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UBC’s validated real-world data automated analytics software is designed for non-programmers and yields insights in minutes that would otherwise require customized complex programming and in-depth knowledge of healthcare coding. End-users of the platform have confidence in the quality of the results it generates whether performing simple feasibility queries to determine counts of diagnoses or medication utilization, creating complex study and comparator populations, or generating data-driven insights for decision making.
Our team of epidemiologists, outcomes researchers, clinicians, statisticians and database specialists have informed the functionality needed to easily showcase value to pharmaceutical decision-makers. Each analytic module was built based on decades of epidemiological strength and best practices in using RWD for evidence generation.
We work closely with clients to ensure that users are designing appropriate queries, correctly interpreting analysis results and efficiently preparing reports to share insights with others. An audit trail is automatically generated and permanently linked to every analysis, ensuring transparency of algorithms as well as reproducibility of results using updated cohorts. Results are considered validated and may be provided to regulators.
The platform supports multiple, disparate global administrative claims and electronic health record databases. The software maps real-world databases to a common data model, providing insights across all phases of drug development and post marketing commitments.
Our subscription-based software provides life science organizations with access to a set of structured content libraries that allows robust query building and rapid descriptive analytics. The easy-to-use, point-and-click web-based interface is leveraged to inform diverse applications.
Through the use of a common data model that standardizes different structures and coding systems of healthcare databases, our platform overcomes many limitations of an increasingly rich but fragmented data landscape. This research-ready platform has been assembled to augment and enable interventional and observational research to flourish with security, privacy and interoperability front of mind: