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 安全网提供者在其社会经济上脆弱的患者中有很大一部分 社区，但很难为患者及时获得高质量的专业护理。 获得特殊护理的访问与值得的健康成果有关，并有可能为健康做出贡献 跨社会经济群体的差异，鉴于他们的资源有限 但是，改善专业访问的方法。 提供者需要了解其人群的专业护理需求。 美国整个美国安全网提供者的eConsult系统为系统性提供了有价值的 测量少数族裔，服务不足人群的专业护理转介模式。 在这个项目中，我们建议在机器学习和自然语言中使用最先进的方法 加工（NLP），以帮助安全网提供者从其电子中提取可操作的，人口广泛的数据 咨询系统。 在洛杉矶，旧金山和纽约市的美国。 合作者，我们将构建NLP系统，以自动将专业请求分类为两个维度： 激励请求的“临床问题”（例如，胸痛）和“问题类型”（例如，请求程序， 对药物管理的帮助）。 确定有希望的国际性目标，以提高专业和护理质量。 在开发了NLP系统之后，我们将分析> 100万个专业请求以描述趋势 安全网患者如何转介专家并检查诊所和 个人提供者。 质量。 有了专业指导，可能会导致新的治疗算法，从而减少对这些算法的需求。 请求和改善其他患者的访问。 该提案是一个“高风险高级奖励”项目，可创建新的研究工具来识别和 评估数据驱动的干预intintions，以改善针对底层人群的专业护理护理。