Peter van Manen: How can Formula 1 racing help ... babies?

80,263 views ・ 2013-08-01

TED


請雙擊下方英文字幕播放視頻。

譯者: Yi-Ting Chung 審譯者: Jessie Lee
00:12
Motor racing is a funny old business.
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賽車是個有趣的老行業
00:14
We make a new car every year,
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每年我們都會造一部新車
00:16
and then we spend the rest of the season
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而當季的其他時間
00:19
trying to understand what it is we've built
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就用來了解我們達成了那些
00:21
to make it better, to make it faster.
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然後想辦法突破、讓車子能跑得更快
00:25
And then the next year, we start again.
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下一年,重新開始
00:28
Now, the car you see in front of you is quite complicated.
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大家眼前的這部車結構相當複雜
00:32
The chassis is made up of about 11,000 components,
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車子底盤大約由一萬一千組零件所構成
00:36
the engine another 6,000,
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引擎則使用了六千組零件
00:38
the electronics about eight and a half thousand.
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電子設備將近八千五百組零件
00:41
So there's about 25,000 things there that can go wrong.
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所以總共約有兩萬五千組零件可能出錯
00:46
So motor racing is very much about attention to detail.
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因此賽車是非常重視細節的
00:51
The other thing about Formula 1 in particular
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尤其是一級方程式賽車
00:54
is we're always changing the car.
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我們不斷地改車
00:56
We're always trying to make it faster.
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總想盡辦法讓它能跑得更快
00:58
So every two weeks, we will be making
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所以每兩週,我們就會製造
01:01
about 5,000 new components to fit to the car.
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大約五千組新零件來組裝
01:05
Five to 10 percent of the race car
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該年度會有 5% 到 10% 的賽車
01:08
will be different every two weeks of the year.
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每兩週就會改頭換面一次
01:11
So how do we do that?
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我們是如何辦到的呢?
01:14
Well, we start our life with the racing car.
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嗯,我們的生活是從賽車開始的
01:17
We have a lot of sensors on the car to measure things.
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我們在車子裡安裝了許多感應器來監測車況
01:21
On the race car in front of you here
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你們眼前的這部賽車
01:23
there are about 120 sensors when it goes into a race.
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比賽時裝了約一百廿個感應器
01:26
It's measuring all sorts of things around the car.
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來測量整部車的車況
01:30
That data is logged. We're logging about
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資料會被記錄下來,我們在資料系統裡
01:32
500 different parameters within the data systems,
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記錄了大約五百種不同的參數
01:36
about 13,000 health parameters and events
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大約一萬三千個健康參數和事件
01:39
to say when things are not working the way they should do,
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車子出狀況時,這些紀錄能派上用場
01:44
and we're sending that data back to the garage
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我們利用遙測技術
以每秒 2-4 兆位元的速率 將資料傳回修車廠
01:47
using telemetry at a rate of two to four megabits per second.
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所以一場兩小時的比賽中 每部車會傳送出
01:52
So during a two-hour race, each car will be sending
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01:55
750 million numbers.
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七億五千萬個數字
01:57
That's twice as many numbers as words that each of us
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這是我們每個人一輩子
02:00
speaks in a lifetime.
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說話用字的兩倍之多
02:02
It's a huge amount of data.
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這是很龐大的資料
02:05
But it's not enough just to have data and measure it.
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但是光有資料或能測量資料是不夠的
02:07
You need to be able to do something with it.
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還必須要懂得如何運用這些資料
02:09
So we've spent a lot of time and effort
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所以我們花了很多時間和精力
02:12
in turning the data into stories
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把數據轉換成故事
02:14
to be able to tell, what's the state of the engine,
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可以解讀引擎的狀況如何、
02:17
how are the tires degrading,
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輪胎的耗損情形、
02:19
what's the situation with fuel consumption?
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車子油耗又是如何?
02:23
So all of this is taking data
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所以這些就是紀錄資料
02:26
and turning it into knowledge that we can act upon.
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並將其轉換為 我們能採取行動的知識
02:29
Okay, so let's have a look at a little bit of data.
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好,讓我們來看看一點點資料
02:32
Let's pick a bit of data from
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這些資料取自於
02:34
another three-month-old patient.
