Closing the loop with an automatic referral population and summarization system

在美国，每年有超过三分之一的病人被转诊到专科医生那里， 占门诊量的一半以上。尽管所有医生都高度重视 初级保健提供者（PCP）和专家，PCP和专家都表示缺乏有效的信息 转介是转介过程中最重要的问题之一。因此，调查一个 改善护理过渡期间沟通的新方法。随着它们的普遍使用，人们认识到， 电子健康记录（EHR）应确保信息在医疗保健系统之间无缝流动， 改善转介程序。但是，在转介过程中缺乏可获得的相关信息仍然是一个问题， 紧迫的问题最近，新兴的深度学习（DL）和自然语言处理（NLP）方法已经 成功地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预后。然而，现有技术不能应用于加工。 来自EHR的异构数据并创建高质量的临床摘要，以传达原因 转诊。响应PA-20-185，该项目将开发和验证一个新的信息学框架， 和合成纵向，多模态EHR数据自动转诊表生成和总结。 虽然转介提供者和专家可以是任何条件的任何类型的提供者，但在此 头痛是一种非常常见的症状， 所有年龄、种族和社会经济地位的人。更重要的是， 头痛转诊在地方和国家循证实践指南中已经定义。因此 卫生信息技术解决方案，使这些数据可访问，将使通信之间 PCP和专家，可以改善数百万患有致残性头痛的患者的护理 紊乱根据我们的初步数据和我们与跨学科数据科学家团队的经验， 我们计划执行具体目标：1）将基于文本的指南转换为基于标准的算法 2）开发模型，以自动填充来自EHR和临床记录的数据， 填写转诊表; 3）创建一个框架来总结纵向临床笔记以填写转诊表; 以及4）以用户为中心的设计方法开发和验证头痛转诊系统。研究 在这个项目中提出的是新颖和创新的，因为它将产生和严格测试新的解决方案， 改善卫生专业人员之间的沟通，以确保提供安全，高质量的护理， 保持护理连续性。该项目的成功将（1）填补我们对以下方面的认识的重要空白： 了解在过渡期间优化患者护理的信息交换类型，以及（2）提供 以证据为基础的解决方案，以促进交流。

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.