Improving Specialty Care Delivery in the Safety Net with Natural Language Processing

通过自然语言处理改善安全网中的专业护理服务

• 批准号: 9789060
• 负责人: Michael Lawrence Barnett
• 金额: $ 9.43万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789060
• 关键词: Acute; Adopted; Adoption; Algorithms; Caring; Chest Pain; Chronic Kidney Failure; Classification; Clinic; Clinical; Communities; Consultations; County; Data; Data Set; Databases; Education; Epidemiology; Face; Federally Qualified Health Center; Gastroesophageal reflux disease; Goals; Health; Health Personnel; Health Services; Health Services Accessibility; Health system; Heart failure; Hospitals; Improve Access; Individual; Intervention; Lead; Los Angeles; Machine Learning; Manuals; Measures; Medical; Medical Education; Medical center; Medication Management; Methods; Minority; Morbidity - disease rate; Natural Language Processing; New York City; Online Systems; Ophthalmology; Outcome; Patients; Pattern; Play; Population; Primary Care Physician; Procedures; Provider; Public Hospitals; Quality of Care; Research; Resources; Retinal Diseases; Role; San Francisco; Specialist; System; Taxonomy; Telemedicine; Text; Time; Transplantation; Triage; Underserved Population; Variant; Visit; automated analysis; care delivery; design; diabetic; disease classification; ethnic minority population; follow-up; health disparity; high reward; high risk; improved; medical specialties; medically underserved; minority communities; mortality; performance tests; programs; racial and ethnic; safety net; screening; socioeconomic disadvantage; socioeconomics; tool; trend; two-dimensional

Project Summary Safety net providers treat a substantial share of socioeconomically vulnerable patients in their communities, but struggle to provide timely access to high quality specialty care for their patients. Delayed access to specialty care is associated with worse health outcomes and potentially contributes to health disparities across socioeconomic groups. Given their limited resources, safety net providers must seek creative approaches to improve specialty access. However, to choose what programs to implement, safety net providers need to understand the specialty care needs of their populations. Fortunately, the adoption of eConsult systems by safety net providers across the US provides a valuable opportunity to systematically measure patterns of specialty care referrals for minority, underserved populations. In this project, we propose using state-of-the-art methods in machine learning and natural language processing (NLP) to help safety net providers extract actionable, population wide data from their electronic consultation systems. We will do this in partnership with three of the most prominent safety net health systems in the US in Los Angeles, San Francisco and New York City. Using specialty request databases from our collaborators, we will build NLP systems to automatically classify specialty requests along two dimensions: the “clinical issue” motivating the request (e.g., chest pain), and the “question type” (e.g., request for a procedure, help with medication management). This automated classification of electronic specialty requests can enable identification of promising targets for interventions to improve specialty access and quality of care. After developing these NLP systems, we will analyze >1 million specialty requests to describe trends in how safety net patients are referred to specialists and examine variation in referral patterns by clinic and individual provider. The goal is to identify the most impactful opportunities to improve specialty access and quality. For example, a high rate of referrals for esophageal reflux, which most PCPs can treat on their own with specialist guidance, could lead to new treatment algorithms, potentially reducing the need for these requests and improving access for other patients. This proposal is a “high-risk high-reward” project that creates new research tools to identify and evaluate data-driven interventions to improve specialty care delivery for underserved populations.

项目摘要 安全网提供者治疗了相当一部分社会经济脆弱的病人， 社区，但努力为他们的病人提供及时的高质量的专业护理。延迟 获得专科护理与更差的健康结果相关，并可能有助于健康 社会经济群体之间的差距。鉴于资源有限，安全网提供者必须寻求创造性的 改善专业准入的方法。但是，要选择实施什么方案，安全网 提供者需要了解其人口的专业护理需求。幸运的是， 美国各地安全网提供商的电子咨询系统提供了一个宝贵的机会， 衡量少数族裔、服务不足人群的专科护理转诊模式。 在这个项目中，我们建议使用机器学习和自然语言中最先进的方法 处理（NLP），以帮助安全网提供商从他们的电子数据库中提取可操作的，人口范围的数据。 协商制度。我们将与三个最著名的安全网卫生系统合作， 在美国的洛杉矶、弗朗西斯科和纽约市。使用我们的专业请求数据库 合作者，我们将建立自然语言处理系统，自动分类专业要求沿沿着两个方面： 激发请求的“临床问题”（例如，胸痛），以及“问题类型”（例如，请求进行手术， 协助药物管理）。这种电子专业请求的自动分类可以使 确定有希望的干预目标，以改善专科准入和护理质量。 在开发这些NLP系统之后，我们将分析超过100万个专业请求，以描述 如何将安全网患者转介给专科医生，并检查各诊所转介模式的差异， 个人供应商。目标是确定最具影响力的机会，以改善专业准入， 质量.例如，食管反流的高转诊率，大多数PCP可以自行治疗 在专家的指导下，可能会导致新的治疗算法，可能会减少对这些治疗算法的需求。 并为其他患者提供更好的服务。 这一建议是一个“高风险高回报”的项目，创造新的研究工具，以确定和 评估数据驱动的干预措施，以改善为服务不足的人群提供的专科护理。