TEDxAustin
Joel Selanikio: The big-data revolution in health care
Joel Selanikio: 意外的醫療保健大數據革新的開端
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收集全球健康數據是一個不完美的科學過程：工作者們走訪民宅敲門詢問，將回答記錄在紙質表單上，然後輸入數據——基於這些漏洞百出的資訊，那些國家作出重大決策。數據專家 Joel Selanikio 談論了過去幾十年在健康醫療數據收集方面所獲得的巨大改變——從開始使用掌上電腦到 Hotmail，到現在使用雲端技術。
Joel Selanikio - Health and technology activist
Dr. Joel Selanikio combines technology and data to help solve global health challenges. Full bio
Dr. Joel Selanikio combines technology and data to help solve global health challenges. Full bio
Double-click the English transcript below to play the video.
00:12
There's an old joke about a cop who's walking his beat
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有一個很老的笑話是說
00:15
in the middle of the night,
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有一個警察在深夜裡巡邏，
00:16
and he comes across a guy under a street lamp
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他遇到一個人在路燈下
00:18
who's looking at the ground and moving from side to side,
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一邊盯著地面看一邊來回走
00:21
and the cop asks him what he's doing.
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警察問他在做什麼。
00:23
The guys says he's looking for his keys.
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那人說他正在找他的鑰匙。
00:25
So the cop takes his time and looks over
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所以警察也花時間幫他找
00:27
and kind of makes a little matrix and looks
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並且做了一些小記號，
00:29
for about two, three minutes. No keys.
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大概花了兩、 三分鐘。
還是沒找到鑰匙。
還是沒找到鑰匙。
00:32
The cop says, "Are you sure? Hey buddy,
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警察說，"嘿，朋友，你確定你的鑰匙
00:35
are you sure you lost your keys here?"
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丟在這裡了嗎？”
00:37
And the guy says, "No, no, actually I lost them
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這個人說 “不，不，其實我把它們丟在了
00:38
down at the other end of the street,
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這條街的另一邊，
00:40
but the light is better here."
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但是這裡的光更亮。”
00:46
There's a concept that people talk about nowadays
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現如今，人們談論著一個概念叫大數據，
00:47
called big data, and what they're talking about
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他們談論的是
00:50
is all of the information that we're generating
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我們通過網際網路互動時
00:52
through our interaction with and over the Internet,
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我們生成的所有資訊，
00:55
everything from Facebook and Twitter
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所有東西，從臉書、推特
00:56
to music downloads, movies, streaming, all this kind of stuff,
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到音樂下載、 電影、 串流媒體
所有這類的東西，
所有這類的東西，
01:01
the live streaming of TED.
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TED 的直播。
01:02
And the folks who work with big data, for them,
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對於那些處理大數據的人們來說，
01:05
they talk about that their biggest problem is
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他們談論的最大問題是
01:07
we have so much information,
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我們有太多的資訊，
01:09
the biggest problem is, how do we organize all that information?
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我們面對的最大問題是，
我們如何整理所有的資訊？
我們如何整理所有的資訊？
01:12
I can tell you that working in global health,
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我告訴你，在全球健康領域工作，
01:15
that is not our biggest problem.
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這不是我們最大的問題。
01:18
Because for us, even though the light
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因為對我們來說，
01:19
is better on the Internet,
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即使網路上的燈更亮，
01:22
the data that would help us solve the problems
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即使網上的數據能幫我們解決一些問題
01:25
we're trying to solve is not actually present on the Internet.
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但我們試圖去解決的問題
實際上卻並不存在於網路上。
實際上卻並不存在於網路上。
01:28
So we don't know, for example, how many people
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所以我們不知道，
01:30
right now are being affected by disasters
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例如，有多少人正在遭受災難
01:32
or by conflict situations.
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或陷入戰爭囹圄之中。
01:35
We don't know for really basically any of the clinics
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我們不知道發展中國家的診所裡
01:39
in the developing world, which ones have medicines
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哪些有藥品，
01:41
and which ones don't.
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哪些沒有藥品。
01:42
We have no idea of what the supply chain is for those clinics.
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我們完全不知道這些診所的供應鏈是怎樣的。
01:45
We don't know -- and this is really amazing to me --
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我們不知道——這真得讓我很吃驚——
01:48
we don't know how many children were born,
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我們不知道有多少孩子出生，
01:51
or how many children there are in Bolivia
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或有多少兒童在玻利維亞
01:54
or Botswana or Bhutan.
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博茨瓦納或不丹。
01:57
We don't know how many kids died last week
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我們不知道在任何這些國家裡，
01:59
in any of those countries.
