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So I have bad news, I have good news,

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and I have a task.

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So the bad news is that we all get sick.

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I get sick. You get sick.

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And every one of us gets sick, and the question really is,

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how sick do we get? Is it something that kills us?

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Is it something that we survive?

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Is it something that we can treat?

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And we've gotten sick as long as we've been people.

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And so we've always looked for reasons to explain why we get sick.

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And for a long time, it was the gods, right?

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The gods are angry with me, or the gods are testing me,

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right? Or God, singular, more recently,

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is punishing me or judging me.

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And as long as we've looked for explanations,

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we've wound up with something that gets closer and closer to science,

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which is hypotheses as to why we get sick,

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and as long as we've had hypotheses about why we get sick, we've tried to treat it as well.

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So this is Avicenna. He wrote a book over a thousand years ago called "The Canon of Medicine,"

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and the rules he laid out for testing medicines

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are actually really similar to the rules we have today,

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that the disease and the medicine must be the same strength,

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the medicine needs to be pure, and in the end we need

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to test it in people. And so if you put together these themes

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of a narrative or a hypothesis in human testing,

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right, you get some beautiful results,

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even when we didn't have very good technologies.

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This is a guy named Carlos Finlay. He had a hypothesis

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that was way outside the box for his time, in the late 1800s.

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He thought yellow fever was not transmitted by dirty clothing.

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He thought it was transmitted by mosquitos.

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And they laughed at him. For 20 years, they called this guy

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"the mosquito man." But he ran an experiment in people,

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right? He had this hypothesis, and he tested it in people.

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So he got volunteers to go move to Cuba and live in tents

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and be voluntarily infected with yellow fever.

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So some of the people in some of the tents had dirty clothes

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and some of the people were in tents that were full

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of mosquitos that had been exposed to yellow fever.

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And it definitively proved that it wasn't this magic dust

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called fomites in your clothes that caused yellow fever.

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But it wasn't until we tested it in people that we actually knew.

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And this is what those people signed up for.

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This is what it looked like to have yellow fever in Cuba

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at that time. You suffered in a tent, in the heat, alone,

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and you probably died.

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But people volunteered for this.

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And it's not just a cool example of a scientific design

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of experiment in theory. They also did this beautiful thing.

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They signed this document, and it's called an informed consent document.

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And informed consent is an idea that we should be

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very proud of as a society, right? It's something that

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separates us from the Nazis at Nuremberg,

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enforced medical experimentation. It's the idea

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that agreement to join a study without understanding isn't agreement.

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It's something that protects us from harm, from hucksters,

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from people that would try to hoodwink us into a clinical

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study that we don't understand, or that we don't agree to.

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And so you put together the thread of narrative hypothesis,

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experimentation in humans, and informed consent,

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and you get what we call clinical study, and it's how we do

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the vast majority of medical work. It doesn't really matter

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if you're in the north, the south, the east, the west.

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Clinical studies form the basis of how we investigate,

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so if we're going to look at a new drug, right,

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we test it in people, we draw blood, we do experiments,

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and we gain consent for that study, to make sure

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that we're not screwing people over as part of it.

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But the world is changing around the clinical study,

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which has been fairly well established for tens of years

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if not 50 to 100 years.

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So now we're able to gather data about our genomes,

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but, as we saw earlier, our genomes aren't dispositive.

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We're able to gather information about our environment.

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And more importantly, we're able to gather information

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about our choices, because it turns out that what we think of

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as our health is more like the interaction of our bodies,

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our genomes, our choices and our environment.

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And the clinical methods that we've got aren't very good

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at studying that because they are based on the idea

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of person-to-person interaction. You interact

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with your doctor and you get enrolled in the study.

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So this is my grandfather. I actually never met him,

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but he's holding my mom, and his genes are in me, right?

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His choices ran through to me. He was a smoker,

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like most people were. This is my son.

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So my grandfather's genes go all the way through to him,

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and my choices are going to affect his health.

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The technology between these two pictures

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cannot be more different, but the methodology

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for clinical studies has not radically changed over that time period.

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We just have better statistics.

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The way we gain informed consent was formed in large part

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after World War II, around the time that picture was taken.

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That was 70 years ago, and the way we gain informed consent,

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this tool that was created to protect us from harm,

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now creates silos. So the data that we collect

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for prostate cancer or for Alzheimer's trials

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goes into silos where it can only be used

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for prostate cancer or for Alzheimer's research.

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Right? It can't be networked. It can't be integrated.

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It cannot be used by people who aren't credentialed.

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So a physicist can't get access to it without filing paperwork.

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A computer scientist can't get access to it without filing paperwork.

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Computer scientists aren't patient. They don't file paperwork.

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And this is an accident. These are tools that we created

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to protect us from harm, but what they're doing

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is protecting us from innovation now.

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And that wasn't the goal. It wasn't the point. Right?

