0:00:00.840,0:00:02.040
Science.

0:00:02.760,0:00:06.176
The very word for many of you conjures[br]unhappy memories of boredom

0:00:06.200,0:00:09.096
in high school biology or physics class.

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But let me assure that what you did there

0:00:12.240,0:00:14.416
had very little to do with science.

0:00:14.440,0:00:16.736
That was really the "what" of science.

0:00:16.760,0:00:19.480
It was the history[br]of what other people had discovered.

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What I'm most interested in as a scientist

0:00:23.080,0:00:25.216
is the "how" of science.

0:00:25.240,0:00:29.056
Because science is knowledge in process.

0:00:29.080,0:00:32.536
We make an observation,[br]guess an explanation for that observation,

0:00:32.560,0:00:34.616
and then make a prediction[br]that we can test

0:00:34.640,0:00:36.560
with an experiment or other observation.

0:00:37.080,0:00:38.416
A couple of examples.

0:00:38.440,0:00:42.016
First of all, people noticed[br]that the Earth was below, the sky above,

0:00:42.040,0:00:45.920
and both the Sun and the Moon[br]seemed to go around them.

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Their guessed explanation

0:00:48.280,0:00:51.360
was that the Earth must be[br]the center of the universe.

0:00:52.240,0:00:55.440
The prediction: everything[br]should circle around the Earth.

0:00:56.120,0:00:57.776
This was first really tested

0:00:57.800,0:01:00.616
when Galileo got his hands[br]on one of the first telescopes,

0:01:00.640,0:01:03.016
and as he gazed into the night sky,

0:01:03.040,0:01:06.736
what he found there was a planet, Jupiter,

0:01:06.760,0:01:10.760
with four moons circling around it.

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He then used those moons[br]to follow the path of Jupiter

0:01:16.160,0:01:19.896
and found that Jupiter[br]also was not going around the Earth

0:01:19.920,0:01:21.880
but around the Sun.

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So the prediction test failed.

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And this led to[br]the discarding of the theory

0:01:28.520,0:01:30.696
that the Earth was the center[br]of the universe.

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Another example: Sir Isaac Newton[br]noticed that things fall to the Earth.

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The guessed explanation was gravity,

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the prediction that everything[br]should fall to the Earth.

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But of course, not everything[br]does fall to the Earth.

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So did we discard gravity?

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No. We revised the theory and said,[br]gravity pulls things to the Earth

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unless there is an equal[br]and opposite force in the other direction.

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This led us to learn something new.

0:02:00.920,0:02:04.176
We began to pay more attention[br]to the bird and the bird's wings,

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and just think of all the discoveries

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that have flown[br]from that line of thinking.

0:02:09.639,0:02:14.776
So the test failures,[br]the exceptions, the outliers

0:02:14.800,0:02:19.360
teach us what we don't know[br]and lead us to something new.

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This is how science moves forward.[br]This is how science learns.

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Sometimes in the media,[br]and even more rarely,

0:02:26.120,0:02:28.536
but sometimes even scientists will say

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that something or other[br]has been scientifically proven.

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But I hope that you understand[br]that science never proves anything

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definitively forever.

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Hopefully science remains curious enough

0:02:43.360,0:02:44.776
to look for

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and humble enough to recognize

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when we have found

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the next outlier,

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the next exception,

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which, like Jupiter's moons,

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teaches us what we don't actually know.

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We're going to change gears[br]here for a second.

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The caduceus, or the symbol of medicine,

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means a lot of different things[br]to different people,

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but most of our[br]public discourse on medicine

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really turns it into[br]an engineering problem.

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We have the hallways of Congress,

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and the boardrooms of insurance companies[br]that try to figure out how to pay for it.

0:03:15.680,0:03:17.296
The ethicists and epidemiologists

0:03:17.320,0:03:20.016
try to figure out[br]how best to distribute medicine,

0:03:20.040,0:03:22.696
and the hospitals and physicians[br]are absolutely obsessed

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with their protocols and checklists,

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trying to figure out[br]how best to safely apply medicine.

