Building a Substance Use Data Commons for Public Health Informatics

项目摘要 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， 酒精滥用还可能导致与医院急诊部门或急救人员反复接触。 虽然物质使用障碍是重复住院的主要原因，但我们分散的数据系统确实如此。 没有产生关于这种治疗不良状况的范围和特征的全面信息， 使提供者能够改善和监测护理质量。关键的社会和行为决定因素强烈 与物质使用有关（例如，院前行为事件）不容易为卫生系统所用，但 它们是可以用来更好地训练人工智能/机器学习（AI/ML）模型的重要数据。在 原则上，医院有能力应对这些挑战。实际上，这些机会是 鉴于目前数据系统的结构和设计支离破碎，经常遗漏这一点。许多患者生活在 药物滥用在药物过量或相关医疗状况后首次访问特定医院， 药物使用，如感染或创伤。物质使用相关的条件是重复的主要原因之一 去医院探望。本补充文件将扩展父R 01的现有工作，其重点是 临床信息学，并建立一个AI/ML就绪的公共卫生信息学物质使用数据共享和共享 这是一个新颖的、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。 我们的目标是促进学术-公共-私营部门的合作，以建立一个数据生态系统。 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。我们将建立一个药物滥用的队列，并将相关数据设计为 AI/ML就绪的数据共享。在我们一年的时间轴内，我们将训练和测试一个AI/ML模型，该模型可以 优先考虑那些结果不佳的风险最高的人，并通过输入发现我们数据源中的重要偏差 健康公平专家。从补充建议中要实现以下目标：（1）建立一个 在主要医院系统和威斯康星州机构中建立物质滥用数据共享;（2）开发和 验证与物质使用相关的健康结果的机器学习工具;（3）检查模型性能 在不同的健康群体（种族/民族群体以及社区）之间。访问来自 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，并优先照顾一些威斯康星州最脆弱 通过AI/ML建模。

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.