It’s hard to measure how much our health has improved over the course of history, though life span is a fairly good indicator. Evolutionary pressures shaped Homo sapiens to have an average life expectancy of roughly thirty years. The logic is easily understood. “Natural selection favors the genes of those with the most descendants,” explains MIT’s Marvin Minsky. “Those numbers tend to grow exponentially with the number of generations, and so natural selection prefers the genes of those who reproduce at earlier ages. Evolution does not usually preserve genes that lengthen lives beyond the amount adults need to care for their young.” Thus, for most of human evolution, men and women would enter puberty in their early teens and have children quickly thereafter. Parents would raise their kids until they reached the child-bearing years, at which point the parents—now thirty-year-old grandparents—became an expensive luxury. In early hominid societies, where life was difficult and food in short supply, an extra pair of grandparents’ mouths to feed meant less food for the children. Thus, evolution built in a fail-safe: a three-decade life span.
Historically, though, as our living conditions improved, this number has improved. In the Neolithic period, life was a nasty, brutish, and short twenty years. This jumped to twenty-six in the Bronze and Iron Ages; and up to twenty-eight in ancient Greece and Rome, making Socrates a seventy-year-old anomaly when he died in 399 BCE. By the early Middle Ages, we pushed into the forties, but our ascendancy was still limited by appallingly high infant mortality rates. During the early sixteen hundreds in England, two-thirds of all children died before the age of four, and the resulting life expectancy was only thirty-five years.
It was the industrial revolution that started us on our trend toward longevity. A more robust food supply coupled with simple public health measures such as building sewers, collecting garbage, providing clean water, and draining mosquito-infested swamps made a huge difference. By the early twentieth century, we had added fifteen years to our historical average, with numbers rising into the low forties. With the creation of modern medicine and hospitals, that mean age skyrocketed into the middle seventies. While centenarians and supercentenarians are becoming increasingly common in the developed world (the verified age record stands at 122 years old), a combination of killers such as lower respiratory infections, AIDs, diarrheal infections, malaria, and tuberculosis, coupled with war and poverty, have played havoc with sub-Saharan Africa, where a large chunk of the population still doesn’t make it much past forty.
Creating a world of health care abundance means addressing the needs at both ends of this spectrum and plenty in the middle. We’ll need to provide clean water, ample nutrition, and smoke-free air. We’ll also need to extinguish already curable ailments such as malaria and learn to detect and prevent those pesky pandemics that seem to threaten our survival with ever-increasing frequency. In the developed world, we’ll need to find new ways to improve quality of life for an increasingly long-lived population. In total, creating a world of health care abundance appears to be a very tall order—except that almost every component of medicine is now an information technology and therefore on an exponential trajectory. And this, my friends, makes it a whole new ball game.
“Code blue, Baker five!” was the urgent page over the loudspeakers, snapping me out my brief slumber. It was four o’clock in the morning, I was catnapping on a stretcher in the hallway of Massachusetts General Hospital. As a third-year medical student, sleep was a rare commodity, and I’d learned to take it whenever and wherever possible. But code blue meant cardiac arrest; Baker five meant the fifth floor of the Baker building. I was just upstairs, on Baker six, now wide awake, adrenaline pumping, already racing down the stairwell. I was the second person to arrive in the room of a sixty-year-old man, less than twenty-four hours post-op from a triple coronary bypass. The resident giving him CPR barked an order my way, and I found myself taking over the chest compressions. It’s the sound I remember most: the cracking of his surgically split sternum under the force of my repeated compressions. It was then that I realized it didn’t matter what I learned in the classroom; none of it prepared me for the reality of this situation and the frailties of the human body.
That classroom learning had started two years earlier at Harvard Medical School. The first year was standard stuff: the basics of normal anatomy and physiology; how it all fits together and is meant to work. The second year was all about pathophysiology: where and how it all goes wrong. And with ten trillion cells in our body, there’s plenty of opportunity for havoc. It was a dizzying amount of information. I remember a single moment when, while studying for my national board exams at the end of that second year, I felt like I had successfully stuffed all the concepts, systems, and terminology into my brain. But that moment was fleeting, especially in the hospital wards, where reality met flesh and blood as it did that early morning on Baker five. In that situation, I realized quickly how much I still had to learn and, even more, how much we really didn’t know.
