The surprising seeds of a big-data revolution in healthcare
-
0:01 - 0:03There's an old joke about a cop
who's walking his beat -
0:03 - 0:04in the middle of the night,
-
0:04 - 0:07and he comes across a guy
under a street lamp -
0:07 - 0:09who's looking at the ground
and moving from side to side, -
0:09 - 0:11and the cop asks him what he's doing.
-
0:11 - 0:13The guys says he's looking for his keys.
-
0:13 - 0:16So the cop takes his time and looks over
-
0:16 - 0:18and kind of makes
a little matrix and looks -
0:18 - 0:20for about two, three minutes. No keys.
-
0:20 - 0:23The cop says, "Are you sure? Hey buddy,
-
0:23 - 0:25are you sure you lost your keys here?"
-
0:25 - 0:27And the guy says, "No,
no, actually I lost them -
0:27 - 0:28down at the other end of the street,
-
0:28 - 0:34but the light is better here."
-
0:34 - 0:36There's a concept that people
talk about nowadays -
0:36 - 0:38called big data,
and what they're talking about -
0:38 - 0:40is all of the information
that we're generating -
0:40 - 0:43through our interaction
with and over the Internet, -
0:43 - 0:45everything from Facebook and Twitter
-
0:45 - 0:49to music downloads, movies,
streaming, all this kind of stuff, -
0:49 - 0:51the live streaming of TED.
-
0:51 - 0:54And the folks who work
with big data, for them, -
0:54 - 0:56they talk about that their biggest
problem is -
0:56 - 0:57we have so much information,
-
0:57 - 1:01the biggest problem is, how do
we organize all that information? -
1:01 - 1:03I can tell you that working
in global health, -
1:03 - 1:06that is not our biggest problem.
-
1:06 - 1:08Because for us, even though the light
-
1:08 - 1:11is better on the Internet,
-
1:11 - 1:13the data that would help
us solve the problems -
1:13 - 1:17we're trying to solve is not
actually present on the Internet. -
1:17 - 1:19So we don't know,
for example, how many people -
1:19 - 1:21right now are being affected by disasters
-
1:21 - 1:23or by conflict situations.
-
1:23 - 1:27We don't know for really
basically any of the clinics -
1:27 - 1:29in the developing world,
which ones have medicines -
1:29 - 1:31and which ones don't.
-
1:31 - 1:34We have no idea of what the supply
chain is for those clinics. -
1:34 - 1:37We don't know -- and this
is really amazing to me -- -
1:37 - 1:40we don't know how many children were born,
-
1:40 - 1:43or how many children there are in Bolivia
-
1:43 - 1:46or Botswana or Bhutan.
-
1:46 - 1:48We don't know how many kids died last week
-
1:48 - 1:49in any of those countries.
-
1:49 - 1:52We don't know the needs
of the elderly, the mentally ill. -
1:52 - 1:56For all of these different
critically important problems -
1:56 - 1:59or critically important areas
that we want to solve problems in, -
1:59 - 2:04we basically know nothing at all.
-
2:04 - 2:06And part of the reason why
we don't know anything at all -
2:06 - 2:09is that the information technology systems
-
2:09 - 2:12that we use in global
health to find the data -
2:12 - 2:15to solve these problems
is what you see here. -
2:15 - 2:17And this is about a 5,000-year-old
technology. -
2:17 - 2:18Some of you may have used it before.
-
2:18 - 2:21It's kind of on its way
out now, but we still use it -
2:21 - 2:23for 99 percent of our stuff.
-
2:23 - 2:27This is a paper form,
and what you're looking at -
2:27 - 2:30is a paper form in the hand
of a Ministry of Health nurse -
2:30 - 2:34in Indonesia who is tramping
out across the countryside -
2:34 - 2:37in Indonesia on, I'm sure,
a very hot and humid day, -
2:37 - 2:40and she is going to be
knocking on thousands of doors -
2:40 - 2:42over a period of weeks or months,
-
2:42 - 2:44knocking on the doors
and saying, "Excuse me, -
2:44 - 2:46we'd like to ask you some questions.
-
2:46 - 2:50Do you have any children? Were
your children vaccinated?" -
2:50 - 2:52Because the only way
we can actually find out -
2:52 - 2:55how many children were vaccinated
in the country of Indonesia, -
2:55 - 2:57what percentage were
vaccinated, is actually not -
2:57 - 3:00on the Internet but by going
out and knocking on doors, -
3:00 - 3:03sometimes tens of thousands of doors.
-
3:03 - 3:06Sometimes it takes months to even years
-
3:06 - 3:07to do something like this.
