Big data is the next big thing in health IT

Big data introduces unique healthcare challenges and opportunities.

During the 2012 HIMSS conference in Las Vegas I was invited by Dell Healthcare, along with a group of health IT experts, to discuss issues in health information technology. The session sparked some passionate discourse about the challenges and opportunities that are important to the health IT industry.

Moderator Dan Briody started the event with a question about things we had seen at HIMSS that had changed our thinking about health IT. Never being shy, I jumped right in and spoke about the issues of payment reform and how the private market is beginning to show signs of disruptive innovation. After a great deal of back and forth among the panelists it seemed we slipped into listing many of the barriers — technological, political and cultural — that health IT faces. I was hoping we would get back to sharing possible solutions, so I made the proposal that big data is the next big thing in health IT (see video below).

When I talk about “big data” I am referring to a dataset that is too large for a typical database software tool to store, manage, and analyze. Obviously, as technology changes and improves, the size of a dataset that would be qualify as “big data” will change as well. There is also a big data difference between healthcare and other industry sectors, since there are different tools available and the required datasets have varying sizes. Since health data is very personal and sensitive, it also has special security and privacy protections. This makes sharing, aggregating, sorting and analyzing the data sometimes challenging.

Another difficulty in making the most of big data in healthcare is those who control different pools of data have different financial incentives. There is a lack of transparency in performance, cost and quality; it is currently structured so that payers who would gain from decreasing revenue to providers, but the providers control the clinical data that is necessary to analyze in order to pay for value. The payers control another pool, which includes claims data. This is not very useful for advanced analysis that will provide real insight. But enabling transparency of the data will help to identify and analyze sources of variability as well as find waste and inefficiencies. Publishing quality and performance data will also help patients make informed health decisions.

The proliferation of digital health information, including both clinical and claims information, is creating some very large datasets. This also creates some significant opportunity. For instance, analyzing and synthesizing clinical records and claims data can help identify patients appropriate for inclusion in a particular clinical trial. These new datasets can also help to provide insight into improved clinical decision making. One great example of this is when an analysis of a database of 1.4 million Kaiser Permanente members helped determine that Vioxx, a popular pain reliever that was widely used by arthritis patients, was dangerous. Vioxx was a big moneymaker for Merck, generating about $2.5 billion in yearly sales, and there was quite a battle to get the drug off the market. Only by having the huge dataset available from years of electronic health records, and tools to properly analyze the data, was this possible.

The big data portion of the Dell think tank discussion is embedded below. You can find video from the full session here.


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