It Began in December, with CVS’s proposed $69 billion buyout of insurer Aetna. In January, three more corporate behemoths—Amazon JPMorgan Chase , and Berkshire Hathaway —said they were forming a joint venture aimed at reducing health care costs and improving outcomes for their combined 1 million or so employees. Then, in March, Cigna said it would buy pharmacy benefits manager Express Scripts for more than $50 billion.
What’s driving this frenzy of health care–related dealmaking? On first glance you might think it’s merely the pursuit of mass itself. Of “scale,” as management types like to say. But in truth, there’s a more powerful catalyst—one so gargantuan and infinitesimal at the same time that it sounds like the answer to a riddle. And that’s data.
More specifically, it’s your data: your individual biology, your health history and ever-fluctuating state of well-being, where you go, what you spend, how you sleep, what you put in your body and what comes out. The amount of data you slough off everyday—in lab tests, medical images, genetic profiles, liquid biopsies, electrocardiograms, to name just a few—is overwhelming by itself. Throw in the stuff from medical claims, clinical trials, prescriptions, academic research, and more, and the yield is something on the order of 750 quadrillion bytes every day—or some 30% of the world’s data production.
These massive storehouses of information have always been there. But now, thanks to a slew of novel technologies, sophisticated measuring devices, ubiquitous connectivity and the cloud, and yes, artificial intelligence, companies can harness and make sense of this data as never before. “It’s not the data,” says Eric Topol, director of the Scripps Translational Science Institute. “It’s the analytics. Up until three-to-five years ago, all that data was just sitting there. Now it’s being analyzed and interpreted. It’s the most radical change happening in health care.”
The quest to retrieve, analyze, and leverage that data has become the new gold rush. And a vanguard of tech titans—not to mention a bevy of hot startups—are on the hunt for it.
Alphabet life sciences arm Verily is aiming to create a “baseline” of human health by tracking all kinds of biometric information from 10,000 volunteers (and is rumored to have an interest in the health insurance business). Apple just released an iPhone feature offering users in several big health systems instant access to their own medical record—an effort that joins its ongoing heart study with Stanford, testing if wearables can detect serious cardiac conditions.
Tapping this reservoir, say many, will ultimately improve patient health and decrease medical costs, which are projected to rise 5.3% in 2018 alone, according to the Centers for Medicare & Medicaid Services. That’s a noble aspiration, certainly. But not lost on anyone is that it’s sure to make for a potentially blockbuster business too. David Friend, managing director at BDO, points out that data-rich Facebook and Google make their money on advertising—a business worth $200 billion, he estimates. “Health care is 15 times bigger than that,” he says. “We spend $3 trillion. In theory, if this is done right, you’ll have 15 Facebooks and 15 Googles. That’s what’s up for grabs.”
Which is why so many old-guard health care companies, from hospitals and insurers to benefits managers and drug and device makers—which together account for one-fifth of the economy—are hastily recombining and reinventing themselves. The realignment promises not only to drastically reshape the health care landscape for companies overall, but for you as well.
Optimists, like Accenture’s chief technology and innovation officer Paul Daugherty, predict that the “information asymmetry” will soon favor patients whose ownership of their own biological data will give them new power.
To see what the new balance of power will look like in the coming years—and what it looks like right now—Fortune interviewed more than three dozen executives at companies across the health care continuum, along with entrepreneurs, doctors, patients, and other experts. Here’s how the big-data revolution is—and isn’t—transforming medicine.
The Data Pill: A New Paradigm for Patients
Right around his eighth birthday, Lindsay Amos noticed her son Jacoby seemed not quite himself. The usually active boy who played hockey and lacrosse was sluggish, and he seemed to be going to the bathroom a lot. When the boy’s doctor took a reading of Jacoby’s blood sugar, the family was told to rush to the ER. On the drive there, the eight-year-old drifted in and out of consciousness. Jacoby’s blood sugar, Amos later learned, was 735 mg/dL, compared with a healthy range of 70–140 mg/dL. The boy was lucky not to have developed diabetic ketoacidosis, or DKA, a sometimes fatal complication in which, owing to prolonged elevated glucose levels, the blood becomes acidic, and organs begin to shut down.