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上星期有多少孩子死去。
02:01
We don't know the needs of the elderly, the mentally ill.
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我們不知道老年人和精神病患者的需要。
02:04
For all of these different critically important problems
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對於所有這些不同的卻非常重要的問題
02:07
or critically important areas that we want to solve problems in,
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或我們想要解決問題的非常重要的領域，
02:10
we basically know nothing at all.
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我們基本上一無所知。
02:15
And part of the reason why we don't know anything at all
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造成我們一無所知的部分原因
02:18
is that the information technology systems
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是我們在全球健康組織工作時，
02:20
that we use in global health to find the data
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用來查找資料，解決問題的資訊技術系統
02:24
to solve these problems is what you see here.
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就是您在這裡看到的。
02:27
And this is about a 5,000-year-old technology.
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這是一個大約有五千年歷史的技術。
02:29
Some of you may have used it before.
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你們有些人此前可能用過。
02:30
It's kind of on its way out now, but we still use it
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它現在已經過時了，
02:32
for 99 percent of our stuff.
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但我們 99% 的工作都使用它。
02:34
This is a paper form, and what you're looking at
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這是紙質表單，
02:38
is a paper form in the hand of a Ministry of Health nurse
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你正在看的是健康部護士
手中的一張紙質表單
手中的一張紙質表單
02:42
in Indonesia who is tramping out across the countryside
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她正徒步穿行在印度尼西亞的鄉下，
02:45
in Indonesia on, I'm sure, a very hot and humid day,
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在印尼，我確信，那是個十分炎熱和潮濕的一天，
02:49
and she is going to be knocking on thousands of doors
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她要在幾週或幾個月內
02:51
over a period of weeks or months,
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去敲千家萬戶的門
02:53
knocking on the doors and saying, "Excuse me,
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邊敲門邊說，"打擾了，
02:56
we'd like to ask you some questions.
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我們想問你一些問題。
02:58
Do you have any children? Were your children vaccinated?"
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你有孩子嗎？你的孩子接種疫苗了嗎?"
03:02
Because the only way we can actually find out
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因為讓我們可以找出
03:03
how many children were vaccinated in the country of Indonesia,
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在印尼有多少兒童接種了疫苗
03:06
what percentage were vaccinated, is actually not
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和他們接種了疫苗的百分比的唯一方法，
03:09
on the Internet but by going out and knocking on doors,
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其實不在網際網路上，
而是通過走出去，敲那些門，
而是通過走出去，敲那些門，
03:12
sometimes tens of thousands of doors.
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有時需要敲千千萬萬的門。
03:15
Sometimes it takes months to even years
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有時需要幾個月甚至幾年
03:17
to do something like this.
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去做這樣的事情。
03:19
You know, a census of Indonesia
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你知道，印尼的人口普查
03:21
would probably take two years to accomplish.
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可能會兩年才能完成。
03:23
And the problem, of course, with all of this is that
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當然，這其中的問題
03:25
with all those paper forms — and I'm telling you
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在於所有那些紙質表單——我要告訴你
03:27
we have paper forms for every possible thing.
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我們什麼事情都有紙質表單。
03:29
We have paper forms for vaccination surveys.
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我們有接種疫苗調查的紙質表單。
03:32
We have paper forms to track people who come into clinics.
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我們用紙質表單來跟蹤到診所來的人。
03:36
We have paper forms to track drug supplies,
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我們用紙質表單來跟蹤藥品供應、
03:38
blood supplies, all these different paper forms
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血液供應，所有這些不同的紙質表單
03:41
for many different topics,
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有許多不同的主題，
03:43
they all have a single common endpoint,
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他們都有一個共同終結點，
03:45
and the common endpoint looks something like this.
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那個共同的終結點看起來就像這樣。
03:48
And what we're looking at here is a truckful o' data.
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我們現在看到的是一卡車的數據。
03:52
This is the data from a single vaccination coverage survey
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這只是一個疫苗接種覆蓋範圍的調查資料
03:57
in a single district in the country of Zambia
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僅僅是尚比亞鄉下的一個地區而已
03:59
from a few years ago that I participated in.
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它們來自於前幾年我參加的一次調查。
04:01
The only thing anyone was trying to find out
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我們唯一想找到的是
04:04
is what percentage of Zambian children are vaccinated,
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尚比亞兒童接種疫苗的百分比，
04:07
and this is the data, collected on paper over weeks
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這是在數週內收集到紙上的數據資料，
04:10
from a single district, which is something like a county
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僅僅是一個地區，
04:13
in the United States.
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有點像我們在美國的一個郡。
04:14
You can imagine that, for the entire country of Zambia,
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你可以想像，整個國家的尚比亞，
04:16
answering just that single question
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回答只是這一個問題
04:20
looks something like this.
