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There's an old joke about a cop

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who's walking his beat
in the middle of the night,

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and he comes across a guy
under a street lamp

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who's looking at the ground
and moving from side to side,

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and the cop asks him what he's doing.

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The guys says he's looking for his keys.

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So the cop takes his time

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and looks over and kind of
makes a little matrix

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and looks for about two, three minutes.

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No keys.

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The cop says, "Are you sure?

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Hey buddy, are you sure
you lost your keys here?"

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And the guy says,
"No, actually I lost them

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down at the other end of the street,

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but the light is better here."

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(Laughter)

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There's a concept that people talk
about nowadays called "big data."

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And what they're talking
about is all of the information

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that we're generating
through our interaction

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with and over the Internet,

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everything from Facebook and Twitter

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to music downloads, movies,
streaming, all this kind of stuff,

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the live streaming of TED.

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And the folks who work
with big data, for them,

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they talk about that their biggest problem

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is we have so much information.

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The biggest problem is: how do we
organize all that information?

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I can tell you that,
working in global health,

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that is not our biggest problem.

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Because for us, even though
the light is better on the Internet,

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the data that would help us solve
the problems we're trying to solve

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is not actually present on the Internet.

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So we don't know, for example,

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how many people right now
are being affected by disasters

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or by conflict situations.

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We don't know for, really,
basically, any of the clinics

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in the developing world,

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which ones have medicines
and which ones don't.

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We have no idea of what
the supply chain is for those clinics.

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We don't know -- and this is really
amazing to me -- we don't know

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how many children were born --
or how many children there are --

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in Bolivia or Botswana or Bhutan.

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We don't know how many kids died last week

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in any of those countries.

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We don't know the needs
of the elderly, the mentally ill.

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For all of these different
critically important problems

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or critically important areas
that we want to solve problems in,

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we basically know nothing at all.

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And part of the reason
why we don't know anything at all

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is that the information technology systems
that we use in global health

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to find the data to solve
these problems is what you see here.

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This is about a 5,000-year-old technology.

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Some of you may have used it before.

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It's kind of on its way out now,

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but we still use it
for 99 percent of our stuff.

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This is a paper form.

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And what you're looking at is a paper form

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in the hand of a Ministry of Health
nurse in Indonesia,

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who is tramping out across the countryside

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in Indonesia on, I'm sure,
a very hot and humid day,

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and she is going to be knocking
on thousands of doors

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over a period of weeks or months,

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knocking on the doors and saying,

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"Excuse me, we'd like to ask
you some questions.

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Do you have any children?
Were your children vaccinated?"

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Because the only way
we can actually find out

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how many children were vaccinated
in the country of Indonesia,

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what percentage were vaccinated,

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is actually not on the Internet,
but by going out and knocking on doors,

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sometimes tens of thousands of doors.

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Sometimes it takes months to even years
to do something like this.

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You know, a census of Indonesia

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would probably take
two years to accomplish.

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And the problem, of course,
with all of this

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is that, with all those paper forms --

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and I'm telling you, we have
paper forms for every possible thing:

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We have paper forms
for vaccination surveys.

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We have paper forms to track
people who come into clinics.

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We have paper forms to track
drug supplies, blood supplies --

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all these different paper forms
for many different topics,

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they all have a single, common endpoint,

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and the common endpoint
looks something like this.

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And what we're looking
at here is a truckful of data.

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This is the data from a single
vaccination coverage survey

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in a single district
in the country of Zambia

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from a few years ago,
that I participated in.

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The only thing anyone
was trying to find out

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is what percentage of Zambian
children are vaccinated,

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and this is the data,
collected on paper over weeks,

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from a single district,

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which is something like a county
in the United States.

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You can imagine that,
for the entire country of Zambia,

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answering just that single question ...

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looks something like this.

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Truck after truck after truck,

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filled with stack after stack
after stack of data.

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And what makes it even worse
is that's just the beginning.

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Because once you've collected
all that data,

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of course, someone --
some unfortunate person --

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is going to have to type that
into a computer.

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When I was a graduate student,

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I actually was that unfortunate
person sometimes.

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I can tell you, I often wasn't
really paying attention.

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I probably made a lot
of mistakes when I did it

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that no one ever discovered,
so data quality goes down.

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But eventually that data, hopefully,
gets typed into a computer,

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and someone can begin to analyze it,

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and once they have
an analysis and a report,

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hopefully, then you can take
the results of that data collection

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and use it to vaccinate children better.

