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Monday, July 23, 2018

Are we "Overcharged" for health care? Will it get even worse?

Recall my recent post citing this book:

Saw some additional stuff of interest via NPR. "Health Insurers Are Vacuuming Up Details About You — And It Could Raise Your Rates."

Insurance companies and data brokers are more and more often using your personal information from social media to predict your health care costs. And they are exploring ways to use that data to determine the rates that you will pay. That's the finding of a new investigation by the nonprofit newsroom ProPublica. Joining us now from our New York studio is their reporter Marshall Allen.
Good morning, Marshall.


Good morning.


All right - so you found that insurance companies are using stuff that we post on Facebook and Twitter and Instagram. What exactly are they gathering?


Well, they're gathering things about your race, your ethnicity, your education level, your TV-watching habits, your marital status, your net worth. They're trying to gather everything they can about us to, in some cases, try and predict what it will cost us to be cared for.


What does me being married or not being married have to do with health insurance?


Well, there's a lot of studies now about social determinants of health - that's kind of the jargon they use to describe it - that show that a lot of a person's health does come from social and economic conditions that they're raised in. And you can kind of see, as a group, how this would happen. So for instance, one of the companies would say, if you're a woman who's recently changed your name, which is something they can tell from public records, maybe you're newly married and so you're about to get pregnant. Or perhaps you are recently divorced and so you're stressed out. And both of those things could lead to higher health care costs.


They are making a heck of a lot of assumptions here. How accurate is this as a method of predicting how healthy people are or are not?


Well, that's what I kept asking them. You know, I had a lot of conversations with a company called LexisNexis Risk Solutions. They're one of the main data brokers who are trafficking in this kind of information. And what they said they've done is that they've linked the personal attributes that we all have to claims data from our medical care costs. And then they use that to draw inferences, which they say are accurate. Now, they haven't done any studies that are available about this. They don't put out any methodology, so we can't really tell how they're doing it. It is a black box. But they say that it is predictive.

I want to point out one other important detail. I wasn't able to nail down whether they're actually using this information to price our health plans right now for a process called underwriting. They're definitely using it to measure our costs and estimate our costs. But what the insurance industry says they're doing is they're using it for case management so they can offer services to help sick people stay healthier.


OK. So they're saying it is a benevolent move on their part.


That's exactly right. They say that this allows them to offer better services for patients.


Is there an argument here that this is exactly what insurance companies do? They get all the data they can on you, and then they figure out how much of a risk you are. I'm just trying to figure out why this is notable. It sort of seems like insurance companies were destined to do this once they knew they could.


Well, it does seem that way, and I think that's a really reasonable question to ask. And you know, they do need to properly assess the risk of each of us so that they can properly price plans so they can know how much we might cost. I mean, that's an important part of the process.

But one thing is this is happening with no public scrutiny. And this is also happening in a way where insurance companies could use the information to discriminate. And that's not something I nailed down with my reporting, but I talked to a lot of experts about how insurance companies do what's called cherry-picking. And by that, I mean they will try and find the healthiest, lowest-cost people and offer them health insurance. And they will try and avoid high-cost health conditions so that they don't have more risk. The Affordable Care Act has made it more difficult to blatantly discriminate, but experts say the discrimination still exists and that this type of information could be used for that purpose.


Marshall Allen is a reporter with ProPublica. Marshall, thank you so much for joining us.


Thank you.
I did a 5-year stint in subprime risk modeling and management (2000 - 2005). Back then we had applicant and customer data elements in our own data warehouse we could not legally use for vetting current and prospective customer risks. Those restraints seem to be long-gone.
[ I wrote all of the bank's White Papers. See my Proprietary Risk Model Scorecard Suite Development paper (large pdf). ]
apropos, see my prior post "(404)^n, the upshot of dirty data." And, my post "The old internet of data, the new internet of things and "Big Data," and the evolving internet of YOU."


From Down Under.
Why Healthy People Should Subsidize the Sick
An introduction to healthcare economics
If there’s one thing that we can all agree on, it’s that healthcare should be cheaper. It’s not that healthcare is overpriced — although if you live in the U.S. you’re probably paying double anyone else in the world — but that it’s a significant cost that takes up a huge portion of our budget and it would be really nice if it were less expensive.

Also, it’s extremely overpriced…
Interesting argument. Will be much unloved over at The CATO Institute.

More to come...

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