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Motor racing is a funny old business.

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We make a new car every year,

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and then we spend the rest of the season

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trying to understand
what it is we've built

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to make it better, to make it faster.

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And then the next year, we start again.

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Now, the car you see in front
of you is quite complicated.

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The chassis is made
up of about 11,000 components,

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the engine another 6,000,

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the electronics
about eight and a half thousand.

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So there's about 25,000 things
there that can go wrong.

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So motor racing is very much
about attention to detail.

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The other thing about Formula
1 in particular

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is we're always changing the car.

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We're always trying to make it faster.

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So every two weeks, we will be making

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about 5,000 new components
to fit to the car.

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Five to 10 percent of the race car

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will be different
every two weeks of the year.

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So how do we do that?

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Well, we start our life
with the racing car.

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We have a lot of sensors
on the car to measure things.

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On the race car in front of you here

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there are about 120 sensors
when it goes into a race.

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It's measuring all sorts
of things around the car.

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That data is logged. We're logging about

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500 different parameters
within the data systems,

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about 13,000 health parameters and events

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to say when things are not
working the way they should do,

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and we're sending that data
back to the garage

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using telemetry at a rate
of two to four megabits per second.

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So during a two-hour race,
each car will be sending

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750 million numbers.

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That's twice as many numbers
as words that each of us

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speaks in a lifetime.

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It's a huge amount of data.

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But it's not enough just
to have data and measure it.

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You need to be able to do
something with it.

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So we've spent a lot of time and effort

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in turning the data into stories

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to be able to tell,
what's the state of the engine,

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how are the tires degrading,

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what's the situation
with fuel consumption?

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So all of this is taking data

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and turning it into knowledge
that we can act upon.

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Okay, so let's have a look
at a little bit of data.

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Let's pick a bit of data from

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another three-month-old patient.

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This is a child, and what you're
seeing here is real data,

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and on the far right-hand side,

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where everything starts getting
a little bit catastrophic,

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that is the patient going
into cardiac arrest.

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It was deemed to be
an unpredictable event.

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This was a heart attack
that no one could see coming.

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But when we look at the information there,

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we can see that things
are starting to become

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a little fuzzy about five minutes
or so before the cardiac arrest.

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We can see small changes

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in things like the heart rate moving.

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These were all undetected
by normal thresholds

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which would be applied to data.

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So the question is, why
couldn't we see it?

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Was this a predictable event?

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Can we look more
at the patterns in the data

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to be able to do things better?

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So this is a child,

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about the same age
as the racing car on stage,

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three months old.

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It's a patient with a heart problem.

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Now, when you look at some of the data
on the screen above,

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things like heart rate, pulse,
oxygen, respiration rates,

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they're all unusual for a normal child,

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but they're quite normal
for the child there,

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and so one of the challenges
you have in health care is,

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how can I look at the patient
in front of me,

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have something which is specific for her,

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and be able to detect when
things start to change,

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when things start to deteriorate?

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Because like a racing car, any patient,

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when things start to go
bad, you have a short time

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to make a difference.

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So what we did is we took a data system

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which we run every two weeks
of the year in Formula 1

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and we installed it
on the hospital computers

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at Birmingham Children's Hospital.

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We streamed data
from the bedside instruments

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in their pediatric intensive care

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so that we could both look
at the data in real time

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and, more importantly, to store the data

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so that we could start to learn from it.

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And then, we applied an application on top

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which would allow us to tease
out the patterns in the data

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in real time so we could
see what was happening,

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so we could determine when
things started to change.

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Now, in motor racing, we're
all a little bit ambitious,

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audacious, a little bit
arrogant sometimes,

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so we decided we would also
look at the children

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as they were being transported
to intensive care.

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Why should we wait
until they arrived in the hospital

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before we started to look?

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And so we installed a real-time link

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between the ambulance and the hospital,

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just using normal 3G
telephony to send that data

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so that the ambulance became an extra bed

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in intensive care.

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And then we started looking at the data.

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So the wiggly lines
at the top, all the colors,

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this is the normal sort of data
you would see on a monitor --

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heart rate, pulse,
oxygen within the blood,

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and respiration.

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The lines on the bottom,
the blue and the red,

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these are the interesting ones.

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The red line is showing
an automated version

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of the early warning score

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that Birmingham Children's Hospital
were already running.

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They'd been running that since 2008,

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and already have stopped cardiac arrests

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and distress within the hospital.

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The blue line is an indication

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of when patterns start to change,

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and immediately, before we even started

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putting in clinical interpretation,

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we can see that the data
is speaking to us.

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It's telling us that something
is going wrong.

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The plot with the red and the green blobs,

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this is plotting different components

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of the data against each other.

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The green is us learning
what is normal for that child.

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We call it the cloud of normality.

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And when things start to change,

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when conditions start to deteriorate,

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we move into the red line.

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There's no rocket science here.

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It is displaying data that exists
already in a different way,

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to amplify it, to provide
cues to the doctors,

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to the nurses, so they can
see what's happening.

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In the same way that a good racing driver

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relies on cues to decide
when to apply the brakes,

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when to turn into a corner,

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we need to help
our physicians and our nurses

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to see when things
are starting to go wrong.

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So we have a very ambitious program.

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We think that the race is on to do
something differently.

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We are thinking big.
It's the right thing to do.

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We have an approach which,
if it's successful,

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there's no reason why it
should stay within a hospital.

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It can go beyond the walls.

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With wireless connectivity these days,

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there is no reason why
patients, doctors and nurses

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always have to be in the same place

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at the same time.

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And meanwhile, we'll take
our little three-month-old baby,

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keep taking it to the track,
keeping it safe,

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and making it faster and better.

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Thank you very much.

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(Applause)