And that’s our first problem. Learning takes time. It takes practice. Our brains process information at a limited pace, but medicine is growing exponentially, and there’s no way we can keep up. Our second problem is a common refrain heard in medical school: five years after graduation, half of what one learns will probably be wrong—but no one knows which half. Regardless of how much medical progress has been made over these past centuries, our third problem is that we’re never really satisfied with our health care. We continually have higher and higher standards, but with humans as the conduits of such care, there will always be limitations on how much information any doctor can know, let alone master.
A recent RAND Corporation report illustrates these points precisely, finding that preventable medical errors in hospitals result in tens of thousands of deaths per year; preventable medication errors occur at least one and a half million times annually; and, on average, adults receive only 55 percent of recommended care, meaning that 45 percent of the time, our doctors get it wrong.
In spite of these dismal numbers, having inaccurate doctors is much better than having no doctor at all. Fifty-seven countries currently don’t have enough health care workers, a deficit of 2.4 million doctors and nurses. Africa has 2.3 health care workers per 1,000 people, compared with the Americas, which have 24.8 per 1,000. Put another way, Africa has 1.3 percent of the world’s health workers caring for 25 percent of the global disease burden.
But things aren’t rosy in the developed world either. The Association of American Medical Colleges warned recently that if training and graduation rates don’t change, the United States could be short 150,000 doctors by 2025. And if America can’t produce enough staff to cover its own medicinal needs, where are we possibly going to find the tenfold increase in health care workers needed to care for the rising billion?
“IBM Watson Vanquishes Human Jeopardy! Foes,” read PCWorld magazine on February 16, 2011. Nearly fourteen years after Deep Blue had beaten world chess champion Garry Kasparov, IBM’s silicon progeny challenged humanity to another battle. This time combat took place on the quiz show Jeopardy! There was $1.5 million in prize money at stake. Meet Watson, a supercomputer named after IBM’s first president, Thomas Watson Sr. Over the course of three days, Watson bested both Brad Rutter, Jeopardy!’s biggest all-time money winner, and Ken Jennings, the show’s record holder for the longest championship streak—meaning two men enter, one computer comes out.
It was something of an inevitable defeat. During the competition, Watson had access to 200 million pages of content, including the full text of Wikipedia. To be fair, the machine didn’t have access to the Internet and could use only what was stored in its 16-terabyte brain. That said, Watson’s brain is a massively parallel system composed of a cluster of ninety IBM Power 750 servers. The end product could handle 500 gigabytes of data per second, or the equivalent of 3.6 billion books per hour.
And that’s only the hardware. The bigger breakthrough was the DeepQA software, which allows Watson to “understand” natural language—for example, the kinds of questions and answers found on Jeopardy! To make this possible, Watson had to not only comprehend context, slang, metaphors, and puns but also gather evidence, analyze data, and generate hypotheses.
Of course, not all good things come in small packages. Right now it takes a medium-sized room to hold Watson. But that’s soon to change. If Moore’s law and exponential thinking have taught us anything, it’s that what fills a room today will soon require no more than a pocket. Moreover, this much computational power could soon be ubiquitous—hosted on one of the many clouds being developed—at little or no cost.
So what can we do with a computer like that? Well, a company called Nuance Communications (which used to be Kurzweil Computer Products, Kurzweil’s first start-up) has teamed with IBM, the University of Maryland Medical School, and Columbia University to send Watson to medical school.
“Watson has the potential to help doctors reduce the time needed to evaluate and determine the correct diagnosis for a patient,” says Dr. Herbert Chase, professor of clinical medicine at Columbia. He also has the ability to develop personalized treatment options for every patient, a capability that Dr. Eliot Siegel, professor and vice chair at Maryland’s Department of Diagnostic Radiology, explains this way: “Imagine a supercomputer that can not only store and collate patient data but also interpret records in a matter of seconds, analyze additional information and research from medical journals, and deliver possible diagnoses and treatments, with the probability of each outcome precisely calculated.”
But delivering correct diagnoses depends on having accurate data, which sometimes don’t come from a conversation with the patient. Even the most brilliant diagnostician needs X-rays, CT scans, and blood chemistries to make the right call. But most of today’s high-tech hospital equipment is large, expensive, and power hungry—unfit for the cost-conscious consumer, let alone the developing world. But now ask yourself that fabled DIY question: What would MacGyver do?
