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Peter van Manen: Better baby care -- thanks to Formula 1

Peter van Manen: Kako utrke Formule 1 mogu pomoći...djeci?

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Tijekom utrke formule 1, automobil šalje stotine milijuna podataka u garažu koje se analiziraju i šalju natrag u realnom vremenu. Zašto ne koristiti ovaj detaljan i rigorozan sustav podataka drugdje, kao u dječjim bolnicama? Peter van Manen će nam reći više.

- Electronic systems expert
Peter van Manen is the Managing Director of McLaren Electronics, which provides data systems to major motorsports series. Full bio

Motor racing is a funny old business.
Utrke automobila star su i smiješan posao.
00:12
We make a new car every year,
Stvaramo nove automobile svake godine,
00:14
and then we spend the rest of the season
a ostatak godine provodimo
00:16
trying to understand what it is we've built
pokušavajući razumjeti što smo napravili
00:19
to make it better, to make it faster.
da bude bolje, da bude brže.
00:21
And then the next year, we start again.
A sljedeće godine, počinjemo iz početka.
00:25
Now, the car you see in front of you is quite complicated.
Automobil koji vidite ispred sebe prilično je kompliciran.
00:28
The chassis is made up of about 11,000 components,
Šasija je izrađena od oko 11.000 komponenti,
00:32
the engine another 6,000,
motor od još 6.000,
00:36
the electronics about eight and a half thousand.
elektronika od oko 8.500.
00:38
So there's about 25,000 things there that can go wrong.
Dakle ovdje je oko 25.000 stvari koje mogu poći po zlu.
00:41
So motor racing is very much about attention to detail.
Utrke automobila su uglavnom obraćanje
pažnje na detalje.
00:46
The other thing about Formula 1 in particular
Druga osobita stvar u Formuli 1
00:51
is we're always changing the car.
je da uvijek mijenjamo automobil.
00:54
We're always trying to make it faster.
Uvijek ga pokušavamo napraviti da bude brži.
00:56
So every two weeks, we will be making
Svaka dva tjedna, napravit ćemo
00:58
about 5,000 new components to fit to the car.
oko 5.000 novih komponenti za automobil.
01:01
Five to 10 percent of the race car
5 do 10 posto trkaćih automobila
01:05
will be different every two weeks of the year.
bit će drugačije svaka dva tjedna tokom godine.
01:08
So how do we do that?
Kako to radimo?
01:11
Well, we start our life with the racing car.
Započinjemo život sa trkaćim automobilom.
01:14
We have a lot of sensors on the car to measure things.
Postoji mnogo senzora u automobilu koji mjere stvari.
01:17
On the race car in front of you here
Trkaći automobil ispred vas
01:21
there are about 120 sensors when it goes into a race.
ima oko 120 senzora kada se utrkuje.
01:23
It's measuring all sorts of things around the car.
Mjere se razne komponente automobila.
01:26
That data is logged. We're logging about
Ti podatci se prijavljuju. Dobivamo oko
01:30
500 different parameters within the data systems,
500 različitih parametara unutar sustava podataka
01:32
about 13,000 health parameters and events
oko 13.000 zdravstvenih parametara i događanja
01:36
to say when things are not working the way they should do,
koja nam govore kada nešto ne radi kako bi trebalo,
01:39
and we're sending that data back to the garage
i te podatke šaljemo natrag u garažu
01:44
using telemetry at a rate of two to four megabits per second.
koristeći telemetriju, brzinom dva
do četri megabita u sekundi.
01:47
So during a two-hour race, each car will be sending
Tijekom dvosatne utrke, svaki automobil će poslati
01:52
750 million numbers.
750 milijuna brojeva.
01:55
That's twice as many numbers as words that each of us
To je dvostruko više brojeva nego riječi koje
01:57
speaks in a lifetime.
mi izgovorimo tokom života.
02:00
It's a huge amount of data.
To je velika količina podataka.
