The number of medical conditions for which the results of genetic testing change the medical management of patients is exponentially increasing. However, a minority of eligible patients receive genetic testing, despite the implications for downstream care. System- (methods to identify eligible patients and return results), clinician( e.g., knowledge, limited workforce), and patient- (e.g. , concerns about costs and adverse effects) level barriers foster uncertainty and a tendency to rely on the status quo - failing to use genomic information to guide medical care. Implementation science methods and frameworks are ideal for addressing this practice gap, especially those that consider multi-level barriers and the role of human decision-making in contexts with uncertainty. Our team has built the infrastructure to address system-barriers to delivering genetic testing across our health system - an integrated system within the electronic health record (EHR) that enables direct ordering and resulting of genetic tests as structured data - now with multiple requests for dissemination. Our team also is using behavioral economics as an implementation science framework to improve healthcare by using nudges (EHR defaults, patient priming) to overcome clinician and patient barriers, concurrently addressing health disparities (e.g., higher practice gaps among racial minorities). Merging these areas, we propose a highly innovative project that will evaluate, for the first time, the use of nudges to clinicians (EHR defaults for either: 1) referring to genetics clinic or 2) ordering for genetic testing) and/or nudges to patients (communication to prime patients about the benefits of genetic testing prior to appointment). In Aim 1, we will develop electronic phenotyping algorithms for 10 clinical conditions, which will drive diagnosis-specific genetics referral and testing; we will refine our nudges working with a Stakeholder Advisory Council. In Aim 2, we will conduct a hybrid type 3 implementation study, using a cluster randomized design with 228 clinicians (physician, Advanced Practice Practitioners) as the unit of randomization (N= 120 clusters) and 16,500 patients with one of the 10 conditions to examine the impact on the rate of genetic testing of: the patient priming nudge, the two clinician nudges, combining the patient and each of the clinician nudges, vs. a generic best practice alert (BPA) (no clinician or patient nudge). We will examine patient (e.g., race), clinician (e.g., specialty), and system (e.g. , community vs. academic center) moderators of nudge effects on genetic testing rate and assess an effectiveness outcome (rate of clinician action following identification of a pathogenic variant). In Aim 3, we will engage in systematic methods to disseminate our EHR integration of genetic testing, EHR-based algorithms, and other materials and systems built for the clinical trial through Epic, PheKB, NHGRl's AnVIL, and GitHub. Our study will be immensely...