Our enterprise-grade RWD/RWE framework enables clinical trial design teams to conduct disease landscaping, identify alternate indications, and understand patient subgroup heterogeneity to create optimized protocols. We integrate HEOR, claims, genomics, lab, and other data sources to model disease states and their progression in a real-world patient population context using graph-based predictive algorithms. 
Simulation of trials based on clinical, operational and outcome-based parameters ensures selection of right protocol variants. 

We enable trial design using synthetic control arms that repurpose historical clinical trial data and RWD to accurately match patients from the experimental arm. We use RWD along with NLP technologies and early biomarker identification to select sites and screen the right patients for trials.