The resolution to those terrifying events struck Amos, who lives in the Denver suburbs, as startlingly casual and vague. Her family received a crash course on Type 1 diabetes, and the life-threatening consequences of both dangerously high and low blood sugar. They were told to count Jacoby’s carbohydrates and check his blood sugar throughout the day—a process that involves pricking a finger and using a diabetes test strip—and to record this all manually in a logbook.
The dance between stress, exercise, insulin, various kinds of food—and what impact all those factors and more have on blood sugar—is notoriously hard to master, and trying to keep Jacoby in a healthy range was both exhausting and frightening. She’d dutifully do the math, but it didn’t seem to matter. Jacoby was on a roller coaster: His glucose level swinging unpredictably to alarming highs (when he’d feel fatigued) to dangerous lows (when he’d feel dizzy).
His life depended on this number. Amos wanted to have an idea of where it stood at all times and for the first few weeks after Jacoby’s diagnosis, she did her best—testing her son’s glucose levels 20 or so times a day, far exceeding the number of test strips their insurance provider covered.
Virta和Omada Health之类数字糖尿病预防和治疗平台还连接起社区和健康教练，教练可以远程监控体重、血糖、饮食和服药等情况。现在还有Proteus Digital Health可食入传感器，都值得《黑镜》拍一集了，这种传感器可以帮助患者（以及医生和家属）随时查看是否服药。Froedtert医院及威斯康星医疗网络医学院首席创新和数字官迈克尔·安德烈斯介绍，每粒药片接触胃酸时都会启动应用程序，传感器把整个过程显示得像游戏一样。根据Froedtert测试，丙肝患者服用Gilead Harvoni等昂贵药物期间，配合传感器按时服药比例达98.6％。按时服药不仅有利于治愈疾病，还能省下不少钱，因为多吃一个月药费用会多出数万美元。这就是Froedtert（而不是患者）支付传感器费用的原因。
This everyday trauma affected millions of American families each year; then, in 2015, a bit of smartphone technology took away some of the worry. A California company called Dexcom connected a continuous glucose monitor (a device that had been around for more than a decade) wirelessly to a smartphone (or smart watch), allowing the user to read, plot, and share blood sugar levels with anyone, at five-minute intervals, all day long—and sending an alert when patients were at risk.
While some experts believe such always-on devices can leave patients with too much information, Amos—who half-jokingly speaks of “stalking” her son’s glucose levels—says it’s a lifesaver. Jacoby is now just another normal third-grader, she says. Rather than leaving class to go to the nurse’s office multiple times a day, he discreetly monitors his blood sugar on his Apple Watch. (Amos watches too, on her iPhone.) And instead of waking every few hours to have his finger pricked, he sleeps through the night.
But the device offers more than just peace of mind. The data it generates has actually helped Amos and Jacoby understand his diabetes and how to manage it. It lets them see what foods make his blood sugar soar and how best to time his insulin shots around complex carbs like pizza. No, his diabetes isn’t cured. But his blood sugar is now predictable and rarely triggers an alarm. It’s remarkable how unremarkable the technology now is in their lives. “It’s no different than a seat belt or a bicycle helmet,” she says.
This is but one way in which smartphones and connected devices are changing the relationship between patients and their health data—and enabling them to improve their health in the process.
Digital diabetes prevention and treatment platforms such as Virta and Omada Health connect users with support communities and health coaches—who can remotely monitor things like weight, blood sugar, diet, and medicine intake. Then there’s Proteus Digital Health’s ingestible sensor, which—with a technology worthy of an episode of Black Mirror—helps patients (and, if they want, their doctors and family members) keep tabs on whether or not they’re taking their meds. By pinging an app every time a pill hits stomach acid, the sensor gamifies the prescription process, as Michael Anderes, the chief innovation and digital officer at Froedtert and the Medical College of Wisconsin Health Network, puts it. Hepatitis C patients on expensive drugs like Gilead’s Harvoni were 98.6% compliant in taking the medicine at the right time when using the sensors, in Froedtert’s experience. That’s not just critical for curing the disease, it’s also a major money saver because an extra month of medication would cost tens of thousands of dollars. Which is why Froedtert (not its patients) foots the bill for the sensor.