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These are all good things.

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However, they also all assume

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at some level

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that the textbook of medicine is closed.

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We start to measure[br]the quality of our health care

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by how quickly we can access it.

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It doesn't surprise me[br]that in this climate,

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many of our institutions[br]for the provision of health care

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start to look a heck of a lot[br]like Jiffy Lube.

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(Laughter)

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The only problem is that[br]when I graduated from medical school,

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I didn't get one of those[br]little doohickeys

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that your mechanic[br]has to plug into your car

0:04:02.760,0:04:05.136
and find out exactly what's wrong with it,

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because the textbook of medicine

0:04:07.280,0:04:08.800
is not closed.

0:04:09.320,0:04:11.160
Medicine is science.

0:04:11.560,0:04:14.240
Medicine is knowledge in process.

0:04:15.280,0:04:16.656
We make an observation,

0:04:16.680,0:04:18.815
we guess an explanation[br]of that observation,

0:04:18.839,0:04:21.456
and then we make a prediction[br]that we can test.

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Now, the testing ground[br]of most predictions in medicine

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is populations,

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and you may remember[br]from those boring days in biology class

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that populations tend to distribute

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around a mean

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as a Gaussian or a normal curve.

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Therefore, in medicine,

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after we make a prediction[br]from a guessed explanation,

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we test it in a population.

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That means that what we know in medicine,

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our knowledge and our know-how,

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comes from populations,

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but extends only as far

0:04:53.640,0:04:55.376
as the next outlier,

0:04:55.400,0:04:56.616
the next exception,

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which, like Jupiter's moons,

0:04:58.400,0:05:00.800
will teach us what we don't actually know.

0:05:02.080,0:05:03.416
Now, I am a surgeon

0:05:03.440,0:05:05.856
who looks after patients with sarcoma.

0:05:05.880,0:05:08.080
Sarcoma is a very rare form of cancer.

0:05:08.720,0:05:10.760
It's the cancer of flesh and bones.

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And I would tell you that every one[br]of my patients is an outlier,

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is an exception.

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There is no surgery I have ever performed[br]for a sarcoma patient

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that has ever been guided[br]by a randomized controlled clinical trial,

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what we consider the best kind[br]of population-based evidence in medicine.

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People talk about thinking[br]outside the box,

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but we don't even have a box in sarcoma.

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What we do have as we take[br]a bath in the uncertainty

0:05:38.840,0:05:42.976
and unknowns and exceptions[br]and outliers that surround us in sarcoma

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is easy access to what I think[br]are those two most important values

0:05:47.560,0:05:49.096
for any science:

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humility and curiosity.

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Because if I am humble and curious,

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when a patient asks me a question,

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and I don't know the answer,

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I'll ask a colleague

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who may have a similar[br]albeit distinct patient with sarcoma.

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We'll even establish[br]international collaborations.

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Those patients will start[br]to talk to each other through chat rooms

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and support groups.

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It's through this kind[br]of humbly curious communication

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that we begin to try and learn new things.

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As an example, this is a patient of mine

0:06:21.320,0:06:23.000
who had a cancer near his knee.

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Because of humbly curious communication

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in international collaborations,

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we have learned that we can repurpose[br]the ankle to serve as the knee

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when we have to remove the knee[br]with the cancer.

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He can then wear a prosthetic[br]and run and jump and play.

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This opportunity was available to him

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because of international collaborations.

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It was desirable to him

0:06:45.920,0:06:48.880
because he had contacted other patients[br]who had experienced it.

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And so exceptions and outliers in medicine

0:06:54.000,0:06:57.960
teach us what we don't know,[br]but also lead us to new thinking.

0:06:59.080,0:07:00.936
Now, very importantly,

0:07:00.960,0:07:04.816
all the new thinking that outliers[br]and exceptions lead us to in medicine

0:07:04.840,0:07:08.200
does not only apply[br]to the outliers and exceptions.

