Let's pool our medical data
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0:00 - 0:03So I have bad news, I have good news,
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0:03 - 0:05and I have a task.
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0:05 - 0:08So the bad news is that we all get sick.
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0:08 - 0:10I get sick. You get sick.
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0:10 - 0:13And every one of us gets sick, and the question really is,
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0:13 - 0:16how sick do we get? Is it something that kills us?
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0:16 - 0:17Is it something that we survive?
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0:17 - 0:19Is it something that we can treat?
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0:19 - 0:22And we've gotten sick as long as we've been people.
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0:22 - 0:26And so we've always looked for reasons to explain why we get sick.
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0:26 - 0:28And for a long time, it was the gods, right?
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0:28 - 0:31The gods are angry with me, or the gods are testing me,
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0:31 - 0:33right? Or God, singular, more recently,
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0:33 - 0:36is punishing me or judging me.
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0:36 - 0:39And as long as we've looked for explanations,
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0:39 - 0:42we've wound up with something that gets closer and closer to science,
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0:42 - 0:45which is hypotheses as to why we get sick,
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0:45 - 0:49and as long as we've had hypotheses about why we get sick, we've tried to treat it as well.
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0:49 - 0:54So this is Avicenna. He wrote a book over a thousand years ago called "The Canon of Medicine,"
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0:54 - 0:56and the rules he laid out for testing medicines
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0:56 - 0:58are actually really similar to the rules we have today,
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0:58 - 1:01that the disease and the medicine must be the same strength,
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1:01 - 1:03the medicine needs to be pure, and in the end we need
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1:03 - 1:06to test it in people. And so if you put together these themes
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1:06 - 1:11of a narrative or a hypothesis in human testing,
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1:11 - 1:13right, you get some beautiful results,
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1:13 - 1:15even when we didn't have very good technologies.
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1:15 - 1:18This is a guy named Carlos Finlay. He had a hypothesis
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1:18 - 1:21that was way outside the box for his time, in the late 1800s.
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1:21 - 1:24He thought yellow fever was not transmitted by dirty clothing.
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1:24 - 1:26He thought it was transmitted by mosquitoes.
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1:26 - 1:28And they laughed at him. For 20 years, they called this guy
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1:28 - 1:32"the mosquito man." But he ran an experiment in people,
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1:32 - 1:35right? He had this hypothesis, and he tested it in people.
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1:35 - 1:40So he got volunteers to go move to Cuba and live in tents
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1:40 - 1:43and be voluntarily infected with yellow fever.
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1:43 - 1:46So some of the people in some of the tents had dirty clothes
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1:46 - 1:47and some of the people were in tents that were full
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1:47 - 1:49of mosquitos that had been exposed to yellow fever.
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1:49 - 1:53And it definitively proved that it wasn't this magic dust
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1:53 - 1:56called fomites in your clothes that caused yellow fever.
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1:56 - 1:59But it wasn't until we tested it in people that we actually knew.
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1:59 - 2:01And this is what those people signed up for.
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2:01 - 2:04This is what it looked like to have yellow fever in Cuba
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2:04 - 2:09at that time. You suffered in a tent, in the heat, alone,
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2:09 - 2:12and you probably died.
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2:12 - 2:15But people volunteered for this.
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2:15 - 2:18And it's not just a cool example of a scientific design
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2:18 - 2:21of experiment in theory. They also did this beautiful thing.
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2:21 - 2:25They signed this document, and it's called an informed consent document.
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2:25 - 2:27And informed consent is an idea that we should be
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2:27 - 2:30very proud of as a society, right? It's something that
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2:30 - 2:32separates us from the Nazis at Nuremberg,
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2:32 - 2:35enforced medical experimentation. It's the idea
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2:35 - 2:39that agreement to join a study without understanding isn't agreement.
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2:39 - 2:43It's something that protects us from harm, from hucksters,
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2:43 - 2:46from people that would try to hoodwink us into a clinical
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2:46 - 2:50study that we don't understand, or that we don't agree to.
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2:50 - 2:54And so you put together the thread of narrative hypothesis,
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2:54 - 2:57experimentation in humans, and informed consent,
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2:57 - 2:59and you get what we call clinical study, and it's how we do
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2:59 - 3:02the vast majority of medical work. It doesn't really matter
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3:02 - 3:05if you're in the north, the south, the east, the west.
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3:05 - 3:09Clinical studies form the basis of how we investigate,
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3:09 - 3:11so if we're going to look at a new drug, right,
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3:11 - 3:14we test it in people, we draw blood, we do experiments,
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3:14 - 3:16and we gain consent for that study, to make sure
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3:16 - 3:19that we're not screwing people over as part of it.