Well, MacGyver would empty his pockets and get the job done with a roll of Scotch tape, a piece of paper napkin, and a ball of spit—which, as it turns out, is exactly the solution we need.
Scotch tape? Really? When Carlos Camara entered his doctoral program at the University of California at Los Angeles, to study high energy-density physics, the last thing he imagined was that he’d soon find himself in a dark room experimenting with Scotch tape, or that this tape could drastically lower health care costs around the world. All he knew at first was that certain materials, when crushed together, create light—which is why, when you crunch a wintergreen Life Saver in your mouth, there’s a little flash. It’s called triboluminescence. Camara was experimenting with triboluminescence in a moderate vacuum and discovered that some materials don’t just release visible light, they also release X-rays. So the question became, which materials? He started working his way through a wide range. Then it happened. Camara unrolled some Scotch tape in the dark. “I was shocked,” he says. “Not only was it one of the brightest materials I had tested, but it also generated X-rays.”
This was big news. It made the cover of Nature, then popped up on an episode of Bones. Soon after its television premiere, Camara teamed up with serial start-up entrepreneur Dale Fox to found Tribogenics, a company aiming to build the world’s smallest and cheapest X-ray machine. Instead of a quarter-million-dollar dishwasher-sized device relying on eighteenth-century technology—basically, vacuum tubes connected to a power supply—the key component of the Tribogenics version (what Camara calls an “X-ray pixel”) costs less than $1, is half the size of a thumb drive, and uses triboluminescence to create X-rays. Groups of these pixels can be arranged into any size or shape. A fourteen-by-seventeen-inch array takes a chest X-ray; a long curve gives you a CT scan. As these pixels require very little energy—less than 1/100th of a traditional X-ray machine—a solar panel or a hand crank can power one. “Imagine an entire radiological suite in a briefcase,” continues Fox. “Something powered with batteries or solar, easily transportable, and capable of diagnosing anything from a broken arm to an abdominal obstruction. It will bring a whole new level of care to field medicine and the developing world.”
Fox sees additional possibilities in mammography. “Today mammography requires an expensive, large, stationary machine that takes a crude, two-dimensional picture. But imagine a ‘bra’ that has tiny X-ray pixel emitters on the top and X-ray sensors on the bottom. It’s self-contained, self-powered, has a 3G or Wi-Fi-enabled network, and can be shipped to a patient in a FedEx box. The patient puts on the bra, pushes a button, and the doctor comes online and starts talking: ‘Hi. All set to take your mammogram? Hold still.’ The X-ray pixels fire, the detectors assemble and transmit the image, and the doctor reads it on the spot. The patient ships back the package, and she’s done. With little time and little money.
The X-ray pixel array is the first step toward what Harvard chemistry professor turned uber-entrepreneur George Whitesides calls “zero-cost diagnostics.” Exactly as it sounds, Whitesides wants to drop the cost of diagnosing disease as low as possible, which, here in MacGyver-land, is pretty low indeed. Toward that end, Whitesides recently turned his attention to the diseases plaguing the rising billion. The only way to develop the vaccines needed to fight HIV, malaria, and tuberculosis is to find a method for accurately and inexpensively diagnosing and monitoring large numbers of patients. You can’t do this with today’s technology.
So Whitesides took a page out of C. K. Prahalad’s BoP development model. Instead of starting with a $100,000 machine and trying to lower its cost by orders of magnitude, he started with the cheapest materials available: a piece of paper about one centimeter on a side, able to wick fluid. Place a pinprick of blood or a drop of urine on the edge of Whitesides’s paper, and the liquid soaks in, migrating through the fibers. A hydrophobic polymer printed on this paper guides the fluid along prescribed channels, toward a set of testing wells, wherein the sample interacts with specific reagents, turning the paper different colors. One chamber tests urine for glucose, turning brown in the presence of sugar. Another turns blue in the presence of protein. Since paper isn’t very expensive, Whitesides’s goal of zero-cost diagnostics isn’t that far fetched. “The major cost is the wax printer,” he says. “These printers are around eight hundred dollars apiece. If you run them twenty-four hours a day, each of them can make some ten million tests per year, so it’s really a solved problem.”
