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

分析

• 批准号: 10447984
• 负责人: Javed Mostafa
• 金额: $ 112.04万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-22 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10447984
• 关键词: Address; African American; Architecture; Awareness; Biological; Caring; Clinical; Collaborations; Communication; Communities; Complex; Comprehensive Health Care; Computer software; Custom; Data; Data Analytics; Data Mart; Development; Education; Elements; Evaluation; Goals; Health Technology; Healthcare; Healthcare Systems; Heart Diseases; Hemorrhage; Housing; Human; Human Resources; Hypertension; Income; Individual; Informatics; Information Systems; Infrastructure; Institution; Intervention; Intervention Studies; Link; Machine Learning; Maternal Health; Maternal Mortality; Maternal-fetal medicine; Measurement; Needs Assessment; North Carolina; Outcome; Patient risk; Patients; Phase; Pilot Projects; Play; Practical Robust Implementation and Sustainability Model; Provider; Public Health Informatics; Research; Research Personnel; Resource-limited setting; Risk; Risk Factors; Role; Site; Socioeconomic Status; Structure; System; Systems Development; Technology; Testing; Translating; Transportation; Trust; Woman; analytical method; base; clinical decision support; clinical risk; design; digital health; experience; falls; forest; health application; health data; health disparity; health disparity populations; high risk; improved; informatics tool; innovation; interest; machine learning algorithm; machine learning method; maternal morbidity; maternal risk; mortality; mortality risk; nutrition; patient portal; population health; racial disparity; risk prediction; risk stratification; severe maternal morbidity; shared decision making; social health determinants; system architecture; transportation access

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.

项目概要 美国各地的非裔美国妇女的孕产妇死亡率比 他们的白人盟友与健康社会决定因素（SDoH）相关的因素，包括 教育、住房、交通和营养被认为可能对此做出贡献 孕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 然而，这些因素之间的复杂关联以及它们在疾病中所起的因果作用。 孕产妇死亡风险增加，尚未得到充分了解，也没有全面的医疗保健 考虑这些综合因素以提供决策和沟通的干预措施 为患者、提供者和社区支持人员提供支持。 孕产妇保健干预 (AMMI) 倡议，这是北卡罗来纳大学研究人员的共同努力 教堂山、杜克大学和维克森林大学旨在通过开发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 美国女性通过整合三个机构（包括临床和 SDoH）的数据。 因素，并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) 澄清并跟踪生物学、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）进行有效且准确的风险预测 确定患者是否属于定义的目标风险组，并且 3) 转化这些风险预测 适合提供者、患者和社区支持组织的干预措施。 该倡议的重点是创建一个先进的技术基础设施，支持互联互通和 这三类利益相关者之间的沟通，目的是建立信任和意识 基于自动策划的决策支持辅助并最终降低患者风险为此， 目标 1，重点是建立系统要求，从组建利益相关者群体开始 汇集患者、提供者和社区支持组织代表参与 在整个项目中，与 AMMI 研究人员一起进行设计和评估，目标 2 重点关注系统。 开发，包括创建 1) 定制的临床和 SDoH 数据集市，2) 临床决策 使用机器学习算法的支持软件，以及 3) 三个针对提供商的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 侧重于试点级别。 系统部署，通过 Epic 集成 AMMI 应用程序以提供信息 目标 4 让利益相关者参与 部署阶段期间和之后的形成性和总结性评估（目标 3），包括 测试软件功能并衡量 AMMI 干预对最终用户的影响。