The Middletown Press (Middletown, CT)

That free health tracker could someday cost you a lot

- By Cathy O’Neil Bloomberg Cathy O’Neil is a mathematic­ian who has worked as a professor, hedge-fund analyst and data scientist. She founded ORCAA, an algorithmi­c auditing company, and is the author of “Weapons of Math Destructio­n.”

Using big data to improve health might seem like a great idea. The way private insurance works, though, it could end up making sick people — or even those perceived as likely to become sick — a lot poorer.

Suppose a company offers you an insurance discount and a free Fitbit if you agree to share your data and submit to a yearly physical. You’re assured that the data will be used only in aggregate, never tied back to specific identities.

If that makes you feel safe, it shouldn’t. The way machine learning works, data can be used against individual­s without being connected directly to names.

Remember that study about how likes on Facebook can indicate sexual and political orientatio­ns? The researcher­s first looked at a pile of data from people with known orientatio­ns, to find patterns in liking behavior. They then built a model that would go the other way, inferring anyone’s orientatio­n from the stuff they happened to like — with a pretty high level of precision.

The same can be done with health. If an algorithm has enough data — on attributes such as weight, height, pre-existing conditions and exercise habits, as well as longer-term health outcomes — it can make prediction­s about the prospects of any given individual.

Wait a second, you might be thinking. Aren’t these tracking devices built to help me stay healthy? Well, some have challenged the idea that Fitbit can reliably track sleep and heart rates. Such devices seem better at figuring out whether you’ve been skipping your morning runs than they are at coaxing you to do them. Even IBM’s Watson has suffered a setback in its efforts to help doctors choose cancer treatments.

In the short term, there’s more money in profiling people as high-risk or low-risk than there is in solving their actual health problems. Granted, the informatio­n people provide to insurance companies might never be used against them personally. But it could ultimately be used against people like them.

Say, for example, lefthanded people with vegetarian diets prove more likely to require expensive medical treatments. Insurance companies might then start charging higher premiums to people with similar profiles — that is, to those the algorithm has tagged as potentiall­y costly. Granted, the Affordable Care Act currently prohibits such discrimina­tion. But that could change if Donald Trump fulfills his promise to repeal the law.

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