The final stop in our MacGyver triad—the spit sample—holds even more promise. This is the input necessary for the aforementioned Lab-on-a-Chip array developed by Dr. Anita Goel at her company, Nanobiosym. Place a drop of saliva (or blood) on Goel’s nanotechnology platforms, and the DNA and RNA signature of any pathogen in your system will get detected, named, and reported to a central supercomputer—aka Dr. Watson. These chips are a serious step toward zero-cost diagnostics, and a critical component in helping to solve a trio of major health care challenges: arresting pandemics, decreasing the threat of bioterrorism, and treating widespread diseases like AIDS. Already mChip, a technology out of Columbia University, is demonetizing and dematerializing the HIV testing process. What once required long doctor visits, a vial of blood, and days or weeks of anxious waiting now needs no visit, a single drop of blood, and a fifteen-minute read, all for under $1 using a microfluidic optical chip smaller than a credit card.
Since Dr. Watson will soon be accessible through a mobile device, and that mobile device has a GPS, the computer can both diagnose your infection and detect an unusually high incidence of, say, flu symptoms in Nairobi—thus alerting WHO to a possible pandemic. Better yet, because the incremental cost of Watson’s diagnosis is simply the expense of computing power (which is really just the cost of electricity), the price comes to pennies. To help accelerate this process, on May 10, 2011, the wireless provider Qualcomm teamed up with the X PRIZE Foundation and announced plans to develop the Qualcomm Tricorder X PRIZE—named after Star Trek’s medical scanning technology. This competition will offer $10 million to the first team able to demonstrate a consumer-friendly low-cost mobile device able to diagnose a patient better than a group of board-certified doctors.
But even this much MacGyver thinking still falls short of our ultimate health care goals, since knowing what’s wrong with a patient is only half the battle. We still need to be able to treat and cure that patient. We’ve already addressed many of the “preventable” ailments, which are obviated through clean water, clean energy, basic nutrition, and indoor plumbing, but there’s also another category to consider: easily treatable and/or curable diseases. Many of these are managed with simple medicines, but others require surgical intervention. In the same way that technology has revolutionized diagnostics, what if it were possible to do the same with surgery?
According to the World Health Organization, age-related cataracts are the world’s largest cause of blindness, accounting for eighteen million cases, primarily in Africa, Asia, and China. Cataracts are a clouding of the eye’s normally transparent lens. Although they can be easily removed, and this form of blindness completely cured, surgical services in many developing countries are inadequate, inaccessible, and too expensive for much of the affected population.
The best chance many have is a nonprofit humanitarian organization called ORBIS International, which teaches cataract surgery in developing countries and operates a Flying Eye Hospital. ORBIS’s refurbished DC-10 swoops into a region with doctors, nurses, and technicians aboard. Once there, they provide treatment to a limited number of cases, and train local physicians. But only so many doctors can be trained this way. Physician and robotics expert Catherine Mohr sees a future without these limitations. “Imagine,” she says, “specialized robots able to conduct this type of simple and repeatable surgery with complete accuracy, at little to no cost.”
The earliest versions of this type of surgical robot, called the da Vinci Surgical System, were built by Mohr’s company, Intuitive Surgical. Da Vinci actually came out of the DARPA’s desire to get surgeons off the front line of the battlefield while still treating the wounded during the first “golden hour” after injury. The best way to do this is with a robot tending the injured soldier, and a telepresent physician running the show from a remote location. In recent years, this technology has evolved rapidly, moving from the battlefield to the surgical suite, initially at the behest of cardiac surgeons looking for ways to operate without splitting the sternum. It was next taken up by surgeons seeking to conduct rapid and repeatable prostatectomies and gastric bypasses. Current iterations, like the MAKO surgical robot, are skilled enough to assist orthopedists with delicate procedures such as knee replacements.
Today’s technology doesn’t completely replace surgeons; instead it enhances their abilities and allows them to operate remotely. “By fully digitizing an image of the injured site being repaired,” explains Mohr, “you put a digital layer between the tissue and the surgeon’s eyes, which can then be augmented with overlaid information or magnification. Also, by digitizing hand movements and placing a digital layer between the surgeon and the robotic instruments, you can take out jitter, make motions more precise, and even transmit the surgical incisions over a long distance, allowing an expert in Los Angeles to conduct surgery in Algiers during their free time without spending twenty hours on airplanes.”
