Health Insurers Are Vacuuming Up Details About You And It Could Raise Your Rates
To an outsider, the fancy booths at a June health insurance
industry gathering in San Diego, Calif., aren't very compelling: a
handful of companies pitching "lifestyle" data and salespeople touting
jargony phrases like "social determinants of health."
But dig deeper and the implications of what they're selling might give many patients pause: a future in which everything you do — the things you buy, the food you eat, the time you spend watching TV — may help determine how much you pay for health insurance.
With little public scrutiny, the health insurance industry has joined forces with data brokers to vacuum up personal details about hundreds of millions of Americans, including, odds are, many readers of this story.
The companies are tracking your race, education level, TV habits, marital status, net worth. They're collecting what you post on social media, whether you're behind on your bills, what you order online. Then they feed this information into complicated computer algorithms that spit out predictions about how much your health care could cost them.
Are you a woman who recently changed your name? You could be newly married and have a pricey pregnancy pending. Or maybe you're stressed and anxious from a recent divorce. That, too, the computer models predict, may run up your medical bills.
Are you a woman who has purchased plus-size clothing? You're considered at risk of depression. Mental health care can be expensive.
Low-income and a minority? That means, the data brokers say, you are more likely to live in a dilapidated and dangerous neighborhood, increasing your health risks.
But dig deeper and the implications of what they're selling might give many patients pause: a future in which everything you do — the things you buy, the food you eat, the time you spend watching TV — may help determine how much you pay for health insurance.
With little public scrutiny, the health insurance industry has joined forces with data brokers to vacuum up personal details about hundreds of millions of Americans, including, odds are, many readers of this story.
The companies are tracking your race, education level, TV habits, marital status, net worth. They're collecting what you post on social media, whether you're behind on your bills, what you order online. Then they feed this information into complicated computer algorithms that spit out predictions about how much your health care could cost them.
Are you a woman who recently changed your name? You could be newly married and have a pricey pregnancy pending. Or maybe you're stressed and anxious from a recent divorce. That, too, the computer models predict, may run up your medical bills.
Are you a woman who has purchased plus-size clothing? You're considered at risk of depression. Mental health care can be expensive.
Low-income and a minority? That means, the data brokers say, you are more likely to live in a dilapidated and dangerous neighborhood, increasing your health risks.
"We sit on oceans of data," said Eric McCulley, director of strategic solutions for LexisNexis Risk Solutions,
during a conversation at the data firm's booth. And he isn't apologetic
about using it. "The fact is, our data is in the public domain," he
said. "We didn't put it out there."
Insurers contend that they
use the information to spot health issues in their clients — and flag
them so they get services they need. And companies like LexisNexis say
the data shouldn't be used to set prices. But as a research scientist
from one company told me: "I can't say it hasn't happened."
At a
time when every week brings a new privacy scandal and worries abound
about the misuse of personal information, patient advocates and privacy
scholars say the insurance industry's data gathering runs counter to its
touted, and federally required, allegiance to patients' medical
privacy. The Health Insurance Portability and Accountability Act, or HIPAA, only protects medical information.
"We
have a health privacy machine that's in crisis," said Frank Pasquale, a
professor at the University of Maryland Carey School of Law who
specializes in issues related to machine learning and algorithms. "We
have a law that only covers one source of health information. They are
rapidly developing another source."
Patient advocates warn that
using unverified, error-prone "lifestyle" data to make medical
assumptions could lead insurers to improperly price plans — for
instance, raising rates based on false information — or discriminate
against anyone tagged as high cost. And, they say, the use of the data
raises thorny questions that should be debated publicly, such as: Should
a person's rates be raised because algorithms say they are more likely
to run up medical bills? Such questions would be moot in Europe, where a
strict law took effect in May that bans trading in personal data.
This year, ProPublica and NPR are investigating
the various tactics the health insurance industry uses to maximize its
profits. Understanding these strategies is important because patients —
through taxes, cash payments and insurance premiums — are the ones
funding the entire health care system. Yet the industry's bewildering
web of strategies and inside deals often has little to do with patients'
needs. As the series' first story showed, contrary to popular belief, lower bills aren't health insurers' top priority.
