The Naked Future (8 page)

Let's assume one of these start-ups, or one not yet conceived, makes it to mainstream adoption. Once that occurs, the personal costs for self-quantification will have collapsed in just a few decades. In the 1980s, when Ray Kurzweil decided to flout the advice of his doctor, take himself off insulin, and begin keeping a detailed log of every meal he ate and what was in it, few other people would have had the patience, know-how, or inclination to attempt anything similar. The behavior at the time seemed positively bizarre. When Kurzweil first began his self-quantification experiments, the costs in terms of time and effort were a bit lower for him than they would be for anybody else, except Stephen Wolfram. Today, they're joined by enterprising people such as Sacha Chua, and the numbers are growing.

We are one app away from becoming Ray Kurzweil.

Here's what that app might look like to you in practice. You would give the program access to your biophysical signals, gleaned from your activity levels, mood analyzers, implants if you have any, e-mail and voice mail, et cetera. The program in turn would give you a rapidly evolving window into your future health. On any given day, you might receive a notification with the following warning: “Dear Patrick, as a result of that stress event you had a couple of weeks ago, the dizzy spell you complained of last night, and the fact that you've recently increased your daily alcohol consumption from two glasses of Merlot to four, your probability for stroke in the next year has just increased to 10 percent.”

Naturally, if you received this message, you would act to avert this stroke before it happened, rendering the prediction incorrect, but still invaluable.

Yet more cloud processing and an abundance of carefully collected personal data aren't the magic ingredients that are going to bring the above scenario to life. Even with the right technology and a seamless interface or analytics engine to take the difficult work out of making usable predictions from your data, the most important component of your changing health picture is other people's health data. Here's the trade-off, the point where our outmoded ideas of privacy begin to get in the way of progress and better health.

In June 2012 a group of researchers from MIT and Columbia created a system that can predict future illness. They call the system the Hierarchical Association Rule Model or HARM (a bizarre but at least memorable acronym for a medical algorithm). It can't tell you what's wrong with you right now; instead, the algorithm determines what you're likely to get
next
on the basis of a current diagnosis combined with certain demographic features such as race, age, and so on. To build it, they used clinical trial data from around 42,000 patient encounters and 2,300 patients, all at least forty years old.
These were people who had signed up to test new medicines so their medical records were more thoroughly filled out than is the norm. The subjects were also encouraged to report back on what they were feeling and experiencing, as such data could have an effect on the drug's marketability. What were the results? It turns out stroke is more predictable than many statisticians had believed, even though the proximate causes for stroke are still difficult to determine.
¹⁴

The system works by finding correlations among thousands of patients sharing their history, not by looking for causes. This is a big departure from the way medicine is traditionally practiced and taught. It also only works when thousands of people elect to share their most personal data.

Future breakthroughs in the application of highly personalized data to medicine will depend tremendously on a willingness to share that sort of information. The question is how to do it in a way that doesn't come back to haunt you. From a researcher's perspective, the problem becomes one of how to build privacy controls that allow your users to share but that allow your model, algorithm, system, or Web site to access the most valuable information.

The fledgling Consent to Research project headed by John Wilbanks is a great example of an organization that fully understands the importance of sharing health data for future medical practice but also understands the risks people take in exposing themselves. Wilbanks points out that more than one in ten people in the United States have a rare disease (defined as a disease that fewer than two hundred thousand people are diagnosed with per year), have a family member with a rare disease, or have a first-degree friend with a rare disease. There are a lot of illnesses that very few people have, but the sheer number of them affects us all.

Wilbanks's sister is one such person. “Our best guess is that she has some kind of psoriatic arthritis. We don't know what kind,” he told me and a few other folks at a Singularity Summit event in San Francisco. “The insurance industry is already quite good at denying her care based on the actuarial tables. So we pay for PET scans out of our own pocket.”

This, in part, is why Wilbanks views the idea of sharing medical data a bit differently than other people do. As far as he's concerned, a system that keeps your information safe but can't find a cure for your sister's disease doesn't work for anyone. “In our family, the potential risk of sharing our data is low compared to the potential benefit that might come.”

One of the fundamental flaws of the big data present, as opposed to the naked future, is that the value or benefits of sharing data is experienced collectively but the risk is experienced
personally.
Wilbanks is trying to get people to share their medical information in a way that creates “data bait” for researchers to solve problems that afflict families like his. “That's a better investment strategy as a non-wealthy family than paying for research into psoriatic arthritis,” he says. “There are people who are good at math who would like to work on something besides hedge funds, but the transaction costs for getting at health data are so high that they won't bother.”

