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Big Data in Healthcare


It is proven now that Hadoop as the most significant data processing platform for big data analytics in Healthcare.

Using Hadoop, researchers can now use data sets that were traditionally impossible to handle. For example a team in Colorado is correlating air quality data with asthma admissions. Life sciences companies use genomic and proteomic data to speed drug development. The Hadoop data processing and storage platform opens up entire new research domains for discovery. Computers are great at finding correlations in data sets with many variables, a task for which humans are ill-suited.

However, for most healthcare providers, the data processing platform is not the real problem, and most healthcare providers don’t have “big data.” A hospital CIO we know plans for future storage growth by estimating 100MB of data generated per patient, per year. A large 600-bed hospital can keep a 20-year data history in a couple hundred terabytes.

Healthcare analytics is generally not being held back by the capability of the data processing platforms. There are a few exceptions in the life sciences, and genomics provides another interesting use case for big data. But for most healthcare providers, the limiting factor is our willingness and ability let data inform and change the way we deliver care. Today, it takes more than a decade for compelling clinical evidence to become common clinical practice. We have known for a long time that babies born at 37 weeks are twice as likely to die from complications like pneumonia and respiratory distress than those born at 39 weeks. Yet 8 percent of births are non-medically necessary pre-term deliveries (i.e. before 39 weeks).

Data from other clinical providers in your geography can be very useful. Claims data give a broad picture but not a deep one. Data from other non-traditional sources also has surprising relevance; in some cases, it’s a better predictor than clinical data. For example: EPA data on geographical toxic chemical load adds additional insight to cancer rates for long-term residents. The CMS-HCC risk adjustment model can help providers understand why patients in their area seem to have higher or lower risk for certain disease conditions. Household size of one increases the risk of readmissions because there is no other caregiver in the home.

The problem we should be talking about in healthcare analytics is NOT what the latest data processing platform can do for us BUT about how we can use data to engage clinicians to help them provide higher quality care. It’s not how much data you have that matters, but how you use it. This is where Datafence comes in. Contact us for further details.