Inside the San Diego Convention Center, there were few qualms about
the way insurance companies were mining Americans' lives for information
— or what they planned to do with the data.
Linking health costs to personal data
The
sprawling convention center was a balmy draw for one of America's
Health Insurance Plans' marquee gatherings. Insurance executives and
managers wandered through the exhibit hall, sampling chocolate-covered
strawberries, champagne and other delectables designed to encourage
deal-making.
Up front, the prime real estate belonged to the
big guns in health data: The booths of Optum, IBM Watson Health and
LexisNexis stretched toward the ceiling, with flat-screen monitors and
some comfy seating. (NPR collaborates with IBM Watson Health on national
polls about consumer health topics.)
To understand the scope
of what they were offering, consider Optum. The company, owned by the
massive UnitedHealth Group, has collected the medical diagnoses, tests,
prescriptions, costs and socioeconomic data of 150 million Americans
going back to 1993, according to its marketing materials.(UnitedHealth
Group provides financial support to NPR.)
The company says it
uses the information to link patients' medical outcomes and costs to
details like their level of education, net worth, family structure and
race. An Optum spokesman said the socioeconomic data is de-identified
and is not used for pricing health plans.
Optum's marketing
materials also boast that it now has access to even more. In 2016, the
company filed a patent application to gather what people share on
platforms like Facebook and Twitter, and to link this material to the
person's clinical and payment information. A company spokesman said in
an email that the patent application never went anywhere. But the
company's current marketing materials say it combines claims and
clinical information with social media interactions.
I had a
lot of questions about this and first reached out to Optum in May, but
the company didn't connect me with any of its experts as promised. At
the conference, Optum salespeople said they weren't allowed to talk to
me about how the company uses this information.
It isn't hard
to understand the appeal of all this data to insurers. Merging
information from data brokers with people's clinical and payment records
is a no-brainer if you overlook potential patient concerns. Electronic
medical records now make it easy for insurers to analyze massive amounts
of information and combine it with the personal details scooped up by
data brokers.
It also makes sense given the shifts in how
providers are getting paid. Doctors and hospitals have typically been
paid based on the quantity of care they provide. But the industry is
moving toward paying them in lump sums for caring for a patient, or for
an event, like a knee surgery. In those cases, the medical providers can
profit more when patients stay healthy. More money at stake means more
interest in the social factors that might affect a patient's health.
Some
insurance companies are already using socioeconomic data to help
patients get appropriate care, such as programs to help patients with
chronic diseases stay healthy. Studies show social and economic aspects
of people's lives play an important role in their health. Knowing these
personal details can help them identify those who may need help paying
for medication or help getting to the doctor.
But patient advocates are skeptical that health insurers have altruistic designs on people's personal information.
The
industry has a history of boosting profits by signing up healthy people
and finding ways to avoid sick people — called "cherry-picking" and
"lemon-dropping," experts say.
Among the classic examples: A
company was accused of putting its enrollment office on the third floor
of a building without an elevator, so only healthy patients could make
the trek to sign up. Another tried to appeal to spry seniors by holding
square dances.
The Affordable Care Act prohibits insurers from
denying people coverage based on pre-existing health conditions or
charging sick people more for individual or small group plans. But
experts said patients' personal information could still be used for
marketing, and to assess risks and determine the prices of certain
plans. And the Trump administration is promoting short-term health
plans, which do allow insurers to deny coverage to sick patients.
Robert
Greenwald, faculty director of Harvard Law School's Center for Health
Law and Policy Innovation, said insurance companies still cherry-pick,
but now they're subtler. The center analyzes health insurance plans to
see if they discriminate. He said insurers will do things like failing
to include enough information about which drugs a plan covers, which
pushes sick people who need specific medications elsewhere. Or they may
change the things a plan covers, or how much a patient has to pay for a
type of care, after a patient has enrolled. Or, Greenwald added, they
might exclude or limit certain types of providers from their networks —
like those who have skill caring for patients with HIV or hepatitis C.
If there were concerns that personal data might be used to cherry-pick or lemon-drop, they weren't raised at the conference.