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一個三個月大的病童
02:37
This is a child, and what you're seeing here is real data,
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他是個小孩子,大家現在看到的是真實數據
02:41
and on the far right-hand side,
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在最右手邊
02:43
where everything starts getting a little bit catastrophic,
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可以看到情況開始變的有點糟糕
02:46
that is the patient going into cardiac arrest.
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這病患的心搏漸漸停止
02:49
It was deemed to be an unpredictable event.
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這是出乎預料的事情
02:53
This was a heart attack that no one could see coming.
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沒人料想到會心臟病發
02:56
But when we look at the information there,
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但是當我們檢查這些資訊時
02:59
we can see that things are starting to become
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我們可以發現,大約在心搏停止前五分鐘
03:01
a little fuzzy about five minutes or so before the cardiac arrest.
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數據開始變得有點模糊不清
03:05
We can see small changes
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我們可以看到如心搏速率等
03:07
in things like the heart rate moving.
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有些微的變化
03:10
These were all undetected by normal thresholds
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這些原本能應用在數據資料上的
03:12
which would be applied to data.
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都沒有被正常閾值偵測到
03:15
So the question is, why couldn't we see it?
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所以問題在於為什麼我們看不到?
03:18
Was this a predictable event?
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這是可以預測的事嗎?
03:20
Can we look more at the patterns in the data
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我們能不能從資料的模式中看出端倪
03:23
to be able to do things better?
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並採取更好的行動呢?
03:27
So this is a child,
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這個小孩
03:29
about the same age as the racing car on stage,
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年紀跟台上這輛賽車相仿
03:33
three months old.
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都是三個月大
03:34
It's a patient with a heart problem.
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這是一名心臟疾病患者
03:37
Now, when you look at some of the data on the screen above,
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現在你看看螢幕上方的資料
03:40
things like heart rate, pulse, oxygen, respiration rates,
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心跳速率、脈搏、含氧量、呼吸速率
03:45
they're all unusual for a normal child,
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都不是一個正常小朋友該有的數據
03:48
but they're quite normal for the child there,
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但是對這個小朋友卻相當正常
03:51
and so one of the challenges you have in health care is,
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因此在醫療照護上所面臨的挑戰之一是
03:55
how can I look at the patient in front of me,
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如何判斷眼前的病人
03:58
have something which is specific for her,
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有她專屬的資料模式
04:01
and be able to detect when things start to change,
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並在情況改變、病情開始惡化時
04:04
when things start to deteriorate?
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有所察覺?
04:06
Because like a racing car, any patient,
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因為跟賽車的道理一樣,任何患者
04:09
when things start to go bad, you have a short time
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遇到緊急狀況時,我們只有極短的時間
04:12
to make a difference.
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扭轉命運
04:14
So what we did is we took a data system
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所以我們使用一個 在一級方程式賽車年度裡
04:17
which we run every two weeks of the year in Formula 1
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每兩週執行一次的資料系統
04:20
and we installed it on the hospital computers
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並將其安裝於
04:23
at Birmingham Children's Hospital.
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伯明罕兒童醫院的電腦裡
04:25
We streamed data from the bedside instruments
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我們從小兒科加護病房內的
04:27
in their pediatric intensive care
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床邊儀器得到數據
04:30
so that we could both look at the data in real time
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如此一來我們可以即時掌握資料
04:33
and, more importantly, to store the data
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而且更重要的是,能夠儲存資料
04:36
so that we could start to learn from it.
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讓我們能開始從資料中學習
04:39
And then, we applied an application on top
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然後,我們運用一個可以即時讓我們
04:44
which would allow us to tease out the patterns in the data
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整理出資料模式的應用程式
04:47
in real time so we could see what was happening,
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我們就能看到發生了什麼事
04:50
so we could determine when things started to change.
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我們就能判斷什麼時候開始有狀況
04:54
Now, in motor racing, we're all a little bit ambitious,
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之於賽車,我們都有點企圖心、
04:58
audacious, a little bit arrogant sometimes,
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有點大膽、有時也有點自負
05:00
so we decided we would also look at the children
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所以我們決定當那些小朋友 被送往加護病房的途中
05:04
as they were being transported to intensive care.
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也要能觀察到他們的資料
05:06
Why should we wait until they arrived in the hospital
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為什麼我們要等到他們抵達醫院
05:09
before we started to look?
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才開始呢?