流行的Apple Watch或Android系统可穿戴健康设备已经普及，提醒各种健康事项，从睡眠呼吸暂停到高血压，甚至是严重心律失常。人们越发关注自己的基因组，希望预测患上某些疾病（如癌症和阿尔茨海默病）的风险，有些暂时还无法实现，有些技术尚不完备（并且有争议）。消费者开始利用Colour Genomics和23andMe等公司非常便宜又方便的基因检测试剂盒，检测结果会提示罹患某些疾病几率较高。支持者认为，了解风险有时可以帮人们采取相应预防措施，从而降低风险。
突然之间，类似家庭基因测试变成必需品：最近的黑色星期五，23andMe的标准DNA测试销量跻身亚马逊前五，差点赶上亚马逊自家出品的智能音箱Echo Dot和多功能高压锅“Instant Pot”。
以密苏里州的药房福利管理结构Express Scripts为例，今年 3月刚刚由信诺保险收购。Express Scripts每年为1亿美国人管理14亿个处方，人们什么时候不按时吃药都知道。不遵医嘱服药每年导致的成本达1000亿美元至3000亿美元，数字差异是由于测算方式不同。成本出现的原因是患者不遵医嘱出现并发症，引起后续治疗。
Express Scripts首席数据官汤姆·亨利表示，已经发现300种可能让病人放弃照处方配药的因素。各项因素包括从基本的人口统计数据（收入水平和邮政编码）到行为数据（根据患者不取处方药后的调查问卷，判断患者健忘程度和拖延倾向）再到直观性较低的因素，如开药者和患者的性别（接受女性医生诊治的男性不遵从医嘱可能性更大）。该公司称，算法准确率达到94％，可以用算法为患者风险评分，并采取不同的提醒服务。亨利称提醒服药的方式“比较温和，不会强迫”。总之，Express Scripts称不遵医嘱服药现象减少了37％，为客户节省了1.8亿美元。
Wearable health trackers like the popular Apple Watch or Android-based devices are now alerting their owners to everything from sleep apnea to hypertension to even serious cardiac arrhythmias. And increasingly, that self-awareness is drilling into our own genomes, helping people—if for now, imperfectly (and controversially)—gauge their risk of developing certain diseases, such as cancer and Alzheimer’s. Consumers are turning to ever-cheaper, spit-and-send genetic test kits offered by companies like Color Genomics and 23andMe, to forewarn them of specific genetic susceptibilities—an awareness that, boosters say, can sometimes enable individuals to take preventive action that may mitigate those risks.
Out of the blue, such at-home gene tests have become consumer must-haves: On the most recent Black Friday, 23andMe’s standard DNA test was one of Amazon’s top five sellers—barely trailing Amazon’s own Echo Dot and the “Instant Pot.”
The selling point for all of these transformative technologies is a simple one: The consumer is in the driver’s seat.
Some big insurers are even discovering that engaging patients with their data is a good way to improve outcomes and control costs. That’s what Mark Bertolini is betting on. Bertolini, the CEO of Aetna, which is looking to combine forces with CVS , believes that consumers can be—and would actively want to be—data-sharing partners, if companies can demonstrate how consumers can benefit from that cooperation. “We have all these rules about protecting data,” says Bertolini. “But if you turn it around and say to the customer, ‘If we have this information about you, we can make this a lot more convenient for you,’ he or she will give you the data. That’s why social media works the way it does.”
With that personalized data, then, the company can build a health plan in concert with those patients, says Bertolini: “We want to say to them, if we build a plan together, there are no copays and there are no authorizations because we built it together.”
Command Central: Better Decisions Through Data
Rodney M. was picking up a birthday present for his wife when the pain hit. The 52-year-old CEO of a communications firm clutched his chest and scrambled to a seat. He felt numb, paralyzed from the waist down. It felt like he’d been “hit by a truck.”
Already a cancer survivor in remission for two years, Rodney had just suffered a whole new kind of medical nightmare—an “aortic dissection,” caused by a tear in the critical artery that supplies the body’s lifeblood.
