Background: Despite the availability of molecularly-targeted agents for the treatment of many cancer types, gaps remain in integrating comprehensive precision oncology decision support tools and services into routine clinical practice. Molecular Tumor Boards (MTBs) have been shown to improve accurate incorporation of precision oncology and oncologic clinical trial enrolment into clinical practice. However, the traditional MTB model is a didactic meeting occurring at regular pre-scheduled cadences which may not align with treatment decisions or schedules of community-based general medical oncologists and advanced practice providers (APPs) without protected time away from clinic. Herein, we report on the utilization of an on-demand virtual MTB (vMTB) implemented at Tennessee Oncology (TO) powered by the Personalized Medicine (PM) team at the Sarah Cannon Research Institute (SCRI). Methods:“MolecularHelp” (MH) decision support services were implemented in September 2021 for oncology providers at TO – a network of over 100 oncologists and 86 APPs practicing across 34 clinics in Tennessee. The MH services request was a structured order that could be placed directly within the electronic health record (EHR) or through patient-protected email within the practice. MH orders initiated a virtual, on-demand interpretation of comprehensive genomic profiling (CGP) reports by a centralized vMTB run by SCRI's PM team and supported by Genospace, SCRI's precision medicine software platform. The PM team – comprised of pharmacologists, cell biologists, human geneticists, and molecular biologists – analyze CGP results and provide expert advice on both standard-of-care (SOC) and clinical research therapeutic options. Both SOC and clinical research targeted therapy options were relayed back to the treating physician and embedded within the EHR. Herein, we report key metrics including MH order frequency, average turnaround time, and subsequent clinical trial enrolment between September 2021 and March 2022. Results: CGP reports from 120 unique patients were reviewed by the vMTB during the collection period. MH orders were initiated by 30 TO providers from 14 different clinic locations across Middle and East Tennessee. The average turnaround time from referral to vMTB interpretation was less than 10 hours. Of the 120 patients reviewed, actionable mutations were identified by the MTB in 103 patients, of whom 27 subsequently enrolled onto clinical trials (15 phase 1 and 12 phase 2/3). Conclusions: An on-demand vMTB is feasible within an engaged community oncology practice with investments in bioinformatics, decision support software tools, and a team of precision oncology experts supported by a robust clinical trial menu. On-demand vMTBs can be widely adopted to enhance clinical trial enrolment. Future directions include studying the impact of vMTBs on patient outcomes over time.