This is one of the more egregious dysfunctions of our health-care system: getting good and useful medical information on a statistically significant population of subjects has never been more expensive. Researchers have estimated the cost for getting patient consent to review medical records at $248 per person.
¹⁵
That's part of the reason why the cost of a clinical drug trial for a single medication can run as high as $5 billion, according to Tuft's researcher Kenneth A. Getz. This is an insane amount of money pointing to an artificial scarcity of health data because the
availability
of potentially useful health data has never been greater. It's not just locked away in blood tests but, again, in our phones, our actions, the daily map of comings and goings.

However, Wilbanks agrees with Wolfram that more data won't equal better health by itself. “The more we measure ourselves, the more stressed we'll be. If we don't figure out ways to get that data into the sorts of models that tell you how likely you are to respond to a drug, we'll just be creating more noise.”

We would be well served by a health-data movement that's open and user powered, much like the Internet itself. This is what
he hopes to offer with the Consent to Research project. “I'm arguing for something open at the core, closed at the edges, and innovative without centralized control.”

Open, innovative, and decentralized are three adjectives that don't apply to health care today. But this is no one's fault but our own. In the twentieth century people consumed health care in the form of hospital visits and medication. The occasion for this consumption was sickness or the symptoms of sickness. In the twenty-first century we have added greater emphasis to the prevention of illness. Diet, exercise, yoga, and wellness have become recognized keys to a long, healthy life. This reprioritization has been a positive step but not wholly transformative. We have remained a customer base. Health care is something you buy from a professional and health is something you make with supplies from Whole Foods.

The next phase of health care—should we choose it—will demand that we become active participants not only in our own health but in the health of others. The data we create has value beyond how we use it to jog more, diet better, and maintain height-weight proportionality. It's more valuable than the results we realize in the singular. My health reveals something about your health, and how you experience health is relevant to me. The next stage in the evolution of health care demands a blurring of the lines between doctor, patient, and researcher. When information about our habits as individuals can paint a full picture of how we are living and dying as a species, we all thrive better individually and collectively. That becomes more apparent when we consider how sharing information will become the best defense against sharing a cold.

#sick

THE
year is 1374. The Black Death is ravaging Europe. At the port of Venice, a ship is approaching from the east. It is met by a small
cammelli
boat, a type of flat-bottomed vessel designed to guide larger ships through the shallow waters of the Venetian lagoon. But this
cammelli
carries a special passenger, one of Venice's three newly appointed Guardians of Health. He boards the tall vessel and quickly sets to work inspecting the crew and cargo where he finds a man who is delirious with fever. The lymph nodes beneath the man's jaw are red and inflamed. The guardian informs the crew that they must leave port. Venice has just instituted an unprecedented protocol: any ship suspected of harboring men infected with the plague must move on for a period of
quaranta giorni—
forty days.

This edict marks the birth of the term “quarantine.”

Why forty days and not fifty, or sixty, or a year? The specificity of this prescribed interval dates back, again, to the ancient Greek thinker Pythagoras and his Doctrine of Critical Days, which suggested that illness in the human body strengthened or weakened on
the basis of natural cycles and rhythms. The most critical of these critical days was day forty, after which point if a diseased person had survived the terrors of tremors of her affliction, she was deemed safe for reentry into society. Many contend that Pythagoras believed the cycles of the moon were a key determinant. As the moon waxed and waned, so, in theory, could sickness.

Pythagoras's doctrine, which went on to influence the ancient surgeon Hippocrates who first turned it into medical practice, was probably an improvement over no system at all but the degree of that improvement is hard to determine. Despite the
quaranta giorni
policy, the Black Death would go on to ravage the city of Venice in the years ahead.

Skip to the year 2020. Twelve-year-old Josh Grant is in the school nurse's office. He doesn't know why.

“I feel fine,” he tells the nurse (let's call her Nurse Gwen).

“That may be so, but you spent a good hour sitting six yards away from your girlfriend, Jessica Stickler, this morning. There's an eighty percent probability that she's going to come down with flu symptoms in the next day. Nothing to worry about. We'll genotype it.”

“She's
not
my girlfriend,” Josh answers. In truth, he's not yet sure if Jessica Stickler is his girlfriend. The prospect fills him with dread. “Anyway, that doesn't mean I have the flu.”

“I know.” Nurse Gwen tries to sound consoling. “Based on the exposure time, there's really only a ten percent chance you're a carrier. But if you are, then there's a sixty percent chance you'll infect Tim Miller during chemistry class later. And Tim's mother has nonrefundable tickets to
Disney Atlantis
this weekend.”

“What does Tim's mother have to do with it?” asks Josh.

“She called us and asked us to pull you out of history class. Normally we would never pull a ten percenter out of class to scan for flu, not when I have ten forty percenters from second period alone.” She laughs and then turns suddenly serious, like a faucet moving from open to shut. “Tim's mother can be rather assertive . . .”

