PDF Summary:Our Bodies, Our Data, by Adam Tanner

Data privacy is a hot topic these days, given the ubiquitous tracking that follows you everywhere on the web. You might want to know that your medical data isn't as private as you might think. Your healthcare providers—pharmacy, hospital, health insurer, lab test provider, and genome sequencers—are continuously selling your medical data to data brokers. Data brokers compile this information into a patient record, which is then resold with records of hundreds of millions of other patients for marketing and industry analysis purposes.

Our Bodies, Our Data is a useful survey of the medical data industry and its current worrisome capabilities. You’ll learn how the industry progressively sold more and more data, how patient records are compiled, and why there’s a market for this data.

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Other Data Sources

Medical data can also be derived from more unexpected sources: Employers can sell their employees’ insurance claims to data miners. Electronic medical records are also fair game—Allscripts provides data to IMS Health, receiving $30 million a year in sales. GE Healthcare sells data from its Centricity EMR.

Then there are data brokers: LexisNexis sells medical claims from many payers, covering 250 million patients. Non-medical data brokers like Experian, Epsilon, and Acxiom sell to medical data miners. These brokers aren’t subject to HIPAA and can sell non-anonymized records.

Miners can also access public data on the internet, for example, from social media. Miners monitor Facebook and Twitter (now X) for mentions of medication, ailments, and keywords like “diagnosed with.” They can also access review sites for doctors and offices, location and exercise data from devices, real estate transactions, and online forum discussions.

Finally, there’s proprietary data—while these haven’t been confirmed to be currently for sale, Tanner says that in the future they could be. This includes any trackable activity online, including Google searches, YouTube videos watched, and ads clicked; profiles and surveys, like on medical websites such as webMD; and data collected by medical devices, such as Apple HealthKit.

Uses of Medical Data

Having hundreds of millions of patient records opens up all kinds of possibilities. Detailed data on which patients take which drugs in which locations can help pharma companies make business decisions, while. researchers can use large datasets for studies. However, the risk is that more unsavory, discriminatory uses can arise.

Here’s how different types of firms can use medical data for their own purposes:

Pharma Companies

Pharmaceutical companies use data to empower their sales force to market drugs to more susceptible doctors. They can get feedback within weeks on whether their efforts to influence doctors are working. Large pharma companies pay $10-40 million per year for IMS Health data, consulting, and services. This helps them understand their competition and breakdown of market share. It also helps them understand patient behaviors such as persistency (how long they keep a drug), compliance (how often they fill a prescription), switching behavior and what specific drugs they switch to, and concomitance (other drugs the patient takes).

Pharma companies also use data to predict which drugs will likely sell and to market treatments for different illnesses. To market medication to patients, they combine demographic data from anonymized sources with information about a specific ailment or treatment, then use identifiable data to target people who fit that profile. For example, if data shows that people who use a particular drug are in their 50s and live in rural Tennessee, marketers can target people with that profile for their drug. They can also target healthcare providers based on what they tend to prescribe or customize to what the doctor’s patients have recently been diagnosed with.

Healthcare Providers

Providers use data to compare cost and quality with their competition. This can help them improve care internally and market to prospective patients. Payers use the data to find billing fraud. Insurers and underwriters get patient consent for the insurer to access named data on prescriptions and tests. Insurers can share info with each other through the Medical Information Bureau.

Other Uses

Other groups that use medical data include financial traders, who use it as information to trade with—for instance, which drugs are popular and which aren’t can influence what stocks will rise and fall. Researchers use it to study long-term outcomes of different treatments, to study the effect of regulation on health outcomes, and for public health studies, such as epidemics, drug use, and health trends.

Employers use data to study patients and spend data to figure out how to reduce costs, as well as to benchmark their costs against other employers. Attorneys use it to contact patients for class action lawsuits. Advertising platforms sell data to Google or Facebook to allow more precise ad targeting of their users.

Potential Bad Faith Uses

There are also more controversial or illegal uses—they’re not currently done publicly, but they’re notable risks. Employers can discriminate against people with higher healthcare costs, lenders may charge higher interest to people with specific conditions, insurers may charge higher premiums to those of a particular profile, blackmailers may use health data to exploit people, and traders may use the health data of CEOs to anticipate changes to stock prices.

Notable Players in Medical Data

Tanner points to some major mining companies in the medical data industry.

IMS Health (Now IQVIA)

IMS Health was founded by Ludwig Wolfgang Frohlich in the mid 1950s. He had a medical advertising agency and developed new marketing practices, like sending direct mail to doctors and telegrams to wholesalers to announce new products. He then partnered with Arthur Sackler, at another ad agency, to split the business between competing pharma companies.

Because it was difficult to prove the effect of marketing on their clients’ bottom line, Frohlich created Intercontinental Medical Statistics (IMS) with David Dubow to research market share, then used this to inform ad clients. Frohlich died in 1971, and Sackler’s brothers inherited the majority of IMS through a tontine. Some suspect Arthur Sackler was the originator of IMS and put Frohlich as its figurehead.