Over the next five to ten years, Mohr predicts a proliferation of smaller, special purpose robots, extending far beyond cataract removal. One might handle glaucoma surgery, another a gastric bypass, while a third performs dental repairs. Mohr thinks the fifteen- to twenty-year horizon is even more exciting. “In the future, we’ll be able detect cancers by monitoring blood, urine, or breath and, once detected, remove them robotically. The robot will find the tiny cancerous lesion, insert a needle, and obliterate it, just like you do a cancerous mole today.”
Cancer is only one of the problems that our aging population will have to face. In fact, when it comes to health care costs and quality of life, caring for the aged is a multitrillion-dollar expense that we’d better get used to. The oldest baby boomers turned sixty-five in 2011. When the trend peaks in 2030, in the United States alone, the number of people over age sixty-five will have soared to 71.5 million. In developed countries, the centenarian population is doubling every decade, pushing the 2009 total of 455,000 to 4.1 million by 2050. And the average annual growth rate of those over eighty is twice as high as the growth rate for those over sixty. In 2050 we’ll have 311 million octogenarians in the world. As the elderly lose the ability to care for themselves, many, according to the National Center for Health Statistics, are sent to nursing homes at an annual cost per person of between $40,000 and $85,000. Bottom line: with hundreds of millions of people soon heading down this road, how will we ever afford it?
For Dr. Dan Barry, the answer is easy: let the robots do the work. Barry brings an eclectic background to this problem, including an MD, a PhD, three Space Shuttle flights, a robotics company, and a starring role as a contestant on the reality TV show Survivor. Barry is also the cochair of the AI and robotics track at SU, where he spends considerable time thinking about how robots can be applied to the future of health care. “The biggest contribution that robots will make to health care is taking care of an aging population: people who have lost spouses or lost the ability to take care of themselves,” he says. “These robots will extend the time they are able to live independently by providing emotional support, social interaction, and assisting them with the basic functional tasks like answering the door, helping them if they fall, or assisting them in the bathroom. They will be willing to listen to the same story twenty-five times and respond appropriately every time. And for some with sexual dysfunction or need, these robots will also play a huge role.”
When will these robots become available, and what will they cost? “Within five years,” continues Barry, “robots will hit the market that can recognize you uniquely, react to your movements and facial expressions with recognizable emotive responses, and perform useful tasks around the home, like clean up while you sleep. Fast-forward fifteen to twenty years, and we’ll be delivering robotic companions that will have real, nuanced conversations, making them able to serve as your friend, your nurse, perhaps even your psychologist.”
The anticipated cost is almost as shocking as their capabilities. “I expect the initial robots will cost on the order of a thousand dollars,” says Barry. He goes on to explain that the cost of a three-dimensional laser range finder has plummeted from $5,000 to about $150 because of new technology and the massive scale of production for Microsoft’s Xbox Kinect. “A five-thousand-dollar laser range finder was the typical way for a robot to navigate a cluttered environment,” he says. “It’s mind boggling how powerful and cheap they’ve become. The result is a tsunami of new code and applications and an explosion in the number of people developing DIY robots. As soon as the price dropped low enough, an army of graduate students began playing, experimenting, and coming up with amazing new applications.”
Just like laser range finders, all other robo-nurse components are on similar price-performance reduction curves. Pretty soon, the requisite sensors and computing power will be nearly free. All that’s left to buy is the mechanical body, which is why Barry believes that $1,000 is the ballpark figure for these bots. So here’s your comparison: if we assume that the majority of the octogenarians in our future will need some form of assisted living care, we can either spend (at today’s costs) trillions of dollars on nursing homes or we can, as Barry suggests, let robots do the work.
In the early 1990s, accomplished neurotrauma surgeon Robert Hariri was growing frustrated with his field, especially with the limitations of the scalpel. “We could do some limited repairs and keep people alive after an accident,” he says, “but surgery couldn’t return them to normal.” So Hariri went looking for ways to restore the natural developmental processes that allow the brain to regrow and rewire itself. In the late 1990s, he realized that he might be able to inject stem cells into patients to treat and potentially cure diseases in the same way that one now injects drugs. Hariri believed that to harness the true potential of cellular medicine he had to ensure a steady source of stem cells for future procedures, so he created his first company to bank both placenta-derived stem cells and cord blood from newborns. Four years later, LifeBank/Anthrogenesis merged with $30 billion pharmaceutical giant Celgene Corporation, which saw the technology’s potential to reinvent medicine.
