IBM's Watson—the same machine that beat Ken Jennings at Jeopardy—is now churning through case histories at Memorial Sloan-Kettering, learning to make diagnoses and treatment recommendations. This is one in a series of developments suggesting that technology may be about to disrupt health care in the same way it has disrupted so many other industries. “n Brazil and India, machines are already starting to do primary care, because there’s no labor to do it. They may be better than doctors. Mathematically, they will follow evidence—and they’re much more likely to be right.
Harley Lukov didn’t need a miracle. He just needed the right diagnosis. Lukov, a 62-year-old from central New Jersey, had stopped smoking 10 years earlier—fulfilling a promise he’d made to his daughter, after she gave birth to his first grandchild. But decades of cigarettes had taken their toll. Lukov had adenocarcinoma, a common cancer of the lung, and it had spread to his liver. The oncologist ordered a biopsy, testing a surgically removed sample of the tumor to search for particular “driver” mutations. A driver mutation is a specific genetic defect that causes cells to reproduce uncontrollably, interfering with bodily functions and devouring organs. Think of an on/off switch stuck in the “on” direction. With lung cancer, doctors typically test for mutations called EGFR and ALK, in part because those two respond well to specially targeted treatments. But the tests are a long shot: although EGFR and ALK are the two driver mutations doctors typically see with lung cancer, even they are relatively uncommon. When Lukov’s cancer tested negative for both, the oncologist prepared to start a standard chemotherapy regimen—even though it meant the side effects would be worse and the prospects of success slimmer than might be expected using a targeted agent.
But Lukov’s true medical condition wasn’t quite so grim. The tumor did have a driver—a third mutation few oncologists test for in this type of case. It’s called KRAS. Researchers have known about KRAS for a long time, but only recently have they realized that it can be the driver mutation in metastatic lung cancer—and that, in those cases, it responds to the same drugs that turn it off in other tumors. A doctor familiar with both Lukov’s specific medical history and the very latest research might know to make the connection—to add one more biomarker test, for KRAS, and then to find a clinical trial testing the efficacy of KRAS treatments on lung cancer. But the national treatment guidelines for lung cancer don’t recommend such action, and few physicians, however conscientious, would think to do these things.
Did Lukov ultimately get the right treatment? Did his oncologist make the connection between KRAS and his condition, and order the test? He might have, if Lukov were a real patient and the oncologist were a real doctor. They’re not. They are fictional composites developed by researchers at the Memorial Sloan-Kettering Cancer Center in New York, in order to help train—and demonstrate the skills of—IBM’s Watson supercomputer. Yes, this is the same Watson that famously went on Jeopardy and beat two previous human champions. But IBM didn’t build Watson to win game shows. The company is developing Watson to help professionals with complex decision making, like the kind that occurs in oncologists’ offices—and to point out clinical nuances that health professionals might miss on their own.
Watson has gotten some media hype already, including articles in Wired and Fast Company. Still, you probably shouldn’t expect to see it the next time you visit your doctor’s office. Before the computer can make real-life clinical recommendations, it must learn to understand and analyze medical information, just as it once learned to ask the right questions on Jeopardy. That’s where Memorial Sloan-Kettering comes in. The famed cancer institute has signed up to be Watson’s tutor, feeding it clinical information extracted from real cases and then teaching it how to make sense of the data. “The process of pulling out two key facts from aJeopardy clue is totally different from pulling out all the relevant information, and its relationships, from a medical case,” says Ari Caroline, Sloan-Kettering’s director of quantitative analysis and strategic initiatives. “Sometimes there is conflicting information. People phrase things different ways.” But Caroline, who approached IBM about the research collaboration, nonetheless predicts that Watson will prove “very valuable”—particularly in a field like cancer treatment, in which the explosion of knowledge is already overwhelming. “If you’re looking down the road, there are going to be many more clinical options, many more subtleties around biomarkers … There will be nuances not just in interpreting the case but also in treating the case,” Caroline says. “You’re going to need a tool like Watson because the complexity and scale of information will be such that a typical decision tool couldn’t possibly handle it all.”
The Cleveland Clinic is also helping to develop Watson, first as a tool for training young physicians and then, possibly, as a tool at the bedside itself. James Young, the executive dean of the Cleveland Clinic medical school, told The Plain Dealer, “If we can get Watson to give us information in the health-care arena like we’ve seen with more-general sorts of knowledge information, I think it’s going to be an extraordinary tool for clinicians and a huge advancement.” And WellPoint, the insurance company, has already begun testing Watson as a support tool for nurses who make treatment-approval decisions.
Whether these experiments show real, quantifiable improvements in the quality or efficiency of care remains to be seen. If Watson tells physicians only what they already know, or if they end up ordering many more tests for no good reason, Watson could turn out to be more hindrance than help. But plenty of serious people in the fields of medicine, engineering, and business think Watson will work (IBM says that it could be widely available within a few years). And many of these same people believe that this is only the beginning—that whether or not Watson itself succeeds, it is emblematic of a quantum shift in health care that’s just now getting under way.