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的，并且在大多数情况下将涉及大型