What is *Our Bodies, Our Data* by Adam Tanner about?

*Our Bodies, Our Data* exposes the multi-billion-dollar trade in anonymized medical records, prescriptions, and insurance claims sold by data brokers to pharmaceutical companies, insurers, and marketers. Adam Tanner investigates how this hidden industry prioritizes profit over patient privacy, highlighting risks of re-identification and the lack of comprehensive health records for actual care.

Who should read *Our Bodies, Our Data*?

This book is essential for healthcare professionals, policymakers, privacy advocates, and anyone concerned about data ethics. It offers critical insights for patients seeking to understand how their medical information is commercialized without consent.

Is *Our Bodies, Our Data* worth reading?

Yes—Tanner’s investigative rigor and clear storytelling make it a landmark work on medical privacy. It sheds light on systemic exploitation in healthcare data markets, offering a compelling case for regulatory reform.

How does medical data get sold without patient consent?

Hospitals, pharmacies, and insurers sell anonymized records to data brokers like IMS Health (now IQVIA), which aggregate billions of patient profiles. These dossiers—stripped of names but retaining identifiers like birth year and doctor details—are monetized for drug marketing and research.

What are the risks of re-identifying anonymized medical data?

Advanced algorithms can cross-reference anonymized data with public records to reveal identities, exposing patients to discrimination, insurance denial, or employment issues. Tanner warns that purported “anonymization” often fails to protect privacy.

How does *Our Bodies, Our Data* critique the healthcare system?

Tanner argues that while corporations profit from patient data, individuals struggle to access unified health records. This paradox undermines care quality while enabling unchecked commercial exploitation.

What solutions does Adam Tanner propose for medical data privacy?

The book advocates for patient ownership of health data, transparency in data sales, and stricter regulations akin to European GDPR. Tanner emphasizes empowering individuals to control how their information is shared.

How does *Our Bodies, Our Data* compare to Tanner’s previous work *What Stays in Vegas*?

While *What Stays in Vegas* explores broader data commercialization, *Our Bodies, Our Data* focuses specifically on healthcare. Both reveal systemic privacy failures, but the latter highlights life-and-death implications of medical data misuse.

데이터의 공포 요약

기업들이 우리의 의료 기록을 판매하여 수십억 달러를 버는 방법

아담 태너

3.8(74 Reviews)

데이터의 공포 요약 개요

당신의 의료 데이터가 동의 없이 판매되고 있습니다. "Our Bodies, Our Data"는 환자 기록을 거래하는 670억 달러 규모의 산업을 폭로합니다. 듀크 대학교의 워싱턴 박사는 이 책을 약국, 보험사, 데이터 브로커들이 당신의 가장 은밀한 건강 비밀을 통해 어떻게 수익을 창출하는지 밝혀내는 "필독서"라고 부릅니다.

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다양한 학습 방식으로 데이터의 공포 요약 살펴보기

데이터의 공포의 같은 아이디어를 담았습니다. 나에게 맞는 목소리, 깊이, 속도를 고르고 언제든 바꿔 보세요.

1장

When Your Health Becomes a Commodity

Did you know that seconds after you share intimate medical details with your doctor, that information begins a journey through a multi-billion dollar marketplace? While your name may be removed, the rest of your medical history-your conditions, medications, test results, and demographic details-is packaged and sold repeatedly to companies you've never heard of. This hidden trade processes millions of health records daily, with firms like IMS Health (now IQVIA) maintaining dossiers on over 500 million patients worldwide. Yet despite this unprecedented commercialization of our most sensitive information, patients themselves often struggle to access their complete medical records during emergencies. This paradox sits at the heart of Adam Tanner's investigative work, which pulls back the curtain on an industry that operates largely without public awareness or consent-one that pharmaceutical giants consider so valuable they'll pay tens of millions annually to access its insights, while privacy advocates warn the practice undermines the very foundation of medical trust.

2장

The Digital Transformation of Your Pharmacy Visit

The modern trade in patient data began innocuously enough at your local pharmacy counter. When psychiatrist Deborah Peel spotted a new computer at her neighborhood Tarrytown Pharmacy in Austin, Texas during the late 1990s, she immediately sensed trouble. The family-owned drugstore had maintained a community feel for decades, but Peel worried the computer would facilitate unauthorized sharing of sensitive patient information. When she confronted owner Brian Newberry, he explained it simplified prescription processing and insurance claims-plus, drug wholesaler McKesson had offered the software for free. What neither fully appreciated was how this computerization would transform pharmacies from mere medication dispensers into data collection points. The digitization that concerned Peel had roots stretching back to 1947, when pharmacy student Ray Gosselin convinced Massachusetts drugstores to share prescription records for his thesis research. Pharmaceutical companies, eager for precise sales data, took interest-one even offered Gosselin $2,000 for his insights. By 1952, Gosselin had transformed his research into a business called the National Prescription Audit, starting with 200 pharmacies and eventually expanding to hundreds more. By the 1980s, pharmacy chains recognized the competitive advantage in these data repositories. Walgreens had installed satellite-linked computers in five Iowa stores by 1981, allowing patients to get refills at any location. By 1997, all Walgreens stores nationwide were connected. This digitization revolutionized pharmaceutical data mining, giving companies easy access to prescription information that could be sold to third parties. Early adopters like Thomas Menighan, who computerized his West Virginia pharmacy in 1979, were surprised when IMS Health offered $50 monthly for copies of his prescription files on floppy disks. The software removed patient names but retained age, doctor information, and medication details. Over time, data miners focused on large chains rather than independent pharmacies, allowing them to collect millions of records in single transactions from companies like CVS and Walgreens. Today, pharmacy chains eagerly profit from selling anonymized prescription copies but remain remarkably reluctant to discuss it. When approached about their practices, most deflect or deny. Walmart's executive VP of global customer insights flatly stated, "We do not share this kind of information externally." Two companies stood apart in their transparency: CVS and Kroger openly confirmed selling anonymized data. "Pretty much everyone who is in the business has some sort of supply arrangement for de-identified prescription data," admitted Per Lofberg, executive VP of CVS Health.

