Patel thinks that on balance, the results of expanded data monitoring will be good. So does David Cutler, a Harvard health-care economist who advised the Obama campaign. He believes that building a data mine could greatly improve how we treat patients. At the moment, randomized controlled trials are the gold standard of medical research. You divide patients into two groups, give one your new treatment, and see how those patients fare compared with a control group that is getting an old treatment, or no treatment at all. These studies have great rigor, but also great limitations: they cover a small number of patients, for a limited time, and they test only what the researchers are looking for. So even though drugs are subjected to the Food and Drug Administration’s scrutiny, horrible side effects may only become obvious later, like the heart problems that forced Merck to pull Vioxx from the market. If a complication appears in only one out of every 10,000 patients or takes years to develop, even a comprehensive trial can easily miss it.
Better data and better analytic tools could allow us to supplement such research with what scientists call “observational data”: say, watching all the patients who take a new drug to see what unexpected conditions pop up. Cutler thinks that mining could detect the subtle relationships that controlled trials often miss. “I’m pretty sure that if your mother has a stroke, the optimal treatment regime very much depends on whether or not your dad is still alive,” he points out. “That affects the kinds of things she can do, the kinds of help she can get. But right now we don’t have any way to look at those kinds of factors and design around them.”
The Obama administration is trying to rectify that. The stimulus and health-care-reform bills contained billions to set standards and help physicians acquire electronic medical-record systems; created a new center that will experiment with payment reforms in Medicare and Medicaid; and established a 15-member advisory board that has a mandate to tie Medicare and Medicaid payments more closely to research on comparative effectiveness. If done right, these steps should push us toward a future of better, more data-driven medical decisions. But they won’t be pushing us very fast. Patel says we’re “very far away” from seemingly minor innovations like letting your doctor know whether you actually filled the prescription she wrote. When it comes to health care, almost everyone seems to be locked inside a silo.
Chalapathy Neti, IBM’s lead researcher on health-care analytics, hopes that the sector’s historical resistance to change can be overcome with technology that combines better monitoring with better diagnostic tools. Simple electronic medical-record systems don’t necessarily offer physicians much; they are often more of a boon to billing departments than to doctors. But as part of IBM’s $100 million global health-care-technology initiative, Neti is overseeing multiple projects that are developing systems for data integration to help physicians improve their treatments. One of them is an HIV database that compares viral-DNA profiles across patients to predict which drug cocktails are likely to work best. Such advances seem to be moving analytics in the direction that Cutler hopes: more-customized medicine.
But Neti also thinks that more-advanced data-management systems, like the ones IBM hopes to sell, will be widely adopted if payment and incentive systems are better aligned with outcomes. It’s easy to justify the expense of such systems in a neonatal intensive-care unit, where the stays are so expensive and the benefits are so obvious. It’s harder for physicians working in small practices to justify even a basic medical-records package, which can cost $100,000 for limited benefits.
If we paid for outcomes rather than for treatments, Neti argues, we’d give providers a financial incentive to have the best possible information to treat their patients. But there’s something of a chicken-and-egg problem: to reward wellness, we first need more data. Patients are not widgets; they are complex and unique. We can’t simply pay more to the doctors with the healthiest patients, because that would just discourage doctors from treating very sick people. We need a way to sift through the data and control for all the complex factors that determine health, before we can start paying doctors based on how well they manage their patients’ care.
This is easy to say, but hard to do. Jim Manzi is the chairman of Applied Predictive Technologies, which helps companies in other industries analyze the kind of data-mining operations that health-care analysts are now eyeing. Manzi says that data mining is relatively simple when you’re, say, trying to analyze the risk of defects in the manufacture of a machine part. But it rapidly becomes complicated when you are trying to engineer a change in human behavior, especially when money is involved.
“When people’s compensation is on the line,” says Manzi, “they suddenly turn into Aristotle.” Many a company has tried to design its sales-force commissions around arcane data analyses that try to control for complex factors, like whether one salesperson’s territory has more customers than another’s. Suddenly, they find the salespeople are all crack statisticians who can explain where the model has gone wrong in the case of their territory. Many of the companies that press forward find themselves, after six or eight months, with a sales force in full revolt. “I’ve seen it many times. Very few data-mining systems survive first contact with reality.”
Based on his experience with other industries, Manzi has a few thoughts on where data mining is likely to succeed in health care—and where it will be an uphill battle. Chemical or biological reactions, he says, will probably be easier to target than behavioral changes. Acute conditions treated in a highly standardized hospital environment will be easier to target than chronic ones treated in the outside world. Patients with singular problems will be easier to target than patients with three—or eight—chronic conditions. Costs will be easier to target than effectiveness.
This suggests that efforts like IBM’s Canadian project are likely to succeed and spread: they offer doctors an obvious way to improve what they are already doing. But making fundamental changes in medical practice will be much more difficult. Fighting disease is relatively simple. Fighting patients, doctors, and all the other stakeholders in the current system may be beyond the powers of even the most advanced computing system.