02:02
But it's not enough just to have data and measure it.
Ali nije dovoljno samo imati podatke i mjeriti ih.
02:05
You need to be able to do something with it.
Treba moći nešto učiniti s njima.
02:07
So we've spent a lot of time and effort
Pa smo potrošili mnogo vremena i truda
02:09
in turning the data into stories
kako bismo pretvorili podatke u priče
02:12
to be able to tell, what's the state of the engine,
koje nam mogu reći kakvo je stanje motora,
02:14
how are the tires degrading,
kako se gume troše,
02:17
what's the situation with fuel consumption?
kakva je situacija s potrošnjom goriva.
02:19
So all of this is taking data
Dakle, to je prikupljanje i pretvaranje
02:23
and turning it into knowledge that we can act upon.
podataka u znanje na koje možemo djelovati.
02:26
Okay, so let's have a look at a little bit of data.
U redu, pogledajmo malu količinu podataka.
02:29
Let's pick a bit of data from
Prikupimo podatke od
02:32
another three-month-old patient.
jednog pacijenta, starog tri mjeseca.
02:34
This is a child, and what you're seeing here is real data,
Ovo je dijete, a ovo što vidite pravi su podatci.
02:37
and on the far right-hand side,
Na desnoj strani ekrana,
02:41
where everything starts getting a little bit catastrophic,
gdje sve postaje pomalo katastrofično,
02:43
that is the patient going into cardiac arrest.
vidimo da pacijentu počinje zatajivati srce.
02:46
It was deemed to be an unpredictable event.
To smatramo nepredviđenim događajem.
02:49
This was a heart attack that no one could see coming.
To je bio srčani udar kojeg nitko
nije mogao predvidjeti.
02:53
But when we look at the information there,
Kada pogledamo informacije ovdje,
02:56
we can see that things are starting to become
možemo vidjeti kako stvari postaju
02:59
a little fuzzy about five minutes or so before the cardiac arrest.
pomalo nejasne oko pet minuta prije zastoja srca.
03:01
We can see small changes
Možemo vidjeti male promjene
03:05
in things like the heart rate moving.
u nekim stvarima poput otkucaja srca.
03:07
These were all undetected by normal thresholds
Sve je bilo neotkriveno normalnim pragovima
03:10
which would be applied to data.
koji bi bili primijenjeni podatcima.
03:12
So the question is, why couldn't we see it?
Pitanje je, zašto to nismo vidjeli?
03:15
Was this a predictable event?
Je li ovo bio očekivani događaj?
03:18
Can we look more at the patterns in the data
Možemo li pogledati malo više na uzorke u podatcima
03:20
to be able to do things better?
da bismo mogli stvari raditi bolje?
03:23
So this is a child,
Dakle ovo je dijete,
03:27
about the same age as the racing car on stage,
otprilike iste starosti kao i vozilo na pozornici,
03:29
three months old.
tri mjeseca staro.
03:33
It's a patient with a heart problem.
Ono je pacijent sa srčanim problemom.
03:34
Now, when you look at some of the data on the screen above,
Sada, kad pogledate u neke podatke na zaslonu,
03:37
things like heart rate, pulse, oxygen, respiration rates,
stvari poput otkucaja srca, pulsa, kisika, udisaja,
03:40
they're all unusual for a normal child,
svi su neobičajeni za normalno dijete,
03:45
but they're quite normal for the child there,
ali oni su prilično normalni za ono dijete,
03:48
and so one of the challenges you have in health care is,
jedan od izazova koje imate u zdravstvu je,
03:51
how can I look at the patient in front of me,
kako mogu pogledati pacijenta ispred sebe,
03:55
have something which is specific for her,
koji ima nešto specifično,
03:58
and be able to detect when things start to change,
kako detektirati kad se pojave promjene,
04:01
when things start to deteriorate?
kada se počinji stvarati greške?
04:04
Because like a racing car, any patient,
Kao i kod trkaćeg automobila, kod svakog pacijenta
04:06
when things start to go bad, you have a short time