들리는 모든 단어를 눈으로 확인하세요

데이터의 공포 요약의 각 단어가 오디오에 맞춰 실시간으로 강조되어 편하게 따라 읽을 수 있습니다.

3장

The Hidden Data Middlemen

The prescription represents just the tip of the informational iceberg. As insurance coverage for prescriptions expanded, a complex network of data processors emerged. These "clearinghouses" or "switches" route claims from pharmacies to insurers while simultaneously collecting valuable medical information. Many pharmacists were shocked to discover their customers' data was being sold without their knowledge. Tery Baskin, who owned two Arkansas drugstores, only learned about this practice when a colleague's patient received a recall notice directly from a drug manufacturer. "The pharmacist, for the most part, didn't even know this was going on," Baskin said. "So you are making money off of my prescriptions? Those patients came to my pharmacy. Now you are taking that data and you are profiting from it, and I'm not even in the know." The Health Insurance Portability and Accountability Act (HIPAA), passed in 1996 and implemented in the early 2000s, governs health data trading in the United States. While it protects identifiable health information containing names, Social Security numbers, addresses or phone numbers, data anonymized by approved methods receives no protection whatsoever. HIPAA's scope is also limited to "covered entities"-healthcare providers, health plans, and healthcare clearinghouses. Other parties can trade health information freely, a significant loophole. The real breakthrough in data collection came when entrepreneurs realized they could monetize the flood of digital medical information flowing through the healthcare system. Fritz Krieger had this realization in 1998 when Cardinal Health, a major drug wholesaler, hired him to monetize the company's data streams. Cardinal's ScriptLINE service, which helped pharmacies maximize insurance reimbursements, received digital copies of every transaction in real-time. When approaching CVS executives, Krieger learned they were already selling data to IMS for $4 million annually with exclusivity agreements. Cardinal's CEO suggested making the pharmacies partners instead of just data sources. This strategy worked brilliantly-CVS, Kmart, and even Walmart joined ArcLight, Krieger's new venture. Soon, the upstart was gathering information on 60% of all US pharmacy sales, expanding the profitable trade in patient data with little consideration of its implications.

4장

The Secretive Giant Behind the Data Trade

IMS Health, a secretive giant in the health data industry with $2.9 billion in revenue in 2015, gathers anonymized medical dossiers on hundreds of millions of patients who know nothing about the company. Despite its massive influence, IMS has historically shunned public attention, with executives routinely declining interview requests. The company's origins trace back to Ludwig Wolfgang Frohlich, who emigrated from Nazi Germany to the United States in 1935, establishing a Madison Avenue medical advertising agency by 1943. He soon befriended Arthur Sackler, and despite being born worlds apart-Frohlich in Germany and Sackler in Brooklyn to immigrant Jewish parents-they shared much in common, including birth dates just weeks apart in 1913 and commitments to self-reliance and hard work. These "med men" revolutionized pharmaceutical advertising during the post-war "miracle drugs" era. Despite the cutthroat competition typical of Madison Avenue, Frohlich and Sackler maintained a secret alliance. Rather than competing for accounts, they met behind closed doors to divide business and share client information. This arrangement allowed them to represent competing drug companies without conflicts of interest. To win new business and prove their advertising effectiveness, Frohlich created Intercontinental Marketing Services (IMS) as a market research company. Ad clients paid for research to see how their products performed against competitors, which the agency would leverage to recommend more advertising-either to protect market leadership or boost lagging products. Frohlich replicated successful market research models by establishing Intercontinental Marketing Services in Germany, sending executive David Dubow to set up the Institut fur Medizinische Statistik, which published its first national market estimates in 1957 using wholesale pharmacy purchase data. This commercial success quickly expanded to other Western European countries. Despite speculation among his staff about his background-with some suspecting Nazi ties while others thought he might be Jewish-Frohlich maintained strict privacy about his past. Research revealed he was born in Giessen before WWI, briefly joined the Nazi youth work brigade after Hitler's rise, and studied briefly at Goethe University before emigrating to New York in 1935. Contrary to what even his closest associates believed, documents confirmed Frohlich was indeed Jewish, a heritage he concealed throughout his life alongside his homosexuality amid persistent American anti-Semitism. IMS moved closer to patient data by soliciting prescription details from doctors, initially offering token gifts like records as compensation. The company eventually increased compensation to about $50 monthly for doctors and paid pharmacies roughly a penny per prescription to share or allow microfilming of dispensing records. By the 1970s, Frohlich's advertising empire had expanded internationally with offices across major cities and annual billings exceeding $43 million. His career ended abruptly when he collapsed during a staff meeting after returning from vacation. Diagnosed with a brain tumor, he died in September 1971 at age 58.

