What is Context Matters?

Built for and by life science professionals to strategically value drugs for global reimbursement. 

Click the image to explore how Context Matters builds smart data

Deep roots in life science, public health, data science, and technology allow the Context Matters team to bridge the traditionally separate worlds of drug development and market access. Applying this expertise, we created the first-to-market comprehensive data model that integrates drug development and market data—delivering a unique, multidimensional perspective from which to understand and solve the challenges of market access. 

Built through a rigorous and meticulous data management process, the Context Matters data model transforms unstructured data from multiple sources into structured, analysis-ready data—we call it smart data. A profoundly powerful and easy-to-use technology platform provides direct access to the data model from which all stakeholders have a richer context for discovering strategic pricing and determining what really matters in creating the optimal value of company innovation. Using our contextual data model, users can answer high-level strategic questions by querying our data in myriad ways to build their blueprint for reimbursement. 

Powerful capabilities

  • Web-based application instantly accessible 24/7 through any browser and with any device from anywhere in the world.
  • Easy-to-use interface to spontaneously and interactively aggregate, visualize, and comprehend the kinds of data-derived insights for which companies traditionally relied on consultants.

Meticulously structured and curated data

  • 6,700+ reimbursement decisions
  • 34,000+ reimbursement documents
  • 3,500+ drugs
  • 100 disease conditions
  • 250+ global biopharmaceutical companies
  • 16,000+ regulatory labels
  • 24,000+ clinical trials outcomes

Comprehensive and thoughtfully designed.

Thoughtfully designed, our data model links the millions of data relationships that allow you to spontaneously ask and answer the questions that matter most:


  • When is overall survival or progression-free survival used as a primary endpoint in a clinical trial vs. a health technology assessment (HTA) agency evaluation?
  • What is the most appropriate comparator for my drug and what is the most appropriate analogue to my drug?
  • I am about to design a head-to-head trial. How can I better understand what comparator or endpoints may be important for an optimal evaluation by a health technology assessment agency?


  • How do the clinical and economic rationales for decisions vary by drug, by drug class, by disease condition, by analogue, etc.?
  • Did a patient access scheme influence or change the initial reimbursement decision? How much did a risk-sharing agreement matter? Was the scheme successful?
  • Which inputs will be successful in an economic model?   
  • What were the submitted base-case incremental cost-effectiveness ratio and quality-adjusted life-year vs. the agency’s reaction?
The insights from your data are —particularly the trends across rare diseases.”
— European HEOR, Biotechnology Company


  • What professional or patient advocacy groups have a significant impact on the evaluation of a drug?
  • How was the economic comparator different from the clinical comparator?


  • What are all of the restrictions that the UK agency (NICE) imposed on drugs that gained limited access?
  • How often is an off-label use of a drug reviewed in a health technology assessment?


  • Show me the time it takes for an orphan drug to be reimbursed.
  • Show me the EMA label at the time of HAS vs. G-BA reimbursement decisions.


  • Show me all of the reimbursement decisions for my competitor.
  • How do I avoid making the same mistakes as my competitor?

See how quickly and easily you can make the right drug pricing and reimbursement decision.