Big Data in Healthcare Today
A number of use cases in healthcare are well suited for a big . Some academic- or research-focused healthcare institutions are either experimenting with big data or using it in advanced research projects. Those institutions draw upon data scientists, statisticians, graduate students, and the like to wrangle the complexities of big data. In the following sections, we’ll address some of those complexities and what’s being done to simplify big data and make it more accessible.
A Brief History of Big Data in Healthcare
In 2001, Doug Laney, now at Gartner, coined the term “the 3 V’s” to define big data–Volume, Velocity, and Variety. Other analysts have argued that this is too simplistic, and there are more things to think about when defining big data. They suggest more V’s such as Variability and Veracity, and even a C for Complexity. We’ll stick with the simpler 3 V’s definition for this piece.
In healthcare, we do have large volumes of data coming in. EMRs alone collect huge amounts of data. Most of that data is collected for recreational purposes according to Brent James of Inter-mountain Healthcare. But neither the volume nor the velocity of data in healthcare is truly high enough to require big data today. Our work with health systems shows that only a small fraction of the tables in an EMR database (perhaps 400 to 600 tables out of 1000s) are relevant to the current practice of medicine and its corresponding analytics use cases. So, the vast majority of the data collection in healthcare today could be considered recreational. Although that data may have value down the road as the number of use cases expands, there aren’t many real use cases for much of that data today.
There is certainly variety in the data, but most systems collect very similar data objects with an occasional tweak to the model. That said, new use cases supporting genomics will certainly require a big data approach.
Health Systems Without Big Data
Most health systems can do plenty today without big data, including meeting most of their analytics and reporting needs. We haven’t even come close to stretching the limits of what healthcare analytics can accomplish with traditional relational databases—and using these databases effectively is a more valuable focus than worrying about big data.
Currently, the majority of healthcare institutions are swamped with some very pedestrian problems such as regulatory reporting and operational dashboards. Most just need the proverbial “air and water” right now, but once basic needs are met and some of the initial advanced applications are in place, new use cases will arrive (e.g. wearable medical devices and sensors) driving the need for big-data-style solutions.