How can Formula 1 racing help ... babies?
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0:00 - 0:03Motor racing is a funny old business.
-
0:03 - 0:05We make a new car every year,
-
0:05 - 0:07and then we spend the rest of the season
-
0:07 - 0:10trying to understand
what it is we've built -
0:10 - 0:13to make it better, to make it faster.
-
0:13 - 0:16And then the next year, we start again.
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0:16 - 0:21Now, the car you see in front
of you is quite complicated. -
0:21 - 0:24The chassis is made
up of about 11,000 components, -
0:24 - 0:27the engine another 6,000,
-
0:27 - 0:30the electronics
about eight and a half thousand. -
0:30 - 0:34So there's about 25,000 things
there that can go wrong. -
0:34 - 0:39So motor racing is very much
about attention to detail. -
0:39 - 0:42The other thing about Formula
1 in particular -
0:42 - 0:44is we're always changing the car.
-
0:44 - 0:47We're always trying to make it faster.
-
0:47 - 0:50So every two weeks, we will be making
-
0:50 - 0:54about 5,000 new components
to fit to the car. -
0:54 - 0:56Five to 10 percent of the race car
-
0:56 - 1:00will be different
every two weeks of the year. -
1:00 - 1:02So how do we do that?
-
1:02 - 1:06Well, we start our life
with the racing car. -
1:06 - 1:10We have a lot of sensors
on the car to measure things. -
1:10 - 1:12On the race car in front of you here
-
1:12 - 1:15there are about 120 sensors
when it goes into a race. -
1:15 - 1:19It's measuring all sorts
of things around the car. -
1:19 - 1:21That data is logged. We're logging about
-
1:21 - 1:24500 different parameters
within the data systems, -
1:24 - 1:28about 13,000 health parameters and events
-
1:28 - 1:32to say when things are not
working the way they should do, -
1:33 - 1:35and we're sending that data
back to the garage -
1:35 - 1:40using telemetry at a rate
of two to four megabits per second. -
1:40 - 1:43So during a two-hour race,
each car will be sending -
1:43 - 1:46750 million numbers.
-
1:46 - 1:49That's twice as many numbers
as words that each of us -
1:49 - 1:50speaks in a lifetime.
-
1:50 - 1:53It's a huge amount of data.
-
1:53 - 1:56But it's not enough just
to have data and measure it. -
1:56 - 1:58You need to be able to do
something with it. -
1:58 - 2:00So we've spent a lot of time and effort
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2:00 - 2:02in turning the data into stories
-
2:02 - 2:05to be able to tell,
what's the state of the engine, -
2:05 - 2:08how are the tires degrading,
-
2:08 - 2:11what's the situation
with fuel consumption? -
2:11 - 2:14So all of this is taking data
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2:14 - 2:18and turning it into knowledge
that we can act upon. -
2:18 - 2:21Okay, so let's have a look
at a little bit of data. -
2:21 - 2:23Let's pick a bit of data from
-
2:23 - 2:26another three-month-old patient.
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2:26 - 2:30This is a child, and what you're
seeing here is real data, -
2:30 - 2:32and on the far right-hand side,
-
2:32 - 2:35where everything starts getting
a little bit catastrophic, -
2:35 - 2:38that is the patient going
into cardiac arrest. -
2:38 - 2:41It was deemed to be
an unpredictable event. -
2:41 - 2:45This was a heart attack
that no one could see coming. -
2:45 - 2:48But when we look at the information there,
-
2:48 - 2:50we can see that things
are starting to become -
2:50 - 2:54a little fuzzy about five minutes
or so before the cardiac arrest. -
2:54 - 2:56We can see small changes
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2:56 - 2:58in things like the heart rate moving.
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2:58 - 3:01These were all undetected
by normal thresholds -
3:01 - 3:03which would be applied to data.
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3:03 - 3:06So the question is, why
couldn't we see it? -
3:06 - 3:09Was this a predictable event?
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3:09 - 3:12Can we look more
at the patterns in the data -
3:12 - 3:15to be able to do things better?
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3:15 - 3:18So this is a child,
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3:18 - 3:21about the same age
as the racing car on stage, -
3:21 - 3:23three months old.
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3:23 - 3:25It's a patient with a heart problem.
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3:25 - 3:29Now, when you look at some of the data
on the screen above, -
3:29 - 3:34things like heart rate, pulse,
oxygen, respiration rates, -
3:34 - 3:37they're all unusual for a normal child,
-
3:37 - 3:40but they're quite normal
for the child there, -
3:40 - 3:44and so one of the challenges
you have in health care is, -
3:44 - 3:47how can I look at the patient
in front of me, -
3:47 - 3:50have something which is specific for her,
-
3:50 - 3:52and be able to detect when
things start to change, -
3:52 - 3:54when things start to deteriorate?
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3:54 - 3:57Because like a racing car, any patient,
-
3:58 - 4:00when things start to go
bad, you have a short time -
4:00 - 4:02to make a difference.
-
4:02 - 4:05So what we did is we took a data system
-
4:05 - 4:08which we run every two weeks
of the year in Formula 1 -
4:08 - 4:11and we installed it
on the hospital computers -
4:11 - 4:13at Birmingham Children's Hospital.
