IBM gets 215 million patient profiles

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http://www.infoworld.com/article/3034609/analytics/ibm-gets-215-million-patient-profiles-in-26-billion-analytics-firm-buy.html

 

IBM has championed Watson Health as a tool to bring high-quality health-care to remote areas, to match patients to clinical trials, and to give health researchers access to massive data sets.

"Lots of the future of progress in healthcare lies in this analytics area," O'Daffer said. "The ability to take data that intersects multiple systems ... and the ability to synthesize that data in a meaningful way to make better decisions is really the key to the next phase of progress."

 

From what I understand of this from reading over the years these companies are a great source of info for pharma data mining it is called they sell your info and your docs prescribing history to pharma according to some books I have read.

Odd thing is they always make it sound like this will help us in some way while I am sure it just helps us be more easily manipulated so we are exactly where big money wants us.

you don't need to say it I know I am jaded.

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Bit more on data mining from me lol practically computer illiterate lol    so here is a face to go with it... cause I can read and have common sense but computers... bit of an mystery to me still.

 

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

 

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.

 

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, 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.

Privacy concerns

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.

If an insurer writes off groups of patients as “not worth the time of a nurse … certain people are going to become sicker and certain people are going to get well,” Martin said. “You have a problem if all of a sudden you’re only using the nurses on certain groups.”

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.

Leading the Big Data revolution

Entrepreneurs like Colin Hill are leading this Big Data revolution.

Fifteen years ago, as a young Cornell physics graduate student, 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.

 

Now, for a given project, GNS culls through millions of medical records, trillions of data points of genetic information, and a huge amount of consumer spending data, using artificial intelligence and machine learning to help companies, hospitals, and insurers figure out how to handle specific patients.

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. Those results helped inform Aetna’s experiment with personalized corporate wellness programs, which included sending members customized vitamin supplements and staging weight-loss contests, among other interventions.

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.

A 6-year-old smoker?

Of course, such analyses are only as good as the underlying data and the algorithm used to mine it. And even advocates of the practice have concerns about the accuracy of some of the personal information they use.

“If you’ve got unreliable data, you’re going to make unreliable decisions,” said Dr. Doug Fridsma, president of the American Medical Informatics Association

For health insurers, Greenspun said, “the real struggle right now is trying to figure out what’s real, what’s not real, where can they get [data], and how much they can trust this.”

 

 

I cherry picked this long article to see it all go to the link.