Josh pulls out his phone and learns from Wikipedia that “the
genotype of an organism is the inherited instructions it carries within its genetic code.”
¹
Before he can consider how that applies to him (or if Wikipedia is even the best source), Nurse Gwen asks him to open his mouth. Soon she's swabbing the inside of his cheek. She scans it using her phone's camera. A moment later the sequencer app makes a
ding
noise, indicating it's done analyzing the influenza strain. Nurse Gwen opens the report on her phone. Her eyes widen and her jaw drops.

“Oh my,” she says in a frightened whisper. She rushes to a cabinet and pulls a small vial from a box marked
TAMIFLU #39
and scans a bar code on the side. She next takes a surgical mask and puts it over her mouth and nose.

“What's going on?” asks Josh.

“We're just taking an extra careful look at your flu,” answers Nurse Gwen.

“I don't have the flu,” Josh repeats. “I feel fine.”

He's no longer so sure of this.

From the word “genotype,” Josh assumes that there must be something in the genetic code of his flu that's causing Nurse Gwen to act this way.

His mother storms into the nurse's office a few minutes later. “I just got a push notification that Josh is now at one hundred percent probability for flu and he was at ten percent an hour ago. What kind of practice do you run here?”

Nurse Gwen hands Josh's mother a mask.

“Put this on,” the nurse commands.

Josh is now terrified.

“But it's just the flu, right?” asks Josh.

“It is the flu,” says Nurse Gwen, “but it's a genetically novel flu strain.” She pulls up a 3-D map of the earth's surface crisscrossed with bright lines connecting points in Asia, Eastern Europe, and the United States. At each point, the color of the line changes.

“I presumed that Josh had the flu we've been tracking for weeks. It's a fairly common one related to H3N2VVI. It originated in the People's Republic of Georgia two years ago and has gone
through several small mutations, here in Hong Kong on December fourth, and here in New York on January fifteenth. Each time a mutation event occurs, the trajectory line changes. The next mutation event was forecast for February nineteenth. Based on the traffic patterns, we believed that this mutation would occur in Mexico City. But Josh's flu isn't H3N2VVI; it's H3NBX. This flu
was
limited to exotic avian megafauna. With Josh, it appears to have made a transgenic leap from birds to mammals.”

“What's an exotic avian megafauna?” Josh feels a distinct sinking feeling.

“Harpy eagles, in this case. Most likely from Central America. They're often smuggled into the States.”

“My twelve-year-old son is not a harpy eagle smuggler.” Josh's mother is adamant on this point. “Look at his Twitter trail.”

“Well . . .” says Josh.

The adults turn to him. His mother becomes very pale.

“The other day, when I was at Ray Bremmer's house tweeting about
Zombie Warz
for Xbox, I wasn't actually playing Xbox. Ray's uncle just got back from Panama with these birds and Ray said I could see them if I promised not to tell
anyone
 . . .”

The sequencer app makes a bell sound. Nurse Gwen checks her phone for the report. She removes her mask. “Good news, it looks like this strain is very mild and isn't Tamiflu-39 resistant, at least not yet.”

Josh's mother seems not to hear. “Of course Ray Bremmer would give you some wild bird flu . . .”

“Ray didn't give Josh this flu,” Nurse Gwen clarifies. “The bird did. The more important question is where the flu is headed now. Looking at your geo-tagged posts, it appears you hung out with David McGill yesterday after the final bell and then with John Brooker this morning at recess. David's posts are now reading that he's at the airport with his father on his way to California. Running the projection map, it appears the Josh Grant flu will show up in Los Angeles tomorrow and from there it will be in Europe, Asia, and Africa by Thursday.”

“Wait,” says Josh in horror. “It's called the
Josh Grant
flu?”

“Just for now,” says Nurse Gwen. “You are the first human to get it and I have to call it
something
when I talk about it with the CDC. I remember not long ago when people contracted the flu they would say they had a bug that was
going around.
Today, we can actually see the bugs and how they spread on a person-to-person level. We can name specific influenza strains after specific mutation points. Every flu can have a name. You are the first person to get what will be a fairly common flu in a couple of months. People will get your flu on subways, on airplanes, on cruise ships, and, of course, in classrooms. The Josh Grant could well be
the
seasonal flu next year. But not to worry, a couple of days of bed rest and liquids and you'll be fine.”

“But
why
name every flu? Why track it if it's not dangerous?” Josh asks.

“By tracking the Josh Grant flu we can keep it from
becoming
dangerous. We can predict how many vaccines we'll need and at which point Tamiflu 39 will cease to be an effective treatment, necessitating the development of a Tamiflu 40, which reminds me . . .”