0:07:08.920,0:07:12.096
It is not that we only learn[br]from sarcoma patients

0:07:12.120,0:07:14.080
ways to manage sarcoma patients.

0:07:14.920,0:07:16.976
Sometimes, the outliers

0:07:17.000,0:07:18.696
and the exceptions

0:07:18.720,0:07:21.960
teach us things that matter quite a lot[br]to the general population.

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Like a tree standing outside a forest,

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the outliers and the exceptions[br]draw our attention

0:07:29.280,0:07:33.616
and lead us into a much greater sense[br]of perhaps what a tree is.

0:07:33.640,0:07:36.136
We often talk about[br]losing the forests for the trees,

0:07:36.160,0:07:37.976
but one also loses a tree

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within a forest.

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But the tree that stands out by itself

0:07:42.880,0:07:45.776
makes those relationships[br]that define a tree,

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the relationships between trunk[br]and roots and branches,

0:07:49.640,0:07:50.880
much more apparent.

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Even if that tree is crooked

0:07:53.080,0:07:56.056
or even if that tree[br]has very unusual relationships

0:07:56.080,0:07:58.376
between trunk and roots and branches,

0:07:58.400,0:08:01.096
it nonetheless draws our attention

0:08:01.120,0:08:03.016
and allows us to make observations

0:08:03.040,0:08:05.280
that we can then test[br]in the general population.

0:08:06.000,0:08:07.976
I told you that sarcomas are rare.

0:08:08.000,0:08:10.640
They make up about one percent[br]of all cancers.

0:08:11.280,0:08:15.240
You also probably know that cancer[br]is considered a genetic disease.

0:08:15.840,0:08:19.176
By genetic disease we mean[br]that cancer is caused by oncogenes

0:08:19.200,0:08:20.576
that are turned on in cancer

0:08:20.600,0:08:23.640
and tumor suppressor genes[br]that are turned off to cause cancer.

0:08:24.160,0:08:26.576
You might think[br]that we learned about oncogenes

0:08:26.600,0:08:28.816
and tumor suppressor genes[br]from common cancers

0:08:28.840,0:08:30.816
like breast cancer and prostate cancer

0:08:30.840,0:08:32.336
and lung cancer,

0:08:32.360,0:08:33.559
but you'd be wrong.

0:08:34.000,0:08:36.895
We learned about oncogenes[br]and tumor suppressor genes

0:08:36.919,0:08:38.135
for the first time

0:08:38.159,0:08:41.600
in that itty-bitty little one percent[br]of cancers called sarcoma.

0:08:42.760,0:08:45.336
In 1966, Peyton Rous got the Nobel Prize

0:08:45.360,0:08:47.376
for realizing that chickens

0:08:47.400,0:08:50.520
had a transmissible form of sarcoma.

0:08:51.320,0:08:54.096
30 years later, Harold Varmus[br]and Mike Bishop discovered

0:08:54.120,0:08:56.656
what that transmissible element was.

0:08:56.680,0:08:58.256
It was a virus

0:08:58.280,0:08:59.696
carrying a gene,

0:08:59.720,0:09:01.160
the src oncogene.

0:09:01.880,0:09:05.536
Now, I will not tell you[br]that src is the most important oncogene.

0:09:05.560,0:09:06.776
I will not tell you

0:09:06.800,0:09:10.296
that src is the most frequently[br]turned on oncogene in all of cancer.

0:09:10.320,0:09:12.760
But it was the first oncogene.

0:09:13.960,0:09:16.296
The exception, the outlier

0:09:16.320,0:09:18.840
drew our attention and led us to something

0:09:19.520,0:09:23.560
that taught us very important things[br]about the rest of biology.

0:09:24.880,0:09:28.976
Now, TP53 is the most important[br]tumor suppressor gene.

0:09:29.000,0:09:31.736
It is the most frequently turned off[br]tumor suppressor gene

0:09:31.760,0:09:33.560
in almost every kind of cancer,

0:09:34.360,0:09:36.656
but we didn't learn about it[br]from common cancers.

