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

  • in the middle of the night,

  • and he comes across a guy under a street lamp

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

  • and the cop asks him what he's doing.

  • The guys says he's looking for his keys.

  • So the cop takes his time and looks over

  • and kind of makes a little matrix and looks

  • for about two, three minutes. No keys.

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

  • are you sure you lost your keys here?"

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

  • down at the other end of the street,

  • but the light is better here."

  • There's a concept that people talk about nowadays

  • called big data, and what they're talking about

  • is all of the information that we're generating

  • through our interaction with and over the Internet,

  • everything from Facebook and Twitter

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

  • the live streaming of TED.

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

  • they talk about that their biggest problem is

  • we have so much information,

  • the biggest problem is, how do we organize all that information?

  • I can tell you that working in global health,

  • that is not our biggest problem.

  • Because for us, even though the light

  • is better on the Internet,

  • the data that would help us solve the problems

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

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

  • right now are being affected by disasters

  • or by conflict situations.

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

  • in the developing world, which ones have medicines

  • and which ones don't.

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

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

  • we don't know how many children were born,

  • or how many children there are in Bolivia

  • or Botswana or Bhutan.

  • We don't know how many kids died last week

  • in any of those countries.

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

  • For all of these different critically important problems

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

  • we basically know nothing at all.

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

  • is that the information technology systems

  • that we use in global health to find the data

  • to solve these problems is what you see here.

  • And this is about a 5,000-year-old technology.

  • Some of you may have used it before.

  • It's kind of on its way out now, but we still use it

  • for 99 percent of our stuff.

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

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

  • in Indonesia who is tramping out across the countryside

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

  • and she is going to be knocking on thousands of doors

  • over a period of weeks or months,

  • knocking on the doors and saying, "Excuse me,

  • we'd like to ask you some questions.

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

  • Because the only way we can actually find out

  • how many children were vaccinated in the country of Indonesia,

  • what percentage were vaccinated, is actually not

  • on the Internet but by going out and knocking on doors,

  • sometimes tens of thousands of doors.

  • Sometimes it takes months to even years

  • to do something like this.

  • You know, a census of Indonesia

  • would probably take two years to accomplish.

  • And the problem, of course, with all of this is that

  • with all those paper formsand I'm telling you

  • we have paper forms for every possible thing.

  • We have paper forms for vaccination surveys.

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

  • We have paper forms to track drug supplies,

  • blood supplies, all these different paper forms

  • for many different topics,

  • they all have a single common endpoint,

  • and the common endpoint looks something like this.

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

  • This is the data from a single vaccination coverage survey

  • in a single district in the country of Zambia

  • from a few years ago that I participated in.

  • The only thing anyone was trying to find out

  • is what percentage of Zambian children are vaccinated,

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

  • from a single district, which is something like a county

  • in the United States.

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

  • answering just that single question

  • looks something like this.

  • Truck after truck after truck

  • filled with stack after stack after stack of data.

  • And what makes it even worse is that

  • that's just the beginning,

  • because once you've collected all that data,

  • of course someone's going to have to --

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

  • When I was a graduate student, I actually was

  • that unfortunate person sometimes.

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

  • I probably made a lot of mistakes when I did it

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

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

  • and someone can begin to analyze it,

  • and once they have an analysis and a report,

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

  • and use it to vaccinate children better.

  • Because if there's anything worse

  • in the field of global public health,

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

  • to die of vaccine-preventable diseases,

  • diseases for which the vaccine costs a dollar.

  • And millions of children die of these diseases every year.

  • And the fact is, millions is a gross estimate because

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

  • What makes it even more frustrating is that

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

  • can take sometimes six months.

  • Sometimes it can take two years to type that information

  • into a computer, and sometimes, actually not infrequently,

  • it actually never happens.

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

  • You just had teams of hundreds of people.

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

  • You probably spent hundreds of thousands of dollars

  • on fuel and photocopying and per diem,

  • and then for some reason, momentum is lost

  • or there's no money left,

  • and all of that comes to nothing

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

  • The process just stops. Happens all the time.

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

  • little data, old data, no data.

  • So back in 1995, I began to think about ways

  • in which we could improve this process.

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

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

  • The top movie of the year was

  • "Die Hard with a Vengeance."

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

  • I was working in the Centers for Disease Control,

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

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

  • was this.

  • Hard for us to imagine, but in 1995,

  • this was the ultimate elite mobile device.

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

  • It was a Palm Pilot.

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

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

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

  • which can hold the capacity of tens of thousands

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

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

  • collect the data electronically, digitally,

  • from the very beginning,

  • we can just put a shortcut right through that whole process

  • of typing,

  • of having somebody type that stuff into the computer.

  • We can skip straight to the analysis

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

  • So that's actually what I began to do.

  • Working at CDC, I began to travel to different programs

  • around the world and to train them in using Palm Pilots

  • to do data collection instead of using paper.

  • And it actually worked great.

  • It worked exactly as well as anybody would have predicted.

  • What do you know? Digital data collection

  • is actually more efficient than collecting on paper.

  • While I was doing it, my business partner, Rose,

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

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

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

  • I realized I had done -- I had been to maybe

  • six or seven programs, and I thought,

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

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

  • to maybe 20 or 30 programs.

  • But the problem is, 20 or 30 programs,

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

  • that is a tiny drop in the bucket.

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

  • just within health, not to mention all of the other fields

  • in developing countries, is enormous.

  • There are millions and millions and millions of programs,

  • millions of clinics that need to track drugs,

  • millions of vaccine programs.

  • There are schools that need to track attendance.

  • There are all these different things

  • for us to get the data that we need to do.

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

  • I was basically hardly going to make any impact

  • by the end of my career.

  • And so I began to wrack my brain

  • trying to think about, you know,

  • what was the process that I was doing,

  • how was I training folks, and what were the bottlenecks

  • and what were the obstacles to doing it faster

  • and to doing it more efficiently?

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

  • I realized -- I identified the main obstacle.

  • And the main obstacle, it turned out,

  • and this is a sad realization,

  • the main obstacle was me.

  • So what do I mean by that?

  • I had developed a process whereby

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

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

  • That means you had to know I existed.

  • Then you had to find the money to pay for me

  • to fly out to your country

  • and the money to pay for my hotel

  • and my per diem and my daily rate.

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

  • if I actually had the time or it fit my schedule

  • and I wasn't on vacation.

  • The point is that anything, any system that depends

  • on a single human being or two or three or five human beings,

  • it just doesn't scale.

  • And this is a problem for which we need to scale

  • this technology and we need to scale it now.

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

  • take myself out of the picture.

  • And, you know, I was thinking,

  • how could I take myself out of the picture

  • for quite some time.

  • You know, I'd been trained that the way that

  • you distribute technology within international development

  • is always consultant-based.

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

  • flying from countries that look pretty much like this

  • to other countries with people with darker skin.

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

  • and you spend time and you spend per diem

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

  • As far as I knew, that was the only way

  • you could distribute technology, and I couldn't figure out a way around it.