Bringing a Vision to Life

Context Matters was born from the realization that there is a common data theme from clinical trial development through market access considerations. Health technology assessment (HTA) agencies outside of the United States were conducting clinical comparative effectiveness and economic evaluations on drugs, yet there was no standardized data approach or searchable capability to understand and compare these assessments. At the same time, information used for these evaluations was based directly on clinical trial data—the same data used for regulatory approval. Disparate data domains, unstructured data, and a lack of understanding as to how the decisions in clinical trial endpoints have an effect very far downstream in reimbursement decisions or comparative and cost-effectiveness calculations were creating a data gap. As a result, the ability of biopharma professionals to make well-informed strategic decisions was being impeded.

This data gap was an opportunity to create a data analytical model that would showcase the relationship between clinical trial design decisions and reimbursement decisions—with the ability to drill into what criteria and comparators would be important for later economic and comparative effectiveness evaluations. 

To address this, we first created an overarching context—starting from the end goal of market access and health outcomes used for both cost and comparative effectiveness evaluations and working back toward the choice of clinical trial endpoints that would have been made at an earlier stage. This required structuring a substantial amount of unstructured data—creating a data architecture that could accommodate disparate domains of data, and utilizing analysts who could parse, match, and categorize across different terminology, language, syntax, and public policy inferences. Every element and data point is subject to the highest level of quality control, rendering an overall flexible data model able to accommodate multiple contexts necessary for complex decision making. Through the integration of our proprietary data model, innovative technology platform, and best-in-class data management process, we have created an elegant solution that relates market access considerations with clinical trial design. Our solution enables a holistic approach that provides pharmaceutical and biotechnology companies the critical data and context needed to develop drugs for both an approvable and a reimbursable label—all at a global level.

Today, we work with more than a dozen large pharmaceutical companies and a biotechnology companies who are using our data and our application to support a myriad of different activities—from strategic planning of market access to understanding specific drugs and disease conditions, to tracking competitive intelligence, to thinking about clinical trial endpoint selection, to track timelines across regulatory and reimbursement decisions that can extend to label extensions and reassessments.