Big Data and Advanced Analysis are Not Dirty Words (in Healthcare)
Much is increasingly made about the new power and application of Big Data and analytics for faster and better decision-making.
In fact, given the current “Hype-cycle,” the words Big and Data used together appear to be becoming irritating. Recently, a number of healthcare industry
experts were even apologizing for the possible over-use of the terms in presentations.
So without focusing on the words, it may be more helpful to simply understand the application, especially in healthcare.
Just crunching more data faster is not the answer; not in healthcare, not in diagnostics, and not in business operations optimization.
As categorized by SAP Chief Medical Officer David Delaney, “The healthcare industry is drowning in data, but starving for information.” Healthcare analytics are only as good as the data, but, unfortunately, often times the data in use can be bad. Taken to the extreme, faster analysis with that bad data could be simply accelerating poor decision-making. But I think this is inaccurate.
From the laboratory perspective, the real power of big data with in-memory analytics is to speed the analytic cycles, completing multiple tests in a fraction of the time as historical database structures. In this way, researchers can more quickly sift through that bad data to eliminate ineffective results, ultimately speeding the best decision-making.
From the perspective of personalized health care, big data and in-memory analytics can find resources and help to coordinate interventions with patients to deliver better care, and can monitor patients and identify strategies to improve outcomes.
There will continue to be growing activity and dialogue around the power of analytics in healthcare; SAP hopes to work more closely across the industry to help move past terminology to focus on delivering real-time, value-based healthcare outcomes.