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3:19 - 3:22But the world is changing around the clinical study,
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3:22 - 3:26which has been fairly well established for tens of years
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3:26 - 3:28if not 50 to 100 years.
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3:28 - 3:31So now we're able to gather data about our genomes,
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3:31 - 3:34but, as we saw earlier, our genomes aren't dispositive.
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3:34 - 3:36We're able to gather information about our environment.
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3:36 - 3:38And more importantly, we're able to gather information
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3:38 - 3:41about our choices, because it turns out that what we think of
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3:41 - 3:44as our health is more like the interaction of our bodies,
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3:44 - 3:47our genomes, our choices and our environment.
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3:47 - 3:50And the clinical methods that we've got aren't very good
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3:50 - 3:53at studying that because they are based on the idea
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3:53 - 3:55of person-to-person interaction. You interact
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3:55 - 3:57with your doctor and you get enrolled in the study.
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3:57 - 3:59So this is my grandfather. I actually never met him,
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3:59 - 4:03but he's holding my mom, and his genes are in me, right?
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4:03 - 4:06His choices ran through to me. He was a smoker,
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4:06 - 4:09like most people were. This is my son.
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4:09 - 4:12So my grandfather's genes go all the way through to him,
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4:12 - 4:15and my choices are going to affect his health.
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4:15 - 4:17The technology between these two pictures
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4:17 - 4:21cannot be more different, but the methodology
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4:21 - 4:25for clinical studies has not radically changed over that time period.
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4:25 - 4:28We just have better statistics.
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4:28 - 4:31The way we gain informed consent was formed in large part
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4:31 - 4:34after World War II, around the time that picture was taken.
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4:34 - 4:38That was 70 years ago, and the way we gain informed consent,
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4:38 - 4:41this tool that was created to protect us from harm,
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4:41 - 4:44now creates silos. So the data that we collect
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4:44 - 4:47for prostate cancer or for Alzheimer's trials
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4:47 - 4:50goes into silos where it can only be used
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4:50 - 4:53for prostate cancer or for Alzheimer's research.
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4:53 - 4:56Right? It can't be networked. It can't be integrated.
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4:56 - 4:59It cannot be used by people who aren't credentialed.
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4:59 - 5:02So a physicist can't get access to it without filing paperwork.
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5:02 - 5:05A computer scientist can't get access to it without filing paperwork.
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5:05 - 5:10Computer scientists aren't patient. They don't file paperwork.
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5:10 - 5:14And this is an accident. These are tools that we created
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5:14 - 5:17to protect us from harm, but what they're doing
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5:17 - 5:19is protecting us from innovation now.
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5:19 - 5:23And that wasn't the goal. It wasn't the point. Right?
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5:23 - 5:25It's a side effect, if you will, of a power we created
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5:25 - 5:28to take us for good.
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5:28 - 5:31And so if you think about it, the depressing thing is that
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5:31 - 5:33Facebook would never make a change to something
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5:33 - 5:36as important as an advertising algorithm
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5:36 - 5:40with a sample size as small as a Phase III clinical trial.
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5:40 - 5:44We cannot take the information from past trials
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5:44 - 5:48and put them together to form statistically significant samples.
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5:48 - 5:51And that sucks, right? So 45 percent of men develop
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5:51 - 5:54cancer. Thirty-eight percent of women develop cancer.
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5:54 - 5:57One in four men dies of cancer.
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5:57 - 6:00One in five women dies of cancer, at least in the United States.
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6:00 - 6:02And three out of the four drugs we give you
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6:02 - 6:06if you get cancer fail. And this is personal to me.
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6:06 - 6:08My sister is a cancer survivor.
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6:08 - 6:12My mother-in-law is a cancer survivor. Cancer sucks.
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6:12 - 6:14And when you have it, you don't have a lot of privacy
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6:14 - 6:17in the hospital. You're naked the vast majority of the time.
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6:17 - 6:21People you don't know come in and look at you and poke you and prod you,
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6:21 - 6:24and when I tell cancer survivors that this tool we created
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6:24 - 6:27to protect them is actually preventing their data from being used,
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6:27 - 6:29especially when only three to four percent of people
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6:29 - 6:32who have cancer ever even sign up for a clinical study,
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6:32 - 6:36their reaction is not, "Thank you, God, for protecting my privacy."
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6:36 - 6:39It's outrage
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6:39 - 6:41that we have this information and we can't use it.
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6:41 - 6:43And it's an accident.
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6:43 - 6:46So the cost in blood and treasure of this is enormous.