He was lucky when the ambulance arrived quickly to take him to Howard County General Hospital, in Columbia, Md.—one in five aortic dissection patients dies before reaching a hospital. But his journey didn’t end there: Within minutes, Rodney was being airlifted to Johns Hopkins in Baltimore—and after a complex, seven-hour surgery, survived.
He didn’t know it at the time, but an artificial intelligence-powered data “command center” at Hopkins helped save his life. “Forty-one minutes. That’s how long it took to get wheels up from the hospital,” says Jim Scheulen, the chief administrative officer tasked with overseeing the medical center’s sprawling emergency medicine unit.
The command center pulls in information from more than a dozen data streams in real time, including patient health records, emergency dispatch service updates, lab results, and tabs on how many hospital beds are available at any given time. Then, through its human-trained algorithms, it makes split-second decisions on triaging patients and getting them where they need to go—prepping the surgical team ahead of time in cases like Rodney’s.
For the hospital, the financial benefits of all this data management are unmistakable. “At Hopkins, there’s been about a 60% increase in the ability to accept complex cancer patients, an over 25% reduction in emergency room boarding [those waiting for an inpatient bed], and a 60% reduction in operating room holds with the command center since its launch in February 2016,” says Jeff Terry, who oversees such projects for GEHealthcare, which built the Hopkins command center. Hopkins’s Scheulen says the technology functionally expanded the hospital’s capacity by 15 or 16 beds without the need to add, well, actual beds. This year, GE plans to announce 10 new command centers covering 30 different hospitals—which, over five years, Terry claims, should yield those medical centers a roughly 4 to 1 return on their investment.
Far less whizbang, but likely more transformative, are recent upgrades to last generation’s big-data breakthrough: the electronic health record (EHR).
It’s hard to find a medical technology more universally hated than the EHR. Doctors complain that it consumes too much of their time, that they don’t work well with other medical records systems, and that they’re still largely indecipherable to patients. The technology’s role in physician burnout has been explored in no less than 593 scholarly articles and one rap video since 2015. But if the big-data mission has found a worthy calling, it is here—transforming electronic health records from a time suck to a viable research tool.
That’s what happened at Lakeland Health, a not-for-profit community health system in southwest Michigan. Lakeland got an EHR system in 2012—but it might as well have been composed of paper and pen. Nurses there recorded vital signs of every patient on charts—and once back at their workstation, would manually reenter the stats into the hospital’s electronic system, a transcription process that ate up 15 to 20 minutes and often resulted in errors. Then, in mid-2016, they switched to a new process that enabled data to be automatically uploaded from patient wristbands or entered by nurses at their bedside on handheld devices.
One change was obvious from the start: Nurses spent less time on data entry and more time tending to patients, says Arthur Bairagee, Lakeland’s chief nursing informatics officer. But more radical—and surprising—was the drop in “code blues”: the warnings that patients were in cardiac and respiratory arrest. They’ve dropped a mammoth 56% since the new system, developed by Anglo-Dutch health giant Philips, was introduced in June 2016. Why? In part, the A.I.-driven warning system built into the monitoring technology, which not only picks up on even subtle changes in vital signs, but also assigns patients risk scores that help nurses prioritize their attention.
“It’s important to be honest and pragmatic about where we are now,” says Roy Smythe, chief medical officer for strategy and innovation at Philips. Many of the new digital and data tools aren’t so much about providing care, but rather about making that care more efficient, smarter, and precisely delivered.
Like so many experts Fortune interviewed—even those who are gung ho about digital health—Smythe cautions against overhyping the new med tech.
“We have overpromised and under–delivered,” says Brennan Spiegel, a physician and director of health services research at the Cedars-Sinai health system. “I consider myself a techno-skeptical techno-philiac. But there are way too many people in the Silicon Valley echo chamber who have never touched hands on a patient and don’t understand how hard digital health is.” Spiegel, who is also a professor of medicine and public health at UCLA, points to high-profile failures he’s personally experienced in the field—including a 2015 Cedars-Sinai project to connect patients, through wearables like Fitbit, Apple Watch, Withings, and others, to electronic health records, which flopped spectacularly. “We didn’t give patients the optimal messaging, and we didn’t invite them in the most compelling way.” So potential participants had little personal rationale to connect and stay engaged with the program because it didn’t present a tangible value proposition. “Digital health is not a computer science or an engineering science; it’s a social science and a behavioral science.”
