# Empowering Interoperable Genomics-Enabled Learning Health Systems at Scale

> **NIH NIH U01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $636,462

## Abstract

PROJECT SUMMARY/ABSTRACT
There is significant potential for patient care enabled by genomics to enable transformational improvements in
health. However, the actual uptake of genomic medicine into clinical care has been limited to date. The
University of Utah (UU) Genomics Learning in the Utah Ecosystem (GLUE) Center will contribute to a
Genomics-Enabled Learning Health Systems (gLHS) Network that will catalyze the wide implementation of
genomic medicine. The GLUE Center will offer the gLHS Network unique expertise, collaborations, and
resources to address key areas of need, including the UU ReImagine EHR Initiative, which has been a pioneer
in leveraging electronic health record (EHR) interoperability standards to improve patient care and the provider
experience at scale; Value-Driven Outcomes, an enterprise platform for assessing and improving care value and
efficiency; the Genetic Cancer Risk Detector (GARDE), a standards-based platform for population-level genetic
screening; the Mendelian Phenotype Search Engine (MPSE), which continuously analyses the EHR to identify
patients most likely to benefit from rapid whole genome sequencing (rWGS); and extensive experience working
with safety-net clinics to reduce healthcare disparities through scalable informatics interventions. As a gLHS
Network site, the GLUE Center will pursue three Aims to contribute our expertise and ensure the success and
broad impact of the network. First, we will provide vision, infrastructure and expertise to the gLHS Network
and enable Network-wide implementation of interoperable interventions, including for pharmacogenomics
(PGx). We will contribute open-source tools and interoperability expertise to enhance the scalability of
interventions chosen for Network-wide dissemination, including for providing PGx guidance. Second, we will
support Network-wide dissemination of the standards-based GARDE clinical decision support platform for
population-based genetic testing. An open-source, standards-based tool that leverages AI and chatbot
technologies, GARDE has been successfully used in a multi-site pragmatic clinical trial to identify, reach,
educate, and facilitate at-home genetic testing of hereditary cancer syndromes. GARDE can be adapted to
facilitate genetic testing for any condition chosen by the Network. Given its public health importance, we
propose the Network focuses on genetic testing for familial hypercholesterolemia. Third, we propose the real-
time identification of critically ill newborns most likely to benefit from rWGS. At Rady Children’s Hospital-
San Diego and the UU Neonatal Intensive Care Unit (NICU), we deployed an automated, open-source pipeline
(MPSE) that prioritizes patients for rWGS using Human Phenotype Ontology terms derived directly from the
EHR. Here, we propose a pilot deployment of MPSE at NICUs across the network for daily, automatic review
of evolving medical records to prioritize newborns for clinically indicated rWGS. Through these efforts, ...

## Key facts

- **NIH application ID:** 10982733
- **Project number:** 1U01HG013784-01
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Kensaku Kawamoto
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $636,462
- **Award type:** 1
- **Project period:** 2024-09-23 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10982733

## Citation

> US National Institutes of Health, RePORTER application 10982733, Empowering Interoperable Genomics-Enabled Learning Health Systems at Scale (1U01HG013784-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10982733. Licensed CC0.

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