데이터의 공포 요약에서 더 많은 것을 얻으세요

매일 새로운 인사이트를 10만 개 이상의 레슨에서 만나보세요
단어가 강조되어 오디오와 실시간으로 동기화됩니다
내 라이브러리를 저장하고 모든 기기에서 이용하세요

5장

The Doctor's Prescription: A Valuable Commodity

After graduating college in 1969, Shel Silverberg joined Lea Associates, which gathered medical data by having doctors complete questionnaires about diagnoses and prescriptions. When IMS acquired Lea in 1972, Silverberg rose to become vice president for international marketing. In 1978, McKesson Corporation hired him away to run Pharmaceutical Data Services (PDS), challenging IMS's monopoly in medical data. By the early 1980s, PDS began ranking individual doctors into tiers based on their prescribing habits, information gathered through surveys that paid doctors $2-10 to report their prescription volumes and medication preferences. This data allowed pharmaceutical companies to strategically target high-prescribing physicians and compensate sales representatives accordingly. Drug companies have used "detailers" since the 1850s to convince doctors to prescribe their medications. Before computers, reps would sweet-talk pharmacists to learn doctors' prescribing habits. By 1988, PDS was gathering prescriptions from 8,000 pharmacies (about 35% of all prescriptions) by paying about a penny per script. IMS Health was initially slow to follow in compiling doctor-specific prescription data, partly to protect sales of its profitable Drug Distribution Data service. After Dun & Bradstreet acquired IMS for nearly $1.7 billion in 1988, IMS finally launched its Xponent service in 1993, marketing it as a precision targeting tool that could dramatically boost pharmaceutical sales. IMS claimed that "winning just one more prescription per week from each prescriber yields an annual gain of $52 million in sales." Doctor-identified data gave pharmaceutical reps a secret weapon as powerful as X-ray vision. Shahram Ahari, who joined Eli Lilly straight out of college in 1998, discovered these reports were "super-top-classified"-reps were forbidden from mentioning them to doctors or bringing laptops containing this data into physicians' offices. During his training, Ahari learned that personality, attractiveness and enthusiasm mattered more than scientific knowledge. His job involved categorizing doctors by personality types and tailoring approaches accordingly. Ahari meticulously recorded personal details about physicians-their families, hobbies, interests-to build seemingly genuine friendships. Using IMS data, he and his partner focused on the top 200 prescribers in their territory of 450 doctors, lavishing the 50 biggest prescribers with expensive dinners at Manhattan's finest restaurants. Though company guidelines officially limited spending to $100 per doctor per meal, managers allowed creative accounting to circumvent these limits for top prescribers. Ahari spent $60,000-$80,000 annually on catering and dining. If doctors didn't increase prescriptions of Eli Lilly drugs after receiving such generosity, the perks would gradually disappear-from gourmet lox to donuts, creating pressure not just from the rep but from office staff accustomed to the freebies.

6장

When Physicians Fight Back

Dr. Peter Klementowicz initially welcomed pharmaceutical representatives to his New Hampshire cardiology practice, rationalizing that their visits provided free samples for his poorest patients. His perspective changed dramatically when two reps brought lunch and showed him a thick printout detailing regional doctors' prescribing habits, pressuring him to change his prescribing patterns. Outraged, he ordered them to leave. Klementowicz was stunned by how thoroughly pharmaceutical companies tracked his prescribing, feeling they had stolen his "intellectual property." While his reaction was particularly strong, most doctors remained unaware for years that sales representatives possessed detailed information about their prescribing patterns. Cindy Rosenwald, Klementowicz's wife and a freshman Democratic representative in New Hampshire, learned about prescription data mining through articles provided by an AARP lobbyist. A 2003 Boston Globe expose revealed the hidden trade in physician-identified data, which shocked and angered Rosenwald, who viewed it as an invasion of privacy likely to increase drug costs. In 2006, Rosenwald proposed legislation to ban commercial use of prescription data. New Hampshire passed the Prescription Information Confidentiality Act in June 2006, prohibiting the use of prescription records for marketing while allowing public health applications. Despite New Hampshire's small size, the legislation sparked similar bills in Vermont and Maine. Data miners fought against the new state laws restricting prescription data use, suing in federal court. Throughout the litigation, data miners argued their First Amendment right to gather prescribing information. Their lawyer Tom Julin positioned them as "publishing companies... similar to newspapers" that simply report on doctors' prescribing decisions. When the Vermont case reached the Supreme Court in April 2011, Justice Kennedy delivered a 6-3 decision favoring IMS and other data miners, ruling that "speech in aid of pharmaceutical marketing" deserved First Amendment protection. The Court found Vermont's law problematic because it selectively restricted pharmaceutical companies while allowing researchers to access the same data. Despite this defeat for privacy advocates, physicians have increasingly restricted pharmaceutical representatives, with one study showing that by 2014, 49% of doctors placed moderate to severe restrictions on sales visits, more than double the 2008 figure. Meanwhile, data miners shifted focus to an even more intimate frontier: comprehensive patient medical histories assembled from doctors, hospitals, pharmacies, insurers, and labs-all without patients' knowledge that their anonymized information has become a commercial product.

