A Bill to Amend the Health Care Act to Prohibit Setting Insurance Prices Based on Data Provide by Brokers


This bill restricts the ability of health care companies to set prices based on data they purchase from brokers.

Pro Arguments

The data may be inaccurate. The data only represents general predictive trends do not apply to all individuals.
Health care. Data could be used to deny individuals health care that they desperately need.
Inequality. Poor individuals may be more likely to have health problems. Since income often correlates with class, these problems could be magnified.
Privacy. Collection of the data violates individual’s privacy.

Con Arguments

Health care cost control. Better predictive data can help control the costs of health care. High costs end up resulting in the deprivation of health care.
Exercise. The collection of health information encourages people to exercise.
Improved health care delivery. Better data means better health care delivery.
Social determinants data will encourage companies to get data from a health care provider
General health care. Big data will generally improve


This article provides the best overview of the issue.

Health Insurers Are Vacuuming Up Details About You — And It Could Raise Your Rates (2018) 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…

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…

t’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.

Here are the data brokers quietly buying and selling your personal information (2019). By buying or licensing data or scraping public records, third-party data companies can assemble thousands of attributes each for billions of people. For decades, companies could buy up lists of magazines subscribers to build targeted advertising audiences. These days, if you use a smartphone or a credit card, it’s not difficult for a company to determine if you’ve just gone through a break-up, if you’re pregnant or trying to lose weight, whether you’re an extrovert, what medicine you take, where you’ve been, and even how you swipe and tap on your smartphone…Piles of personal data are flowing to political consultants attempting to influence your vote (like Cambridge Analytica) and to government agencies pursuing non-violent criminal suspects (like U.S. Immigration and Customs Enforcement). Meanwhile, people-search websites, accessible to virtually anyone with a credit card, can be a goldmine for doxxers, abusers, and stalkers.

Insurance companies like Kaiser, Aetna, Anthem and UnitedHealth have been making big data pays (2018). This article lists all of the companies that collect data and how they use it. Anthem, which recently began building out its AI team with the hire of former Googler Udi Manber to lead its AI group, has a subsidiary called HealthCore, which acts as its health outcomes research arm. HealthCore has information on almost 60 million individuals in the U.S. and uses that to give healthcare decision makers actionable intelligence.  In May, HealthCore partnered with Boston Health Economics’ Instant Health Data platform, allowing direct access to its Integrated Research Database for the first time.  Anthem is the third largest healthcare insurance company in the country, covering 40 million people or 6.2 percent of the market.


Health Insurers Are Vacuuming Up Details About You — And It Could Raise Your Rates (2018) 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.

Data Brokers and Health Insurer Partnerships Could Result in Insurance Discrimination (2018). Health privacy experts are concerned that insurance companies will raise rates or deny patients coverage based on risk factors identified when insurers mine claims data with consumer data provided by commercial data brokers. According to an investigation by National Public Radio and ProPublica, insurers are collaborating with data brokers to help track consumers’ data points, such as their race, education level, TV habits, marital status, net worth, social media posts, ZIP code, credit score, online shopping trends, child bearing decisions, and much more. These factors are then fed into complicated algorithms that predict healthcare costs based on these factors. But privacy advocates warn that the use of this “lifestyle” data, which is sold by brokers such as LexisNexis, to make medical hypotheses “could lead insurers to improperly price plans — for instance raising rates based on false information — or discriminate against anyone tagged as high cost.” For instance, data analysts say that a person who purchases plus-sized clothing is at risk for depression, or items purchased for an impending pregnancy can signal that person’s healthcare costs are going up. Or, people who downsize their homes and people whose parents didn’t finish high school tend to have higher healthcare costs, analysts told ProPublica.

Call It What It Is: Health Insurers Use Your Data To Discriminate Against You (2018) The industry says insights from this data are used to improve case management. But, they backpedal when discussions about linkage of data with clinical records abound. Europe is keen on understanding the complexities around data misuse and abuse, so they enacted legislation that prohibits such practices by affirming the individual owns their data, can withdraw it and must be consented for its use. Given the lightning speed of technological advances, health insurers are circumventing HIPAA privacy laws (that they are somewhat bound to) that only hold up in the doctor’s office, hospital room or between a health professional and patient anyway. And, they are using information, the type that the average person would think they otherwise had a reasonable expectation of privacy for, for their own interpretation, false or not, for objectionable purposes.

