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病人被转诊给专科医生，以及专科医生的就诊