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看起來這樣的。
04:22
Truck after truck after truck
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一車一車又一車
04:24
filled with stack after stack after stack of data.
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裝滿一堆一堆又一堆的數據資料。
04:28
And what makes it even worse is that
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並且更糟糕的是，
04:29
that's just the beginning,
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這只是開始，
04:31
because once you've collected all that data,
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因為一旦你收集了所有的資料，
04:33
of course someone's going to have to --
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當然有一些人必須要去——
04:35
some unfortunate person is going to have to type that into a computer.
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有些不幸的人必須要把它們輸入到電腦上。
04:38
When I was a graduate student, I actually was
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當我是研究生的時候，
04:40
that unfortunate person sometimes.
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實際上我有時就是那不幸的人。
04:42
I can tell you, I often wasn't really paying attention.
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我可以告訴你，我那時經常分心。
04:45
I probably made a lot of mistakes when I did it
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我大概做了很多錯誤的輸入，
04:47
that no one ever discovered, so data quality goes down.
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沒有人會發現，所以數據資料的品質會下降。
04:50
But eventually that data hopefully gets typed into a computer,
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但是最終希望那些數據被輸入到了電腦上，
04:53
and someone can begin to analyze it,
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那樣有人就可以開始分析它們，
04:55
and once they have an analysis and a report,
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一旦他們得出了一份分析和報告，
04:57
hopefully then you can take the results of that data collection
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但願你能使用那些收集來的數據結果
05:01
and use it to vaccinate children better.
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來更好地為孩子們接種疫苗。
05:03
Because if there's anything worse
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因為如果在全球公共健康領域
05:06
in the field of global public health,
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有任何更糟的事情發生，
05:08
I don't know what's worse than allowing children on this planet
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我不知道世上有什麼比讓孩子們
05:11
to die of vaccine-preventable diseases,
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死於原本疫苗可預防的疾病更糟糕的事情，
05:14
diseases for which the vaccine costs a dollar.
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那些疾病的疫苗成本也就 1 美元。
05:17
And millions of children die of these diseases every year.
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但是，每年有數百萬的兒童死於這些疾病。
05:20
And the fact is, millions is a gross estimate because
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而且事實是，數百萬只是一個粗略的估計，
05:24
we don't really know how many kids die each year of this.
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因為我們真的不知道
每年究竟有多少孩子死於這些疾病。
每年究竟有多少孩子死於這些疾病。
05:27
What makes it even more frustrating is that
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讓人更加沮喪的是，
05:29
the data entry part, the part that I used to do as a grad student,
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數據輸入的部分，
也就是我作為研究生時曾做過的工作，
也就是我作為研究生時曾做過的工作，
05:32
can take sometimes six months.
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有時會長達六個月之久。
05:34
Sometimes it can take two years to type that information
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有時，可以用兩年時間把那些資訊輸入到電腦
05:37
into a computer, and sometimes, actually not infrequently,
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有時，其實經常發生，
05:40
it actually never happens.
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那些數據根本不會被輸入。
05:42
Now try and wrap your head around that for a second.
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現在試著想一想這一點。
05:44
You just had teams of hundreds of people.
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你只有一個幾百人的團隊。
05:47
They went out into the field to answer a particular question.
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他們要去實地回答一個特定的問題。
05:49
You probably spent hundreds of thousands of dollars
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你很可能花掉了成百上千美元
05:51
on fuel and photocopying and per diem,
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在燃料、複印和每日生活開支上，
05:55
and then for some reason, momentum is lost
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然後出於某些原因，比如沒了勁頭兒
05:58
or there's no money left,
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或者沒錢了，
05:59
and all of that comes to nothing
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所有這一切都落空了
06:01
because no one actually types it into the computer at all.
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因為實際上根本沒人把數據輸入到電腦裡。
06:04
The process just stops. Happens all the time.
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這個過程就中斷了。但這常常發生。
06:07
This is what we base our decisions on in global health:
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這就是我們在在全球健康組織裡頭的決策基礎：
06:10
little data, old data, no data.
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少數據，舊數據，沒數據。
06:15
So back in 1995, I began to think about ways
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所以，我們再回到 1995 年，我開始思考
06:18
in which we could improve this process.
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我們可以改善這一過程的方法。
06:20
Now 1995, obviously that was quite a long time ago.
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1995 年，很明顯，那是很久以前了。
06:23
It kind of frightens me to think of how long ago that was.
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想想那有多久以前真得讓我有點害怕。
06:25
The top movie of the year was
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那年最精彩的電影是
06:27
"Die Hard with a Vengeance."
