There are mechanisms in place to reward innovative and “life-changing” drugs. Most of these mechanisms exist in the regulatory process, but what about the reimbursement process? Are “life-changing” drugs rewarded by health technology assessment (HTA) agencies with positive reimbursement decisions? This original research applies a data-driven approach to evaluate the key drivers of HTA agency decisions for drugs that provided significant improvement in benefits over existing therapies.
Last summer, we used the term "Smart Data" to explain the opportunity we see for applying technology and information to strategic drug development decisions. At that time, there was a lot of hype around Big Data, and we made the distinction that the value for biopharma was not just in having high volumes of a variety of data with a frequent velocity, but in being able to make sense of the data.
When the New York Times recently published an article, "For Big Data Scientists 'Janitor Work' Is Key Hurdle to Insights" it struck a chord with us. Context Matters is not a "Big Data" company, but in the process of curating and standardizing disparate sources of drug development data, we confront many of the same issues and challenges.
During the frenzy over the rise of Big Data and its promise to transform just about everything, an interesting backlash has also emerged: Big Data skepticism. After all, it’s only logical to ask the simple but very important question: Do all this data actually mean anything? In a recent interview with journalist Charlie Rose, Freakonomics authors Steven Leavitt and Steven Dubner made the point that ideas, talent, and good questions are the key elements that turn data into useful insights. They went on to say that these three elements remain in remarkably short supply, particularly within conventional thinking – including around Big Data. And they are not alone.
A recent New York Times op-ed looked at the ascendance of big data, noting that the World Economic Forum has compared the rise of big data to “transformative innovations like the steam locomotive, electricity grids, steel, air-conditioning and the radio.” Author James Glanz points to lackluster productivity growth since the emergence of big data (which he dates back to 2005) and asks why we aren’t seeing more obvious productivity benefits from this supposedly “transformative” development. At Context Matters, we’re not really surprised that big data hasn’t lived up to its promise yet. It’s always been our belief that the presence of data alone (even in vast quantities) is not inherently valuable. You have to ask the right questions to gain real value from data of any size.
Volume, variety, and velocity. How many times have you heard this when looking for a definition of big data? But what does that really mean, and how is big data actually changing the way we make decisions?