Big Data: Healthcare must move beyond the hype

The biggest hindrance to progress is that tools like artificial intelligence, cognitive computing and machine learning conjure images of sci-fi movies rather than real-world uses. Here’s what one Harvard Medical School expert said needs to happen next.
By Bill Siwicki
07:06 AM
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Big data beyond hype

Harvard Medical School assistant professor Leonard D’Avolio said the term Big Data needs to be clarified before healthcare providers can make significant strides with related technologies. D’Avolio and colleagues are building an organization called Cyft to deal with these issues.

The hype surrounding so-called Big Data – the computational analysis of vast data sets to uncover patterns, trends and associations – is “bi-polar.” That’s how Leonard D’Avolio, an assistant professor at Harvard Medical School, describes all the chatter around this technology.

“Either we are reading about how Big Data will cure cancer or about how it’s foolish to believe Big Data will replace doctors,” D’Avolio said. “I think the narrative should be in the middle, where we are talking about these technologies as tools that could be used to complement the work of not just clinicians but also healthcare administrators, operational leaders and others. Big Data is another set of technologies with pros and cons.”

D’Avolio points to research firm Gartner’s Hype Cycle description of the evolution of new technologies as a way healthcare leaders can better grasp what is happening with Big Data today, bearing in mind that Big Data still is more or less a new set of tools. 

“New technology evolution starts with a peak of inflated expectations, then hits a trough of disillusionment, then a slope of enlightenment, and finally a plateau of productivity,” D’Avolio said of the Gartner description. “The point is: Whenever a new technology comes out around which great promise is ascribed, it usually starts as this magical thing that is understood by few and only accessible to PhDs and only operated by people in white coats in sterile rooms and only available to those with multimillion-dollar budgets. But then, eventually, the mystical mainframe becomes a cell phone. But it takes a while to understand how to align the pros and cons of a new technology with actual business needs.”

Good design and application of technology involves matching what is technically possible with what organizations really need to do, D’Avolio explained.

“We have yet to go through that process in healthcare with Big Data,” he added. “Additionally, the fact that we even call this complex set of technologies by this umbrella term Big Data has not helped us demystify the technology for the industry. One thing hindering my progress is the fact that we refer to this as Big Data and machine learning and cognitive computing and artificial intelligence, and that one conjures up images of the evil computer network SkyNet in the ‘Terminator’ films.”

Until the industry stops referring to this huge suite of very different technologies as Big Data, it will be difficult to figure out the jobs for which these varied technologies are best suited, he added.

“The relatively few companies that have been able to harness the power of these technologies have had trouble mapping the technologies to the very real business and clinical needs of potential customers,” D’Avolio said. “So it has been a bit of hammers seeking nails, which is natural in this very early stage of a new technology being introduced. But when new technologies are surrounded by so much promise and potential and are fueled by vendors and journalists, it is sometimes difficult for those trying to implement the technologies to battle that promise and do the necessary work to find the needs for these technologies.” 


  Related stories ahead of  Big Data & Analytics Forum in Boston, Oct. 24-25. 
⇒ Tips for reading Big Data results correctly
⇒ Small hospital makes minor investment in analytics and reaps big rewards 
 MIT professor's quick primer on two types of machine learning for healthcare
⇒ Must-haves for machine learning to thrive in healthcare


Twitter: @SiwickiHealthIT
Email the writer: bill.siwicki@himssmedia.com

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