The Patient Equation

10Good Data

In building models that work, good data management and system infrastructure are the costs of entry; there is just no way around it. Machine learning and artificial intelligence are nice buzzwords, but you need humans to set the systems up, write the rules, figure out what kinds of data need to be collected in the first place, and work to get it all “fit for purpose” from an analytic point of view. Systems have to speak to each other—in a way that produces useful output and not merely garbage. There are so many examples of bad data out there, and lessons to learn—from the Mars Lander and from the failure of IBM Watson in the oncology area, to name two well-publicized disasters.

But even though those situations tell us what can go wrong, that knowledge doesn't, on its own, get us to something that will go right. I've been working in this area for my entire adult life, and I know data. I know what happens when it's not aggregated, standardized, and analyzed the way it needs to be to produce useful insights—and I know its power when the pieces are all properly in place.

The Failure of Watson

IBM's Watson supercomputer was supposed to revolutionize cancer treatment. A 2013 article in Wired announced, “IBM's Watson is better at diagnosing cancer than human doctors,” that it had “a breadth of knowledge no human doctor can match,” and that it had a “successful diagnosis rate for lung cancer [of] 90 percent, compared to 50 percent for human doctors.”¹

Five years later, ...