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"終極警探3"。
06:28
As you can see, Bruce Willis had a lot more hair back then.
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正如您所看到的，
布魯斯 · 威利斯那時還有很多頭髮。
布魯斯 · 威利斯那時還有很多頭髮。
06:31
I was working in the Centers for Disease Control,
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我那時正在疾病控制中心工作，
06:34
and I had a lot more hair back then as well.
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那時我也有很多頭髮。
06:37
But to me, the most significant thing that I saw in 1995
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但對我來說，在1995年，
我看到的最有意義的事
我看到的最有意義的事
06:40
was this.
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是這個。
06:41
Hard for us to imagine, but in 1995,
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我們很難想像，但是在1995 年，
06:44
this was the ultimate elite mobile device.
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這是那時候最精英的移動裝置。
06:48
Right? It wasn't an iPhone. It wasn't a Galaxy phone.
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對吧？這不是一部 iPhone，
也不是三星 Galaxy 手機。
也不是三星 Galaxy 手機。
06:50
It was a Palm Pilot.
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它是個掌上電腦。
06:52
And when I saw the Palm Pilot for the first time, I thought,
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當我第一次看到掌上電腦時，我想，
06:55
why can't we put the forms on these Palm Pilots
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為什麼我們不能把那些表格放到掌上電腦上？
06:58
and go out into the field just carrying one Palm Pilot,
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然後去實地調查時就只帶著一個掌上電腦，
07:00
which can hold the capacity of tens of thousands
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可以容納成千上萬的紙質表單。
07:04
of paper forms? Why don't we try to do that?
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我們為什麼不試著這樣做呢？
07:06
Because if we can do that, if we can actually just
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因為如果我們能那麼做，
07:09
collect the data electronically, digitally,
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我們實際上就可以電子化、數字化地收集數據，
07:11
from the very beginning,
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從一開始，
07:13
we can just put a shortcut right through that whole process
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我們就在整個過程中獲得了一個捷徑，
07:16
of typing,
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就不用打字，
07:19
of having somebody type that stuff into the computer.
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不用讓人把那些數據輸入電腦裡了。
07:21
We can skip straight to the analysis
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我們可以直接進行分析，
07:23
and then straight to the use of the data to actually save lives.
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然後直接用那些數據去實際地挽救生命了。
07:26
So that's actually what I began to do.
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所以那就是我開始做的事。
07:29
Working at CDC, I began to travel to different programs
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在疾病預防控制中心工作時，
我開始到全世界不同的部門
我開始到全世界不同的部門
07:32
around the world and to train them in using Palm Pilots
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並且培訓他們用掌上電腦
07:36
to do data collection instead of using paper.
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收集數據，而不是用紙張。
07:39
And it actually worked great.
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實際工作進展得很順利。
07:41
It worked exactly as well as anybody would have predicted.
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它就像每個人預計得那樣運作良好。
07:43
What do you know? Digital data collection
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你知道嗎？電子數據收集
07:46
is actually more efficient than collecting on paper.
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實際上比用紙張收集高效很多。
07:48
While I was doing it, my business partner, Rose,
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當我在做這件事的時候，
我的生意夥伴，蘿絲，
我的生意夥伴，蘿絲，
07:50
who's here with her husband, Matthew, here in the audience,
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她和她的丈夫，馬修，就在觀眾席裡，
07:53
Rose was out doing similar stuff for the American Red Cross.
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蘿絲也去美國紅十字會做了同樣的事情。
07:56
The problem was, after a few years of doing that,
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問題是，在那幾年之後，
07:58
I realized I had done -- I had been to maybe
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我意識到我已經完成了——我可能已經去了
08:01
six or seven programs, and I thought,
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2718
6 個或 7 個組織，我覺得，
08:04
you know, if I keep this up at this pace,
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你知道，如果再以這種速度繼續下去，
08:06
over my whole career, maybe I'm going to go
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我整個職業生涯，也許我要去
08:08
to maybe 20 or 30 programs.
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也許 20 或 30 個組織。
08:10
But the problem is, 20 or 30 programs,
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但問題是，20或30個組織，
08:13
like, training 20 or 30 programs to use this technology,
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給 20 或 30 個組織培訓使用這種技術，
08:16
that is a tiny drop in the bucket.
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這簡直就是杯水車薪。
08:18
The demand for this, the need for data to run better programs,
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這種要讓數據更好運行的需求，
08:22
just within health, not to mention all of the other fields
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僅僅是在健康領域，其他領域暫且不談，
08:25
in developing countries, is enormous.
