By Marshall Allen
ProPublica, July 17, 2018
To an outsider, the fancy booths at last month’s health insurance industry gathering in San Diego 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’s 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 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.
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 have little to do with patients’ needs. As the series’ first story showed, contrary to popular belief, lower bills aren’t health insurers’ top priority.
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. 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 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.
But patient advocates are skeptical 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.
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.
The LexisNexis booth was emblazoned with the slogan “Data. Insight. Action.” The company said it uses 442 non-medical 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.
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.
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.”
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.
By Don McCanne, M.D.
It’s obvious that massive amounts of data are being collected on each one of us, and this data is being purchased by the private health insurance companies. They say that this data is only being used to help patients get the care they need, but does that really fit with the business model of the insurers?
Are they really paying for this data in order to be able to pay for more care that patients would not be receiving except for the wisdom of the insurers in identifying the medical needs of their clients and then arranging for additional care?
When you listen to the quarterly reports of the major health insurers, they boast about increased revenues, increased profits, and low medical loss ratios – the latter being important because it shows that they are being successful in limiting the amount of health care that they pay for. That seems to be the opposite of their avowed use of these massive pools of data to obtain more care for their clients.
Furthermore, when the United States is already infamous for its profoundly wasteful administrative excesses in health care, doesn’t it seem illogical that we would expand these excesses through this superfluous, massive data management?
The full ProPublica article is quite long, but it is worth reading if you wish to understand more details about this nefarious mismanagement of our personal data.
If we had a single payer, improved Medicare for all, would our public stewards be accessing this massive amount of data in order to stratify the needs of various sectors of patients, avoiding those with greater needs? Of course not. The system would be designed to serve all patients well, including those with the greatest needs. The sooner we get rid of the private insurers, the better.
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