓶颈，并评估我们的解决方案对临床医生行为和患者健康结果的影响。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着的信息。最后，我们通过扩展电子健康记录功能，使其超越简单的文档输入系统，成为医生有用的参考和决策工具，从而为信息学研究做出贡献

: 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