Analytics & Machine-learning for Maternal-health Interventions (AMMI): A Cross-CTSA Collaboration

项目总结 美国各地的非裔美国妇女的孕产妇死亡率比 他们的白人同行。与健康的社会决定因素有关的因素，包括 教育、住房、交通和营养都被认为是潜在的原因。 产妇健康结果的差异以及包括高血压和心脏病在内的临床危险因素 疾病。然而，这些因素之间的复杂联系，以及它们在 产妇死亡率增加的风险尚未得到很好的了解，也没有全面的卫生保健 综合考虑这些因素的干预措施，以提供决策和沟通 为患者、提供者和社区支持工作者提供支持。分析学与机器学习 对于孕产妇健康干预(AMMI)倡议，这是北卡罗来纳大学研究人员的合作努力- 教堂山，杜克和维克森林，旨在通过开发一种机器学习来解决这些差距- 加强卫生技术框架，以降低非洲孕产妇死亡的下游风险- 美国女性。通过集成包括临床和SDoH在内的三个机构的数据 通过构建基于这些数据的机器学习应用程序，AMMI的目标是：1) 阐明和跟踪生物学、临床和SDoH因素对特定母亲的贡献 与最终死亡相关的发病率，2)进行有效和准确的风险预测，以 确定患者是否属于定义的目标风险组，以及3)转换这些风险预测 转变为适合提供者、患者和社区支持组织的干预措施。一把钥匙 该倡议的重点是创建先进的技术基础设施，支持互联互通和 这三类利益攸关方之间的沟通，目的是建立信任和认识 基于自动管理的决策支持辅助工具，并最终降低患者风险。为此， 目标1，专注于建立系统需求，从形成一个利益相关者小组开始 这将患者、提供者和社区支持组织的代表聚集在一起， 在整个项目期间与AMMI研究人员一起进行设计和评估。目标2侧重于系统 开发，包括创建1)定制的临床和SDoH数据集市，2)临床决策 支持使用机器学习算法的软件，以及3)针对提供商的三个面向用户的应用程序， 患者和社区支持人员，以及AMMI研究人员。目标3侧重于试行级别 系统部署，通过EPIC集成AMMI应用程序以提供信息 对提供者、患者和社区支持人员的干预。AIM 4让利益相关者参与 在部署阶段(目标3)期间和之后进行形成性和总结性评价，包括 测试软件功能并测量AMMI干预对最终用户的影响。

PROJECT SUMMARY African-American women across the US experience alarmingly higher rates of maternal mortality than their white counterparts. Factors associated with social determinants of health (SDoH), including education, housing, transportation, and nutrition are recognized as potentially contributiing to this disparity in maternal health outcomes, along with clinical risk factors including hypertension and heart disease. However, the complex associations among these factors, along with the causal role they play in increased risk for maternal mortality, are not well understood, nor are there comprehensive health care interventions that take these combined factors into account to provide decision and communication support for patients, providers, and community support workers. The Analytics and Machine-learning for Maternal-health Interventions (AMMI) initiative, a collaborative effort from researchers at UNC- Chapel Hill, Duke, and Wake Forest, aims to address these gaps by developing a machine learning- enhanced health technology framework to reduce downstream risk of maternal mortality in African- American women. By integrating data across the three institutions that includes both clinical and SDoH factors, and by building machine learning applications grounded in this data, AMMI’s goals are to: 1) clarify and track contributions of biological, clinical, and SDoH factors toward specific maternal morbidities associated with eventual mortality, 2) conduct efficient and accurate risk predictions to determine whether patients fall into defined target risk groups, and 3) translate these risk predictions into interventions appropriate for providers, patients, and community support organizations. A key focus of the initiative is to create an advanced technology infrastructure supporting connectivity and communication among these three types of stakeholders, with the goal of building trust and awareness based on automatically curated decision support aids and ultimately mitigating patient risk. To this end, Aim 1, focused on establishing system requirements, begins with the formation of a stakeholder group that brings together patient, provider, and community support organization representatives to engage in design and evaluation with AMMI researchers throughout the project. Aim 2 focuses on systems development, including the creation of 1) a custom-built clinical and SDoH data mart, 2) clinical decision support software using machine learning algorithms, and 3) three user-facing apps aimed at providers, patients and community support personnel, and AMMI researchers. Aim 3 focuses on pilot-level deployment of the system, integrating the AMMI apps through Epic to provide informational interventions to providers, patients, and community support personnel. Aim 4 engages stakeholders in formative and summative evaluation during and after the deployment phase (Aim 3), including both testing of the software function and measurement of the impact of AMMI interventions on end users.