들리는 모든 단어를 눈으로 확인하세요

데이터의 공포 요약의 각 단어가 오디오에 맞춰 실시간으로 강조되어 편하게 따라 읽을 수 있습니다.

7장

The Comprehensive Patient Profile

Unlike early atomic bomb survivor studies or the famous Framingham Heart Study where participants gave informed consent, most people unknowingly contribute to commercial longitudinal data files. Data miners compile anonymized dossiers on hundreds of millions of people from insurance claims, pharmacy prescriptions, electronic health records, and lab tests. Each source provides different perspectives on the same patient's treatment, creating comprehensive profiles that help researchers develop new insights while simultaneously creating privacy risks as more data elements become linked together. Commercial analysis of longitudinal patient files gained momentum in the 1970s when American companies sought ways to reduce soaring healthcare costs. Companies like General Motors began sending insurance claims data to Boston University for analysis, where Professor Richard Egdahl and Dr. Paul Gertman developed techniques to mine this information. The creation of Medicare and Medicaid in 1965 accelerated the computerization of claims data, creating vast information repositories and opportunities for processing firms. Insurance claims data proved valuable because of its uniformity compared to varied physicians' notes or lab reports. While some pioneers like Gertman refused to sell patient information to data miners or pharmaceutical companies, concerned about re-identification risks, other entrepreneurs embraced this opportunity. Ernie Ludy founded MedStat Systems in 1981, aggregating claims data from major corporations like Ford and General Electric. His business model offered companies free analysis in exchange for permission to sell their anonymized data to drugmakers and researchers. This approach proved lucrative-Ludy sold MedStat to Thomson Corporation for $339 million in 1994. By 1997, IMS expanded into longitudinal patient data collection, initially assembling nearly one million anonymized electronic medical records and insurance claims from over 1.5 million US patients. This represented a dramatic evolution from their earlier focus on aggregate sales data and prescription tracking. As competition intensified, IMS secured more patient data sources, including a $10 million investment in Allscripts in 2000. Originally a medication repackaging service, Allscripts pivoted to electronic prescribing and later electronic medical records, giving it access to valuable patient data. According to former IMS executive Bob Merold, "Most of where IMS is getting their clinical data from is Allscripts." Over the past two decades, major health insurers have established data analysis companies that monetize their claims information. UnitedHealth created Optum, Anthem operates HealthCore, and Blue Cross Blue Shield's Blue Health Intelligence maintains data on 125 million people dating back to 2005. Companies not traditionally associated with healthcare have joined this lucrative market. IBM acquired Explorys, Phytel, and Truven, gaining access to data on over 300 million patients, while LexisNexis has built what it calls the largest medical claims warehouse with 1.2 billion claims covering 250 million patients.

8장

The Myth of Anonymity

Despite industry assurances about anonymization, many experts warn that advanced computing makes re-identification increasingly possible. Surprisingly, even MSA's technical director Jani Syed admits: "No matter how much data obfuscation you do, if you have enough data, it's always possible to identify a particular person. It's not that hard." Computer scientist Latanya Sweeney has built her career exposing these vulnerabilities. The first Black woman to earn a computer science PhD from MIT, Sweeney made headlines in 1997 by identifying Governor William Weld in supposedly anonymized insurance records using just birth date, ZIP code, and gender. She calculated these three data points could identify 87.1% of Americans. Even without traditional identifiers, unique patterns in our data can reveal our identities. Two New York Times reporters re-identified a woman among 657,000 anonymized AOL users by analyzing her search patterns. Princeton researchers identified Netflix users by comparing rental histories with public IMDB reviews. Anthony Tockar re-identified celebrities in NYC taxi data, even tracking passengers from sex clubs to their homes. Medical data is particularly vulnerable-a 46-year-old patient seeing doctors in two specific locations, with certain height, weight, and allergies, becomes increasingly identifiable. With people generating constant digital traces through phones, health apps, internet searches, and purchases, re-identification becomes increasingly feasible. Beyond re-identification risks, data breaches expose millions of medical records directly. Between 2009 and 2016, the US Department of Health and Human Services recorded over 1,300 breaches involving at least 500 people each, exposing more than 170 million patient files. The scale is staggering: Premera Blue Cross reported attackers accessed 11 million records containing names, birth dates, Social Security numbers, bank information, and clinical data. Anthem suffered an even larger breach affecting 78.8 million people. Companies trading in medical data are reluctant to discuss security and anonymization flaws. When approached, IMS Health's officials declined detailed discussions, with spokesman Tor Constantino providing only a brief statement emphasizing the company's commitment to privacy and security. In a revealing 2002 filing to the European Commission, IMS argued against excessive anonymization requirements, claiming it would be "highly impractical" to ensure no conceivable method could re-identify individuals.