But it’s not just Celgene that wants in on this action. “We all start out as a single fertilized egg that develops into a complex organism of ten trillion cells, made up of over two hundred tissue types, each working twenty-four/seven at specialized functions,” says Dr. Daniel Kraft, a specialist in bone marrow transplantation (a form of stem cell therapy) and chair of the medicine track at SU. “Stem cells drive this incredible process of differentiation, growth, and repair. They have the ability to revolutionize many aspects of health care like almost nothing else in the pipeline.”
Dr. Hariri agrees:
The potential for this technology is immense. In the next five to ten years, we’re going to be able to use stem cells to correct chronic autoimmune diseases such as rheumatoid arthritis, multiple sclerosis, ulcerative colitis, Crohn’s disease, and scleroderma. After that, I think neurodegenerative diseases will be the next big frontier; this is when we’ll reverse the effects of Parkinson’s disease, Alzheimer’s disease, even stroke. And it’ll be affordable too. Cell manufacturing technology has seen vast improvements over the past decade. To give you an idea, we’ve gone from thinking that cell therapy would cost over $100,000, to believing that we can do it for about $10,000. Over the next decade, I think we can lower costs significantly more. So we’re speaking about the potential for “curing” chronic diseases and revitalizing key organs for less than the price of a new laptop.
And should your liver or kidney fail before you have a chance to revitalize it, fear not, there is another solution. One of Hariri’s issued patents, “The renovation and repopulation of cadaveric organs and tissue matrices by stem cells,” is the basis for growing new and transplantable organs in the lab, which is an approach that tissue-engineering pioneer Anthony Atala of Wake Forest University Medical Center has demonstrated successfully.
“There is a huge need for organs worldwide,” says Atala. “In the past decade, the number of patients on the organ transplant waiting list has doubled, while the number of actual transplants has remained flat. Thus far, we’ve been able to grow human ears, fingers, urethras, heart valves, and whole bladders in the lab.”
Atala’s next major challenge is to grow one of the most intricate organs in the human body: the kidney. About 80 percent of patients on the transplant list are waiting for a kidney. In 2008 there were over sixteen thousand kidney transplants in the United States alone. To accomplish this feat, he and his team have moved beyond the use of cadaveric organs and tissue matrices and are literally “3-D printing” early versions of the organ. “We started by using a normal desktop ink-jet printer that we rigged to print layers of cells one at a time,” he explains. “We’ve been able to print an actual minikidney in a few hours.” While the full organ may need another decade of work, Atala is cautiously optimistic, given that sections of his printed kidney tissue are already excreting a urine-like substance.
“Whether it’s organ regeneration or repairing tissues affected by aging, trauma, or disease,” says Dr. Kraft, “this fast-moving field will impact almost every clinical arena. The recent invention of induced pluripotent stem cells, which can be generated by reprogramming a patient’s own skin cells, gives us controversy-free access to this powerful technology. And with the coming convergence of stem cells, tissue engineering, and 3-D printing, we’ll soon have an incredibly potent arsenal for achieving health care abundance.”
While many believe that stem cells will soon give us the ability to repair and replace failed organs, if P4 medicine does its job, the situation might never get that desperate. P4 stands for “predictive, personalized, preventative, and participatory,” and it’s where health care is heading. Combine cheap, ultrafast, medical-grade genome sequencing with massive computing power, and we’re en route to the first two categories: predictive and personalized medicine.
During the past decade, sequencing costs have dropped from Craig Venter’s historic $100 million genome in 2001 to an anticipated $1,000 version of equal accuracy. Companies such as Illumina, Life Technologies, and Halcyon Molecular are vying for the trillion-dollar sequencing market. Soon every newborn will have his or her genome sequenced. Genetic profiles will be part of standard patient care. Cancer victims will have their tumors DNA analyzed, with the results linked to a massive data correlation effort. If done properly, all three efforts will yield a myriad of useful predictions, changing medicine from passive and generic to predictive and personalized. In short, each of us will know what diseases our genes have in store for us, what to do to prevent their onset, and, should we become ill, which drugs are most effective for our unique inheritance.
