Data integration and management
Brings together any and all real-world data sources in a single platform. Automatically maps data to a common data model, and refreshes cohorts and outputs as new data arrives.
End-to-end capabilities for gleaning new scientific and commercial insights from real-world evidence – from data management, to cohort extraction, to advanced analytics and more.
Achieve faster time to insight and gain a competitive advantage.
Gather relevant real-world data from a variety of sources.
Easily integrate real-world data from internal and third-party sources – point-of-care systems, electronic medical records, insurance claims, patient-reported outcomes, third-party data providers and more. An automated process cleanses and prepares the data, and maps it to a common data model for side-by-side comparison. You spend less time preparing data and more time learning from it.
Gain fast, easy access to advanced analytics and data visualization tools.
Even nontechnical users can navigate, visualize and explore huge amounts of data quickly with an easy-to-use, point-and-click interface to powerful, in-memory analytics. A library of prebuilt analytical models infused with innovative algorithms provides access to a wide range of analytic tools – from simple descriptive statistics, to predictive analytics, to machine learning methods. You can also create meaningful reports and share them via mobile devices to support decisions on brand strategy, treatment regimens, pricing, reimbursement, formulary access and more.
Visualize and analyze cohort data.
An interactive drag-and-drop cohort builder supports complex cohort queries with temporal relationships – no coding required. You can easily explore cohort characteristics and the effect of inclusion/exclusion criteria on patient populations to determine study feasibility. Cohort definitions can be saved for reuse, modified and applied to other real-world data assets for comparisons across populations, saving time and resources. You can also validate results and do further analysis using in-memory analysis and visualization in SAS or other technologies (e.g., R, Python and third-party visualization tools).
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