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Private insurers are data mining our personal information

Insurers want to nudge you to better health. So they’re data mining your shopping lists

By Rebecca Robbins
STAT, December 15, 2015

Health insurers are scooping up huge quantities of personal information in a bid to figure out when you’re likely to get sick — and to design interventions to keep you healthy.

Insurance companies have always had access to your medical records, and in some cases your genetic data, too. Now, they’re paying data miners to sift through information on everything from what model car you drive to how many hours you sleep, from which magazines you read to where you shop and what you buy.

The goal: To decipher patterns that will allow them to steer you away from health emergencies. And to save themselves a whole lot of money in the process.

Shopping at home-improvement stores, for instance, turns out to be a great predictor of mental health. If you suddenly stop shopping at Lowe’s, your insurance company may suspect that you’re depressed, (Deloitte’s Dr. Harry) Greenspun said.

And if you drive a foreign-made car, you’re more likely to lose your eligibility for Medicaid in the coming year, according to Chris Coloian, president of Predilytics, a Massachusetts health-care data analytics company recently acquired by the firm Welltok.

Not all of this information is useful in crafting interventions to keep patients healthy. But with the help of data miners, insurers are finding that some patterns can make for powerful tools.

The intervention can be as simple as determining patients’ ethnicities to make sure they’re receiving information in the right language. Or it can be as aggressive as sending a patient a free digital scale — unprompted — if, say, she has congestive heart failure; unexpected weight gain from pooling fluids can be a sign the condition is worsening.

Privacy advocates worry that insurers are using all this highly personal, often sensitive information without informed consent and with little transparency or accountability.

Long before Big Data, of course, health insurers made decisions about which patients to prioritize. But using an algorithm to determine how and when to intervene raises troubling risks, said Kirsten Martin, an assistant professor at George Washington University who studies business ethics and Big Data.

Health insurers say they don’t deny care to anyone based on algorithms; they just use the data to customize the approach to each patient.

In one popular intervention, for instance, three health insurers in the Northeast designed outreach around neighborhood demographics. They staged outdoor health fairs for customers in walkable neighborhoods. Those more likely to drive everywhere were targeted instead with messages about healthy behaviors on social media, Coloian said.

Fifteen years ago, as a young Cornell physics graduate student, (Colin) Hill got swept up in the excitement around the Human Genome Project. He and a fellow physics graduate student started a company that aimed to harness the promise of all that new data.

For nearly a decade, GNS Healthcare worked mostly with pharmaceutical companies, mining genomic and lab test data to help discover drugs and evaluate them. But the firm, based in Cambridge, Mass., has since expanded its focus.

GNS helped the insurance giant Aetna predict which of about 37,000 policyholders were most at risk of developing metabolic syndrome or seeing their condition worsen within a year, using an analysis that took into account demographic variables including ethnicity, cigarette usage, and nightly hours of sleep.

GNS will also rank patients by how much return on investment the insurer can expect if it targets them with particular interventions, such as sending a text message reminding them to refill a prescription or sending a nurse to their home for a checkup. For example, the firm helped a group that manages pharmacy benefits for Regence Blue Cross Blue Shield’s policyholders in the Northwest make decisions about how to target patients who skip their pills.

All patients, of course, should take the medication prescribed to them, “but as a health plan with precious finite resources, where do you focus your energy?” asked Hill, the chief executive of GNS. The algorithm, he said, can tell the insurer not to waste time and money trying to get certain patients to take their pills — but to spend resources on other patients instead.

http://www.statnews.com/2015/12/15/insurance-big-data/

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Comment:

By Don McCanne, M.D.

The private insurers are masters at innovation. Now they are paying data miners to sift through extensive personal information on each of their clients. Is this really to keep patients healthy, as the insurers claim, or is it to simply to introduce an opaque process to create a new pathway to medical underwriting to benefit their own bottom line?

Big Data now impacts all of us. The attack on our privacy has placed all of us in the Emperor’s new clothes (nobody told you?). Although the Affordable Care Act greatly limited medical underwriting (smoking, age, etc.), insurers can introduce new innovations based on personal data that could have the same impact (e.g., telling the insurer not to waste time or money on certain patients, etc.).

We should ask, who should be partnering with the patient to provide the best health outcomes? Should it be the health care team with a mission to protect and improve the patient’s health, or should it be a private insurance company with a mission to compete for greater business success in the marketplace?

This blatant invasion of our privacy is one more reason that we should get rid of the private insurers and replace them with a well designed single payer national health program - an institution that would be expected to protect our privacy (think of HIPPA).