Key Drivers of HTA Decisions for "Life-Changing" Drugs

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.

Building a Smart Data Model

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.

The Rise of Smart Data: The Next Step in the Evolution of Big Data

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.

What’s Wrong with This Picture? Big Data, Insight, and Asking the Right Questions

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.