Superfreakonomics_ global cooling, patri - Steven D. Levitt [34]
As Azyxxi’s benefits became clear, many other hospitals came calling. So did, eventually, Microsoft, which bought it, Craig Feied and all. Microsoft renamed it Amalga and, within the first year, installed the system in fourteen major hospitals, including Johns Hopkins, New York–Presbyterian, and the Mayo Clinic. Although it was developed in an ER, more than 90 percent of its use is currently in other hospital departments. As of this writing, Amalga covers roughly 10 million patients at 350 care sites; for those of you keeping score at home, that’s more than 150 terabytes of data.
It would have been enough if Amalga merely improved patient outcomes and made doctors more efficient. But such a massive accumulation of data creates other opportunities. It lets doctors seek out markers for diseases in patients who haven’t been diagnosed. It makes billing more efficient. It makes the dream of electronic medical records a straightforward reality. And, because it collects data in real time from all over the country, the system can serve as a Distant Early Warning Line for disease outbreaks or even bioterrorism.
It also allows other, non-medical people—people like us, for instance—to repurpose its data to answer other kinds of questions, such as: who are the best and worst doctors in the ER?
For a variety of reasons, measuring doctor skill is a tricky affair.
The first is selection bias: patients aren’t randomly assigned to doctors. Two cardiologists will have two sets of clientele who may differ on many dimensions. The better doctor’s patients may even have a higher death rate. Why? Perhaps the sicker patients seek out the best cardiologist, so even if he does a good job, his patients are more likely to die than the other doctor’s.
It can therefore be misleading to measure doctor skill solely by looking at patient outcomes. That is generally what doctor “report cards” do and, though the idea has obvious appeal, it can produce some undesirable consequences. A doctor who knows he is being graded on patient outcomes may “cream-skim,” turning down the high-risk patients who most need treatment so as to not tarnish his score. Indeed, studies have shown that hospital report cards have actually hurt patients precisely because of this kind of perverse physician incentive.
Measuring doctor skill is also tricky because the impact of a doctor’s decisions may not be detectable until long after the patient is treated. When a doctor reads a mammogram, for instance, she can’t be sure if there is breast cancer or not. She may find out weeks later, if a biopsy is ordered—or, if she missed a tumor that later kills the patient, she may never find out. Even when a doctor gets a diagnosis just right and forestalls a potentially serious problem, it’s hard to make sure the patient follows directions. Did he take the prescribed medication? Did he change his diet and exercise program as directed? Did he stop scarfing down entire bags of pork rinds?
The data culled by Craig Feied’s team from the WHC emergency room turn out to be just the thing to answer some questions about doctor skill. For starters, the data set is huge, recording some 620,000 visits by roughly 240,000 different patients over nearly eight years, and the more than 300 doctors who treated them.
It contains everything you might want to know about a given patient—anonymized, of course, for our analysis—from the moment she walks, rolls, or is carried through the ER door until the time she leaves the hospital, alive or otherwise. The data include demographic information; the patient’s complaint upon entering the ER; how long it took to see a doctor; how the patient was diagnosed and treated; whether the patient was admitted to the hospital, and the length of stay; whether the patient was later readmitted; the total cost of the treatment; and if or when the patient died. (Even if the patient