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Because if there's anything worse
in the field of global public health --

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I don't know what's worse
than allowing children on this planet

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to die of vaccine-preventable diseases --

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diseases for which
the vaccine costs a dollar.

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And millions of children die
of these diseases every year.

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And the fact is, millions
is a gross estimate,

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because we don't really know
how many kids die each year of this.

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What makes it even more frustrating
is that the data-entry part,

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the part that I used to do
as a grad student,

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can take sometimes six months.

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Sometimes it can take two years
to type that information into a computer,

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And sometimes, actually not infrequently,

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it actually never happens.

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Now try and wrap your head
around that for a second.

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You just had teams of hundreds of people.

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They went out into the field
to answer a particular question.

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You probably spent hundreds
of thousands of dollars

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on fuel and photocopying and per diem.

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And then for some reason, momentum is lost

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or there's no money left,

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and all of that comes to nothing,

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because no one actually types it
into the computer at all.

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The process just stops.

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Happens all the time.

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This is what we base
our decisions on in global health:

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little data, old data, no data.

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So back in 1995,

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I began to think about ways
in which we could improve this process.

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Now 1995 -- obviously,
that was quite a long time ago.

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It kind of frightens me to think
of how long ago that was.

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The top movie of the year
was "Die Hard with a Vengeance."

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As you can see, Bruce Willis
had a lot more hair back then.

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I was working in the Centers
for Disease Control

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and I had a lot more
hair back then as well.

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But to me, the most significant
thing that I saw in 1995

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was this.

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Hard for us to imagine, but in 1995,

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this was the ultimate elite mobile device.

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It wasn't an iPhone.
It wasn't a Galaxy phone.

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It was a PalmPilot.

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And when I saw the PalmPilot
for the first time, I thought,

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"Why can't we put the forms
on these PalmPilots?

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And go out into the field
just carrying one PalmPilot,

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which can hold the capacity
of tens of thousands of paper forms?

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Why don't we try to do that?

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Because if we can do that,

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if we can actually just collect
the data electronically, digitally,

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from the very beginning,

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we can just put a shortcut right
through that whole process

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of typing, of having somebody type
that stuff into the computer.

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We can skip straight to the analysis

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and then straight to the use
of the data to actually save lives."

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So that's what I began to do.

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Working at CDC, I began to travel
to different programs around the world

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and to train them in using
PalmPilots to do data collection,

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instead of using paper.

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And it actually worked great.

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It worked exactly as well
as anybody would have predicted.

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What do you know?

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Digital data collection is actually
more efficient than collecting on paper.

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While I was doing it, my business partner,

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Rose, who's here with her husband,
Matthew, here in the audience,

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Rose was out doing similar stuff
for the American Red Cross.

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The problem was,
after a few years of doing that,

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I realized -- I had been to maybe
six or seven programs --

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and I thought, you know,
if I keep this up at this pace,

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over my whole career,

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maybe I'm going to go
to maybe 20 or 30 programs.

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But the problem is, 20 or 30 programs,

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like, training 20 or 30 programs
to use this technology,

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that is a tiny drop in the bucket.

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The demand for this, the need
for data to run better programs

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just within health -- not to mention
all of the other fields

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in developing countries -- is enormous.

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There are millions and millions
and millions of programs,

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millions of clinics
that need to track drugs,

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millions of vaccine programs.

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There are schools
that need to track attendance.

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There are all these different things
for us to get the data that we need to do.

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And I realized if I kept up
the way that I was doing,

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I was basically hardly
going to make any impact

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by the end of my career.

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And so I began to rack my brain,

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trying to think about, what
was the process that I was doing?

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How was I training folks,

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and what were the bottlenecks
and what were the obstacles

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to doing it faster
and to doing it more efficiently?

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And, unfortunately, after thinking
about this for some time,

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I identified the main obstacle.

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And the main obstacle, it turned out --

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and this is a sad realization --

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the main obstacle was me.

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So what do I mean by that?

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I had developed a process

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whereby I was the center
of the universe of this technology.

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If you wanted to use this technology,
you had to get in touch with me.

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That means you had to know I existed.

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Then you had to find the money
to pay for me to fly out to your country

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and the money to pay for my hotel
and my per diem and my daily rate.

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So you could be talking
about 10- or 20- or 30,000 dollars,

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if I actually had the time
or it fit my schedule

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and I wasn't on vacation.

