Closing the loop with an automatic referral population and summarization system

在美国，每年有超过三分之一的患者被转介到专科医生那里，专家就诊 占门诊就诊人数的一半以上。尽管所有的医生都非常重视 初级保健提供者和专家，初级保健提供者和专家都指出缺乏有效的信息 移交是转介过程中最重要的问题之一。因此，至关重要的是要调查 在护理过渡期间改善沟通的新方法。随着它们的普遍使用，人们认识到 电子健康记录(EHR)应确保医疗保健系统之间的信息无缝流动 改进转诊流程。但是，在转介过程中缺乏可获得和相关的信息仍然是一个问题 紧迫的问题。最近，新兴的深度学习和自然语言处理方法已经 已成功应用于从电子病历中提取相关信息和生成文本摘要 改善护理质量和患者结局。然而，现有的技术不能应用于工艺 来自电子病历的异类数据，并创建高质量的临床摘要以传达原因 转介。响应PA-20-185，该项目将开发和验证一个新的信息学框架，以收集 并合成纵向、多模式的EHR数据，用于自动生成和汇总推荐表。 虽然推荐提供者和专家可以是任何情况下的任何类型的提供者，但这里的重点是 应用于初级保健的头痛，因为它是一种非常常见的症状和影响 所有年龄、种族和社会经济地位的人。更重要的是，需要的相关信息 头痛转诊已在地方和国家循证实践指南中定义。因此，a 使这些数据可访问的卫生信息技术解决方案将增强 专科医生和专科医生，可以改善数百万患有致残性头痛的患者的护理 精神错乱。根据我们的初步数据和我们与数据科学家和 医生们，我们计划执行特定的目标：1)将基于文本的指南转换为基于标准的算法 用于电子实施；2)开发模型以自动填充来自电子病历和临床记录的数据，以 填写转诊表；3)创建框架，汇总纵向临床病历，填写转诊表； 4)采用以用户为中心的设计方法，开发并验证头痛转诊系统。这项研究 在这个项目中提出的是新颖和创新的，因为它将产生并严格测试新的解决方案，以 改善卫生专业人员之间的沟通，确保提供安全、高质量的护理和 保持护理的连续性。这个项目的成功将(1)填补我们在以下方面的重要知识空白 了解将在过渡期间优化患者护理的信息交换类型，并(2)提供 以证据为基础的解决方案使交流成为可能。

In the United States, more than a third of patients are referred to a specialist each year, and specialist visits constitute more than half of outpatient visits. Even though all physicians highly value communication between primary care providers (PCPs) and specialists, both PCPs and specialists cite the lack of effective information transfer as one of the most significant problems in the referral process. Therefore, it is critical to investigate a new method to improve communication during care transitions. With their ubiquitous use, it is recognized that electronic health records (EHRs) should ensure a seamless flow of information across healthcare systems to improve the referral process. But, a lack of accessible and relevant information in the referral process remains a pressing problem. Recently, emerging deep learning (DL) and natural language processing (NLP) methods have been successfully applied in extracting pertinent information from EHRs and generating text summarization to improve care quality and patient outcomes. However, existing technologies cannot be applied to process heterogeneous data from EHRs and create high-quality clinical summaries for communicating a reason for referral. Responding to PA-20-185, this project will develop and validate a novel informatics framework to collect and synthesize longitudinal, multimodal EHR data for automatic referral form generation and summarization. While the referring provider and specialist can be any type of provider for any condition, the focus in this application has been on headache for primary care, because it is an extremely common symptom and affects people of all ages, races, and socioeconomic statuses. More importantly, relevant information needed for headache referrals has been defined in local and national evidence-based practice guidelines. Therefore, a health information technology solution to make these data accessible will empower communication between PCPs and specialists, which can improve the care of millions of patients suffering from disabling headache disorders. Based on our preliminary data and our experience with an interdisciplinary team of data scientists and physicians, we plan to execute specific aims: 1) Convert text-based guidelines into a standards-based algorithm for electronic implementation; 2) develop models to automatically populate data from EHR and clinical notes to fill the referral form; 3) create a framework to summarize the longitudinal clinical notes to fill out the referral form; and 4) develop and validate the headache referral system with a user-centered design approach. The research proposed in this project is novel and innovative because it will produce and rigorously test new solutions to improve the communication between health professonals to ensure that safe, high-quality care is provided and care continuity is maintained. The success of this project will (1) fill important gaps in our knowledge of understanding the types of information exchange that will optimize patient care during transitions and (2) provide evidence-based solutions to enable the exchange.