0:09:36.680,0:09:39.096
We learned about it[br]when doctors Li and Fraumeni

0:09:39.120,0:09:40.696
were looking at families

0:09:40.720,0:09:42.736
and they realized that these families

0:09:42.760,0:09:45.280
had way too many sarcomas.

0:09:45.920,0:09:47.696
I told you that sarcoma is rare.

0:09:47.720,0:09:50.896
Remember that a one[br]in a million diagnosis,

0:09:50.920,0:09:53.056
if it happens twice in one family,

0:09:53.080,0:09:55.480
is way too common in that family.

0:09:56.640,0:09:59.336
The very fact that these are rare

0:09:59.360,0:10:00.800
draws our attention

0:10:01.760,0:10:04.000
and leads us to new kinds of thinking.

0:10:05.480,0:10:06.936
Now, many of you may say,

0:10:06.960,0:10:08.496
and may rightly say,

0:10:08.520,0:10:10.416
that yeah, Kevin, that's great,

0:10:10.440,0:10:12.496
but you're not talking[br]about a bird's wing.

0:10:12.520,0:10:16.000
You're not talking about moons[br]floating around some planet Jupiter.

0:10:16.520,0:10:18.056
This is a person.

0:10:18.080,0:10:21.336
This outlier, this exception,[br]may lead to the advancement of science,

0:10:21.360,0:10:22.560
but this is a person.

0:10:24.280,0:10:25.896
And all I can say

0:10:25.920,0:10:28.280
is that I know that all too well.

0:10:29.760,0:10:33.160
I have conversations with these patients[br]with rare and deadly diseases.

0:10:33.800,0:10:35.736
I write about these conversations.

0:10:35.760,0:10:38.056
These conversations are terribly fraught.

0:10:38.080,0:10:39.896
They're fraught with horrible phrases

0:10:39.920,0:10:43.160
like "I have bad news"[br]or "there's nothing more we can do."

0:10:43.760,0:10:46.960
Sometimes these conversations[br]turn on a single word:

0:10:47.760,0:10:48.960
"terminal."

0:10:52.920,0:10:55.840
Silence can also be rather uncomfortable.

0:10:57.360,0:10:59.936
Where the blanks are in medicine

0:10:59.960,0:11:01.816
can be just as important

0:11:01.840,0:11:04.080
as the words that we use[br]in these conversations.

0:11:05.080,0:11:06.616
What are the unknowns?

0:11:06.640,0:11:08.840
What are the experiments[br]that are being done?

0:11:09.680,0:11:11.376
Do this little exercise with me.

0:11:11.400,0:11:14.616
Up there on the screen,[br]you see this phrase, "no where."

0:11:14.640,0:11:15.920
Notice where the blank is.

0:11:16.680,0:11:19.880
If we move that blank one space over

0:11:20.640,0:11:22.216
"no where"

0:11:22.240,0:11:24.936
becomes "now here,"

0:11:24.960,0:11:26.856
the exact opposite meaning,

0:11:26.880,0:11:29.080
just by shifting the blank one space over.

0:11:31.680,0:11:33.256
I'll never forget the night

0:11:33.280,0:11:35.520
that I walked into[br]one of my patients' rooms.

0:11:36.280,0:11:37.936
I had been operating long that day

0:11:37.960,0:11:39.976
but I still wanted to come and see him.

0:11:40.000,0:11:43.200
He was a boy I had diagnosed[br]with a bone cancer a few days before.

0:11:43.840,0:11:46.896
He and his mother had been meeting[br]with the chemotherapy doctors

0:11:46.920,0:11:48.136
earlier that day

0:11:48.160,0:11:51.136
and he had been admitted[br]to the hospital to begin chemotherapy.

0:11:51.160,0:11:53.336
It was almost midnight[br]when I got to his room.

