| | February 20179CIOReviewand unfounded assumptions, no matter how obvious, is simply too risky. Let me demonstrate the importance of this notion with a non-healthcare example--the self-driving car.The self-driving car is a multi-ton predictive analytics processor on wheels. When algorithms inside the car's computer predict the vehicle will hit a pedestrian unless an intervention occurs. The car engages the brakes (intervention) as the result of a proven predictive model. Recently we have seen how disaster (and even death) can occur when an intervention (or lack of intervention) is based on faulty analytics. These life-and-death stakes are just as high in healthcare, and interventions derived from faith-based assumptions can be deadly. Getting to the Next PlateauMy naiveté may be showing, but I believe we will eventually reach the Predictive Analytics plateau in healthcare. But the road is winding and complex. In my opinion, the trigger we need will be value-based healthcare (secret code for risk based contracting). Sharing risk successfully means understanding costs, and understanding healthcare costs requires lots and lots of data. Once these risk sharing contracts are in place, managing populations of patients and networks of providers, will be even more data intensive. As a result of this trend, wise healthcare organizations and vendors are creating analytics technology architectures. These technologies will be used to first produce, dare I say it, dashboards. But necessary analytics components like warehouses, data normalization structures, data governance techniques, data lakes and more are being developed procured, and implemented. With cloud-based technologies, the analytics price tag is looking better and better. Bottom line--we are starting to see the emergence of cost effective technology architectures capable of producing meaningful predictive models. Once in place, these will be the platforms for predictive analytics. But when it comes to predictive analytics, technology is not nearly enough. Having people with the skills capable of producing sound predictive models is the key. Today these skilled professionals are rare, and this skills gap will be our biggest hurdle in the foreseeable future. Other than academic medical centers we rarely see these types of individuals in a typical healthcare delivery organization. This is not easy stuff to teach or learn. First and foremost, this new workforce must be well schooled in the scientific method. Practitioners must be able to devise testable questions (scientists would call this `forming a hypothesis'). Once a hypothesis is formed it must be rigorously tested with sound statistical techniques. A thorough understanding of the data--its strengths, weaknesses, inclusions and exclusions--is an absolute requirement. Data integration, normalization, technology architectures, data governance and more all go into the calculus of predictive analytics. I could go on for hours, but the bottom line is obvious. We must learn and teach a new kind of workforce if we are to devise evidence base interventions capable of changing healthcare for the better.Without interventions created by sound predictive analytics we will never fully realize the value of healthcare data. Technology is not the problem, and with the proliferation of EMRs we have plenty of data. We know how to train qualified professionals, but sadly the process of creating this new workforce is slow-moving and embryonic. Are we ready to travel beyond dashboards, and move to predictive analytics in healthcare? Sadly, appears the answer to this question is a resounding `Not Yet'. Predictive analytics, using sound statistical and sampling techniques for minimizing risk, must form the foundation of an interventionKirk KirkseyPredictive analytics, using sound statistical and sampling techniques for minimizing risk, must form the foundation of an interventionKirk Kirksey
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