Predictive Analytics And The Health Care Industry

1002 Words Oct 30th, 2015 5 Pages
Before proceeding to review a range of predictive analytic algorithms, it is important to know how critical predictive analytics is to the health care industry. The growth rate of US healthcare expenditures, increasing annually by nearly 5% in real terms over the last decade and a major contributor to the high national debt levels projected over the next two decades. McKinsey estimates that Big Data can enable more than $300 billion savings per year in US healthcare, with two-thirds of that through reductions of around 8% to national healthcare expenditures. Imagine if there were health care analytics in the middle ages. The black plague could have been avoided saving millions of lives of people as it would have been easy to single out the root cause: water. The advent of modern analytics in healthcare systems has so much potentiality that it can provide cost-savings, better patient outcomes, identify at-risk population, predict individual’s future healthcare needs, faster development of treatments and medical breakthroughs. Predictive analytics is essential to any platform and to the Healthcare industry it is paramount. According to McKinsey a potential savings of 300 billion $ annually in US healthcare is estimated. By 2016, 4.9 million people will use remote health monitoring devices, 142 million healthcare and medical app downloads and the health care data which was 500 petabytes in 2012 is expected to be exploded to 25000 petabytes by 2020.
So what constitutes to data…
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