At
the IBM Watson Health booth, Kevin Ruane, a senior consulting
scientist, told me that the company surveys 80,000 Americans a year to
assess lifestyle, attitudes and behaviors that could relate to health
care. Participants are asked whether they trust their doctor, have
financial problems, go online, or own a Fitbit and similar questions.
The responses of hundreds of adjacent households are analyzed together
to identify social and economic factors for an area.
Ruane said
he has used IBM Watson Health's socioeconomic analysis to help
insurance companies assess a potential market. The ACA increased the
value of such assessments, experts say, because companies often don't
know the medical history of people seeking coverage. A region with too
many sick people, or with patients who don't take care of themselves,
might not be worth the risk.
Ruane acknowledged that the
information his company gathers may not be accurate for every person.
"We talk to our clients and tell them to be careful about this," he
said. "Use it as a data insight. But it's not necessarily a fact."
In
a separate conversation, a salesman from a different company joked
about the potential for error. "God forbid you live on the wrong street
these days," he said. "You're going to get lumped in with a lot of bad
things."
The LexisNexis booth was emblazoned with the slogan
"Data. Insight. Action." The company said it uses 442 nonmedical
personal attributes to predict a person's medical costs. Its cache
includes more than 78 billion records from more than 10,000 public and
proprietary sources, including people's cellphone numbers, criminal
records, bankruptcies, property records, neighborhood safety and more.
The information is used to predict patients' health risks and costs in
eight areas, including how often they are likely to visit emergency
rooms, their total cost, their pharmacy costs, their motivation to stay
healthy and their stress levels.
People who downsize their
homes tend to have higher health care costs, the company says. As do
those whose parents didn't finish high school. Patients who own more
valuable homes are less likely to land back in the hospital within 30
days of their discharge. The company says it has validated its scores
against insurance claims and clinical data. But it won't share its
methods and hasn't published the work in peer-reviewed journals.
McCulley,
LexisNexis' director of strategic solutions, said predictions made by
the algorithms about patients are based on the combination of the
personal attributes. He gave a hypothetical example: A high school
dropout who had a recent income loss and doesn't have a relative nearby
might have higher-than-expected health costs.
But couldn't that same type of person be healthy?
"Sure," McCulley said, with no apparent dismay at the possibility that the predictions could be wrong.
McCulley
and others at LexisNexis insist the scores are only used to help
patients get the care they need and not to determine how much someone
would pay for their health insurance. The company cited three different
federal laws that restricted them and their clients from using the
scores in that way. But privacy experts said none of the laws cited by
the company bar the practice. The company backed off the assertions when
I pointed that the laws did not seem to apply.
LexisNexis
officials also said the company's contracts expressly prohibit using the
analysis to help price insurance plans. They would not provide a
contract. But I knew that in at least one instance a company was already
testing whether the scores could be used as a pricing tool.
Before
the conference, I'd seen a press release announcing that the largest
health actuarial firm in the world, Milliman, was now using the
LexisNexis scores.
I tracked down Marcos Dachary, who works in
business development for Milliman. Actuaries calculate health care risks
and help set the price of premiums for insurers. I asked Dachary if
Milliman was using the LexisNexis scores to price health plans and he
said: "There could be an opportunity."
The scores could allow
an insurance company to assess the risks posed by individual patients
and make adjustments to protect themselves from losses, he said. For
example, he said, the company could raise premiums or revise contracts
with providers.
It's too early to tell whether the LexisNexis
scores will actually be useful for pricing, he said. But he was excited
about the possibilities. "One thing about social determinants data – it
piques your mind," he said.
Dachary acknowledged the scores
could also be used to discriminate. Others, he said, have raised that
concern. As much as there could be positive potential, he said, "there
could also be negative potential."
Erroneous inferences from group data
It's
that negative potential that still bothers data analyst Erin Kaufman,
who left the health insurance industry in January. The 35-year-old from
Atlanta had earned her doctorate in public health because she wanted to
help people, but one day at Aetna, her boss told her to work with a new
data set.
To her surprise, the company had obtained personal
information from a data broker on millions of Americans. The data
contained each person's habits and hobbies, like whether they owned a
gun, and if so, what type, she said. It included whether they had
magazine subscriptions, liked to ride bikes or run marathons. It had
hundreds of personal details about each person.