데이터의 공포 요약에서 더 많은 것을 얻으세요

매일 새로운 인사이트를 10만 개 이상의 레슨에서 만나보세요
단어가 강조되어 오디오와 실시간으로 동기화됩니다
내 라이브러리를 저장하고 모든 기기에서 이용하세요

9장

The Patient's Right to Control Their Data

Deborah Peel evolved into a privacy activist through her psychiatry work. After becoming chief of psychiatry at Austin's Brackenridge Hospital in 1979, she began advocating on mental health issues, eventually testifying before federal panels about privacy concerns. In 2004, she founded Patient Privacy Rights, running it on a shoestring budget funded largely by family and friends. Peel regularly features patients sharing privacy violation stories at her annual conferences. In 2015, Deanna Fei spoke about being one of two AOL employees whose premature babies had prompted CEO Tim Armstrong to announce benefit cuts. Another speaker was Alina, a bipolar lawyer whose intimate psychiatric records-including details of childhood sexual abuse and family problems-became accessible to all doctors in her health system. When she complained, officials claimed their practices complied with HIPAA regulations, leaving her to choose between privacy and quality care: "You shouldn't have to choose between privacy and the best possible care." After many hours with Deborah Peel, Tanner asked what she really wants. Her answer was passionate and direct: pharmacies, doctors, hospitals, and middlemen should obtain permission before sharing medical data, even anonymized. "The point is just ask us," she insisted. "They don't want to ask, because they want to steal it!" Those opposed to patient opt-outs argue science needs complete data sets. Tom Brogan warns that excluding patients with serious conditions creates "massive holes." Others cite administrative complexity-Jonathan Wald notes that blocking one pathway doesn't stop "thirty other pathways" for data distribution. Controlling one's medical data privacy remains difficult, yet some promising models are emerging. Rhode Island became the first state to allow residents to opt out of anonymized insurance claims sharing, with only 1.5% (about 15,000 people) choosing to do so by mid-2015. Since 2001, the pioneering Framingham study has given participants control over whether their data is shared with commercial entities, with less than 5% opting out. Many former data industry executives now support giving patients more control, with some even suggesting financial compensation ($1-15 per patient) for commercial use of their data.

10장

A System That Fails Patients

The unfettered commercial trade in anonymized medical data has created a troubling paradox: HIPAA has enabled a vast market for intimate information that undermines patient trust while creating privacy vulnerabilities, yet patients still struggle to access their own complete records. Despite Obama's 2008 promise to computerize health records and $31 billion in subsidies through HITECH, hundreds of different electronic medical systems still can't communicate effectively. Even tech leaders like Craig Barrett (Intel), Denny Briley (pharmacy records pioneer), and Neal Patterson (Cerner CEO) can't access their complete medical histories, with Patterson's cancer-stricken wife carrying "shopping bags" of records to appointments. The problem persists because health systems want to retain patients within their networks and vendors create "switching barriers" to lock in customers. Epic, founded by Judy Faulkner in 1979 and now worth billions, has been criticized for lack of interoperability despite exchanging 21 million records monthly. Government failed by not requiring compatibility before distributing billions in subsidies. As David Blumenthal, Obama's health information czar, admitted: "There was no business case for interoperability." Even tech giants have failed to solve the medical records problem. Google abandoned its Google Health service in 2011, admitting it couldn't achieve "widespread adoption in the daily health routines of millions of people." Microsoft's HealthVault outlasted Google's effort but still struggled, with only two million people signed up by 2014, far below their ten-million goal. In Vermont's remote Underhill region lives Larry Weed, a doctor in his nineties who pioneered computerized medical records half a century ago. In 1969, Weed moved to Vermont to develop PROMIS (Problem-Oriented Medical Information System), allowing doctors to record patient histories consistently and access medical information decades before the Internet. But Weed's disruptive approach alienated many doctors who resisted having their notes visible to others. The hospital administration prioritized billing systems over patient-centered records. At his Vermont home, Weed remains energetic and passionate in his nineties, demonstrating his knowledge couplers and expressing disgust with the business world's profit focus. After hours of rapid-fire explanation, he admits to being depressed that medicine hasn't embraced his innovations: "The disaster in medicine is far bigger than most people realize."

들리는 모든 단어를 눈으로 확인하세요

데이터의 공포 요약의 각 단어가 오디오에 맞춰 실시간으로 강조되어 편하게 따라 읽을 수 있습니다.

