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.

项目摘要 美国各地的非洲裔美国妇女的孕产妇死亡率高得惊人， 他们的白色同伴。与健康的社会决定因素（SDoH）相关的因素，包括 教育、住房、交通和营养被认为是潜在的原因 产妇健康结果的差异，沿着临床风险因素，包括高血压和心脏病 疾病然而，这些因素之间的复杂联系，沿着它们在 产妇死亡风险增加的问题没有得到很好的理解，也没有全面的保健服务 将这些综合因素考虑在内干预措施， 为患者、提供者和社区支持工作者提供支持。分析和机器学习 产妇健康干预（AMMI）倡议，一个合作的努力，从研究人员在2010年， 查佩尔山、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， 加强保健技术框架，减少非洲产妇死亡率的下游风险- 美国女人通过整合包括临床和SDoH在内的三家机构的数据， 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） 阐明并跟踪生物学、临床和SDoH因素对特定孕产妇的影响 与最终死亡率相关的发病率，2）进行有效和准确的风险预测， 确定患者是否属于定义的目标风险组，以及3）翻译这些风险预测 转化为适合提供者、患者和社区支持组织的干预措施。一个关键 该计划的重点是建立一个先进的技术基础设施，支持互联互通， 在这三类利益相关者之间进行沟通，目的是建立信任和认识 基于自动策划的决策支持辅助，并最终减轻患者风险。为此目的， 目标1，集中于建立系统需求，从形成一个涉众组开始 它将患者、提供者和社区支持组织代表聚集在一起， 在整个项目的设计和评估与AMMI研究人员。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）针对供应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侧重于试点 系统部署，通过Epic集成AMMI应用程序， 对提供者、患者和社区支持人员的干预。目标4让利益攸关方参与 部署阶段期间和之后的形成性和总结性评价（目标3），包括 测试软件功能并衡量AMMI干预措施对最终用户的影响。