Big Data Could Set Insurance Premiums. Minorities Could Pay the Price. (2018). Existing health disparities mean that data will consistently show members of certain groups to be more likely to need more health care. What will happen, then, if this data starts being used against those groups? We know, for example, that Black women are much more likely to experience serious complications from pregnancy than white women. So, health insurers might conclude that a woman who is Black and recently married is likely to cost them more money than a white woman in the same position. Even in cases where they don’t have accurate race data, insurers might draw the same conclusion for women who purchase Black hair-care products or those who have tweeted about television shows like Atlanta or Scandal. More broadly, people who live in poor neighborhoods and neighborhoods of color are much more likely to have health problems than those in affluent neighborhoods. The ProPublica piece quotes one health data vendor joking, “God forbid you live on the wrong street these days … You’re going to get lumped in with a lot of bad things.” Is it fair to make health care more expensive for people based on zip code or race?

How Data Brokers And Pharmacies Commercialize Our Medical Data (2018). Putting this all together, Facebook knows the perfect airbrushed you that exists only in fantasy – it is the data brokers and data repurposers like Walgreens that know the real you behind closed doors. Yet, they have been largely absent from the societal conversation of the last few weeks about the privacy of our data and our rights (or lack thereof) to control what companies are allowed to access and do with our information. In a world in which our most intimate medical ailments are merely commodity data points to be bought and sold about us, making money for everyone but us, is the Facebook story really the most important one or should we be talking about just how many companies today “own” our digitized selves?


Why You Should Demand More Surveillance—Of Your Health Records (2013) Yet the vast majority of your health care data remains unused, discarded and ignored. It sits idle when it could be applied today to improve the delivery of health care — including yours — and advance medical science. A few examples: Say you’re a woman with a constellation of symptoms that bounce you from emergency care, to psychiatry, to urology for a urinary tract infection. What might your underlying problem be? Eventually, the health care system would figure out that you’re a victim of domestic abuse. But surveillance of health records could yield that answer faster — and help you sooner. Just by tracking the patterns of emergency-room diagnoses across an entire state, researchers found in a recent study that we could identify domestic abuse an average of two years before the system would normally recognize it.

Health Firms Are Looking at Personal Data (2019) Research shows that medical care exerts less influence on a person’s health than social factors such as access to housing, transportation and nutritious food. About 80% of a person’s state of health is the result of socioeconomic status, physical environment, health behaviors and biology, according to a 2013 report by the National Conference of State Legislatures. By analyzing social and medical data, and harnessing advances in machine learning, health organizations aim to spot at-risk patients who would benefit from prevention programs or referral to a social worker.

Top Benefits of Big Data in the Healthcare Industry (2018). The healthcare industry faces multiple challenges, ranging from new disease outbreaks to maintaining an optimal operational efficiency. Big data analytics can help in solving these healthcare challenges. With the vast amount of data available in the healthcare sector like financial, clinical, R&D, administration and operational data, big data can derive meaningful insights to improve the operational efficiency of the industry.

Can Big Data Solve the Health Insurance Transparency Problem? (2018). More detailed quality data is on the way, and Bind is also working on analytics that can identify provider referral patterns, which can allow the company to attach more customized copays to certain treatment choices while alerting patients of their options.  “If Doctor Jones tends to send her patients straight to surgery, but Doctor Singh is more likely to recommend physical therapy first, and one achieves better long-term outcomes than the other, patients should be able to use that information to budget and plan on what’s best for them,” said Fast.

How Healthcare Payers and Providers Can Harness Big Data for Big Results (2018). Healthcare providers can enhance health outcomes through the power of big data through a combination of trend analysis and making better treatment decisions with access to historical information. Leveraging a big data solution to manage patient medical information can also cut down on duplicate records, which cost health systems an average of $96 per record to manage. Compounding the problem is that repeated tests or delayed treatments caused by duplicate records increases costs by $1,100 per patient, and more than 100,00 people die every year because of mistaken identity or “wrong patient” errors. Looking at the benefits of effective big data management holistically, it’s clear that healthcare payers and providers have a lot to gain from leveraging their data effectively. Healthcare payers can offer a more nuanced relationship with plan members, and healthcare providers can make better care decisions – all through maintaining and leveraging their big data.

Medical Internet of Things and Big Data in Healthcare (2016). A new category of “personalised preventative health coaches” (Digital Health Advisors) will emerge. These workers will possess the skills and the ability to interpret and understand health and well-being data. They will help their clients avoid chronic and diet-related illness, improve cognitive function, achieve improved mental health and achieve improved lifestyles overall. As the global population ages, such roles will become increasingly important.