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It's a side effect, if you will, of a power we created

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to take us for good.

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And so if you think about it, the depressing thing is that

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Facebook would never make a change to something

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as important as an advertising algorithm

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with a sample size as small as a Phase III clinical trial.

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We cannot take the information from past trials

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and put them together to form statistically significant samples.

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And that sucks, right? So 45 percent of men develop

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cancer. Thirty-eight percent of women develop cancer.

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One in four men dies of cancer.

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One in five women dies of cancer, at least in the United States.

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And three out of the four drugs we give you

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if you get cancer fail. And this is personal to me.

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My sister is a cancer survivor.

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My mother-in-law is a cancer survivor. Cancer sucks.

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And when you have it, you don't have a lot of privacy

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in the hospital. You're naked the vast majority of the time.

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People you don't know come in and look at you and poke you and prod you,

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and when I tell cancer survivors that this tool we created

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to protect them is actually preventing their data from being used,

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especially when only three to four percent of people

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who have cancer ever even sign up for a clinical study,

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their reaction is not, "Thank you, God, for protecting my privacy."

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It's outrage

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that we have this information and we can't use it.

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And it's an accident.

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So the cost in blood and treasure of this is enormous.

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Two hundred and twenty-six billion a year is spent on cancer in the United States.

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Fifteen hundred people a day die in the United States.

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And it's getting worse.

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So the good news is that some things have changed,

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and the most important thing that's changed

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is that we can now measure ourselves in ways

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that used to be the dominion of the health system.

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So a lot of people talk about it as digital exhaust.

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I like to think of it as the dust that runs along behind my kid.

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We can reach back and grab that dust,

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and we can learn a lot about health from it, so if our choices

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are part of our health, what we eat is a really important

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aspect of our health. So you can do something very simple

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and basic and take a picture of your food,

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and if enough people do that, we can learn a lot about

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how our food affects our health.

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One interesting thing that came out of this — this is an app for iPhones called The Eatery —

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is that we think our pizza is significantly healthier

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than other people's pizza is. Okay? (Laughter)

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And it seems like a trivial result, but this is the sort of research

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that used to take the health system years

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and hundreds of thousands of dollars to accomplish.

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It was done in five months by a startup company of a couple of people.

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I don't have any financial interest in it.

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But more nontrivially, we can get our genotypes done,

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and although our genotypes aren't dispositive, they give us clues.

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So I could show you mine. It's just A's, T's, C's and G's.

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This is the interpretation of it. As you can see,

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I carry a 32 percent risk of prostate cancer,

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22 percent risk of psoriasis and a 14 percent risk of Alzheimer's disease.

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So that means, if you're a geneticist, you're freaking out,

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going, "Oh my God, you told everyone you carry the ApoE E4 allele. What's wrong with you?"

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Right? When I got these results, I started talking to doctors,

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and they told me not to tell anyone, and my reaction is,

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"Is that going to help anyone cure me when I get the disease?"

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And no one could tell me yes.

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And I live in a web world where, when you share things,

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beautiful stuff happens, not bad stuff.

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So I started putting this in my slide decks,

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and I got even more obnoxious, and I went to my doctor,

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and I said, "I'd like to actually get my bloodwork.

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Please give me back my data." So this is my most recent bloodwork.

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As you can see, I have high cholesterol.

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I have particularly high bad cholesterol, and I have some

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bad liver numbers, but those are because we had a dinner party with a lot of good wine

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the night before we ran the test. (Laughter)

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Right. But look at how non-computable this information is.

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This is like the photograph of my granddad holding my mom

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from a data perspective, and I had to go into the system

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and get it out.

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So the thing that I'm proposing we do here

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is that we reach behind us and we grab the dust,

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that we reach into our bodies and we grab the genotype,

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and we reach into the medical system and we grab our records,

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and we use it to build something together, which is a commons.

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And there's been a lot of talk about commonses, right,

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here, there, everywhere, right. A commons is nothing more

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than a public good that we build out of private goods.

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We do it voluntarily, and we do it through standardized

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legal tools. We do it through standardized technologies.

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Right. That's all a commons is. It's something that we build

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together because we think it's important.

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And a commons of data is something that's really unique,

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because we make it from our own data. And although

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a lot of people like privacy as their methodology of control

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around data, and obsess around privacy, at least

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some of us really like to share as a form of control,

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and what's remarkable about digital commonses

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is you don't need a big percentage if your sample size is big enough

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to generate something massive and beautiful.

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So not that many programmers write free software,

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but we have the Apache web server.

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Not that many people who read Wikipedia edit,

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but it works. So as long as some people like to share

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as their form of control, we can build a commons, as long as we can get the information out.

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And in biology, the numbers are even better.

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So Vanderbilt ran a study asking people, we'd like to take

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your biosamples, your blood, and share them in a biobank,

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and only five percent of the people opted out.

