Data Integration and Quality Core

数据集成和质量核心

• 批准号: 10274378
• 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/10274378
• 关键词: Artificial Intelligence; Clinical Data; Clinical Research; Collaborations; Common Data Element; Country; Data; Data Analyses; Data Collection; Data Management Resources; Development; Devices; 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; base; 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.

约翰霍普金斯大学 AITC 的目标是深度数据密集型，并且在大多数情况下将涉及大量数据 整合不同临床数据的努力。数据集成和质量核心将促进 试点研究调查人员之间的联系以及开发新产品所需的适当数据连接 以及改善老年人健康的适销对路产品。患者风险的范围， 干预参数和结果包含大量电子健康记录 (EHR) 数据。 该提案的目的是 1) 确保所有受支持的 AITC 项目都经过审查和优化，以 建立在数据质量和管理之上的数据质量和利用的最高标准 约翰霍普金斯大学医学院和公共卫生学院的可用资源，2) 为不同的数据整合和集成提供一个通用平台，利用可用的 霍普金斯大学的资源非常适合此目的。约翰霍普金斯精准医疗平台 (PMAP) 为数据集成和分析提供了安全、强大且灵活的基于云的框架。 我们的核心将审查所有概念提案和试点应用，并帮助确保，3) 协调 在可行的情况下，将跨源和域的通用数据元素转化为规范标准。我们将 使用富含 HL7 FHIR 源的 OHDSI-OMOP 标准来获取电子健康记录数据，开放 用于设备数据集成的 mHealth 和 CommonHealth 以及通用术语服务增强 FHIR 术语服务器功能通过 UMLS、caDSR 和 NCI 同义词库进行了增强 语义数据集成。完成这些目标将有助于确保相关数据模式 包括患者报告的信息、调查和传感器数据将被整合到连贯的渲染中 可以维持机器学习发现或统计评估的推理。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人工智能或技术相关数据 通过该 AITC 提交的开发申请将经过审查和组织，以便能够 可快速用于开发旨在改善健康和福祉的特定产品 是老年人。这项工作的重要内容是约翰霍普金斯大学精准医学的发展 分析平台 (PMAP)，一个为批准的临床研究构建的数据收集和分析系统 根据患者的临床数据开发并维护 约翰霍普金斯医学院和约翰霍普金斯应用物理实验室加速 生物医学发现。我们在开发和实施该数据平台方面的经验将 使全国各地的试点研究调查人员能够参与其中。在此基础上以及数据平台方面的专业知识 和电子健康记录研究，我们建议支持所有试点的开发和完成 JH AITC 内的项目根据以下具体目标。