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Medical Students Crunch Big Data To Spot Health Trends

(Left to right) NYU medical students Brian Chao, Michael Lui, Hye Min Choi, and Varun Vijay take the team approach to learning about the anatomy of cells, and how disease can disrupt them. Analyzing big data sets is now a routine part of their studies, too.

Medicine, meet Big Data.

For generations, physicians have been trained in basic science and human anatomy to diagnose and treat the patient immediately in front of them.

But now, massive stores of data about what works for which patients are literally changing the way medicine is practiced.

"That's how we make decisions; we make them based on the truth and the evidence that are present in those data," says Marc Triola, an associate dean for educational informatics at New York University's medical school.

Figuring out how to access and interpret all that data is not a skill that most physicians learned in medical school — but that's changing.

"If you don't have these skills, you could really be at a disadvantage," says Triola, "in terms of the way you understand the quality and the efficiency of the care you're delivering."

That's why every first and second year student at the NYU School of Medicine is required to do what's called a 'health care by the numbers' project. Students are given access to an enormous database with more than 5 million anonymous records — information on every hospital patient in the state for the preceding two years. "Their age, their race and ethnicity, what zip code they came from," Triola lists, as well as their diagnosis, procedures and the bills paid on their behalf.

The project, funded in part by an effort by the American Medical Association to update what and how medical students are taught, also includes a companion database for roughly 50,000 outpatients. It's called the Lacidem Care Group. (Lacidem? That's "medical," backwards.) The database contains information from NYU's own faculty practices — scrubbed to ensure that neither the patients nor the doctors can be identified. Students use analytic tools provided by the project to "look at quality measures for things like heart failure, diabetes, smoking and high blood pressure," Triola says, "and drill down and look at the performance of the practice as a whole, and [the performance of] individual doctors."

Some med students — including Micah Timen, now in his second year — have taken to the assignment with relish. Timen likes numbers. A lot. A former accountant before applying to med school, he keeps a spreadsheet to track his study hours in preparation for a test. Regarding an upcoming test on the digestive system, for example, Timen says, "I know I have 18 hours and 40 minutes left to make sure I feel comfortable walking into my exam."

For his database project, Timen wanted to know if the cost to patients of hip replacement surgery around the state vary as much as the cost of a fast-food hamburger. Timen says they tried comparing the varying prices of a hip replacement using The Economist magazine's famous Big Mac Index, which measures purchasing power between currencies. "But when you call McDonald's, they don't give you prices over the phone," he says. So he tried Plan B: "Burger King gave it to me."

Using the "Whopper Index" instead, Timen found, not surprisingly, that the price of a giant burger sandwich is higher in New York City than, say, Albany. So, too was the amount of money patients paid for a hip replacement. But the margin was much wider for health care than for hamburgers, meaning patients are paying more in some places than simple geography would suggest. Timen says he'd like to explore why that might be, "but unfortunately med school is a little bit time consuming," so that may have to wait.

Meanwhile, these classes appeal not just to "data junkies," like Timen, but also to those who were not already steeped in crunching data.

"I really have no statistical background," says Justin Feit, also a second-year student. "I don't even know how to use Excel well."

So Feit was partnered with Jessica Lynch, who already has a Ph.D. in physics. Lynch says that if medicine wasn't moving in the direction of including more interpretation of big data sets, "I don't know if I would have gone into medicine."

Together Feit and Lynch looked at the rates of cesarean births around the state, and found that the rates of C-section — like the cost of a hip replacement — varies widely. But their project will get more than just a grade. A faculty member at NYU is using it as part of a bigger research project headed for publication.

Triola says he hopes that will happen more and more.

"With literally millions of records, these in-class student projects often involved more patients than the published literature," he says. "It's incredible."

And the concept of having students learn to use health data is catching on quickly. Triola says NYU has offered its database and program to other medical schools, and seven are already incorporating it into their curricula.

Copyright 2015 Kaiser Health News. To see more, visit

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