As healthcare buzz goes, “Big Data” has got to be one of the most thrown around phrases of the moment, nipping at the heels of “Obamacare” and maintaining a slight edge over “patient engagement.” I’m also fairly confident it is one of the least understood phrases in healthcare right now. It is used in every part of the industry – from physicians to vendors to payers, and even by some patients. But, as with any industry, subtle shifts in definition occur as different groups throw the phrase around.
On the macro level, Big Data, as defined by Wikipedia, refers to the “collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization.”
On the not-so-micro level, the concept of Big Data as applied to healthcare gets bigger by the day. More and more digital data is being created as:
It stands to reason that Big Data should ideally deliver big financial results, and that does seem possible according to a McKinsey report released earlier this year. The consulting firm estimates the use of big data could save healthcare stakeholders up to $450 billion in healthcare costs. That amount is nothing to sneeze at, to be sure. However, definitions and ROI estimates can only take my understanding of this concept so far.
I was fortunate to be able to attend a Big Data event recently hosted by the Technology Association of Georgia featuring IBM’s Watson was the main attraction, supported by panelists from Emory Healthcare and Blue Cross Blue Shield of Georgia. Many consumers are familiar with Watson thanks to its appearance on Jeopardy a few years ago. It has evolved since then, and now seems to be ushering in a new era of Big Data decision-making support tools for physicians.
Physicians can “ask” Watson for a list of possible diagnoses based on patient symptoms and other relevant data. Watson then “thinks” for a few seconds (literally), and offers up a list of potential diagnoses, each assigned a level of confidence. Watching a demo of Watson in action was intriguing, and I’ll be interested to see how well this scales up or down – from the smallest physician’s office to the largest integrated health system.
Feedback from the audience alluded to the fact that an earlier era of decision-making support tools was cut short thanks to physicians’ lack of enthusiasm. Laborious data input, interrupted workflows and lack of confidence in results were some of the detractors cited. Now that we’re in the era of Big Data, however, decision-making support tools may see a tipping point in physician acceptance.
As the story goes, one ED doc who has worked on the Watson team is happy that he may finally have someone to argue diagnoses with when he’s the sole MD in the department and has mere seconds to make life-changing decisions.
I’d venture to bet that the higher-quality outcomes Watson can help physicians achieve will be far greater than its $77,147 in Jeopardy winnings.