Maternal Health Data Innovation and Coordination Hub

项目摘要 约翰霍普金斯大学(JHU)致力于加强创新研究和实践的协调 通过与美国国立卫生研究院(NIH)和 他们实施人人享有母亲健康和妊娠结局愿景(改善)倡议 受赠人。该项目的总体目标是建立和维护产妇保健数据创新 和协调中心，以支持孕产妇健康研究中心的卓越，并促进重复使用 它们产生的数据。该项目将由一个多学科的孕产妇保健小组实施 JHU彭博公共卫生学院的专家和生物统计学家，以及信息学和数据科学 JHU医学院的专家，在由主题组成的专家局的支持下 卫生公平、生物伦理学、卫生经济学、患者安全、患者和家庭参与方面的专家 研究。主要项目活动是建立和维护一个安全的、基于云的协调平台 受控访问和面向公众的数据中心网站；使用修改后的 Delphi方法；支持使用通用数据模型；提供数据收集和分析工具 集成的质量保证工作流程；支持使用传统和人工的统计分析 智能/机器学习技术；准备数据并与NIH存储库共享；提供技术支持 协助和技能指导、培训和专业发展研究机会 中心/改进受赠者。我们的提案有技术和概念上的创新领域。最值得注意的是， 拟将数据中心与现有研究协调平台整合，并演示 可行性--JHU的精密药物分析平台(PMAP)。它利用了观察到的健康数据 科学与信息学(OHDSI)开源社区与观察性医学成果 合作伙伴(OMOP)，受雇于NIH资助的大型研究。OMOP以标准临床为基础 术语；能够将感兴趣的变量提取、摄取、整理到观察性研究中 注册；并具有数据存储、安全、分析和在参与站点之间传输的能力。也是 创新是拟议的培训和职业发展机会，包括学费奖学金，数据 挑战奖，以及导师计划。我们预计，这些活动将导致短期和 中间成果(例如，改进的数据科学能力；生成可查找、可访问、 可互操作和可重复使用的数据)，从长远来看，这将促进改善产妇健康的研究 结果和促进公平。过程和结果评估将确定我们的项目在多大程度上 正在成功地支持研究中心。来自研究项目的数据科学方法和发现将是 通过报告、同行评议文章和科学演示在数据中心网站上传播。

Project Summary The Johns Hopkins University (JHU) seeks to strengthen the coordination of innovative research and practice efforts in maternal health through collaboration with the National Institutes of Health (NIH) and their Implementing a Maternal Health and Pregnancy Outcomes Vision for Everyone (IMPROVE) initiative grantees. The overarching goals of this project are to establish and maintain a Maternal Health Data Innovation and Coordination Hub to support Maternal Health Research Centers of Excellence, and to facilitate the reuse of the data they generate. The project will be implemented by a multidisciplinary team of maternal health experts and biostatisticians at JHU’s Bloomberg School of Public Health, and informatics and data science specialists at JHU’s School of Medicine, with support from an Experts’ Bureau comprised of subject matter experts in health equity, bioethics, health economics, patient safety, patient and family engagement in research. Key project activities are to establish and maintain a secure, cloud-based coordination platform with controlled access, and a public-facing Data Hub website; develop common data elements using a modified Delphi approach; support the use of a common data model; provide data collection and analysis tools with integrated quality assurance workflows; provide support for statistical analyses using traditional and artificial intelligence/machine learning techniques; prepare and share data with NIH repositories; provide technical assistance and skills coaching, training, and professional development opportunities to Research Centers/IMPROVE grantees. Our proposal has technical and conceptual areas of innovation. Most notably, the proposed integration of the Data Hub with an existing research coordination platform with demonstrated feasibility -- JHU’s Precision Medicine Analytics Platform (PMAP). It utilizes the Observational Health Data Science and Informatics (OHDSI) open-source community and the Observational Medical Outcomes Partnership (OMOP), employed by large NIH-funded research. OMOP is based upon standard clinical terminologies; enables extraction, ingestion, collation of variables of interest into an observational research registry; and has the capability for data storage, security, analysis, and transfer among participating sites. Also innovative are the proposed training and career development opportunities, including tuition scholarships, data challenge awards, and a mentorship program. We anticipate that these activities will lead to short-term and intermediate outcomes (e.g. improved data science capabilities; generation of findable, accessible, interoperable, and reusable data), which, over the long-term, will advance research to improve maternal health outcomes and promote equity. Process and outcomes evaluations will ascertain the extent to which our project is successfully supporting Research Centers. Data science methods and findings from research projects will be disseminated on the Data Hub website, through reports, peer-reviewed articles, and scientific presentations.