In February, 2011, IBM’s Watson took on two reigning Jeopardy champions and whupped them. But, now, this history-making AI computer system may be facing its toughest battle: the Human Genome.
Many of the experts taking the stage at the CES Digital Health Summit 2015 this week invoked the name of Watson, praising the promise of aggregated data, touting its ability to offer treatment recommendations based on an unprecendented, superhuman volume of experiences, guidelines, clinical studies, whitepapers, EMR data, HCP notes from around the world, and patient information.
They salivated at the thought of aggregating reams of real-time, real-world data from a patient over weeks and months (vs. the single point-in-time of an exam) to get a larger and more accurate picture of that patient’s journey, in order to better assess and match therapy to need.
Exciting stuff that has dozens of VC firms investing in start-ups and companies that can make the dream come true, and deliver, in the words of Wainwright Fishburn, Jr., partner at Cooley, LLC, “organization and analysis of data that can lead to efficiency and lower cost of chronic care.” Corinne Savill, Head Business Development & Licensing at Novatis Pharma AG, saw the Watsonesque analysis of data helping pharma companies move from the traditional business model of selling pills to answer the new clarion call of success: “Selling Positive Outcomes To Patients.” (And Payors.)
At the same time, and sometimes in the same talk, many of these digital health experts also praised the powerful potential of mapping a patient’s unique and personal human genome. They predicted that within just a few years, the $1000 genome will be the $100 genome, and with that new ubiquity comes the chance to know specifically what treatment will be effective for that individual, develop drugs that work better on certain genetic make-ups, and even “prevent” disease by identifying a patient’s predisposition to conditions.
So, the question is: If I know my patient’s individual genome (predictive), and therefore know what treatment will work best for him/her, do I really need to know what the aggregated data of current and past (retrospective) says?
Wouldn’t matching a therapy exactly to a patient’s singular biology ultimately drive the same efficiencies and positive outcomes of which Watson dreams?
In fact, Dr. James Zackheim, PhD, VP & Neupro Patient Solutions Leader, UCB, Inc. takes the vision even further, coining the phrase “Adaptive Medicine.” whereby a physician establishes optimial treatment based on genetic mapping, but then can continually adjust dosing and combination of therapies, based on that patient’s changing/improving biology and condition– all thanks to aggregated data.
Hey, maybe Watson and The Human Genome can actually crunch happily ever after.
