Empowering Interoperable Genomics-Enabled Learning Health Systems at Scale

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, the GLUE Center will contribute critical and unique expertise that will help ensure the Network is successful in its mission to discover and disseminate effective and equitable strategies for enabling genomic medicine at scale.