11장

Reclaiming Our Medical Identity

Medicine remains a business that caters to the real customer-the patient-far less than other economic sectors. As marketing evolved, patients' needs weren't prioritized. Doctors, insurers, and drugmakers had little incentive to share medical data with patients, instead lessening privacy while denying individuals control of their own information. We now stand at a precipice where medical data can either be treated securely on behalf of individuals or left to market forces. If the market takes charge-and big pharma wields enormous political and financial leverage-outside businesses will know increasingly more about us, potentially leading to discrimination, exclusion, and humiliation. In a more benign alternative, patients gain greater control over their medical destinies while doctors and pharmaceutical companies still prosper but must cede some control. Patients would have authority over their records and determine how their intimate data contribute to medical science. Companies buying and selling patient information have obscured their activities from the public, treating us with condescension. This approach is both wrong and counterproductive-many people would gladly share health information if they understood the purpose and had reason to cooperate. The US medical system must transform to put patients in control of their records, requiring clear consent before data sharing, even when anonymized. Despite the big data revolution, randomized, double-blind, controlled clinical trials remain medicine's gold standard. The for-profit trade in longitudinal patient data has delivered neither the revolutionary breakthroughs promised by data miners nor the catastrophic privacy violations feared by advocates. When pressed for examples of significant insights, industry executives struggle to provide specific cases beyond vague "cost efficiencies." Many data miners are honorable professionals genuinely seeking to improve patient health while making money. "I don't think there are any evil characters in all of this," says Kris Joshi of health-data company Emdeon. "IMS is not evil, pharmaceutical companies are not evil, and patients are definitely not evil, and neither are insurance companies evil." Yet as former IMS official Bob Merold cautions about longitudinal medical data: "There is a lot of goodness about it. There are huge amounts of potential evil about it." The question becomes how to harness big data in medicine while protecting individual interests. Corporations trading patient data claim to help science, but may not offer the best societal approach. Health issues represent our most intimate secrets, with greater downside risks than other personal data. What's needed is simple: more transparency, consent, and control. HIPAA protections should extend to all health information, not just "individually identifiable" data, and these protections should apply broadly beyond current "covered entities" to include health device makers, fitness trackers, smartphone apps, and DNA services. After all, it's our data.

★★★★★

데이터의 공포 요약 완독을 축하합니다

“23일째 매일 사용하고 있어요. 이제 제 일상의 한 부분이 되었습니다.”

jayallen

데이터의 공포 요약의 핵심 주제

medical data mining
patient privacy rights
healthcare commercialization
anonymized data brokerage
pharmaceutical market intelligence

데이터의 공포 요약의 명문장

We do not share this kind of information externally.
Pretty much everyone...has some sort of supply arrangement.
Patients came to my pharmacy.
HIPAA's scope is also limited to 'covered entities'.
IMS has historically shunned public attention.

데이터의 공포 요약의 등장인물

Adam TannerAuthor and investigative journalist
Deborah PeelPsychiatrist and patient privacy advocate
Ray GosselinFounder of the National Prescription Audit
Fritz KriegerEntrepreneur who monetized medical data streams
Tery BaskinPharmacist who discovered unauthorized data sales

저자 소개

『우리의 몸, 우리의 데이터: 기업들은 어떻게 우리의 의료 기록을 팔아 수십억 달러를 버는가(Our Bodies, Our Data: How Companies Make Billions Selling Our Medical Records)』의 저자인 아담 태너(Adam Tanner)는 데이터 프라이버시와 개인 정보의 상업화 분야에서 선도적인 권위자입니다.

하버드 대학교 양적 사회과학 연구소(Institute for Quantitative Social Science)의 펠로우이자 발칸 반도, 샌프란시스코, 모스크바 등지에서 활동한 전직 로이터 통신 특파원인 태너는 조사 보도의 엄격함과 글로벌 데이터 생태계에 대한 깊은 이해를 결합합니다.

워싱턴 포스트의 주목할 만한 저서로 선정된 『라스베이거스에서 남는 것: 개인 데이터의 세계—대기업의 생명줄—그리고 우리가 아는 프라이버시의 종말(What Stays in Vegas: The World of Personal Data—Lifeblood of Big Business—and the End of Privacy as We Know It)』을 포함한 그의 저작들은 산업계가 어떻게 민감한 정보로부터 이익을 얻는지를 폭로하는 동시에 프라이버시 논쟁을 재편하고 있습니다. CNN, NPR, BBC 등의 플랫폼에서 자주 논평가로 활동하는 태너의 통찰력은 수십 년간의 현장 취재와 학술 연구에서 비롯되었습니다.

그는 리비아 혁명 보도로 2012년 퓰리처상 최종 후보에 오른 로이터 팀의 일원이었으며, 이는 시스템적 진실을 밝히려는 그의 헌신을 강조해 줍니다. 『우리의 몸, 우리의 데이터』는 디지털 시대의 혁신, 이윤, 그리고 개인의 권리 사이에 숨겨진 기회비용을 조명하려는 그의 사명을 이어가고 있습니다.

