How will Hadoop impact and/or change healthcare analytics?

In February of this year, HIMSS Journal released a report on big data, Big data analytics in healthcare: promise and potential. In the report, the authors list 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. 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 I 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.

Every day, there are more than 4.75 billion content items shared on Facebook (including status updates, wall posts, photos, videos, and comments), more than 4.5 billion “Likes,” and more than 10 billion messages sent. More than 250 billion photos have been uploaded to Facebook, and more than 350 million photos are uploaded every day on average. Facebook adds 500 terabytes a day to their Hadoop warehouse.

Southwest’s fleet of 607 Boing 737 aircraft generate 262,224 terabytes of data every day. They don’t store it all (yet), but the planes’ instrumentation produce that much data.

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).

The problem we should be talking about in healthcare analytics is not what the latest data processing platform can do for us. We should be talking 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. At our upcoming September Healthcare Analytics Summit, national experts and healthcare executives will lead an interactive discussion on how Healthcare Analytics has gone from a “Nice To Have” to a “Must Have” in order to support the requirements of healthcare transformation.(Source :Healthcatalyst)

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