Closing the loop with an automatic referral population and summarization system

通过自动转介人群和汇总系统形成闭环

• 批准号: 10720778
• 负责人: Yifan Peng
• 金额: $ 71.2万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10720778
• 关键词: Affect; Age; Algorithms; Automated Abstracting; Caring; Clinical; Communication; Continuity of Patient Care; Data; Data Scientist; Disparate; Electronic Health Record; Electronics; Ensure; Generations; Guidelines; Headache; Headache Disorders; Health; Healthcare Systems; Informatics; Knowledge; Methods; Modeling; Natural Language Processing; Outpatients; Patient Care; Patient-Focused Outcomes; Patients; Persons; Physicians; Population; Primary Care; Process; Provider; Public Health; Quality of Care; Race; Research; Socioeconomic Status; Specialist; Strategic Planning; System; Technology; Testing; Text; Translations; United States; United States National Library of Medicine; Visit; care outcomes; common symptom; deep learning; electronic health data; electronic health information; empowerment; evidence base; evidence based guidelines; experience; health information technology; heterogenous data; improved; innovation; interdisciplinary approach; interoperability; multimodality; novel; primary care provider; success; user centered design

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.

在美国，每年有超过三分之一的患者被转诊至专科医生处，并且专科医生就诊 占门诊就诊人数的一半以上。尽管所有医生都高度重视之间的沟通 初级保健提供者 (PCP) 和专家，PCP 和专家都表示缺乏有效信息 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) 应确保医疗保健系统之间的信息无缝流动 改进转介流程。但是，在转介过程中缺乏可访问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 已成功应用于从电子病历中提取相关信息并生成文本摘要 提高护理质量和患者治疗效果。但现有技术无法应用于加工 来自 EHR 的异质数据并创建高质量的临床摘要以传达原因 转介。为了响应 PA-20-185，该项目将开发并验证一个新颖的信息学框架来收集 合成纵向、多模式 EHR 数据，用于自动转诊表格生成和总结。 虽然转介提供者和专家可以是针对任何情况的任何类型的提供者，但重点在于 应用已用于初级保健的头痛，因为这是一种极其常见的症状，并且影响 所有年龄、种族和社会经济地位的人。更重要的是，需要相关信息 地方和国家循证实践指南已对头痛转诊进行了定义。因此，一个 使这些数据可访问的卫生信息技术解决方案将增强人们之间的沟通 PCP 和专家可以改善数百万患有致残性头痛的患者的护理 失调。根据我们的初步数据以及我们与数据科学家跨学科团队合作的经验， 对于医生来说，我们计划执行特定目标：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) 开发模型自动填充 EHR 和临床记录中的数据 填写推荐表； 3) 创建一个框架来总结纵向临床记录以填写转诊表； 4) 采用以用户为中心的设计方法开发和验证头痛转诊系统。研究 该项目中提出的方案新颖且具有创新性，因为它将产生并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 保持护理的连续性。该项目的成功将（1）填补我们在以下方面的知识空白： 了解可在过渡期间优化患者护理的信息交换类型，以及 (2) 提供 基于证据的解决方案促进交流。