IMS Health developed the Drug Distribution Data service. It was acquired for $1.7 billion in 1988, under the same umbrella as AC Nielsen. It started providing doctor profiles in 1993, went public in 2014, and had $2.9 billion in revenue by 2015. Half of the revenue came from information, and half from services. It made the logical step of providing consulting to its clients once they had the data.

In 2016, IMS merged with competitor Quintiles. (Shortform note: The joint company was renamed IQVIA in 2017.) The firm is said to have 90% of global market share.

PDS (Pharmaceutical Data Services), Source International

PDS began with doctor surveys, then started buying prescription data from pharmacies. It was funded by pharma companies like Merck and Pfizer. It was purchased in 1988 by IMS execs Handel Evans and Dennis Turner. Afterward, it pushed into doctor-identified information, getting accurate data on prescriptions and sales per doctor. PDS was eventually renamed as Source International, and it was acquired by Symphony in 2012.

Verispan

Verispan was created by McKesson and Quintiles in 2002. It produced an annual revenue of $100 million, with its biggest customer paying $6 million per year. It was acquired by Surveillance Data Inc. (SDI), then by IMS in 2011.

ArcLight

ArcLight was started by Fritz Krieger from Cardinal Health. It challenged IMS by providing data reports more quickly and giving large pharmacies equity in ArcLight to avoid upfront fees. Retail company Walmart became a client—it had stopped working with IMS because competitors could figure out individual store revenue numbers from IMS reports.

ArcLight went out of business in the 2000s, a few years after trying to create anonymized patient dossiers. It had difficulty matching anonymized data due to the limitations of computer processing power.

Other Players

There are many other data brokers and companies involved with medical data, including Cardinal Health, ScriptLINE, Symphony Health, and MedStat. MedStat was sold to Thomson Corporation in 1994 for $339 million, then sold in 2012 for $1.25 billion to form Truven, and then sold again to IBM Watson in 2016 for $2.6 billion.

Insurers like UnitedHealth, Anthem, and Blue Cross Blue Shield created data analysis companies like Optum, HealthCore, and Blue Health Intelligence, respectively. Other data brokers include LexisNexis, with its medical claims warehouse; Medivo; and ExamOne and its rival Milliman, which gather prescription data for life insurance.

There’s also Practice Fusion, which gave away electronic medical record (EMR) technology for free, to sell ads to users and sell patient data to brokers. In 2014, it sought between $50,000 and $2 million for longitudinal data sets. And finally, there are startups like Betterpath and Carenity.

The Forces For and Against Medical Data

Tanner describes some ways in which the sale of medical data has been supported, as well as ways in which it has been opposed.

Forces That Propel Patient Data Selling

Forces that have propelled patient data selling over the past decades include:

A general public indifference about privacy. A majority of people do not opt out of anonymized sharing when given the option, and sites like Facebook and Google train people to expect a lack of privacy in their everyday life.

Improved earnings. Selling data is a high-margin boost to the bottom line for data sources (like pharmacies and hospitals). Medical confidentiality doesn’t help much: HIPAA only protects health data with identifiable info and only applies to providers, payers, and clearinghouses (so-called “covered entities”). Other parties can bypass this with a loophole. Furthermore, digitization of healthcare info makes the transfer of patient data easier than mailing in receipts like in olden days.

Purported benefits to people. Manufacturers believe their drugs are good, so in their view, selling their drugs more effectively can only help more people. Pharma salespeople believe they’re educating doctors about better medications. Doctors believe more data access helps research studies. Shared medical data is actually useful for research, and it’s legally difficult to block commercial uses while allowing research uses.

Marketing spending by pharma companies. This is powered by more drugs available in the late 20th century along with readily available reimbursement from insurers. Generics and me-too drugs require more effective marketing to attract attention.

Effectiveness of salespeople. Physicians are too busy to read the latest literature, so they’re swayed by salespeople’s pitches.

Forces That Oppose Patient Data Selling

On the other hand, forces that have counteracted the sale of patient data include:

Negative public sentiment. There’s a general fear of privacy invasion. For example, Korea has fought against the sale of anonymized data, and some patients ask, “If I pay you cash, will you keep my medical records private?” Public sentiment can also include fear of being discriminated against using your medical record, as this discrimination could result in higher rates for life and health insurance, job rejection, and blackmail. On top of that, providers feel manipulated by being tracked for their prescribing behavior. Increasingly, providers are refusing to see salespeople.

Regulatory changes. For instance, gifts to doctors from pharma are now largely prohibited. Current guidelines are for gifts not to exceed $100, and they should be medically related (such as stethoscopes, not golf trips). Some states tried outlawing the use of prescription data for marketing, such as New Hampshire, Vermont, and Maine, but this was struck down by the Supreme Court, which hesitated to bar pharma but not researchers from accessing doctor-identified data.

Resistance continues in other areas. The FDA is clamping down on me-too drugs, which will decrease the marketing demand to separate drugs that are largely identical. Insurance companies are now seeking higher co-pays for branded drugs vs. generics, which limits the marketing efficacy of branded drugs. And among researchers, there’s a perception that it’s more difficult to get accurate insights from medical record data compared to a randomized controlled trial.