0:11:53.360,0:11:55.536
He was asleep, but I found his mother

0:11:55.560,0:11:57.136
reading by flashlight

0:11:57.160,0:11:58.616
next to his bed.

0:11:58.640,0:12:01.440
She came out in the hall[br]to chat with me for a few minutes.

0:12:02.280,0:12:04.376
It turned out that[br]what she had been reading

0:12:04.400,0:12:06.576
was the protocol[br]that the chemotherapy doctors

0:12:06.600,0:12:07.840
had given her that day.

0:12:08.200,0:12:09.440
She had memorized it.

0:12:11.200,0:12:14.736
She said, "Doctor Jones, you told me

0:12:14.760,0:12:16.936
that we don't always win

0:12:16.960,0:12:18.240
with this type of cancer,

0:12:19.680,0:12:23.160
but I've been studying this protocol,[br]and I think I can do it.

0:12:23.960,0:12:27.536
I think I can comply[br]with these very difficult treatments.

0:12:27.560,0:12:30.536
I'm going to quit my job.[br]I'm going to move in with my parents.

0:12:30.560,0:12:32.520
I'm going to keep my baby safe."

0:12:35.320,0:12:36.520
I didn't tell her.

0:12:37.840,0:12:40.760
I didn't stop to correct her thinking.

0:12:41.680,0:12:43.936
She was trusting in a protocol

0:12:43.960,0:12:47.176
that even if complied with,

0:12:47.200,0:12:49.600
wouldn't necessarily save her son.

0:12:51.960,0:12:53.160
I didn't tell her.

0:12:54.360,0:12:55.760
I didn't fill in that blank.

0:12:57.080,0:12:59.056
But a year and a half later

0:12:59.080,0:13:01.800
her boy nonetheless died of his cancer.

0:13:03.400,0:13:04.720
Should I have told her?

0:13:05.360,0:13:07.616
Now, many of you may say, "So what?

0:13:07.640,0:13:08.896
I don't have sarcoma.

0:13:08.920,0:13:10.816
No one in my family has sarcoma.

0:13:10.840,0:13:12.296
And this is all fine and well,

0:13:12.320,0:13:15.016
but it probably doesn't[br]matter in my life."

0:13:15.040,0:13:16.296
And you're probably right.

0:13:16.320,0:13:19.000
Sarcoma may not matter[br]a whole lot in your life.

0:13:21.040,0:13:23.376
But where the blanks are in medicine

0:13:23.400,0:13:24.720
does matter in your life.

0:13:26.520,0:13:28.816
I didn't tell you one dirty little secret.

0:13:28.840,0:13:33.216
I told you that in medicine,[br]we test predictions in populations,

0:13:33.240,0:13:34.496
but I didn't tell you,

0:13:34.520,0:13:36.736
and so often medicine never tells you

0:13:36.760,0:13:39.616
that every time an individual

0:13:39.640,0:13:41.736
encounters medicine,

0:13:41.760,0:13:45.800
even if that individual is firmly[br]embedded in the general population,

0:13:47.360,0:13:49.736
neither the individual[br]nor the physician knows

0:13:49.760,0:13:52.440
where in that population[br]the individual will land.

0:13:53.040,0:13:55.736
Therefore, every encounter with medicine

0:13:55.760,0:13:57.200
is an experiment.

0:13:57.920,0:13:59.936
You will be a subject

0:13:59.960,0:14:01.640
in an experiment.

0:14:02.560,0:14:07.400
And the outcome will be either[br]a better or a worse result for you.

0:14:08.320,0:14:10.336
As long as medicine works well,

0:14:10.360,0:14:13.376
we're fine with fast service,

0:14:13.400,0:14:16.840
bravado, brimmingly[br]confident conversations,

0:14:17.720,0:14:19.376
but when things don't work well,

0:14:19.400,0:14:21.240
sometimes we want something different.

0:14:22.520,0:14:25.800
A colleague of mine[br]removed a tumor from a patient's limb.

