Is Data the Pivot Point in Rising Health Care Cost Curve?

Loraine Lawson
Slide Show

Big Data: Eight Facts and Eight Fictions

I love it when technology people start to focus on a new area, because they always seem to offer a fresh view, even when the topic is well dissected. I think that’s one reason why tech is known for lowering costs in all industries, except one: health care.

MIT Technology Review recently published an excellent package, “A Cure for Health-Care Costs.” At the heart of the articles is this question: Why is it that technology raises the costs of health care, rather than lowering it, and how can we change that?

“Computers make things better and cheaper. In health care, new technology makes things better, but more expensive,” quips Jonathan Gruber, an economist at MIT who leads a heath-care group at the National Bureau of Economic Research, in one article.


It’s an excellent look at all the ways technology innovation has contributed to the problem, but now is being used to create solutions.

As the articles explain, some of the problems reside in the economics of health care. There’s no incentive for anyone to cut costs, except possibly insurers. Doctors seldom even know how much a procedure costs. Many doctors won’t even answer emails with pictures of rashes, because they can’t bill for it.

And there’s no incentive for patients to ask for cheaper options: In fact, it’s just the opposite. Fear might push patients to pursue costly procedures.

In some ways, that sounds like a good thing. You don’t want your doctor deciding what’s too expensive for you or choosing cut-rate procedures just because they’re cheaper.

But this is where we come to the less well-articulated problem with health care: the utter disregard for data.

For instance, in “The Costly Paradox of Health Care Technology,” Jonathan S. Skinner divides treatments into three bins: treatments with the greatest — and known — benefits; procedures whose benefits are substantial for some patients, but not all patients (and, he points out, many more patients get the procedure even when it’s less clear the procedure will be beneficial); and finally, treatments whose benefits are small or supported by little scientific evidence.

To be clear, Skinner isn’t just any writer. He’s the James Freedman Presidential Professor in the department of economics at Dartmouth College and a professor at the Dartmouth Institute for Health Policy & Clinical Practice at the Geisel School of Medicine.

His findings:

“Most of the spending growth is generated by the third category, which the U.S. health-care system is uniquely, and perversely, designed to encourage. Unlike many countries, the U.S. pays for nearly any technology (and at nearly any price) without regard to economic value. This is why, since 1980, health-care spending as a percentage of gross domestic product has grown nearly three times as rapidly in the United States as it has in other developed countries, while the nation has lagged behind in life-expectancy gains.”

But Loraine, you may say: Nobody wants to be that one person who it could have helped, if they’d only tried it. After all, can you put a price on human life?

Valid points — when the data is murky. But there are cases where it’s clear the procedure won’t work, and yet, in the U.S., there’s a good chance you can get the procedure anyway.

I have to wonder, too, how much our misunderstanding of statistics and other math principles plays into this. After all, as a friend once told me, a 2 percent chance of getting a disease is meaningless when you’re in that 2 percent.

And, of course, the MIT package includes a Big Data story about a project at Mount Sinai Medical Center that’s led by Jeff Hammerbacher, the man known as Facebook’s first data scientist.

The project will crunch data from patients’ records, which includes plasma samples and a bio bank with 26,735 patient DNA records.

People often complain about Big Data being a vague term, but in this case, there’s no doubt it’s Big Data. In fact, this project’s data is so big, it could “easily be the biggest job for ‘big data’ yet,” the article notes.

The data was used for a program that uses patients’ health records, demographics and disease information to predict which patients were at the highest risk of returning to the hospital. The pilot study found the program cut readmissions by half.

After reading the series, I’m convinced that data — managing it, understanding it, and using it — will be key to innovating health care and reducing costs.



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