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在發展中國家，這需求是巨大的。
08:27
There are millions and millions and millions of programs,
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4010
有成千上萬難以計數的組織，
08:31
millions of clinics that need to track drugs,
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2535
數以百萬的診所需要跟蹤藥品情況，
08:34
millions of vaccine programs.
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數以百萬的疫苗組織。
08:35
There are schools that need to track attendance.
193
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2057
有很多學校需要紀錄出勤率。
08:37
There are all these different things
194
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2005
有各種各樣這樣不同的事情，
08:39
for us to get the data that we need to do.
195
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2095
並且我意識到，
08:41
And I realized, if I kept up the way that I was doing,
196
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4526
如果我繼續以我以前的方式，
08:46
I was basically hardly going to make any impact
197
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在我的職業生涯結束之前，
08:49
by the end of my career.
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我基本上很難做出什麼影響。
08:51
And so I began to wrack my brain
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於是，我開動腦筋
08:53
trying to think about, you know,
200
521647
1143
努力思考，
08:54
what was the process that I was doing,
201
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1518
我在做的過程是什麼樣的，
08:56
how was I training folks, and what were the bottlenecks
202
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2856
我怎麼培訓人們，我遇到的瓶頸是什麼
08:59
and what were the obstacles to doing it faster
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2813
更快速、更有效的阻礙？
09:01
and to doing it more efficiently?
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我意識到——我確定了主要的阻礙。
09:03
And unfortunately, after thinking about this for some time,
205
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3143
不幸的是，在思考這一段時間以後
09:06
I realized -- I identified the main obstacle.
206
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3452
我意識到——我確定了主要的阻礙。
09:10
And the main obstacle, it turned out,
207
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1977
這個主要的阻礙出現了，
09:12
and this is a sad realization,
208
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1835
並且這個發現讓我很沮喪。
09:13
the main obstacle was me.
209
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2268
這個主要的障礙就是我本人。
09:16
So what do I mean by that?
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2196
這是什麼意思呢？
09:18
I had developed a process whereby
211
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2488
我已經開發了這樣一個過程
09:20
I was the center of the universe of this technology.
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5045
我是這項技術的中心。
09:25
If you wanted to use this technology, you had to get in touch with me.
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如果您想要使用這項技術，你必須聯絡我。
09:28
That means you had to know I existed.
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2106
這意味著你必須知道我的存在。
09:30
Then you had to find the money to pay for me
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1474
然後你不得不拿出錢來付給我
09:32
to fly out to your country
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1486
飛到你的國家
09:33
and the money to pay for my hotel
217
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1548
拿錢來支付我的旅館
09:35
and my per diem and my daily rate.
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2760
和我的每日支出和日常開銷。
09:38
So you could be talking about 10,000 or 20,000 or 30,000 dollars
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2949
所以你可以說是 1 萬或 2 萬或 3 萬美元
09:41
if I actually had the time or it fit my schedule
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2582
如果我真的有時間
或著能把這件事提上日程
或著能把這件事提上日程
09:43
and I wasn't on vacation.
221
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1947
而且我沒有在度假。
09:45
The point is that anything, any system that depends
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這個問題的關鍵在於任何事，任何系統
09:48
on a single human being or two or three or five human beings,
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2870
只要是依靠一兩個或三五個人，
09:51
it just doesn't scale.
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1736
它就不會成規模。
09:53
And this is a problem for which we need to scale
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2021
問題就是我們需要傳播
09:55
this technology and we need to scale it now.
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2997
這項技術，並且我們現在就需要擴大它的規模。
09:58
And so I began to think of ways in which I could basically
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2222
所以我開始想
10:00
take myself out of the picture.
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能讓我把自己脫離出來的方法。
10:02
And, you know, I was thinking,
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你知道，我在想，
10:07
how could I take myself out of the picture
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我如何把自己脫離出來？
10:09
for quite some time.
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1809
想了相當一段時間。
10:11
You know, I'd been trained that the way that
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2157
你知道，我習慣於認為
10:13
you distribute technology within international development
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2722
在國際發展中傳播科技
10:16
is always consultant-based.
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2027
總是基於諮詢顧問模式的。
10:18
It's always guys that look pretty much like me
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2977
總是看起來有些像我一樣的人
10:21
flying from countries that look pretty much like this
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609153
2301
從一些像這裡的國家飛到
10:23
to other countries with people with darker skin.
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3106
有著深色皮膚人群的國家。
10:26
And you go out there, and you spend money on airfare
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2445
你去到那些國家，你要花機票錢
10:29
and you spend time and you spend per diem
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3510
要花時間，在日常生活上還要花錢，
10:32
and you spend [on a] hotel and you spend all that stuff.