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The point is that anything,

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any system that depends
on a single human being

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or two or three or five human beings --

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it just doesn't scale.

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And this is a problem for which
we need to scale this technology,

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and we need to scale it now.

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And so I began to think of ways
in which I could basically

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take myself out of the picture.

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And, you know, I was thinking,

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"How could I take myself
out of the picture?"

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for quite some time.

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I'd been trained that the way
you distribute technology

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within international development

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is always consultant-based.

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It's always guys
that look pretty much like me,

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flying from countries
that look pretty much like this

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to other countries
with people with darker skin.

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And you go out there,
and you spend money on airfare

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and you spend time and you spend per diem

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and you spend for a hotel
and all that stuff.

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As far as I knew, that was the only way
you could distribute technology,

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and I couldn't figure out a way around it.

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But the miracle that happened --

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I'm going to call it Hotmail for short.

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You may not think of Hotmail
as being miraculous,

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but for me it was miraculous,

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because I noticed, just as I
was wrestling with this problem --

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I was working in sub-Saharan
Africa, mostly, at the time --

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I noticed that every sub-Saharan
African health worker

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that I was working with
had a Hotmail account.

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And it struck me, "Wait a minute --

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I know the Hotmail people surely didn't
fly to the Ministry of Health in Kenya

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to train people in how to use Hotmail.

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So these guys are distributing technology,
getting software capacity out there,

247
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but they're not actually
flying around the world.

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I need to think about this more."

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While I was thinking about it,

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people started using even more
things like this, just as we were.

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They started using LinkedIn and Flickr
and Gmail and Google Maps --

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all these things.

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Of course, all of these things
are cloud based

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and don't require any training.

255
00:11:23,583 --> 00:11:25,259
They don't require any programmers.

256
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They don't require consultants.

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Because the business model
for all these businesses

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requires that something be so simple
we can use it ourselves,

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00:11:32,280 --> 00:11:33,574
with little or no training.

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You just have to hear about it
and go to the website.

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And so I thought, what would happen
if we built software

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to do what I'd been consulting in?

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Instead of training people
how to put forms onto mobile devices,

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00:11:46,621 --> 00:11:49,922
let's create software that lets them
do it themselves with no training

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and without me being involved.

266
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And that's exactly what we did.

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So we created software called Magpi,
which has an online form creator.

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No one has to speak to me,

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you just have to hear about it
and go to the website.

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You can create forms,
and once you've created the forms,

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you push them to a variety
of common mobile phones.

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Obviously, nowadays, we've moved
past PalmPilots to mobile phones.

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And it doesn't have to be a smartphone,
it can be a basic phone,

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like the phone on the right,
the basic Symbian phone

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that's very common
in developing countries.

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And the great part about this
is it's just like Hotmail.

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It's cloud based,

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and it doesn't require any training,
programming, consultants.

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But there are some
additional benefits as well.

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Now we knew when we built this system,

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the whole point of it,
just like with the PalmPilots,

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was that you'd be able to collect the data

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and immediately upload
the data and get your data set.

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00:12:39,134 --> 00:12:42,119
But what we found, of course,
since it's already on a computer,

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we can deliver instant maps
and analysis and graphing.

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00:12:44,778 --> 00:12:46,849
We can take a process that took two years

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00:12:46,873 --> 00:12:49,881
and compress that
down to the space of five minutes.

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00:12:50,222 --> 00:12:52,377
Unbelievable improvements in efficiency.

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00:12:52,740 --> 00:12:56,448
Cloud based, no training,
no consultants, no me.

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00:12:57,520 --> 00:12:59,791
And I told you that in the first few years

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of trying to do this
the old-fashioned way,

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going out to each country,

293
00:13:03,211 --> 00:13:07,432
we probably trained about 1,000 people.

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What happened after we did this?

295
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In the second three years,

296
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we had 14,000 people find the website,

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sign up and start using it
to collect data:

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00:13:15,373 --> 00:13:17,283
data for disaster response,

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00:13:17,307 --> 00:13:21,741
Canadian pig farmers
tracking pig disease and pig herds,

300
00:13:21,765 --> 00:13:23,646
people tracking drug supplies.