The Aetna data
team merged the data with the information it had on patients it insured.
The goal was to see how people's personal interests and hobbies might
relate to their health care costs.
But Kaufman said it felt
wrong: The information about the people who knitted or crocheted made
her think of her grandmother. And the details about individuals who
liked camping made her think of herself. What business did the insurance
company have looking at this information? "It was a data set that
really dug into our clients' lives," she said. "No one gave anyone
permission to do this."
In a statement,
Aetna said it uses consumer marketing information to supplement its
claims and clinical information. The combined data helps predict the
risk of repeat emergency room visits or hospital admissions. The
information is used to reach out to members and help them and plays no
role in pricing plans or underwriting, the statement said.
Kaufman
said she had concerns about the accuracy of drawing inferences about an
individual's health from an analysis of a group of people with similar
traits. Health scores generated from arrest records, homeownership and
similar material may be wrong, she said.
Pam Dixon, executive
director of the World Privacy Forum, a nonprofit that advocates for
privacy in the digital age, shares Kaufman's concerns. She points to a
study by the analytics company SAS, which worked in 2012 with an unnamed
major health insurance company to predict a person's health care costs
using 1,500 data elements, including the investments and types of cars
people owned.
The SAS study said higher health care costs could
be predicted by looking at things like ethnicity, watching TV and
mail-order purchases.
"I find that enormously offensive as a list," Dixon said. "This is not health data. This is inferred data."
Data scientist Cathy O'Neil said drawing conclusions about health
risks on such data could lead to a bias against some poor people. It
would be easy to infer they are prone to costly illnesses based on their
backgrounds and living conditions, said O'Neil, author of the book Weapons of Math Destruction,
which looked at how algorithms can increase inequality. That could lead
to poor people being charged more, making it harder for them to get the
care they need, she said. Employers, she said, could even decide not to
hire people with data points that could indicate high medical costs in
the future.
O'Neil said the companies should also measure how the scores might discriminate against the poor, sick or minorities.
American
policymakers could do more to protect people's information, experts
said. In the United States, companies can harvest personal data unless a
specific law bans it, although California just passed legislation that
could create restrictions, said William McGeveran, a professor at the
University of Minnesota Law School. Europe, in contrast, passed a strict
law called the General Data Protection Regulation, which went into effect in May.
"In Europe, data protection is a constitutional right," McGeveran said.
Pasquale,
the University of Maryland law professor, said health scores should be
treated like credit scores. Federal law gives people the right to know
their credit scores and how they're calculated. If people are going to
be rated by whether they listen to sad songs on Spotify or look up
information about AIDS online, they should know, Pasquale said. "The
risk of improper use is extremely high," he said. "And data scores are
not properly vetted and validated and available for scrutiny."
A creepy walk down memory lane
As
I reported this story I wondered how the data vendors might be using my
personal information to score my potential health costs. So, I filled
out a request on the LexisNexis website
for the company to send me some of the personal information it has on
me. A week later, a somewhat creepy, 182-page walk down memory lane
arrived in the mail. Federal law only requires the company to provide a
subset of the information it collected about me. So that's all I got.
LexisNexis
had captured details about my life going back 25 years, many that I'd
forgotten. It had my phone numbers going back decades and my home
addresses going back to my childhood in Golden, Colo. Each location had a
field to show whether the address was "high risk." Mine were all blank.
The company also collects records of any liens and criminal activity,
which, thankfully, I didn't have.
My report was boring, which
isn't a surprise. I've lived a middle-class life and grown up in good
neighborhoods. But it made me wonder: What if I had lived in "high-risk"
neighborhoods? Could that ever be used by insurers to jack up my rates —
or to avoid me altogether?
I wanted to see more. If LexisNexis
had health risk scores on me, I wanted to see how they were calculated
and, more importantly, whether they were accurate. But the company told
me that if it had calculated my scores it would have done so on behalf
of its client, my insurance company. So, I couldn't have them.
source : https://www.npr.org/sections/health-shots/2018/07/17/629441555/health-insurers-are-vacuuming-up-details-about-you-and-it-could-raise-your-rates