데이터의 공포 요약 다운로드

데이터의 공포 요약을 무료 PDF 또는 EPUB으로 받으세요. 인쇄하거나 오프라인에서 언제든 읽을 수 있습니다.

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BeFreed는 호기심 넘치는 글로벌 커뮤니티를 하나로 연결합니다

4.7

평균 평점

앱 평가 7.84천 개 이상

BeFreed 커뮤니티

정말이지 아직 앱을 다 써 보지도 않았는데, 며칠 써 본 것만으로도 깊은 인상을 받았어요… BeFreed는 제가 써 본 어떤 학습 앱과도 차원이 달라요. 몰입감이 엄청나고 집중력도 실제로 좋아져서, 스마트폰을 하염없이 스크롤하는 분들께 딱이에요!

@ladyInfinity

정확히 23일 전에 BeFreed를 구입했는데, 그날부터 하루도 빠짐없이 쓰고 있어요. 제 일상 업무 흐름과 학습 습관에 완전히 자리 잡았어요.

@jayallen

솔직히 이 앱은 제 기대를 전부 뛰어넘었어요. 어떤 주제든 오디오로 만들어 달라고 할 수 있고, 결과물이 놀라워요. 제 전문 분야는 심리치료 쪽이고 여러 학문이 얽혀 있는데도 답변이 아주 정확해요.

@Raguipa

제일 고마운 건 스크롤하는 시간이 확 줄었다는 거예요. 검색하는 시간은 줄고 흡수하는 시간은 늘었어요. 오디오북 전권, 팟캐스트, 학습 플랜의 조합이 정말 훌륭해요.

@colonyofcreatorsNGO

저는 24년째 PhotoReading 속진 학습 강사로 일하고 있어요… 책과 독서, 배움이 제 전문인데, BeFreed는 정보를 소화하기 쉽게 전달하는 혁신적인 방식을 정말 잘 구현했어요.

@BeFreed user

단순한 책 요약 앱이 아니에요. '재미' 스타일을 써 봤는데, 전통적인 방식보다 훨씬 나은 요약이고 아이디어를 이해하기도 쉬워요. 이것만으로도 값어치를 해요.

@austinakon

이 앱이 정말 좋아요. 며칠 써 봤는데 듣는 걸 멈출 수가 없어요. 시작하기에 이보다 좋을 수 없어요.

@jcrules328

정말 마음에 들어요. 한 달 정도 써 봤는데 숨은 보석을 찾은 기분이에요. BeFreed로 제가 원하는 주제를 직접 만들 수 있어서 좋고, 목소리도 훌륭한 데다 내레이션 선택지가 무궁무진해요.

@DanielCZ

정말이지 아직 앱을 다 써 보지도 않았는데, 며칠 써 본 것만으로도 깊은 인상을 받았어요… BeFreed는 제가 써 본 어떤 학습 앱과도 차원이 달라요. 몰입감이 엄청나고 집중력도 실제로 좋아져서, 스마트폰을 하염없이 스크롤하는 분들께 딱이에요!

@ladyInfinity

정확히 23일 전에 BeFreed를 구입했는데, 그날부터 하루도 빠짐없이 쓰고 있어요. 제 일상 업무 흐름과 학습 습관에 완전히 자리 잡았어요.

@jayallen

솔직히 이 앱은 제 기대를 전부 뛰어넘었어요. 어떤 주제든 오디오로 만들어 달라고 할 수 있고, 결과물이 놀라워요. 제 전문 분야는 심리치료 쪽이고 여러 학문이 얽혀 있는데도 답변이 아주 정확해요.

@Raguipa

제일 고마운 건 스크롤하는 시간이 확 줄었다는 거예요. 검색하는 시간은 줄고 흡수하는 시간은 늘었어요. 오디오북 전권, 팟캐스트, 학습 플랜의 조합이 정말 훌륭해요.

@colonyofcreatorsNGO

저는 24년째 PhotoReading 속진 학습 강사로 일하고 있어요… 책과 독서, 배움이 제 전문인데, BeFreed는 정보를 소화하기 쉽게 전달하는 혁신적인 방식을 정말 잘 구현했어요.

@BeFreed user

단순한 책 요약 앱이 아니에요. '재미' 스타일을 써 봤는데, 전통적인 방식보다 훨씬 나은 요약이고 아이디어를 이해하기도 쉬워요. 이것만으로도 값어치를 해요.

@austinakon

이 앱이 정말 좋아요. 며칠 써 봤는데 듣는 걸 멈출 수가 없어요. 시작하기에 이보다 좋을 수 없어요.

@jcrules328

정말 마음에 들어요. 한 달 정도 써 봤는데 숨은 보석을 찾은 기분이에요. BeFreed로 제가 원하는 주제를 직접 만들 수 있어서 좋고, 목소리도 훌륭한 데다 내레이션 선택지가 무궁무진해요.

@DanielCZ

유용한 정보와 아이디어를 8~15분짜리 팟캐스트 스타일 오디오로 압축해서 들을 수 있다는 게 정말 좋아요. 원래 팟캐스트는 군더더기가 많아서 안 좋아했는데, 여기는 그걸 싹 걷어냈어요.