kada stvari krenu krivo, imate vrlo kratko vrijeme
04:09
to make a difference.
za raditi razliku.
04:12
So what we did is we took a data system
Mi smo uzeli sustav podataka
04:14
which we run every two weeks of the year in Formula 1
s kojim svakih dva tjedna u godini vozimo Formulu 1
04:17
and we installed it on the hospital computers
i instalirali ga na bolnička računala
04:20
at Birmingham Children's Hospital.
u dječjoj bolnici Birmingham.
04:23
We streamed data from the bedside instruments
Prenosili smo podatke s instrumenata
koji su bili na krevetu
04:25
in their pediatric intensive care
njihovim pedijatrima na intenzivnoj njezi
04:27
so that we could both look at the data in real time
mogli smo gledati u podatke u stvarnom vremenu
04:30
and, more importantly, to store the data
a što je još važnije, mogli smo pohraniti te podatke
04:33
so that we could start to learn from it.
kako bismo mogli učiti iz njih.
04:36
And then, we applied an application on top
Tada smo počeli primjenjivati aplikaciju
04:39
which would allow us to tease out the patterns in the data
koja nam je dopustila da pročešljamo
po obrascima unutar podataka
04:44
in real time so we could see what was happening,
u stvarnom vremenu te smo mogli
vidjeti što se događa,
04:47
so we could determine when things started to change.
mogli smo utvrditi kada su se stvari počele mijenjati.
04:50
Now, in motor racing, we're all a little bit ambitious,
Sada, u automobilskim utrkama,
svi smo malo ambiciozni,
04:54
audacious, a little bit arrogant sometimes,
odvažni, a ponekad pomalo arogantni,
04:58
so we decided we would also look at the children
zato smo odlučili da ćemo gledati djecu
05:00
as they were being transported to intensive care.
koja su prevezena na intenzivnu njegu.
05:04
Why should we wait until they arrived in the hospital
Zašto da čekamo da dođu u bolnicu
05:06
before we started to look?
prije nego što ih počnemo pregledavati?
05:09
And so we installed a real-time link
Instalirali smo vezu u realnom vremenu
05:11
between the ambulance and the hospital,
između kola hitne pomoći i bolnice
05:14
just using normal 3G telephony to send that data
koristeći samo normalnu 3G
telefoniju za poslati te podatke
05:16
so that the ambulance became an extra bed
tako su kola hitne pomoći postala dodatni ležaj
05:20
in intensive care.
na intenzivnoj njezi.
05:23
And then we started looking at the data.
Počeli smo gledati u podatke.
05:26
So the wiggly lines at the top, all the colors,
valovite linije na vrhu, svih boja,
05:30
this is the normal sort of data you would see on a monitor --
ovo su normalne vrste podataka
koje se vide na zaslonu --
05:32
heart rate, pulse, oxygen within the blood,
otkucaji srca, puls, kisik u krvi,
05:36
and respiration.
i disanje.
05:39
The lines on the bottom, the blue and the red,
Linije na dnu, plava i crvena,
05:42
these are the interesting ones.
vrlo su zanimljive.
05:45
The red line is showing an automated version
Crvena linija prikazuje automatiziranu verziju
05:46
of the early warning score
o ranom upozorenju
05:49
that Birmingham Children's Hospital were already running.
koje dječja bolnica Birmingham već vidi.
05:51
They'd been running that since 2008,
Oni to pokreću od 2008,
05:53
and already have stopped cardiac arrests
i već su zaustavili zastoje srca
05:56
and distress within the hospital.
i nevolje unutar bolnice.
05:58
The blue line is an indication
Plava linija pokazuje
06:01
of when patterns start to change,
kada se uzorak počinje mijenjati,
06:03
and immediately, before we even started
i trenutno, prije nego počne
06:06
putting in clinical interpretation,
klinička obrada,
06:08
we can see that the data is speaking to us.
možemo vidjeti što nam taj podatak govori.
06:10
It's telling us that something is going wrong.
Govori da nešto nije uredu.
06:13
The plot with the red and the green blobs,