But rapid DNA sequencing is only the beginning of today’s biotech renaissance. We are also unraveling the molecular basis for disease and taking control of our body’s gene expression, which together can create an era of personalized and preventative medicine. One example is the potential to cure what the WHO now recognizes as a global epidemic: obesity. The genetic culprit here is the fat insulin receptor gene that instructs our body to hold on to every calorie we consume. This was a helpful gene in the era before the invention of Whole Foods and McDonald’s, when early hominids could never be certain about their next harvest or even their next meal. But in our fast-food nation, this genetic edict has become a death sentence.
However, a new technology called RNA interference (RNAi) turns off specific genes by blocking the messenger RNA they produce. When Harvard researchers used RNAi to shut off the fat insulin receptor in mice, the animals consumed plenty of calories but remained thin and healthy. As an added bonus, they lived almost 20 percent longer, obtaining the same benefit as caloric restriction, without the painful sacrifice of an extreme diet.
Participatory medicine is the fourth category of our health care future. Powered by technology, each of us is becoming the CEO of our own health. The mobile phone is being transformed into a mission control center where our body’s real-time data can be captured, displayed, and analyzed, empowering each of us to make important health decisions day by day, moment by moment. Personal genomics companies such as 23 and Me and Navigenics, meanwhile, allow users to gain a deeper understanding of their genetic makeup and its health implications. But equally important is the effect of our environment and daily choices—which is where a new generation of sensing technology comes into play.
“Sensors have plummeted in cost, size, and power consumption,” explains Thomas Goetz, executive editor of Wired and author of The Decision Tree: Taking Control of Your Health in the New Era of Personalized Medicine. “An ICBM guidance sensor from the 1960s used to cost one hundred thousand dollars and weigh many kilograms. Now that same capability fits on a chip and costs less than a dollar.” Taking advantage of these breakthroughs, members of movements such as Quantified Self are increasing self-knowledge through self-tracking. Today they’re tracking everything from sleep cycles, to calories burned, to real-time electrocardiogram signals. Very soon, should one choose to go this route, we’ll have the ability to measure, record, and evaluate every aspect of our lives: from our blood chemistries, to our exercise regimen, to what we eat, drink, and breathe. Never again will ignorance be a valid excuse for not taking care of ourselves.
As should be apparent, the field of health care is entering a period of explosive transformation. However, the major drivers here are not just technological. As the baby boomers age, there is no amount of money that the richest among them won’t spend for a little more quality time with their loved ones. Thus, every new technology inevitably finds its way into the service of health, driven by an older, wealthier, and more motivated population.
In the same way that Wall Street tycoons talking on briefcase-sized mobile phones in the 1970s underwrote the development of the hundreds of millions of Nokia handsets now scattered through sub-Saharan Africa, so too will the billions of health care research dollars and entrepreneurial inventions described in this chapter soon benefit all nine billion of us. And given the rigorous, somewhat calcified, nature of the first-world health care regulatory process, there’s every reason to believe that more than a few of these groundbreaking technologies will first make their way to less bureaucratic regions of the developing world before being legally allowed onto Main Street, USA.
While the developing world will certainly benefit from these high-tech cures, the truth of the matter is that the majority of their needs are still basic: bed nets and antimalarial drugs; antibiotics to combat bronchitis and diarrhea; education about the realities of HIV and the necessity of contraception. In many cases, the remedies exist, but the necessary infrastructure does not. However, there are now a host of mobile-phone-enabled education programs that can help. Project Masiluleke, for example, in South Africa, uses text messages to broadcast an HIV-awareness bulletin. Johnson & Johnson’s Text4Baby effort has served more than twenty million pregnant women and new parents in China, India, Mexico, Bangladesh, South Africa, and Nigeria. This is also where technophilanthropists like Bill Gates and his war on malaria can make a huge difference. Ultimately, however, meeting the medical needs of the entire world means empowering the rising billion with the basic resources—food, water, energy, and education—while at the same time driving forward the breakthroughs outlined in this chapter. If we can do this, we can create an age of health care abundance.