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I'm from Tennessee. It's not the most science-positive state

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in the United States of America. (Laughter)

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But only five percent of the people wanted out.

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So people like to share, if you give them the opportunity and the choice.

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And the reason that I got obsessed with this, besides the obvious family aspects,

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is that I spend a lot of time around mathematicians,

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and mathematicians are drawn to places where there's a lot of data

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because they can use it to tease signals out of noise.

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And those correlations that they can tease out, they're not

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necessarily causal agents, but math, in this day and age,

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is like a giant set of power tools

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that we're leaving on the floor, not plugged in in health,

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while we use hand saws.

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If we have a lot of shared genotypes, and a lot of shared

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outcomes, and a lot of shared lifestyle choices,

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and a lot of shared environmental information, we can start

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to tease out the correlations between subtle variations

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in people, the choices they make and the health that they create as a result of those choices,

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and there's open-source infrastructure to do all of this.

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Sage Bionetworks is a nonprofit that's built a giant math system

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that's waiting for data, but there isn't any.

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So that's what I do. I've actually started what we think is

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the world's first fully digital, fully self-contributed,

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unlimited in scope, global in participation, ethically approved

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clinical research study where you contribute the data.

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So if you reach behind yourself and you grab the dust,

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if you reach into your body and grab your genome,

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if you reach into the medical system and somehow extract your medical record,

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you can actually go through an online informed consent process --

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because the donation to the commons must be voluntary

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and it must be informed -- and you can actually upload

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your information and have it syndicated to the

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mathematicians who will do this sort of big data research,

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and the goal is to get 100,000 in the first year

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and a million in the first five years so that we have

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a statistically significant cohort that you can use to take

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smaller sample sizes from traditional research

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and map it against,

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so that you can use it to tease out those subtle correlations

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between the variations that make us unique

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and the kinds of health that we need to move forward as a society.

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And I've spent a lot of time around other commons.

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I've been around the early web. I've been around

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the early creative commons world, and there's four things

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that all of these share, which is, they're all really simple.

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And so if you were to go to the website and enroll in this study,

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you're not going to see something complicated.

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But it's not simplistic. These things are weak intentionally,

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right, because you can always add power and control to a system,

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but it's very difficult to remove those things if you put them in at the beginning,

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and so being simple doesn't mean being simplistic,

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and being weak doesn't mean weakness.

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Those are strengths in the system.

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And open doesn't mean that there's no money.

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Closed systems, corporations, make a lot of money

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on the open web, and they're one of the reasons why the open web lives

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is that corporations have a vested interest in the openness

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of the system.

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And so all of these things are part of the clinical study that we've created,

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so you can actually come in, all you have to be is 14 years old,

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willing to sign a contract that says I'm not going to be a jerk,

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basically, and you're in.

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You can start analyzing the data.

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You do have to solve a CAPTCHA as well. (Laughter)

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And if you'd like to build corporate structures on top of it,

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that's okay too. That's all in the consent,

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so if you don't like those terms, you don't come in.

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It's very much the design principles of a commons

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that we're trying to bring to health data.

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And the other thing about these systems is that it only takes

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a small number of really unreasonable people working together

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to create them. It didn't take that many people

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to make Wikipedia Wikipedia, or to keep it Wikipedia.

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And we're not supposed to be unreasonable in health,

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and so I hate this word "patient."

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I don't like being patient when systems are broken,

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and health care is broken.

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I'm not talking about the politics of health care, I'm talking about the way we scientifically approach health care.

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So I don't want to be patient. And the task I'm giving to you

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is to not be patient. So I'd like you to actually try,

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when you go home, to get your data.

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You'll be shocked and offended and, I would bet, outraged,

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at how hard it is to get it.

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But it's a challenge that I hope you'll take,

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and maybe you'll share it. Maybe you won't.

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If you don't have anyone in your family who's sick,

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maybe you wouldn't be unreasonable. But if you do,

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or if you've been sick, then maybe you would.

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And we're going to be able to do an experiment in the next several months

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that lets us know exactly how many unreasonable people are out there.

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So this is the Athena Breast Health Network. It's a study

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of 150,000 women in California, and they're going to

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return all the data to the participants of the study

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in a computable form, with one-clickability to load it into

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the study that I've put together. So we'll know exactly

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how many people are willing to be unreasonable.

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So what I'd end [with] is,

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the most beautiful thing I've learned since I quit my job

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almost a year ago to do this, is that it really doesn't take

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very many of us to achieve spectacular results.

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You just have to be willing to be unreasonable,

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and the risk we're running is not the risk those 14 men

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who got yellow fever ran. Right?

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It's to be naked, digitally, in public. So you know more

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about me and my health than I know about you. It's asymmetric now.

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And being naked and alone can be terrifying.

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But to be naked in a group, voluntarily, can be quite beautiful.

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And so it doesn't take all of us.

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It just takes all of some of us. Thank you.

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(Applause)