05:11
And so we installed a real-time link
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因此我們在救護車及醫院之間
05:14
between the ambulance and the hospital,
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安裝了一個即時連結機制
05:16
just using normal 3G telephony to send that data
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只需使用一般 3G 通訊傳送資料
05:20
so that the ambulance became an extra bed
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這樣一來,救護車就等同於
05:23
in intensive care.
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加護病房裡的床位了
05:26
And then we started looking at the data.
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然後我們開始看那些數據
05:30
So the wiggly lines at the top, all the colors,
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上面那些波浪狀的線,所有顏色
05:32
this is the normal sort of data you would see on a monitor --
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都是我們在監測器上 會看到的正常數據資料
05:36
heart rate, pulse, oxygen within the blood,
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心跳速率、脈搏、血中的含氧量
05:39
and respiration.
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及呼吸速率
05:42
The lines on the bottom, the blue and the red,
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底部的藍線和紅線
05:45
these are the interesting ones.
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滿有趣的
05:46
The red line is showing an automated version
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紅線代表自動感應的
05:49
of the early warning score
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預警分數
05:51
that Birmingham Children's Hospital were already running.
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這些資料是伯明罕兒童醫院 已經跑過的數據
他們從 2008 年就開始跑這些數據
05:54
They'd been running that since 2008,
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05:56
and already have stopped cardiac arrests
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而且已經成功阻止了
05:58
and distress within the hospital.
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院內心搏停止和心臟窘迫的案例
06:01
The blue line is an indication
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而藍線呢
06:03
of when patterns start to change,
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是資料模式開始產生變化的指標
06:06
and immediately, before we even started
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立即地,甚至在我們開始
06:08
putting in clinical interpretation,
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臨床解讀這些數據之前
06:10
we can see that the data is speaking to us.
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數據已經在對我們說話
06:13
It's telling us that something is going wrong.
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告訴我們情況不妙
06:16
The plot with the red and the green blobs,
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這張有紅點與綠點的圖表
06:20
this is plotting different components
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標示出相互對立資料的不同成分
06:23
of the data against each other.
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標示出相互對立資料的不同成分
06:25
The green is us learning what is normal for that child.
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綠色是我們所知那位病童的正常情況資料
06:29
We call it the cloud of normality.
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我們稱之為常態雲圖
06:32
And when things start to change,
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當情況開始轉變、
06:34
when conditions start to deteriorate,
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病情開始惡化時
06:37
we move into the red line.
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我們會移到紅線上
06:39
There's no rocket science here.
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這完全不涉及艱深的學問
06:41
It is displaying data that exists already in a different way,
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只是以不同的方式呈現既有的資料
06:45
to amplify it, to provide cues to the doctors,
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詳述它、提供醫生護士更多線索
06:48
to the nurses, so they can see what's happening.
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他們就能了解正在發生什麼事
06:51
In the same way that a good racing driver
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同樣地,一位優秀的賽車手
06:54
relies on cues to decide when to apply the brakes,
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也仰賴線索來決定他何時該煞車、
06:58
when to turn into a corner,
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何時該轉彎
06:59
we need to help our physicians and our nurses
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我們須要幫助我們的醫生和護士
07:02
to see when things are starting to go wrong.
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讓他們在情況不妙時能夠察覺
07:06
So we have a very ambitious program.
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所以我們有一個野心勃勃的計畫
07:09
We think that the race is on to do something differently.
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我們認為賽車該採取不同的行動了
07:14
We are thinking big. It's the right thing to do.
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我們目標很高,這是對的事情
07:17
We have an approach which, if it's successful,
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我們有這個方法,而且如果它成功了
07:20
there's no reason why it should stay within a hospital.
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我們就沒理由只在醫院裡運用它
這方法能走出醫院之牆
07:23
It can go beyond the walls.
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07:24
With wireless connectivity these days,
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現在到處都有無線連結
07:26
there is no reason why patients, doctors and nurses
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沒有理由病人、醫生和護士
07:30
always have to be in the same place
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為何總是必須同時在同一個地方
07:32
at the same time.
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為何總是必須同時在同一個地方
07:34
And meanwhile, we'll take our little three-month-old baby,
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此刻,我們會讓這個三個月大的寶寶
07:38
keep taking it to the track, keeping it safe,
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安安全全地待在軌道上
07:42
and making it faster and better.
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跑得更快更好
07:44
Thank you very much.
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謝謝大家
07:45
(Applause)
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(掌聲)
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