Eric Topol, at Scripps, who’s also a renowned cardiologist, sounds a similar cautionary note. “There’s a tremendous amount of promise, but so much is unfulfilled,” he says, owing to a variety of systemic roadblocks. Among the challenges, he says, are America’s rigid and “long in the tooth” medical establishment—half of U.S. doctors are over 50—which is resistant to changing its ways “unless it’s going to lead to higher compensation.”
Fighting the “Failure Problem”
To hear the folks at Amgen tell it, big data has upended the California biotech’s drug development process and significantly reshaped its pipeline. That story begins in 2011, when R&D chief Sean Harper, started making trips to Iceland. He was trying to solve his company’s—which is also the industry’s—“failure problem,” which is summed up by the fact that 90% of drug candidates fail to make it to market.
Discovering new medicines is a wildly expensive and inefficient endeavor. Companies will often invest billions of dollars and many years chasing a promising scientific hypothesis. Pharmaceutical scientists hope for a moment of chemical serendipity, despite often not fully understanding the complexity of the biological mechanisms they’re targeting—or why something might fail in humans when it works so neatly in a mouse model.
For Harper, Iceland seemed to offer an unparalleled pool of health-related data. The collection of that data—the genetic sequences of 160,000 citizens, along with their medical and genealogical records—was made possible by the Icelandic government, and the storage and analysis of that data was overseen by deCode, a Reykjavík-based human genetics outfit that, since its founding in 1996, had struggled to stay afloat financially.
Despite its solvency issues, deCode had become a prolific publisher of genetic discovery. Its trove of data allowed the company to mine the population for genetic variants and connect those variants to clinical outcomes in diseases ranging from cancer to schizophrenia. As the cost of sequencing plummeted in sync with the rise of computer processing power, Harper saw an undervalued asset for drug discovery: Amgen bought the company in 2012 for $415 million.
That purchase has utterly transformed Amgen’s R&D process. Prior to the deCode acquisition, only 15% of Amgen candidate molecules had been validated against specific genetic targets. After the purchase, Amgen began evaluating all of its drug candidates against deCode’s database. The review exposed some clear losers; in the case of 5% of its candidate molecules, there was evidence the agent wouldn’t work. Managers killed those programs (including one highly anticipated drug aimed at coronary disorders that was about to head into human trials) and prioritized others where there was a clear genetic target for the drug. Amgen also green-lighted more than a dozen drugs for which it found confirmation in deCode’s genetic data.
Today, three-quarters of Amgen’s pipeline is based on genetic insights largely gleaned from the database, says Harper, and the company has more than earned its investment back.
While having genetic validation for a target is no guarantee of success—scientists still have to figure out how to drug the target safely and effectively, and meet myriad other biological challenges—it does offer a head start. Says Harper, “If you can increase your rate of return by 50%, that’s enormous.”
Regeneron, a biotech that was looking to partner with deCode around the same time Amgen bought it, has a similar strategy. But rather than buying up a genetic research outfit, it decided to build its own in the form of the Regeneron Genetics Center (RGC), an ambitious four-year old effort to sequence as many exomes (the protein-encoding part of the genome) as possible, pair them with medical records, and accelerate drug development.
Lots of people talk about the promise of using genetics in pharma R&D, says Jeff Reid, Regeneron’s chief of genome bioinformatics, “but they don’t have a vision for sample flow.” Translation: They don’t have the data. Reid joined Regeneron when he learned the company had partnered with Geisinger (see “Keystone Care”), a Pennsylvania-based health care system with which it planned to collect and sequence samples from 100,000 consenting patients who also have comprehensive medical records. “They wanted to take this data and actually improve the care of patients,” says Aris Baras, who heads up the RGC.
The company has since partnered with more than 60 other sources including the UK Biobank, which has recruited 500,000 participants. Baras and Reid say the scale and diversity of its growing data set, along with the ability to make all of these discoveries in-house, are critical. The work has so far spawned 50 target biology programs.