301
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One of my favorite examples, the IRC,
International Rescue Committee,

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00:13:27,609 --> 00:13:30,822
they have a program
where semi-literate midwives,

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00:13:30,846 --> 00:13:33,249
using $10 mobile phones,

304
00:13:33,273 --> 00:13:36,574
send a text message
using our software, once a week,

305
00:13:36,598 --> 00:13:39,074
with the number of births
and the number of deaths,

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00:13:39,098 --> 00:13:42,670
which gives IRC something that no one
in global health has ever had:

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00:13:42,694 --> 00:13:46,216
a near-real-time system
of counting babies,

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00:13:46,240 --> 00:13:47,874
of knowing how many kids are born,

309
00:13:47,898 --> 00:13:50,540
of knowing how many children
there are in Sierra Leone,

310
00:13:50,564 --> 00:13:52,787
which is the country
where this is happening,

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00:13:52,811 --> 00:13:54,564
and knowing how many children die.

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00:13:55,394 --> 00:13:56,967
Physicians for Human Rights --

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00:13:56,991 --> 00:13:59,583
this is moving a little bit
outside the health field --

314
00:13:59,607 --> 00:14:04,390
they're basically training people
to do rape exams in Congo,

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00:14:04,414 --> 00:14:05,863
where this is an epidemic,

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00:14:05,887 --> 00:14:07,680
a horrible epidemic,

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00:14:07,704 --> 00:14:10,880
and they're using our software
to document the evidence they find,

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00:14:10,904 --> 00:14:12,878
including photographically,

319
00:14:12,902 --> 00:14:15,805
so that they can bring
the perpetrators to justice.

320
00:14:16,956 --> 00:14:20,549
Camfed, another charity
based out of the UK --

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00:14:20,573 --> 00:14:23,286
Camfed pays girls' families
to keep them in school.

322
00:14:24,165 --> 00:14:27,640
They understand this is the most
significant intervention they can make.

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00:14:27,664 --> 00:14:31,504
They used to track the disbursements,
the attendance, the grades, on paper.

324
00:14:31,528 --> 00:14:34,875
The turnaround time between a teacher
writing down grades or attendance

325
00:14:34,899 --> 00:14:37,734
and getting that into a report
was about two to three years.

326
00:14:37,758 --> 00:14:38,909
Now it's real time.

327
00:14:38,933 --> 00:14:42,116
And because this is such a low-cost
system and based in the cloud,

328
00:14:42,140 --> 00:14:46,129
it costs, for the entire five countries
that Camfed runs this in,

329
00:14:46,153 --> 00:14:48,018
with tens of thousands of girls,

330
00:14:48,042 --> 00:14:50,750
the whole cost combined
is 10,000 dollars a year.

331
00:14:51,154 --> 00:14:53,115
That's less than I used to get

332
00:14:53,139 --> 00:14:56,035
just traveling out for two weeks
to do a consultation.

333
00:14:58,139 --> 00:15:01,608
So I told you before that when
we were doing it the old-fashioned way,

334
00:15:01,632 --> 00:15:05,427
I realized all of our work was really
adding up to just a drop in the bucket --

335
00:15:05,451 --> 00:15:07,173
10, 20, 30 different programs.

336
00:15:08,036 --> 00:15:09,449
We've made a lot of progress,

337
00:15:09,473 --> 00:15:11,029
but I recognize that right now,

338
00:15:11,053 --> 00:15:14,417
even the work that we've done
with 14,000 people using this

339
00:15:14,441 --> 00:15:15,904
is still a drop in the bucket.

340
00:15:15,928 --> 00:15:18,658
But something's changed,
and I think it should be obvious.

341
00:15:18,682 --> 00:15:20,837
What's changed now is,

342
00:15:20,861 --> 00:15:24,163
instead of having a program
in which we're scaling at such a slow rate

343
00:15:24,187 --> 00:15:27,498
that we can never reach
all the people who need us,

344
00:15:27,522 --> 00:15:31,104
we've made it unnecessary
for people to get reached by us.

345
00:15:31,128 --> 00:15:36,131
We've created a tool
that lets programs keep kids in school,

346
00:15:36,155 --> 00:15:40,094
track the number of babies that are born
and the number of babies that die,

347
00:15:40,118 --> 00:15:43,895
catch criminals and successfully
prosecute them --

348
00:15:43,919 --> 00:15:48,341
to do all these different things
to learn more about what's going on,

349
00:15:48,365 --> 00:15:49,666
to understand more,

350
00:15:49,690 --> 00:15:51,006
to see more ...

351
00:15:51,911 --> 00:15:53,816
and to save lives and improve lives.

352
00:15:55,920 --> 00:15:57,072
Thank you.

353
00:15:57,096 --> 00:16:01,365
(Applause)