Data Integration and Quality Core

数据集成和质量核心

• 批准号: 10678984
• 负责人: CHRISTOPHER G CHUTE
• 金额: $ 16.63万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678984
• 关键词: Acceleration; Artificial Intelligence; Clinical Data; Clinical Research; Collaborations; Common Data Element; Country; Data; Data Analyses; Data Collection; Data Management Resources; Development; Devices; Disparate; Elderly; Electronic Health Record; Ensure; Evaluation; Fast Healthcare Interoperability Resources; Feeds; Goals; Health; Intervention; Laboratories; Machine Learning; Modality; NCI Thesaurus; Outcome; Patient risk; Patients; Personal Satisfaction; Physics; Pilot Projects; Public Health Schools; Reporting; Research; Research Personnel; Resources; Secure; Semantics; Services; Source; Surveys; System; Systems Analysis; Technology; Terminology; Unified Medical Language System; cloud based; data framework; data integration; data quality; experience; flexibility; heuristics; high standard; improved; inquiry-based learning; mHealth; medical schools; precision medicine; resilience; sensor; technology development

The goals of the Johns Hopkins AITC are profoundly data intensive, and in most cases will involve large efforts that incorporate disparate clinical data. The Data Integration and Quality Core will facilitate the connections between pilot study investigators and appropriate data connection needed to develop new and marketable products that improve the health of older adults. The spectrum of patient risks, intervention parameters, and outcomes comprise a large swath of electronic health record (EHR) data. The aims of this proposal are 1) to ensure that all supported AITC projects are reviewed and optimized to the highest standards of data quality and utilization building on the data quality and management resources available across the Johns Hopkins School of Medicine and the School of Public Health, 2) to provide a common platform for disparate data consolidation and integration, leveraging available resources at Hopkins ideally suited for this purpose. The Johns Hopkins Precision Medicine Platform (PMAP) provides a secure, robust, and flexible cloud-based framework for data integration and analyses. Our core will review all concept proposals and pilot applications and help to ensure, and 3) to harmonize common data elements across sources and domains into a canonical standard where practical. We will use the OHDSI-OMOP standards enriched with HL7 FHIR feeds for Electronic Health Record data, Open mHealth and CommonHealth for device data integration, and Common Terminology Services enhance FHIR Terminology Server functionality augmented with UMLS, caDSR, and the NCI Thesaurus for semantic data integration. Completion of these aims will help to ensure that related modalities of data including patient reported information, surveys, and sensor data will be integrated into coherent renderings that can sustain inferencing for machine learning discovery or statistical evaluation. We will also help to assure that any AI or technology related data collected as part of any artificial intelligence or technology development application that comes thru this AITC will be vetted and organized in such a way that it can be quickly utilized in the development of specific products that are meant to improve the health and well- being of older adults. Important in this effort is the development of the Johns Hopkins Precision Medicine Analytics Platform (PMAP), a data collection and analysis system built for approved clinical research based upon clinical data of patients was developed and is maintained as a collaboration between the Johns Hopkins School of Medicine and the Johns Hopkins Applied Physics Laboratory to accelerate biomedical discovery. Our experience in the development and implementation this data platform will enable pilot study investigators from across the country. Building on this, and expertise in data platform and electronic health record research, we propose to support the development and completion of all pilot projects within the JH AITC according to the following specific aims.

约翰·霍普金斯AITC的目标是密集的数据密集型，在大多数情况下，将涉及大量数据 结合不同临床数据的努力。数据集成和质量核心将有助于 试点研究调查人员与开发新的数据连接之间的联系 以及可改善老年人健康的可销售产品。患者风险的范围， 干预参数和结果包括大量电子健康记录（EHR）数据。 该提案的目的是1）确保对所有支持的AITC项目进行审查和优化，以确保 数据质量和利用率的最高标准在数据质量和管理上 约翰·霍普金斯医学院和公共卫生学院可用的资源，2） 提供一个共同的平台来进行不同的数据整合和集成，利用可用的 霍普金斯的资源非常适合此目的。约翰·霍普金斯精密医学平台 （PMAP）为数据集成和分析提供了一个安全，健壮和灵活的基于云的框架。 我们的核心将审查所有概念建议和试点应用程序，并有助于确保； 3） 跨源和域的通用数据元素在实用的情况下为规范标准。我们将 使用富含HL7 FHIR馈送的OHDSI-OMOP标准进行电子健康记录数据，打开 用于设备数据集成的MHealth和CommonHealth，以及共同术语服务增强 FHIR术语服务器功能功能通过UMLS，CADSR和NCI词库增强 语义数据集成。这些目标的完成将有助于确保数据的相关方式 包括患者报告的信息，调查和传感器数据将集成到连贯的渲染中 可以维持机器学习发现或统计评估的推断。我们还将帮助 确保作为任何人工智能或技术的一部分收集的任何AI或技术相关数据 通过此AITC进行的开发应用程序将通过以下方式进行审查和组织 迅速用于开发旨在改善健康和良好健康的特定产品 是老年人。这项工作重要的是约翰·霍普金斯精密医学的发展 分析平台（PMAP），该数据收集和分析系统为批准的临床研究构建 根据患者的临床数据，开发了 约翰·霍普金斯医学院和约翰·霍普金斯（Johns Hopkins）应用物理实验室加速 生物医学发现。我们在开发和实施方面的经验这个数据平台将 启用来自全国各地的试点研究调查员。以此为基础，以及数据平台上的专业知识 和电子健康记录研究，我们建议支持所有飞行员的开发和完成 根据以下特定目的，JH AITC中的项目。