Big Data in Healthcare
by Dave Wilson on Nov 7, 2011
Being in the healthcare space my entire career, I had no idea what all the fuss was about when Big Data started to be the topic of the day. Sounded like a large file to me – mammography images can be 60Mb each – and aside from the potential joke about large mammo images, what was the big deal?
So I did some research. “Big Data” refers to a volume of data too large to be harnessed and used in meaningful ways. In other words, Big Data is an accumulation of data that is going to waste and has no immediate meaning, mostly because no one can do anything with it due to its size.
Healthcare providers are quickly becoming inundated with Big Data. Governments are unintentionally driving big data warehouses through health information exchanges, diagnostic imaging repositories and electronic health Records. Eighty percent of the data is unstructured, and it is accumulating to the point where it can be called Big Data. Now, on an individual basis, each patient record has value and meaning to the patient–obviously. But as we look at the growing accumulation of data, the opportunities are endless to drive meaningful analytics out of the volumes of data available.
Data warehouses are simply that–a warehouse for data. Data has no meaning on its own, and as providers create these warehouses, there needs to be a shift in how we think about the potential use of data. Data warehouses need to become information or content repositories. Information is a useful tool that results from the analysis of data driving decision making. Sounds like marketing fluff right? Let me demonstrate.
A diabetic patient monitors their blood sugar multiple times per day. This value gets stored electronically–this value is data. On its own it has little meaning (aside from the obvious immediate value to the patient) in the big picture of things. Now take that patient and all the patients in the region and their blood sugar values for the last 5 years. Analysis of this data could lead to trends—important information that can drive preventative health measures. This leads to better patient care, improved quality of life, and lower healthcare costs.
Much of this data is available today in separate repositories, isolated applications and local data warehouses. The potential to combine the blood sugar data with nursing notes keywords, weather forecasts, and other related and unrelated data can help drive this analysis. The challenge becomes getting access to this data and then overcoming the interoperability aspects. Problem is, we don’t know what we don’t know. Questions we would never ask today can be asked when the restrictions are lifted – Is there a correlation between diabetic hospital admissions and the weather pattern?
A content cloud could answer some of these challenges. Consolidate and aggregate data from multiple sources, and at the same time capture the relevant meta data associated with the data against which analytics can be run. Meta data can help manage the massive amounts of data being generated (the Big Data) and provide a way to correlate this data into meaningful information. This content then can be accessed by researchers and scientists to analyze.
Big Data is, and will continue to be, a major problem for healthcare providers. One estimate has healthcare Big Data sized at 150 exabytes and growing at a phenomenal rate of 1.2 exabytes per year. The possibilities of tapping into that information are endless. It has been a challenge for pharmaceutical and biotech companies for years – but that’s another discussion.