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2112
花錢在旅館上，等等這些東西上。
10:34
As far as I knew, that was the only way
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1851
那時，據我所知，這是唯一傳播技術的方法，
10:36
you could distribute technology, and I couldn't figure out a way around it.
242
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我想不出其他解決辦法。
10:39
But the miracle that happened,
243
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2671
但是奇蹟發生了，
10:42
I'm going to call it Hotmail for short.
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2750
我把它簡稱為 Hotmail。
10:45
Now you may not think of Hotmail as being miraculous,
245
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2181
現在你可能不覺得的 Hotmail 有多神奇，
10:47
but for me it was miraculous, because I noticed,
246
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2913
但對我來說那是個奇蹟，
10:50
just as I was wrestling with this problem,
247
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2566
因為，正當我為解決這一問題而絞盡腦汁的時候，
10:52
I was working in sub-Saharan Africa mostly at the time.
248
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3414
那時我大部分時間都在撒哈拉南部非洲工作。
10:56
I noticed that every sub-Saharan African health worker
249
644242
2589
我注意到每個和我工作的
撒哈拉南部非洲的健康事業工作者
撒哈拉南部非洲的健康事業工作者
10:58
that I was working with had a Hotmail account.
250
646831
4108
都使用 Hotmail 帳戶。
11:02
And I thought, it struck me,
251
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2144
我想，它啓發了我，
11:05
wait a minute, I know that the Hotmail people
252
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2615
等一下，我知道 Hotmail 公司的人
11:07
surely didn't fly to the Ministry of Health of Kenya
253
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2716
肯定不會飛到肯亞健康部去
11:10
to train people in how to use Hotmail.
254
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2711
培訓人們如何使用 Hotmail。
11:13
So these guys are distributing technology.
255
661125
2487
所以這些人正在傳播技術。
11:15
They're getting software capacity out there
256
663612
2004
他們讓軟件應用能力傳播到那裡，
11:17
but they're not actually flying around the world.
257
665616
2009
但他們實際上並不在世界各地飛行。
11:19
I need to think about this some more.
258
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1560
關於這個我需要多想想。
11:21
While I was thinking about it, people started using
259
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2173
當我正在想這個的時候，人們開始使用
11:23
even more things just like this, just as we were.
260
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3200
更多的東西，就像我們以前一樣。
11:26
They started using LinkedIn and Flickr
261
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1210
他們開始使用 LinkedIn 和 Flickr
11:27
and Gmail and Google Maps, all these things.
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2761
Gmail 和谷歌地圖，所有這些事情。
11:30
Of course, all of these things are cloud-based
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2726
當然，所有這些東西都是基於雲端的
11:33
and don't require any training.
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2206
不需要任何培訓。
11:35
They don't require any programmers.
265
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1600
他們不需要任何的程式員。
11:37
They don't require any consultants, because
266
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1709
因為他們不需要任何顧問，
11:38
the business model for all these businesses
267
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2394
因為所有這些企業的商業模式
11:41
requires that something be so simple we can use it ourselves
268
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2997
需要的東西簡單到我們可以自己使用它
11:44
with little or no training.
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1185
只要很少或根本不需要訓練。
11:45
You just have to hear about it and go to the website.
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2614
你只是聽說過它，然後登錄那個網站。
11:47
And so I thought, what would happen if we built software
271
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4365
所以，如果我們開發軟體來做我的諮詢工作
11:52
to do what I'd been consulting in?
272
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2011
會發生什麼呢？
11:54
Instead of training people how
273
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1434
我們不是培訓人們
11:55
to put forms onto mobile devices,
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2850
如何將表單放到移動裝置上，
11:58
let's create software that lets them do it themselves
275
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2284
是創建可以讓他們自己做事的軟體
12:00
with no training and without me being involved?
276
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1890
不需培訓，也不需要我參與。
12:02
And that's exactly what we did.
277
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1804
而這正是我們所做的。
12:04
So we created software called Magpi,
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3684
所以，我們創建了 Magpi 軟體
12:08
which has an online form creator.
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1877
它有一個在線表單創建者。
12:10
No one has to speak to me.
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1151
不需要有人和我說話。
12:11
You just have to hear about it and go to the website.
281
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2694
你只需要聽說過它，然後登錄那個網站。
12:14
You can create forms, and once you've created the forms,
282
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2747
您可以創建表單，一旦你創建了表單，
12:16
you push them to a variety of common mobile phones.
283
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2340
你可以把他們推送到各種常見的手機上。
12:19
Obviously nowadays, we've moved past Palm Pilots
284
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2475
很明顯的是，我們已經用移動電話
12:21
to mobile phones.
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1328
取代了掌上電腦。
12:22
And it doesn't have to be a smartphone.
