Background: Immunotherapy (IO) utility in bladder cancer (UC) has expanded into multiple stages of disease. Employing IO optimally requires mastery of clinical trial data, patient eligibility criteria, and interpretation of biomarkers and determination of treatment sequencing. Given the nuanced therapeutic decision-making, education was developed in partnership between Medscape Oncology and Society for Immunotherapy of Cancer (SITC) to assist oncologists in improving their performance surrounding the management of patients with advanced UC. Methods: A virtual patient simulation (VPS) continuing medical education (CME)-certified activity depicting 2 advanced UC cases was made available to oncologist members of Medscape. The cases depicted 1) a patient with newly diagnosed metastatic UC with comorbidities and PDL1+ disease and 2) a patient with advanced UC progressing on platinum therapy with no actionable mutations. The VPS platform captures real-life decision making process of oncologists in an EHR-like format supported by an extensive database of diagnostic and treatment possibilities. Learners were able to interact with patients via video, order lab tests, assess patients, make diagnoses, and order treatments matching the scope and depth of actual practice. Tailored clinical guidance (CG) employing up-to-date evidence-based and faculty recommendations was provided after each decision point. Decisions were collected pre- and post-CG and analyzed using McNemar’s test to determine p-values. Data were collected from 4/28/20 to 7/13/20. Results: Analyses from oncologists (n = 51-66) found significant improvement in performance measured pre- to-post CG: Case 1: Ordering appropriate testing to determine patient eligibility for therapy (39% pre; 65% post; p < .001) Prescribing appropriate therapy based on patient- and disease-specific factors (38% pre; 77% post; p < .001) Providing appropriate counseling and follow-up for a patient receiving treatment (65% pre; 80% post; p < .01) Case 2: Ordering appropriate testing to determine patient eligibility for therapy (39% pre; 57% post; p < .01) Prescribing appropriate therapy based on patient- and disease-specific factors (25% pre; 41% post; p < .01) Providing appropriate counseling and follow-up for a patient receiving treatment (71% pre; 82% post; p < .05). Conclusions: This activity demonstrates the value of providing oncologists a simulation platform to practice and master clinical decision-making of the limitless possible diagnostic and therapeutic options in the management of advanced UC. Insights from rationales for each clinical decision point uncover continued gaps for oncologists on guideline recommendations, efficacy outcomes, or molecular implications. They also highlight barriers including limited experience or confidence with IO.