An NLP Approach to Generating Patient Record Summaries

生成患者记录摘要的 NLP 方法

• 批准号: 7925659
• 负责人: NOEMIE ELHADAD
• 金额: $ 45.69万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2012-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7925659
• 关键词: Address; Allergic; Caring; Clinic; Clinical; Cognitive; Complex; Data; Data Analyses; Decision Making; Educational process of instructing; Electronic Health Record; Evaluation Studies; Face; Failure; Feasibility Studies; Goals; Hand; Health; Health Status; Informatics; Information Resources; Interview; Kidney Diseases; Knowledge; Laboratories; Lead; Link; Marshal; Medical; Medical History; MedlinePlus; Methods; Natural Language Processing; Outcome; Patient Care; Patients; Personal Health Records; Pharmaceutical Preparations; Physicians; Procedures; Process; Records; Research; Resources; Solutions; Source; Structure; Surveys; System; Techniques; Testing; Text; Time; Visit; Work; data mining; design; health literacy; information gathering; knowledge base; literate; medical schools; meetings; novel; research study; satisfaction; stem; tool

: 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

: 该提案的长期目标是提高医生获取、处理和处理信息的方式 通过向他们提供自动生成的、全面的、最新的患者记录中出现的信息摘要来整理医疗信息。在患者护理时，医生通常必须快速处理与患者相关的潜在大量信息。如果不能有效地做到这一点，可能会导致提供次优的护理。一些电子健康记录系统提供自动生成的“封面页”，旨在帮助医生全面了解特定患者的情况，但这些信息是从患者记录中的结构化数据字段中得出的，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。我们正在以之前在摘要和自然语言处理方面的工作为基础，并利用我们在研究信息需求和临床医生决策的认知研究方面的专业知识来构建患者记录摘要器，以收集记录中的信息叙述（非结构化）以及结构化部分。我们专注于为肾病患者提供摘要，因为他们通常有复杂的病史，有多种病症、手术和药物。提供全面、最新的图表总结对于普通医生，尤其是肾病专家来说非常有价值。将实现以下三个目标：（1）进行形成性研究，以确定医生在审查患者病历时如何优先考虑并在心理上表征相关信息； (2) 创建一套自动化方法来选择患者记录中的显着信息并将其组织成连贯的摘要； (3) 评估与摘要器的使用相关的功效、效率和医生用户满意度。该提案的主要优点是我们正在解决信息过载问题（电子健康记录使用的瓶颈），并评估我们的解决方案对临床医生的行为和患者的健康结果的影响。此外，我们建议使用新颖的自然语言处理、基于知识的数据挖掘方法来提取和组织显着信息。最后，我们通过扩展电子健康记录功能，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，为信息学研究做出贡献