Building a Substance Use Data Commons for Public Health Informatics

为公共卫生信息学建立药物使用数据共享区

• 批准号: 10411763
• 负责人: Majid Afshar
• 金额: $ 31.06万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10411763
• 关键词: Accident and Emergency department; Address; Area; Artificial Intelligence; Atlases; Automobile Driving; Award; Behavioral; COVID-19 pandemic; Caring; Cessation of life; Clinical; Clinical Informatics; Clinical Research; Cloud Computing; Collaborations; Communities; Complex; County; Data; Data Commons; Data Engineering; Data Linkages; Data Sources; Drug usage; Electronic Health Record; Emergency Medicine; Emergency Situation; Emergency medical service; Environment; Epidemic; Ethnic group; Event; Fire - disasters; Fostering; Goals; Grant; Health; Health Insurance Portability and Accountability Act; Health Policy; Health Services; Health system; Hospitals; Infection; Information Systems; Institutes; Intervention; Lead; Link; Machine Learning; Medical; Medicine; Mental Health; Modeling; Monitor; Natural Language Processing; Neighborhoods; Opioid; Outcome; Overdose; Parents; Patients; Performance; Persons; Plant Roots; Positioning Attribute; Privatization; Provider; Public Health; Public Health Informatics; Public Policy; Quality of Care; Race; Research; Running; Secure; Services; Social Work; Societies; Software Design; Structure; Substance Use Disorder; Surface; System; Testing; Time; TimeLine; Training; Translational Research; Trauma; United States National Institutes of Health; Universities; Visit; Wisconsin; Work; addiction; alcohol misuse; blind; care systems; cohort; comorbidity; data ecosystem; data harmonization; data infrastructure; data management; data modeling; data quality; data resource; data sharing; deep learning; deprivation; design; effective intervention; electronic structure; evidence base; first responder; health disparity; health equity; health information technology; high risk; hospital readmission; improved; indexing; interest; learning strategy; neural network; non-opioid analgesic; novel; opioid misuse; overdose death; predictive modeling; risk stratification; screening; social; social determinants; social factors; substance misuse; substance use; tool

PROJECT SUMMARY Substance misuse comprises a complex set of conditions, often associated with comorbidities and social factors, that are the root cause of misuse and can lead to poor outcomes. Opioid misuse, non-opioid illicit use, and alcohol misuse can also lead to repeated encounters with hospital emergency departments or first-responders. Although substance use disorders are a leading cause of repeat hospital visits, our fragmented data systems do not generate comprehensive information on the scope and character of this poorly treated condition that would allow providers to improve and monitor the quality of care. Crucial social and behavioral determinants strongly linked with substance use (e.g., pre-hospital behavioral events) are not readily available to health systems, but they are important data that can be used to better train artificial intelligence/machine learning (AI/ML) models. In principle, hospitals are well-positioned to address these challenges. In practice, these opportunities are frequently missed given the fragmented structure and design of current data systems. Many patients living with substance misuse visit a specific hospital for the first time after an overdose or a related medical condition of drug use such as infection or trauma. Substance use-related conditions are among the top reasons for repeat visits to the hospital. This supplemental will expand on the existing work of the Parent R01, which is focused in clinical informatics, and build an AI/ML-ready public health informatics Substance Use Data Commons and share a novel, all-inclusive prediction model that will help guide clinical interventions and regional health policy. We aim to foster an academic-public-private collaboration to build a data ecosystem in this supplemental grant that will harmonize data across a Wisconsin regional hospital, pre-hospital agencies like fire, and public health agencies for the first time. We will build a cohort with substance misuse with linked data that are engineered as an AI/ML-ready data commons. During our one-year timeline, we will train and test an AI/ML model that can prioritize those at the highest risk for poor outcomes and uncover important biases in our data sources with input by health equity experts. The following goals are to be accomplished from the supplement proposal: (1) build a Substance Misuse Data Commons across a major hospital system and Wisconsin agencies; (2) develop and validate a machine learning tool for substance use-related health outcomes; and (3) examine model performance across health disparate groups (race/ethnic groups as well as neighborhoods). Access to combined data from hospitals, public health agencies, and first responder agencies could provide a comprehensive data resource that would allow us to reliably identify, risk stratify, and prioritize care for some of Wisconsin's most vulnerable residents through AI/ML modeling.

项目概要 药物滥用包括一系列复杂的情况，通常与合并症和社会因素有关， 这是滥用的根本原因，并可能导致不良结果。阿片类药物滥用、非阿片类药物非法使用，以及 滥用酒精还可能导致多次遇到医院急诊科或急救人员。 尽管物质使用障碍是重复去医院就诊的主要原因，但我们分散的数据系统确实 无法生成有关这种治疗不当的情况的范围和特征的全面信息 允许提供者改进和监控护理质量。重要的社会和行为决定因素 卫生系统不易获得与物质使用相关的信息（例如，院前行为事件），但 它们是重要的数据，可用于更好地训练人工智能/机器学习 (AI/ML) 模型。在 原则上，医院有能力应对这些挑战。在实践中，这些机会是 鉴于当前数据系统的分散结构和设计，经常会被遗漏。许多患者与 药物滥用 服药过量或出现相关医疗状况后首次前往特定医院就诊 使用药物，例如感染或外伤。与物质使用相关的情况是重复的主要原因之一 去医院就诊。本补充文件将扩展 Parent R01 的现有工作，重点是 临床信息学，并构建支持 AI/ML 的公共卫生信息学物质使用数据共享并共享 一种新颖的、包罗万象的预测模型，将有助于指导临床干预和区域卫生政策。 我们的目标是通过这笔补充赠款促进学术、公私合作，建立数据生态系统 这将协调威斯康星州地区医院、消防等院前机构和公共卫生部门的数据 机构首次。我们将通过链接数据建立一个药物滥用队列，这些数据被设计为 AI/ML 就绪的数据共享。在一年的时间内，我们将训练和测试一个 AI/ML 模型，该模型可以 优先考虑那些面临不良结果风险最高的人，并通过输入发现我们数据源中的重要偏差 由健康公平专家提出。补充提案要实现以下目标：（1）建立 主要医院系统和威斯康星州机构的药物滥用数据共享； (2) 开发和 验证与药物使用相关的健康结果的机器学习工具； (3) 检查模型性能 跨健康不同群体（种族/族裔群体以及社区）。访问组合数据 医院、公共卫生机构和急救机构可以提供全面的数据资源 这将使我们能够可靠地识别、风险分层并优先考虑对威斯康星州一些最弱势群体的护理 居民通过人工智能/机器学习建模。