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6:46 - 6:50Two hundred and twenty-six billion a year is spent on cancer in the United States.
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6:50 - 6:53Fifteen hundred people a day die in the United States.
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6:53 - 6:56And it's getting worse.
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6:56 - 6:59So the good news is that some things have changed,
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6:59 - 7:00and the most important thing that's changed
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7:00 - 7:03is that we can now measure ourselves in ways
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7:03 - 7:06that used to be the dominion of the health system.
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7:06 - 7:08So a lot of people talk about it as digital exhaust.
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7:08 - 7:11I like to think of it as the dust that runs along behind my kid.
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7:11 - 7:13We can reach back and grab that dust,
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7:13 - 7:16and we can learn a lot about health from it, so if our choices
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7:16 - 7:18are part of our health, what we eat is a really important
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7:18 - 7:21aspect of our health. So you can do something very simple
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7:21 - 7:23and basic and take a picture of your food,
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7:23 - 7:26and if enough people do that, we can learn a lot about
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7:26 - 7:27how our food affects our health.
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7:27 - 7:32One interesting thing that came out of this — this is an app for iPhones called The Eatery —
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7:32 - 7:34is that we think our pizza is significantly healthier
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7:34 - 7:38than other people's pizza is. Okay? (Laughter)
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7:38 - 7:41And it seems like a trivial result, but this is the sort of research
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7:41 - 7:44that used to take the health system years
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7:44 - 7:46and hundreds of thousands of dollars to accomplish.
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7:46 - 7:50It was done in five months by a startup company of a couple of people.
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7:50 - 7:52I don't have any financial interest in it.
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7:52 - 7:55But more nontrivially, we can get our genotypes done,
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7:55 - 7:58and although our genotypes aren't dispositive, they give us clues.
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7:58 - 8:01So I could show you mine. It's just A's, T's, C's and G's.
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8:01 - 8:03This is the interpretation of it. As you can see,
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8:03 - 8:05I carry a 32 percent risk of prostate cancer,
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8:05 - 8:1022 percent risk of psoriasis and a 14 percent risk of Alzheimer's disease.
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8:10 - 8:12So that means, if you're a geneticist, you're freaking out,
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8:12 - 8:16going, "Oh my God, you told everyone you carry the ApoE E4 allele. What's wrong with you?"
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8:16 - 8:20Right? When I got these results, I started talking to doctors,
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8:20 - 8:22and they told me not to tell anyone, and my reaction is,
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8:22 - 8:26"Is that going to help anyone cure me when I get the disease?"
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8:26 - 8:29And no one could tell me yes.
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8:29 - 8:31And I live in a web world where, when you share things,
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8:31 - 8:34beautiful stuff happens, not bad stuff.
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8:34 - 8:36So I started putting this in my slide decks,
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8:36 - 8:39and I got even more obnoxious, and I went to my doctor,
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8:39 - 8:41and I said, "I'd like to actually get my bloodwork.
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8:41 - 8:43Please give me back my data." So this is my most recent bloodwork.
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8:43 - 8:46As you can see, I have high cholesterol.
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8:46 - 8:48I have particularly high bad cholesterol, and I have some
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8:48 - 8:51bad liver numbers, but those are because we had a dinner party with a lot of good wine
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8:51 - 8:54the night before we ran the test. (Laughter)
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8:54 - 8:59Right. But look at how non-computable this information is.
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8:59 - 9:02This is like the photograph of my granddad holding my mom
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9:02 - 9:05from a data perspective, and I had to go into the system
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9:05 - 9:07and get it out.
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9:07 - 9:11So the thing that I'm proposing we do here
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9:11 - 9:13is that we reach behind us and we grab the dust,
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9:13 - 9:16that we reach into our bodies and we grab the genotype,
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9:16 - 9:19and we reach into the medical system and we grab our records,
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9:19 - 9:22and we use it to build something together, which is a commons.
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9:22 - 9:25And there's been a lot of talk about commonses, right,
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9:25 - 9:28here, there, everywhere, right. A commons is nothing more
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9:28 - 9:31than a public good that we build out of private goods.
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9:31 - 9:34We do it voluntarily, and we do it through standardized
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9:34 - 9:37legal tools. We do it through standardized technologies.
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9:37 - 9:40Right. That's all a commons is. It's something that we build
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9:40 - 9:42together because we think it's important.