0:14:26.920,0:14:28.736
He was concerned about this tumor.

0:14:28.760,0:14:31.776
In our physician conferences,[br]he talked about his concern

0:14:31.800,0:14:33.216
that this was a type of tumor

0:14:33.240,0:14:35.800
that had a high risk[br]for coming back in the same limb.

0:14:36.680,0:14:38.656
But his conversations with the patient

0:14:38.680,0:14:40.776
were exactly what a patient might want:

0:14:40.800,0:14:42.056
brimming with confidence.

0:14:42.080,0:14:45.096
He said, "I got it all[br]and you're good to go."

0:14:45.120,0:14:46.856
She and her husband were thrilled.

0:14:46.880,0:14:50.960
They went out, celebrated, fancy dinner,[br]opened a bottle of champagne.

0:14:52.040,0:14:54.336
The only problem was a few weeks later,

0:14:54.360,0:14:57.456
she started to notice[br]another nodule in the same area.

0:14:57.480,0:15:01.616
It turned out he hadn't gotten it all[br]and she wasn't good to go.

0:15:01.640,0:15:04.480
But what happened at this juncture[br]absolutely fascinates me.

0:15:05.200,0:15:06.816
My colleague came to me and said,

0:15:06.840,0:15:09.560
"Kevin, would you mind[br]looking after this patient for me?"

0:15:10.240,0:15:13.456
I said, "Why, you know the right thing[br]to do as well as I do.

0:15:13.480,0:15:15.096
You haven't done anything wrong."

0:15:15.120,0:15:19.600
He said, "Please, just look[br]after this patient for me."

0:15:21.200,0:15:22.736
He was embarrassed --

0:15:22.760,0:15:24.160
not by what he had done,

0:15:25.154,0:15:27.040
but by the conversation that he had had,

0:15:27.760,0:15:29.200
by the overconfidence.

0:15:30.600,0:15:33.216
So I performed[br]a much more invasive surgery

0:15:33.240,0:15:36.376
and had a very different conversation[br]with the patient afterwards.

0:15:36.400,0:15:38.736
I said, "Most likely I've gotten it all

0:15:38.760,0:15:41.176
and you're most likely good to go,

0:15:41.200,0:15:44.360
but this is the experiment[br]that we're doing.

0:15:45.040,0:15:47.056
This is what you're going to watch for.

0:15:47.080,0:15:48.976
This is what I'm going to watch for.

0:15:49.000,0:15:52.936
And we're going to work together[br]to find out if this surgery will work

0:15:52.960,0:15:54.280
to get rid of your cancer."

0:15:54.920,0:15:56.856
I can guarantee you, she and her husband

0:15:56.880,0:15:59.800
did not crack another bottle of champagne[br]after talking to me.

0:16:01.600,0:16:04.456
But she was now a scientist,

0:16:04.480,0:16:07.840
not only a subject in her experiment.

0:16:09.960,0:16:11.576
And so I encourage you

0:16:11.600,0:16:15.056
to seek humility and curiosity

0:16:15.080,0:16:16.280
in your physicians.

0:16:16.760,0:16:19.736
Almost 20 billion times each year,

0:16:19.760,0:16:23.696
a person walks into a doctor's office,

0:16:23.720,0:16:26.000
and that person becomes a patient.

0:16:27.320,0:16:30.840
You or someone you love[br]will be that patient sometime very soon.

0:16:31.840,0:16:33.480
How will you talk to your doctors?

0:16:34.640,0:16:35.840
What will you tell them?

0:16:36.760,0:16:38.280
What will they tell you?

0:16:40.600,0:16:42.816
They cannot tell you

0:16:42.840,0:16:44.360
what they do not know,

0:16:45.560,0:16:49.120
but they can tell you when they don't know

0:16:50.280,0:16:51.640
if only you'll ask.

0:16:52.160,0:16:55.000
So please, join the conversation.

0:16:56.200,0:16:57.416
Thank you.

0:16:57.440,0:16:59.080
(Applause)