286
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1132
它不需要是智慧型手機。
12:24
It can be a basic phone like the phone on the right there,
287
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2707
它可以是非智慧型手機，就像右邊的這個一樣，
12:26
you know, the basic kind of Symbian phone
288
734733
1336
你知道，這種非智慧型的塞班手機
12:28
that's very common in developing countries.
289
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2466
在發展中國家非常普遍。
12:30
And the great part about this is, it's just like Hotmail.
290
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3999
最精彩的部分是，它就像 Hotmail 一樣。
12:34
It's cloud-based, and it doesn't require any training,
291
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2334
它是基於雲端運算的，並不需要任何培訓，
12:36
programming, consultants.
292
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2040
程式設計和顧問諮詢。
12:38
But there are some additional benefits as well.
293
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1936
就像用掌上電腦一樣，最重要的一點是，
12:40
Now we knew, when we built this system,
294
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1955
現在我們知道，當我們建立了這個系統，
12:42
the whole point of it, just like with the Palm Pilots,
295
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2293
就像用掌上電腦一樣，最重要的一點是
12:45
was that you'd have to, you'd be able to
296
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2604
你必須而且你能夠
12:47
collect the data and immediately upload the data and get your data set.
297
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3191
收集數據並立即上載數據
以獲取您的數據庫。
以獲取您的數據庫。
12:50
But what we found, of course, since it's already on a computer,
298
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2437
但是我們發現，當然，因為數據已經在電腦上了，
12:53
we can deliver instant maps and analysis and graphing.
299
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3188
我們可以提供即時圖像並且分析畫面。
12:56
We can take a process that took two years
300
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2251
我們可以將一個需要花費兩年時間的過程
12:58
and compress that down to the space of five minutes.
301
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3222
壓縮到只要五分鐘的時間。
13:01
Unbelievable improvements in efficiency.
302
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2506
令人難以置信的效率提升。
13:04
Cloud-based, no training, no consultants, no me.
303
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4766
基於雲端的，不需要培訓、 不需要顧問，不需要我。
13:09
And I told you that in the first few years
304
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2323
我告訴過你們，在最初的幾年中
13:11
of trying to do this the old-fashioned way,
305
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1827
試圖用老方法做這個的時候，
13:13
going out to each country,
306
781407
1292
到每個國家去，
13:14
we reached about, I don't know,
307
782699
3054
我們達到了大約，我不確定，
13:17
probably trained about 1,000 people.
308
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2118
大概培訓了1000 人左右。
13:19
What happened after we did this?
309
787871
1803
在我們創建了軟體之後，發生了什麼事？
13:21
In the second three years, we had 14,000 people
310
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2506
在第二個三年中，有14000 人
13:24
find the website, sign up, and start using it to collect data,
311
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3193
找到網站、 註冊，並開始使用它來收集數據，
13:27
data for disaster response,
312
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1502
疾病反饋的數據，
13:28
Canadian pig farmers tracking pig disease and pig herds,
313
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4748
加拿大養豬業者用來跟蹤豬的疾病和豬飼料，
13:33
people tracking drug supplies.
314
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2415
人們跟蹤藥品供應。
13:36
One of my favorite examples, the IRC,
315
804038
1942
我最喜歡的例子之一，
13:37
International Rescue Committee,
316
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1629
IRC，國際救援委員會
13:39
they have a program where semi-literate midwives
317
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3237
他們有一個程式，能讓半文盲的助產士
13:42
using $10 mobile phones
318
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用 10 美元的手機
13:45
send a text message using our software
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使用我們的軟體每週發送一次短消息
13:47
once a week with the number of births
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匯報出生人數量和死亡數量，
13:49
and the number of deaths, which gives IRC
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這給了全球救援委員會
13:52
something that no one in global health has ever had:
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在全球健康組織前所未見的東西：
13:54
a near real-time system of counting babies,
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一個近乎即時紀錄新生嬰兒數量的系統，
13:58
of knowing how many kids are born,
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知道多少孩子出生，
13:59
of knowing how many children there are
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知道兒童的數量，
14:01
in Sierra Leone, which is the country where this is happening,
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在塞拉里昂，人們正在使用它，
14:04
and knowing how many children die.
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並且知道有多少兒童死去。
14:07
Physicians for Human Rights --
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人權組織的醫師——
14:08
this is moving a little bit outside the health field —
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現在我們所說的有點超出了健康的領域——
14:11
they are gathering, they're basically training people
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他們在收集，他們基本上是培訓人們
14:14
to do rape exams in Congo, where this is an epidemic,
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做關於強姦的測驗，在剛果這是一種流行病，
14:17
a horrible epidemic,
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一個可怕的流行病，
14:19
and they're using our software to document
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他們可以使用我們的軟體紀錄
14:21
the evidence they find, including photographically,
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他們找到的證據，包括圖像上的，
14:24
so that they can bring the perpetrators to justice.
