# Data Integration and Quality Core

> **NIH NIH P30** · JOHNS HOPKINS UNIVERSITY · 2024 · $166,348

## Abstract

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 accordin...

## Key facts

- **NIH application ID:** 10848417
- **Project number:** 5P30AG073104-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** CHRISTOPHER G CHUTE
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $166,348
- **Award type:** 5
- **Project period:** 2021-09-30 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10848417

## Citation

> US National Institutes of Health, RePORTER application 10848417, Data Integration and Quality Core (5P30AG073104-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10848417. Licensed CC0.

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