-
4:14 - 4:16We streamed data
from the bedside instruments -
4:16 - 4:18in their pediatric intensive care
-
4:18 - 4:22so that we could both look
at the data in real time -
4:22 - 4:25and, more importantly, to store the data
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4:25 - 4:28so that we could start to learn from it.
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4:28 - 4:32And then, we applied an application on top
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4:32 - 4:36which would allow us to tease
out the patterns in the data -
4:36 - 4:38in real time so we could
see what was happening, -
4:38 - 4:42so we could determine when
things started to change. -
4:42 - 4:46Now, in motor racing, we're
all a little bit ambitious, -
4:46 - 4:49audacious, a little bit
arrogant sometimes, -
4:49 - 4:52so we decided we would also
look at the children -
4:52 - 4:55as they were being transported
to intensive care. -
4:55 - 4:57Why should we wait
until they arrived in the hospital -
4:58 - 4:59before we started to look?
-
4:59 - 5:02And so we installed a real-time link
-
5:02 - 5:05between the ambulance and the hospital,
-
5:05 - 5:09just using normal 3G
telephony to send that data -
5:09 - 5:11so that the ambulance became an extra bed
-
5:11 - 5:14in intensive care.
-
5:14 - 5:18And then we started looking at the data.
-
5:18 - 5:21So the wiggly lines
at the top, all the colors, -
5:21 - 5:24this is the normal sort of data
you would see on a monitor -- -
5:24 - 5:28heart rate, pulse,
oxygen within the blood, -
5:28 - 5:31and respiration.
-
5:31 - 5:33The lines on the bottom,
the blue and the red, -
5:33 - 5:35these are the interesting ones.
-
5:35 - 5:38The red line is showing
an automated version -
5:38 - 5:39of the early warning score
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5:39 - 5:42that Birmingham Children's Hospital
were already running. -
5:42 - 5:44They'd been running that since 2008,
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5:44 - 5:47and already have stopped cardiac arrests
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5:47 - 5:49and distress within the hospital.
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5:49 - 5:52The blue line is an indication
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5:52 - 5:54of when patterns start to change,
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5:54 - 5:57and immediately, before we even started
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5:57 - 5:58putting in clinical interpretation,
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5:58 - 6:01we can see that the data
is speaking to us. -
6:01 - 6:05It's telling us that something
is going wrong. -
6:05 - 6:08The plot with the red and the green blobs,
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6:09 - 6:11this is plotting different components
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6:11 - 6:14of the data against each other.
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6:14 - 6:18The green is us learning
what is normal for that child. -
6:18 - 6:20We call it the cloud of normality.
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6:20 - 6:23And when things start to change,
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6:23 - 6:25when conditions start to deteriorate,
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6:25 - 6:27we move into the red line.
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6:27 - 6:29There's no rocket science here.
-
6:29 - 6:33It is displaying data that exists
already in a different way, -
6:33 - 6:36to amplify it, to provide
cues to the doctors, -
6:37 - 6:39to the nurses, so they can
see what's happening. -
6:39 - 6:42In the same way that a good racing driver
-
6:42 - 6:46relies on cues to decide
when to apply the brakes, -
6:46 - 6:48when to turn into a corner,
-
6:48 - 6:51we need to help
our physicians and our nurses -
6:51 - 6:54to see when things
are starting to go wrong. -
6:54 - 6:57So we have a very ambitious program.
-
6:57 - 7:02We think that the race is on to do
something differently. -
7:02 - 7:05We are thinking big.
It's the right thing to do. -
7:05 - 7:08We have an approach which,
if it's successful, -
7:08 - 7:11there's no reason why it
should stay within a hospital. -
7:11 - 7:13It can go beyond the walls.
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7:13 - 7:15With wireless connectivity these days,
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7:15 - 7:18there is no reason why
patients, doctors and nurses -
7:18 - 7:20always have to be in the same place
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7:20 - 7:22at the same time.
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7:22 - 7:26And meanwhile, we'll take
our little three-month-old baby, -
7:26 - 7:30keep taking it to the track,
keeping it safe, -
7:30 - 7:33and making it faster and better.
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7:33 - 7:34Thank you very much.
-
7:34 - 7:39(Applause)
- Title:
- How can Formula 1 racing help ... babies?
- Speaker:
- Peter van Manen
- Description:
-
During a Formula 1 race, a car sends hundreds of millions of data points to its garage for real-time analysis and feedback. So why not use this detailed and rigorous data system elsewhere, like ... at children’s hospitals? Peter van Manen tells us more.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 07:56
Krystian Aparta edited English subtitles for Better baby care -- thanks to Formula 1 | ||
Jenny Zurawell edited English subtitles for Better baby care -- thanks to Formula 1 | ||
Thu-Huong Ha approved English subtitles for Better baby care -- thanks to Formula 1 | ||
Thu-Huong Ha edited English subtitles for Better baby care -- thanks to Formula 1 | ||
Thu-Huong Ha edited English subtitles for Better baby care -- thanks to Formula 1 | ||
Thu-Huong Ha edited English subtitles for Better baby care -- thanks to Formula 1 | ||
Morton Bast accepted English subtitles for Better baby care -- thanks to Formula 1 | ||
Morton Bast edited English subtitles for Better baby care -- thanks to Formula 1 |