@BeFreed user

박사 과정을 마무리하는 중이라 낯선 자료를 많이 읽어야 해요… BeFreed에서는 프롬프트만 입력하면 앱이 자료를 찾아서 오디오 팟캐스트로 만들어 줘요. BeFreed의 과정이 NotebookLM보다 더 매끄럽게 느껴져요.

@Brad

아침을 준비하거나 산책하거나 출퇴근할 때 들을 것을 YouTube에서 자주 찾곤 했는데, BeFreed는 광고도 군더더기도 없이 훨씬 더 딱 맞는 걸 들려줘요!

@BeFreed user

이 플랫폼의 가장 큰 장점은 활용도예요. 다루지 못하는 주제가 말 그대로 하나도 없어요. 무엇을 던져도 다 소화해요… 제한이 전혀 없으면서 약속을 실제로 지키는 학습 도구는 정말 드물어요.

@jayallen

BeFreed는 환상적이에요. 디자인이 편해서 헤매는 시간은 줄고 배우는 시간은 늘었어요. 오디오북, 팟캐스트, 학습 플랜의 조합은 천재적이에요. 제 하루가 완전히 달라졌어요.

@BeFreed user

처음엔 이탈리아어로 팟캐스트를 만드는 방법을 이해하는 데 시간이 좀 걸렸는데, 알고 나니까 — 와! 정말 대단해요! 어떤 주제든 설명해 달라고 하면 정말 똑똑하게 잘 설명해 줘요!

@matteo77

BeFreed는 제가 매일 쓰는 오디오북 앱이 됐어요… 제일 마음에 드는 건 텍스트를 넣으면 이동 중에도 들을 수 있는 오디오로 만들어 준다는 점이에요.

@kotanzu1

유용한 정보와 아이디어를 8~15분짜리 팟캐스트 스타일 오디오로 압축해서 들을 수 있다는 게 정말 좋아요. 원래 팟캐스트는 군더더기가 많아서 안 좋아했는데, 여기는 그걸 싹 걷어냈어요.

@BeFreed user

박사 과정을 마무리하는 중이라 낯선 자료를 많이 읽어야 해요… BeFreed에서는 프롬프트만 입력하면 앱이 자료를 찾아서 오디오 팟캐스트로 만들어 줘요. BeFreed의 과정이 NotebookLM보다 더 매끄럽게 느껴져요.

@Brad

아침을 준비하거나 산책하거나 출퇴근할 때 들을 것을 YouTube에서 자주 찾곤 했는데, BeFreed는 광고도 군더더기도 없이 훨씬 더 딱 맞는 걸 들려줘요!

@BeFreed user

이 플랫폼의 가장 큰 장점은 활용도예요. 다루지 못하는 주제가 말 그대로 하나도 없어요. 무엇을 던져도 다 소화해요… 제한이 전혀 없으면서 약속을 실제로 지키는 학습 도구는 정말 드물어요.

@jayallen

BeFreed는 환상적이에요. 디자인이 편해서 헤매는 시간은 줄고 배우는 시간은 늘었어요. 오디오북, 팟캐스트, 학습 플랜의 조합은 천재적이에요. 제 하루가 완전히 달라졌어요.

@BeFreed user

처음엔 이탈리아어로 팟캐스트를 만드는 방법을 이해하는 데 시간이 좀 걸렸는데, 알고 나니까 — 와! 정말 대단해요! 어떤 주제든 설명해 달라고 하면 정말 똑똑하게 잘 설명해 줘요!

@matteo77

BeFreed는 제가 매일 쓰는 오디오북 앱이 됐어요… 제일 마음에 드는 건 텍스트를 넣으면 이동 중에도 들을 수 있는 오디오로 만들어 준다는 점이에요.

@kotanzu1

웹에서 BeFreed가 어떻게 논의되고 있는지 더 보기

지금 바로 학습 여정을 시작하세요

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데이터의 공포 요약의 명문장

데이터의 공포 요약의 등장인물

저자 소개

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BeFreed는 호기심 넘치는 글로벌 커뮤니티를 하나로 연결합니다

다양한 학습 방식으로 데이터의 공포 요약 살펴보기

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The Digital Transformation of Your Pharmacy Visit

들리는 모든 단어를 눈으로 확인하세요

The Hidden Data Middlemen

The Secretive Giant Behind the Data Trade

데이터의 공포 요약에서 더 많은 것을 얻으세요

The Doctor's Prescription: A Valuable Commodity

When Physicians Fight Back

들리는 모든 단어를 눈으로 확인하세요

The Comprehensive Patient Profile

The Myth of Anonymity

데이터의 공포 요약에서 더 많은 것을 얻으세요

The Patient's Right to Control Their Data

A System That Fails Patients

들리는 모든 단어를 눈으로 확인하세요

Reclaiming Our Medical Identity

데이터의 공포 요약 완독을 축하합니다

다른 사용자들도 좋아한 책

더 알아보기