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這樣，他們可以將犯罪者繩之以法。
14:28
Camfed, another charity based out of the U.K.,
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Camfed，總部在英國的另一個慈善機構，
14:32
Camfed pays girls' families to keep them in school.
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Camfed，支付給女孩的家庭，好讓她們繼續上學。
14:36
They understand this is the most significant intervention
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他們明白這是他們能做的最有意義的干預
14:38
they can make. They used to track the dispersements,
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他們過去用紙張來跟蹤支出，
14:41
the attendance, the grades, on paper.
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出勤，成績。
14:43
The turnaround time between a teacher
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從一個老師
14:44
writing down grades or attendance
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寫下成績或出勤率
14:46
and getting that into a report was about two to three years.
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然後大約兩至三年後得出報告。
14:49
Now it's real time, and because this is such
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現在這是一個實時系統，並且因為這是如此
14:51
a low-cost system and based in the cloud, it costs,
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低成本的系統，以雲計算為基礎，它的成本，
14:54
for the entire five countries that Camfed runs this in
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對於 Camfed 資助的五個國家
14:57
with tens of thousands of girls,
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數以萬計的女孩子們，
14:59
the whole cost combined is 10,000 dollars a year.
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所有成本加起來每年只需要一萬美金。
15:03
That's less than I used to get
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這比我以前
15:04
just traveling out for two weeks to do a consultation.
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出去做兩週諮詢的花費要少。
15:10
So I told you before that
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所以，我之前告訴過你
15:12
when we were doing it the old-fashioned way, I realized
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當我們在用老式方法做的時候，
15:14
all of our work was really adding up to just a drop in the bucket --
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我意識到，我們所有的工作加起來
不過就是杯水車薪。
不過就是杯水車薪。
15:17
10, 20, 30 different programs.
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只是10、 20、 30個不同的組織，
15:19
We've made a lot of progress, but I recognize
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我們已經取得了很大進展，但我承認
15:21
that right now, even the work that we've done
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現在，即使我們所做的工作
15:23
with 14,000 people using this,
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有14000 人使用它，
15:26
is still a drop in the bucket. But something's changed.
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這仍是杯水車薪。但是已經發生了一些改變
15:29
And I think it should be obvious.
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而且我認為它應該明顯。
15:30
What's changed now is,
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現在的改變是，
15:32
instead of having a program in which we're scaling at such a slow rate
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我們過去用的低效率的方式已經被取代
15:36
that we can never reach all the people who need us,
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我們再也不用到需要我們的人那裡去，
15:39
we've made it unnecessary for people to get reached by us.
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我們已經做到不需要有人來找我們了。
15:43
We've created a tool that lets programs
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我們創造了一種工具，可以讓程式
15:46
keep kids in school, track the number of babies
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可以讓孩子們留在學校，
15:49
that are born and the number of babies that die,
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跟蹤嬰兒的出生和死亡數量，
15:52
to catch criminals and successfully prosecute them,
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逮捕罪犯並成功起訴他們，
15:55
to do all these different things to learn more
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來做這些不同的事情從而瞭解更多
15:58
about what's going on, to understand more, to see more,
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關於正在發生什麼，以便能瞭解更多，看得更多，
16:03
and to save lives and improve lives.
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來拯救生命和改善生活。
16:07
Thank you.
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謝謝。
16:09
(Applause)
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（掌聲）
ABOUT THE SPEAKER
Joel Selanikio - Health and technology activistDr. Joel Selanikio combines technology and data to help solve global health challenges.
Why you should listen
A practicing pediatrician, former Wall Street computer consultant, and former epidemiologist at the Centers for Disease Control, Dr. Joel Selanikio is the CEO of DataDyne, a social business working in fields such as international development and global health.
Selanikio started to experiment with electronic data capture back when the Palm Pilot was cutting edge technology. In the years since then, he has helped to experiment with the growing potential and availability of technology--and the growing ubiquity of the cloud. Combining the two has led to systems such as Magpi mobile data collection software. Previously known as "EpiSurveyor," the service now has over 20,000 users in more than 170 countries.
Selanikio holds a bachelor's degree from Haverford College, a medical degree from Brown University, and he is a graduate of the Epidemic Intelligence Service fellowship of the CDC. He continues to practice clinical pediatrics as an Assistant Professor at Georgetown University and on the Emergency Response Team of the International Rescue Committee.
Joel Selanikio | Speaker | TED.com