Hidden Figures: The Untapped Value of Medical Records
A random assortment of health information doesn’t mean much if it doesn’t meet at least two critical criteria, says oncologist and former Duke professor Amy Abernethy: quality and context. “Anyone who doesn’t understand the core aspects of practicing medicine can’t understand how messy it is,” says Abernethy, who four years ago became the chief medical officer at Flatiron Health, a startup backed by Google Ventures (GV).
Take cancer records—Flatiron’s specialty—as an example. Many of the most useful nuggets in an oncology EHR (about half, in fact) may reside in doctors’ notes that aren’t structured into specific data fields. These are the sorts of observations that can’t be neatly packaged into categories on a form.
“Historically, these electronic records are billing and collection tools, documentation we have to comply with to get paid,” explains Jeffrey Patton, a physician and CEO of Tennessee Oncology, a community-based health system that treats the largest number of cancer patients in the state and is one of hundreds of community cancer centers that now uses Flatiron’s system.
Flatiron’s selling point, ironically, is humans. When it comes to this type of data, it seems, people can figure out things a purely computer-driven system might miss. The real challenge isn’t to gather the data, but to “clean it up,” says Abernethy. “And that’s really hard without an understanding of context.”
Flatiron now has data from 20% of active cancer patients in the U.S., “and it’s extremely well structured,” says Daniel O’Day, CEO of Roche Pharmaceuticals, a unit of Roche Holding AG, which snapped up Flatiron in a $1.9 billion deal announced in February. “What set Flatiron apart was that it was able to create regulatory grade, real-world data,” O’Day tells Fortune—data that O’Day claims is so well curated, that it “could have theoretically replaced the ‘control’ arm” of one of Roche’s own clinical trials for the cancer immunotherapy drug Tecentriq.
In theory, Flatiron’s system could have a broader impact on clinical trials accrual—for generations, one of the most stubborn challenges in cancer drug research—making it easier, for example, to match up specific patients with appropriate drug studies.
That, indeed, is an area where IBM Watson has already found some success. In March, the Mayo Clinic reported that using Big Blue’s advanced cognitive computing system increased enrollment in clinical trials for breast cancer by 80%. “Watson is able to give us faster, better matching of patients to potential clinical trials that our oncologists wouldn’t have otherwise been able to see,” Mayo Clinic CIO Christopher Ross told the trade publication MobiHealthNews.
Even organizations which previously only had distant relationships with patients—pharmacy benefit managers, for instance—are, because of the vast kingdoms of data they oversee, well positioned to draw insights that might improve population health and lower costs.
Consider Express Scripts, the Missouri-based PBM that just announced its sale to insurer Cigna in March. Express Scripts administers 1.4 billion prescriptions for 100 million Americans each year—and it knows when you don’t take your meds. Such nonadherence costs between $100 billion and $300 billion a year, depending on which estimate one believes. That cost comes when patients suffer complications from not following the doctor’s orders.
The company has identified 300 different factors that can help determine the likelihood that a patient will not fill a prescription says Express Scripts’ chief data officer Tom Henry. They range from that basic demographic data (income level and zip code) to behavioral data (one’s level of forgetfulness and tendency to procrastinate, gleaned from surveys the company does after patients fail to pick up their prescriptions) to less intuitive things like the genders of the prescriber and patient (men with a woman physician are more likely to not follow orders). The company uses the algorithm, which it says is validated and 94% accurate, to assign risk scores to patients and target them with varying modes of outreach—Henry says those efforts are “soft touch, nothing Orwellian.” Nonetheless, Express Scripts claims this work has reduced nonadherence by 37% and saved its clients more than $180 million.
As with any revolution, many rush into action without considering weighty questions about what comes next and what unintended consequences may arise. In this social experiment, those questions cover everything from patient privacy to the ethical dilemma of warning someone about a risk they can’t avoid. To many digital health evangelists, big data is the “magic bullet” we’ve been waiting for. The question is, where exactly that bullet strikes.
A version of this article appears in the April 2018 issue of Fortune with the headline “Big Data Meets Biology.”