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9:42 - 9:45And a commons of data is something that's really unique,
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9:45 - 9:48because we make it from our own data. And although
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9:48 - 9:50a lot of people like privacy as their methodology of control
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9:50 - 9:53around data, and obsess around privacy, at least
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9:53 - 9:56some of us really like to share as a form of control,
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9:56 - 9:58and what's remarkable about digital commonses
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9:58 - 10:01is you don't need a big percentage if your sample size is big enough
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10:01 - 10:04to generate something massive and beautiful.
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10:04 - 10:07So not that many programmers write free software,
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10:07 - 10:09but we have the Apache web server.
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10:09 - 10:12Not that many people who read Wikipedia edit,
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10:12 - 10:16but it works. So as long as some people like to share
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10:16 - 10:19as their form of control, we can build a commons, as long as we can get the information out.
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10:19 - 10:22And in biology, the numbers are even better.
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10:22 - 10:24So Vanderbilt ran a study asking people, we'd like to take
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10:24 - 10:28your biosamples, your blood, and share them in a biobank,
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10:28 - 10:30and only five percent of the people opted out.
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10:30 - 10:33I'm from Tennessee. It's not the most science-positive state
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10:33 - 10:36in the United States of America. (Laughter)
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10:36 - 10:38But only five percent of the people wanted out.
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10:38 - 10:42So people like to share, if you give them the opportunity and the choice.
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10:42 - 10:47And the reason that I got obsessed with this, besides the obvious family aspects,
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10:47 - 10:50is that I spend a lot of time around mathematicians,
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10:50 - 10:53and mathematicians are drawn to places where there's a lot of data
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10:53 - 10:56because they can use it to tease signals out of noise.
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10:56 - 10:59And those correlations that they can tease out, they're not
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10:59 - 11:03necessarily causal agents, but math, in this day and age,
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11:03 - 11:05is like a giant set of power tools
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11:05 - 11:09that we're leaving on the floor, not plugged in in health,
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11:09 - 11:11while we use hand saws.
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11:11 - 11:16If we have a lot of shared genotypes, and a lot of shared
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11:16 - 11:19outcomes, and a lot of shared lifestyle choices,
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11:19 - 11:21and a lot of shared environmental information, we can start
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11:21 - 11:24to tease out the correlations between subtle variations
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11:24 - 11:30in people, the choices they make and the health that they create as a result of those choices,
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11:30 - 11:32and there's open-source infrastructure to do all of this.
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11:32 - 11:35Sage Bionetworks is a nonprofit that's built a giant math system
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11:35 - 11:40that's waiting for data, but there isn't any.
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11:40 - 11:44So that's what I do. I've actually started what we think is
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11:44 - 11:48the world's first fully digital, fully self-contributed,
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11:48 - 11:53unlimited in scope, global in participation, ethically approved
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11:53 - 11:56clinical research study where you contribute the data.
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11:56 - 11:59So if you reach behind yourself and you grab the dust,
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11:59 - 12:01if you reach into your body and grab your genome,
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12:01 - 12:04if you reach into the medical system and somehow extract your medical record,
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12:04 - 12:08you can actually go through an online informed consent process --
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12:08 - 12:10because the donation to the commons must be voluntary
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12:10 - 12:13and it must be informed -- and you can actually upload
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12:13 - 12:16your information and have it syndicated to the
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12:16 - 12:19mathematicians who will do this sort of big data research,
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12:19 - 12:21and the goal is to get 100,000 in the first year
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12:21 - 12:24and a million in the first five years so that we have
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12:24 - 12:28a statistically significant cohort that you can use to take
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12:28 - 12:30smaller sample sizes from traditional research
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12:30 - 12:32and map it against,
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12:32 - 12:35so that you can use it to tease out those subtle correlations
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12:35 - 12:37between the variations that make us unique
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12:37 - 12:41and the kinds of health that we need to move forward as a society.
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12:41 - 12:44And I've spent a lot of time around other commons.
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12:44 - 12:47I've been around the early web. I've been around
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12:47 - 12:49the early creative commons world, and there's four things
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12:49 - 12:53that all of these share, which is, they're all really simple.
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12:53 - 12:56And so if you were to go to the website and enroll in this study,
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12:56 - 12:58you're not going to see something complicated.
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12:58 - 13:03But it's not simplistic. These things are weak intentionally,
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13:03 - 13:06right, because you can always add power and control to a system,
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13:06 - 13:10but it's very difficult to remove those things if you put them in at the beginning,
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13:10 - 13:12and so being simple doesn't mean being simplistic,
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13:12 - 13:15and being weak doesn't mean weakness.
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13:15 - 13:17Those are strengths in the system.
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13:17 - 13:20And open doesn't mean that there's no money.
