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There's an old joke about a cop who's walking his beat

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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 and looks over

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and kind of makes a little matrix and looks

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for about two, three minutes. No keys.

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The cop says, "Are you sure? Hey buddy,

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are you sure you lost your keys here?"

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And the guy says, "No, 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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There's a concept that people talk about nowadays

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called big data, and what they're talking about

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is all of the information that we're generating

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through our interaction 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 is

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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

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is better on the Internet,

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the data that would help us solve the problems

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we're trying to solve is not actually present on the Internet.

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So we don't know, for example, how many people

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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, which ones have medicines

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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 --

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we don't know how many children were born,

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or how many children there are in Bolivia

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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

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that we use in global health to find the data

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to solve these problems is what you see here.

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And 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, but we still use it

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for 99 percent of our stuff.

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This is a paper form, and what you're looking at

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is a paper form in the hand of a Ministry of Health nurse

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in Indonesia 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, "Excuse me,

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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, is actually not

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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

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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 is that

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with all those paper forms — and I'm telling you

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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,

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blood supplies, all these different paper forms

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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 o' 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, which is something like a county

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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

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that's just the beginning,

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because once you've collected all that data,

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of course someone's going to have to --

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some unfortunate person is going to have to type that into a computer.

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When I was a graduate student, I actually was

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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

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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 because

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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

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the data entry part, 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

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into a computer, 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. 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, I began to think about ways

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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

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"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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Right? It wasn't an iPhone. It wasn't a Galaxy phone.

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It was a Palm Pilot.

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And when I saw the Palm Pilot for the first time, I thought,

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why can't we put the forms on these Palm Pilots

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and go out into the field just carrying one Palm Pilot,

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which can hold the capacity of tens of thousands

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of paper forms? Why don't we try to do that?

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Because if we can do that, if we can actually just

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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,

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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 actually what I began to do.

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Working at CDC, I began to travel to different programs

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around the world and to train them in using Palm Pilots

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to do data collection 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? Digital data collection

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is actually more efficient than collecting on paper.

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While I was doing it, my business partner Rose,

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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 done -- I had been to maybe

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six or seven programs, and I thought,

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

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over my whole career, maybe I'm going to go

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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

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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 wrack my brain

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trying to think about, you know,

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what was the process that I was doing,

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how was I training folks, and what were the bottlenecks

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and what were the obstacles to doing it faster

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and to doing it more efficiently?

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And unfortunately, after thinking about this for some time,

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I realized, 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 whereby

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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

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to fly out to your country

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and the money to pay for my hotel

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and my per diem and my daily rate.

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So you could be talking about 10,000 or 20,000 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, any system that depends

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on a single human being 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

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this technology 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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You know, I'd been trained that the way that

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you distribute technology 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 [on a] hotel and you spend all that stuff.

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As far as I knew, that was the only way

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you could distribute technology, 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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Now you may not think of Hotmail as being miraculous,

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but for me it was miraculous, because I noticed,

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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 I thought, it struck me,

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wait a minute, I know that the Hotmail people

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surely didn't fly to the Ministry of Health of Kenya

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to train people in how to use Hotmail.

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So these guys are distributing technology.

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They're getting software capacity out there

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but they're not actually flying around the world.

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I need to think about this some more.

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While I was thinking about it, people started using

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even more things just like this, just as we were.

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They started using LinkedIn and Flickr

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and Gmail and Google Maps, 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.

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They don't require any programmers.

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They don't require any consultants, because

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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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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

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to put forms onto mobile devices,

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let's create software that lets them do it themselves

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with no training and without me being involved?

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And that's exactly what we did.

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So we created software called Magpi,

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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 Palm Pilots

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to mobile phones.

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And it doesn't have to be a smartphone.

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It can be a basic phone like the phone on the right there,

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you know, the basic kind of 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, and it doesn't require any training,

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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 Palm Pilots,

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was that you'd have to, you'd be able to

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collect the data and immediately upload the data and get your data set.

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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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We can take a process that took two years

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and compress that down to the space of five minutes.

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Unbelievable improvements in efficiency.

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Cloud-based, no training, no consultants, no me.

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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,

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we reached about, I don't know,

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probably trained about 1,000 people.

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What happened after we did this?

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In the second three years, we had 14,000 people

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find the website, sign up, and start using it to collect data,

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data for disaster response,

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Canadian pig farmers tracking pig disease and pig herds,

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people tracking drug supplies.

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One of my favorite examples, the IRC,

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International Rescue Committee,

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they have a program where semi-literate midwives

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using $10 mobile phones

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send a text message using our software

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once a week with the number of births

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and the number of deaths, which gives IRC

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something that no one in global health has ever had:

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a near real-time system of counting babies,

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of knowing how many kids are born,

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of knowing how many children there are

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in Sierra Leone, which is the country where this is happening,

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and knowing how many children die.

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Physicians for Human Rights --

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this is moving a little bit outside the health field —

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they are gathering, they're basically training people

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to do rape exams in Congo, where this is an epidemic,

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a horrible epidemic,

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and they're using our software to document

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the evidence they find, including photographically,

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so that they can bring the perpetrators to justice.

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Camfed, another charity based out of the U.K.,

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Camfed pays girls' families to keep them in school.

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They understand this is the most significant intervention

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they can make. They used to track the dispersements,

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the attendance, the grades, on paper.

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The turnaround time between a teacher

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writing down grades or attendance

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and getting that into a report was about two to three years.

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Now it's real time, and because this is such

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a low-cost system and based in the cloud, it costs,

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for the entire five countries that Camfed runs this in

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with tens of thousands of girls,

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the whole cost combined is 10,000 dollars a year.

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That's less than I used to get

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just traveling out for two weeks to do a consultation.

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So I told you before that

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when we were doing it the old-fashioned way, I realized

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all of our work was really adding up to just a drop in the bucket --

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10, 20, 30 different programs.

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We've made a lot of progress, but I recognize

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that right now, even the work that we've done

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with 14,000 people using this,

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is still a drop in the bucket. But something's changed.

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And I think it should be obvious.

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What's changed now is,

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instead of having a program in which we're scaling at such a slow rate

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that we can never reach all the people who need us,

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we've made it unnecessary for people to get reached by us.

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We've created a tool that lets programs

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keep kids in school, track the number of babies

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that are born and the number of babies that die,

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to catch criminals and successfully prosecute them,

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to do all these different things to learn more

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about what's going on, to understand more, to see more,

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and to save lives and improve lives.

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Thank you.

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(Applause)