protocolSection identificationModule nctId: NCT06377033, orgStudyIdInfo id: 10877980, briefTitle: Using the EHR to Advance Genomic Medicine Across a Diverse Health System, statusModule overallStatus: NOT_YET_RECRUITING, startDateStruct date: 2024-05-01, primaryCompletionDateStruct date: 2026-12-31, completionDateStruct date: 2027-06-30, studyFirstPostDateStruct date: 2024-04-22, lastUpdatePostDateStruct date: 2024-04-22, sponsorCollaboratorsModule leadSponsor name: University of Pennsylvania, class: OTHER, descriptionModule briefSummary: Given the expansion of indications for genetic testing and our understanding of conditions for which the results change medical management, it is imperative to consider novel ways to deliver care beyond the traditional genetic counseling visit, which are both amenable to large-scale implementation and sustainable. The investigators propose an entirely new approach for the implementation of genomic medicine, supported by the leadership of Penn Medicine, investigating the use of non-geneticist clinician and patient nudges in the delivery of genomic medicine through a pragmatic randomized clinical trial, addressing NHGRI priorities. Our application is highly conceptually and technically innovative, building upon expertise and infrastructure already in place.Innovative qualities of our proposal include: 1) Cutting edge EHR infrastructure already built to support genomic medicine (e.g., partnering with multiple commercial genetic testing laboratories for direct test ordering and results reporting in the EHR); 2) Automated EHR-based direct ordering or referring by specialist clinicians (i.e., use of replicable modules that enable specialist clinicians to order genetic testing through Epic Smartsets, including all needed components, such as populated gene lists, smartphrases, genetic testing, informational websites and acknowledgement e-forms for patient signature); 3) EHR algorithms for accurate patient identification (i.e., electronic phenotype algorithms to identify eligible patients, none of which currently have phenotype algorithms present in PheKB; 4) Behavioral economics-informed implementation science methods: This trial will be the first to evaluate implementation strategies informed by behavioral economics, directed at clinicians and/or patients, for increasing the use of genetic testing; further it will be the first study in this area to test two forms of defaults as a potential local adaptation to facilitate implementation (ordering vs. referring); and 5) Dissemination: In addition to standard dissemination modalities,PheKB95, GitHub and Epic Community Library, the investigators propose to disseminate via AnVIL (NHGRI's Genomic Data Science Analysis, Visualization, and Informatics Lab-Space). Our results will represent an entirely new paradigm for the provision of genomic medicine for patients in whom the results of genetic testing change medical management., conditionsModule conditions: Genetic Predisposition, conditions: Paraganglioma, conditions: Pheochromocytoma, conditions: ALS, conditions: Parkinson Disease, conditions: Polyneuropathies, conditions: Frontotemporal Dementia, conditions: Alzheimer Disease, conditions: Cardiomyopathy Non-ischemic, conditions: Thoracic Aortic Aneurysm, designModule studyType: INTERVENTIONAL, phases: NA, designInfo allocation: RANDOMIZED, interventionModel: FACTORIAL, primaryPurpose: HEALTH_SERVICES_RESEARCH, maskingInfo masking: QUADRUPLE, whoMasked: PARTICIPANT, whoMasked: CARE_PROVIDER, whoMasked: INVESTIGATOR, whoMasked: OUTCOMES_ASSESSOR, enrollmentInfo count: 1000, type: ESTIMATED, armsInterventionsModule interventions name: Behavioral nudge, outcomesModule primaryOutcomes measure: Rate of Genetic Testing, secondaryOutcomes measure: Rate of Genetic Test Orders/Referrals, secondaryOutcomes measure: Rate of Patient Engagement, secondaryOutcomes measure: Rate of Pathogenic variants and VUS, secondaryOutcomes measure: Rate of Clinician actions, eligibilityModule sex: ALL, minimumAge: 18 Years, stdAges: ADULT, stdAges: OLDER_ADULT, contactsLocationsModule, hasResults: False