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13:20 - 13:23Closed systems, corporations, make a lot of money
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13:23 - 13:26on the open web, and they're one of the reasons why the open web lives
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13:26 - 13:29is that corporations have a vested interest in the openness
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13:29 - 13:31of the system.
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13:31 - 13:35And so all of these things are part of the clinical study that we've created,
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13:35 - 13:39so you can actually come in, all you have to be is 14 years old,
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13:39 - 13:41willing to sign a contract that says I'm not going to be a jerk,
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13:41 - 13:43basically, and you're in.
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13:43 - 13:45You can start analyzing the data.
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13:45 - 13:49You do have to solve a CAPTCHA as well. (Laughter)
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13:49 - 13:53And if you'd like to build corporate structures on top of it,
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13:53 - 13:56that's okay too. That's all in the consent,
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13:56 - 13:58so if you don't like those terms, you don't come in.
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13:58 - 14:01It's very much the design principles of a commons
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14:01 - 14:04that we're trying to bring to health data.
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14:04 - 14:07And the other thing about these systems is that it only takes
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14:07 - 14:10a small number of really unreasonable people working together
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14:10 - 14:13to create them. It didn't take that many people
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14:13 - 14:17to make Wikipedia Wikipedia, or to keep it Wikipedia.
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14:17 - 14:19And we're not supposed to be unreasonable in health,
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14:19 - 14:21and so I hate this word "patient."
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14:21 - 14:24I don't like being patient when systems are broken,
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14:24 - 14:27and health care is broken.
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14:27 - 14:31I'm not talking about the politics of health care, I'm talking about the way we scientifically approach health care.
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14:31 - 14:34So I don't want to be patient. And the task I'm giving to you
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14:34 - 14:37is to not be patient. So I'd like you to actually try,
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14:37 - 14:40when you go home, to get your data.
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14:40 - 14:43You'll be shocked and offended and, I would bet, outraged,
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14:43 - 14:46at how hard it is to get it.
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14:46 - 14:48But it's a challenge that I hope you'll take,
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14:48 - 14:51and maybe you'll share it. Maybe you won't.
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14:51 - 14:52If you don't have anyone in your family who's sick,
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14:52 - 14:55maybe you wouldn't be unreasonable. But if you do,
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14:55 - 14:57or if you've been sick, then maybe you would.
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14:57 - 15:01And we're going to be able to do an experiment in the next several months
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15:01 - 15:04that lets us know exactly how many unreasonable people are out there.
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15:04 - 15:06So this is the Athena Breast Health Network. It's a study
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15:06 - 15:10of 150,000 women in California, and they're going to
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15:10 - 15:12return all the data to the participants of the study
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15:12 - 15:15in a computable form, with one-clickability to load it into
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15:15 - 15:18the study that I've put together. So we'll know exactly
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15:18 - 15:20how many people are willing to be unreasonable.
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15:20 - 15:23So what I'd end [with] is,
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15:23 - 15:26the most beautiful thing I've learned since I quit my job
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15:26 - 15:29almost a year ago to do this, is that it really doesn't take
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15:29 - 15:33very many of us to achieve spectacular results.
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15:33 - 15:36You just have to be willing to be unreasonable,
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15:36 - 15:38and the risk we're running is not the risk those 14 men
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15:38 - 15:40who got yellow fever ran. Right?
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15:40 - 15:43It's to be naked, digitally, in public. So you know more
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15:43 - 15:46about me and my health than I know about you. It's asymmetric now.
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15:46 - 15:50And being naked and alone can be terrifying.
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15:50 - 15:55But to be naked in a group, voluntarily, can be quite beautiful.
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15:55 - 15:56And so it doesn't take all of us.
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15:56 - 15:59It just takes all of some of us. Thank you.
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15:59 - 16:05(Applause)
- Title:
- Let's pool our medical data
- Speaker:
- John Wilbanks
- Description:
-
When you're getting medical treatment, or taking part in medical testing, privacy is important; strict laws limit what researchers can see and know about you. But what if your medical data could be used -- anonymously -- by anyone seeking to test a hypothesis? John Wilbanks wonders if the desire to protect our privacy is slowing research, and if opening up medical data could lead to a wave of health care innovation.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 16:25
Morton Bast edited English subtitles for Let's pool our medical data | ||
Morton Bast edited English subtitles for Let's pool our medical data | ||
Thu-Huong Ha approved English subtitles for Let's pool our medical data | ||
Thu-Huong Ha edited English subtitles for Let's pool our medical data | ||
Morton Bast accepted English subtitles for Let's pool our medical data | ||
Morton Bast edited English subtitles for Let's pool our medical data | ||
Joseph Geni added a translation |