The beauty of these highly complex, cloud-based algorithms, Kohane says, is that they can function at the clinical site on commodity-level hardware that costs a few thousand dollars. "In some sense, we are heading in this direction anyway, using humans instead of computers," he says. "A lot of hospitals use off-shore radiologists in another part of the world, like India or Australia, so that overnight the x-rays are read by doctors. But what if you didn't even have to wait for it to be done overnight? You could have it done for much less money, much faster."
Comparison to EHR
With respect to the potential impact on care delivery, the advent of machine learning in medicine can be somewhat compared to the rollout of electronic health records over the past decade. Harvard's Kohane says he believes the process will go smoother this time.
"Although we were thrilled that the HITECH Act invested in the process, it was pretty clear at the time that the available shovel-ready technology was state of the art for the 1980s and it was not going to be comparable to what our kids were using for video games. That was a predicable outcome," Kohane says. "This looks different. This will be adopted because it gives productivity and financial gain and accuracy right away when you implement it, as opposed to the promissory note around EHRs, which has not yet really shown itself to be robust."
IBM Watson Health's Jain was involved in the EHR rollout at Cleveland Clinic, and he says "absolute lessons" from that experience can apply to any kind of technology innovation.
John Commins is a content specialist and online news editor for HealthLeaders, a Simplify Compliance brand.