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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 One 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
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is, 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 One
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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)