Gwen leaves the conversation to pick up a few extra shares of a pharmaceutical company that produces influenza medication. Josh's mother calls her workplace to take the rest of the week off as Josh collects his book bag and shuffles toward the car. His body will recover from the Josh Grant flu in two days. His relationship with Jessica Stickler, sadly, may not survive.

•   •   •

PERHAPS
you aren't yet convinced that the naked future offers any improvement to compensate for the sacrifice of privacy that it demands. Certainly, this new era will distribute rewards and punishments unfairly and unequally (sort of like the Old Testament depiction of God). But consider that every year millions of people in the United States truck themselves down to clinics for flu shots and wind up getting the flu anyway. According to epidemiologists, flu shots are 70 percent effective in the general population at most.
The reason? Every shot contains an (inactive) mixture of only the three virus strains that epidemiologists believe are going to be prevalent in the coming season.
²

In the last several years, that has included strains of H3N2 (the base of the swine flu virus and several other influenza strains common in mammals), H1N1 (the famous bird flu), and a variety of influenza B strains, which are considered less dangerous and more likely to strike later in the flu season. But this is a small percentage of the types of flu known to be in existence.

The Centers for Disease Control and Prevention (CDC) almost apologetically states on its Web site, “It's not possible to predict with certainty which flu viruses will predominate during a given season. Flu viruses are constantly changing (called ‘antigenic drift')—they can change from one season to the next or they can even change within the course of one flu season. Experts must pick which viruses to include in the vaccine many months in advance in order for vaccine to be produced and delivered on time.” Perhaps it's a sign of how far medicine has advanced that we, like naive children, simply assume the shots we get will actually work.
³

In the last several years, the emergence of superlarge, publicly accessible databases of virus sequences such as the Global Initiative on Sharing All Influenza Data (GISAID)
⁴
and the National Institutes of Health's GenBank
⁵
have greatly reduced bureaucratic barriers to finding and sharing the most current information about new influenza observations.

Wider use of sequencing technology could lead to earlier detection of new types of flu, which would help pharmaceutical companies create better vaccines. Today, devices like Life Science's Ion Proton can sequence all 3 billion base pairs of the human genome in less than a day for a price of $1,000, according to the machine's makers. With just eight ribonucleic acid (RNA) segments, influenza is an exponentially simpler organism to sequence than the human genome. But sequencing influenza is very rarely done at a nurse's office—what epidemiologists call “the point of surveillance.” Instead, when flu samples are collected they're usually sent
to a county or state public health lab, by which point a great deal of time has been lost.
⁶

Collecting samples from birds and animals that are showing flu symptoms is, arguably, a more important step in curbing the spread of new deadly flu types. But that sort of sampling doesn't happen very often. As the editors of
Nature
pointed out in a recent Op-Ed:

“Just 7 of the 39 countries with more than 100 million poultry in 2010 collected more than 1,000 avian flu samples between 2003 and 2011. Eight countries—Brazil, Morocco, the Philippines, Colombia, Ecuador, Algeria, Venezuela and the Dominican Republic—collected none at all . . .”
^7
,
8

The current state of flu detection leaves much to be desired. Yet the Josh Grant scenario outlined above could become reality within a decade. You can see its initial outlines today.

The date is Tuesday, April 10, 2012. Bioinformatics professor Daniel Janies is at the NIH in Bethesda, Maryland, to discuss his creation, an interactive map that plots the spread of specific flu strains around the world. It also allows researchers to draw inferences as to where those strains will go next and how the strain will evolve. To do this, the Supramap, as it's named, makes use of the details that often find their way into reports epidemiologists write for public health officials but that don't always seem important to people who aren't epidemiologists. This includes such facts as when the variation was first spotted and whether the host organism was wild or farm raised. “We can put all that information together because all kinds of information [have] a time and place,” says Janies. “Every point on the earth is an observation, every point in space is an inference, not just an evolutionary midpoint, as a phylogeneticist would create, but also a
geographic
midpoint” [emphasis mine].

The map on the display behind him shows the progression of the H1N1 strain out of Asia. A series of lines are shooting up and across the earth's surface; some are close together, others stretch
over continents. Most are bluish green. They come together to create what looks like a strange scaffold on top of a Google Earth map, linking Indonesia to Korea and Japan. At various junctures, the green lines turn white, then red, and arch higher and higher. The lines represent the movement of chickens, ducks, geese, humans, et cetera, across Asia and tell the epidemiologists which sort of animal brought what strain of the flu where. A goose brought it to Guodong, China, in 1996; a chicken—transported via a person—brought it to Indonesia after that.