grafički podaci s crvenom i zelenom mrljom,
06:16
this is plotting different components
to su grafički podaci različitih komponenti
06:20
of the data against each other.
od međusobnih podataka.
06:23
The green is us learning what is normal for that child.
Zelena nas uči što je normalno za to dijete.
06:25
We call it the cloud of normality.
To zovemo oblak normalnosti.
06:29
And when things start to change,
A kada se stvari počinju mijenjati,
06:32
when conditions start to deteriorate,
kada se uvijeti počinju mijenjati,
06:34
we move into the red line.
prelazimo u crvenu liniju.
06:37
There's no rocket science here.
Ovdje nema raketne tehnologije.
06:39
It is displaying data that exists already in a different way,
To prikazuje podatke koji već
postoje u drugom obliku,
06:41
to amplify it, to provide cues to the doctors,
za pojačati ih, osigurati signale doktorima,
06:45
to the nurses, so they can see what's happening.
sestrama, kako bi vidjeli što se događa.
06:48
In the same way that a good racing driver
Na isti način dobar vozač automobilskih utrka
06:51
relies on cues to decide when to apply the brakes,
oslanja se na signale da odluči kada će početi kočiti,
06:54
when to turn into a corner,
kada će skrenuti u zavoj,
06:58
we need to help our physicians and our nurses
mi moramo pomoći svojim doktorima i sestrama
06:59
to see when things are starting to go wrong.
da vide kada stvari krenu u krivom smjeru.
07:02
So we have a very ambitious program.
Stoga imamo vrlo ambiciozan program.
07:06
We think that the race is on to do something differently.
Mislimo da je utrka mjesto
gdje možemo učiniti nešto drukčije.
07:09
We are thinking big. It's the right thing to do.
Razmišljamo na veliko. To je ispravna stvar za učiniti.
07:14
We have an approach which, if it's successful,
Imamo pristup koji je uspješan,
07:17
there's no reason why it should stay within a hospital.
nema razloga zašto bi stajalo unutar bolnice.
07:20
It can go beyond the walls.
Može se smjestiti iza zidova.
07:22
With wireless connectivity these days,
Bežičnom vezom,
07:24
there is no reason why patients, doctors and nurses
nema razloga da su pacijenti, doktori i sestre
07:26
always have to be in the same place
uvijek na istom mjestu,
07:30
at the same time.
u isto vrijeme.
07:32
And meanwhile, we'll take our little three-month-old baby,
U međuvremenu, mi ćemo
našu tri mjeseca staru bebu,
07:34
keep taking it to the track, keeping it safe,
nastaviti pratiti, čuvati sigurnom,
07:38
and making it faster and better.
i raditi da bude brže i bolje.
07:42
Thank you very much.
Hvala vam puno.
07:44
(Applause)
(Pljesak)
07:45
Translated by Kristina Gottwald
Reviewed by Senzos Osijek

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About the speaker:

Peter van Manen - Electronic systems expert
Peter van Manen is the Managing Director of McLaren Electronics, which provides data systems to major motorsports series.

Why you should listen

To say that Peter van Manen has a high-speed job would be an understatement. As Managing Director of McLaren Electronics, which provides electronics and data collection software to motorsports events, he and his team work in real time during a race to improve cars on about 500 different parameters. That's about 750 million data points in two hours.

But recently van Manen and his team have been wondering: Why can't the extremely precise and subtle data-collection and analysis systems used in motorsports be applied elsewhere, for the benefit of all? They have applied their systems to ICU units at Birmingham Children's Hospital with real-time analysis that allows them to proactively prevent cardiac arrests. The unit has seen a 25 percent decrease in life-threatening events. And it's just the beginning.

More profile about the speaker
Peter van Manen | Speaker | TED.com