OBJECTIVES:  A growing number of decision makers are embracing performance-based contracts as the avenue to shift the pharmaceutical business paradigm from selling drugs to selling outcomes. Meaningful performance-based contracts require anticipating clinical outcomes under usual circumstances of care at a time when little evidence exists outside of clinical trials for the new intervention. A novel tool based on bridging-to-effectiveness modeling was developed to anticipate the real-world performance of new drugs. METHODS: The tool was built as an R/Shiny-based web interface. The user selects a therapeutic area with a set of outcomes, interventions and real-world practice scenarios to be evaluated. Data on key patient and drug usage characteristics (effectiveness drivers) and drug efficacy can then be entered either as patient-level datasets or via summary statistics. Real-world outcomes for the selected interventions are predicted over time using a longitudinal Bayesian model with default prior parameter distributions. The tool then jointly simulates the dynamics of outcomes, exposure and effectiveness drivers for any user-defined virtual cohort. Results in terms of comparative effectiveness are displayed along with confidence intervals. RESULTS:  Two case examples were run with the tool to predict real-world outcomes under various scenarios: prediction of survival in renal-cell carcinoma patients and of hospitalization rates in schizophrenic patients with anexposure first channeled to most severe patients after launch [1]. CONCLUSIONS: The tool allows for rapid prediction and visualisation of real-life outcomes with confidence intervals, accounting for all evidence available to the user. This tool could save months of modeling time, e.g., in the context of fast-paced performance-based contract negotiations.  1. Schneeweiss S et al. Clin Pharmacol Ther. 2011 Dec;90(6):777-90. https://www.ncbi.nlm.nih.gov/pubmed/22048230