-
3:07 - 3:09You know, a census of Indonesia
-
3:09 - 3:11would probably take
two years to accomplish. -
3:11 - 3:14And the problem, of course,
with all of this is that -
3:14 - 3:16with all those paper forms
— and I'm telling you -
3:16 - 3:18we have paper forms
for every possible thing. -
3:18 - 3:21We have paper forms
for vaccination surveys. -
3:21 - 3:24We have paper forms to track
people who come into clinics. -
3:24 - 3:27We have paper forms
to track drug supplies, -
3:27 - 3:30blood supplies, all these
different paper forms -
3:30 - 3:31for many different topics,
-
3:31 - 3:34they all have a single common endpoint,
-
3:34 - 3:36and the common endpoint
looks something like this. -
3:36 - 3:41And what we're looking at here
is a truckful o' data. -
3:41 - 3:45This is the data from a single
vaccination coverage survey -
3:45 - 3:47in a single district
in the country of Zambia -
3:47 - 3:49from a few years
ago that I participated in. -
3:49 - 3:52The only thing anyone
was trying to find out -
3:52 - 3:55is what percentage of Zambian
children are vaccinated, -
3:55 - 3:58and this is the data,
collected on paper over weeks -
3:58 - 4:01from a single district,
which is something like a county -
4:01 - 4:03in the United States.
-
4:03 - 4:05You can imagine that,
for the entire country of Zambia, -
4:05 - 4:08answering just that single question
-
4:08 - 4:10looks something like this.
-
4:10 - 4:13Truck after truck after truck
-
4:13 - 4:16filled with stack after stack
after stack of data. -
4:16 - 4:18And what makes it even worse is that
-
4:18 - 4:20that's just the beginning,
-
4:20 - 4:22because once you've collected
all that data, -
4:22 - 4:23of course someone's going to have to --
-
4:23 - 4:26some unfortunate person is going
to have to type that into a computer. -
4:26 - 4:29When I was a graduate
student, I actually was -
4:29 - 4:30that unfortunate person sometimes.
-
4:30 - 4:33I can tell you, I often wasn't
really paying attention. -
4:33 - 4:35I probably made a lot
of mistakes when I did it -
4:35 - 4:38that no one ever discovered,
so data quality goes down. -
4:38 - 4:41But eventually that data hopefully
gets typed into a computer, -
4:41 - 4:43and someone can begin to analyze it,
-
4:43 - 4:46and once they have
an analysis and a report, -
4:46 - 4:49hopefully then you can take
the results of that data collection -
4:49 - 4:51and use it to vaccinate children better.
-
4:51 - 4:54Because if there's anything worse
-
4:54 - 4:56in the field of global public health,
-
4:56 - 4:59I don't know what's worse
than allowing children on this planet -
4:59 - 5:02to die of vaccine-preventable diseases,
-
5:02 - 5:06diseases for which the vaccine
costs a dollar. -
5:06 - 5:09And millions of children die
of these diseases every year. -
5:09 - 5:12And the fact is, millions
is a gross estimate because -
5:12 - 5:15we don't really know how many kids
die each year of this. -
5:15 - 5:18What makes it even more
frustrating is that -
5:18 - 5:21the data entry part, the part
that I used to do as a grad student, -
5:21 - 5:23can take sometimes six months.
-
5:23 - 5:25Sometimes it can take two years
to type that information -
5:26 - 5:28into a computer, and sometimes,
actually not infrequently, -
5:28 - 5:30it actually never happens.
-
5:30 - 5:33Now try and wrap your head
around that for a second. -
5:33 - 5:35You just had teams of hundreds of people.
-
5:35 - 5:37They went out into the field
to answer a particular question. -
5:37 - 5:40You probably spent hundreds
of thousands of dollars -
5:40 - 5:44on fuel and photocopying and per diem,
-
5:44 - 5:46and then for some reason, momentum is lost
-
5:46 - 5:47or there's no money left,
-
5:47 - 5:50and all of that comes to nothing
-
5:50 - 5:53because no one actually types
it into the computer at all. -
5:53 - 5:56The process just stops.
Happens all the time. -
5:56 - 5:59This is what we base our decisions
on in global health: -
5:59 - 6:04little data, old data, no data.
-
6:04 - 6:06So back in 1995, I began
to think about ways -
6:06 - 6:08in which we could improve this process.
-
6:08 - 6:11Now 1995, obviously
that was quite a long time ago. -
6:11 - 6:14It kind of frightens me to think
of how long ago that was. -
6:14 - 6:16The top movie of the year was
-
6:16 - 6:17"Die Hard with a Vengeance."
-
6:17 - 6:20As you can see, Bruce Willis
had a lot more hair back then. -
6:20 - 6:22I was working in the Centers
for Disease Control, -
6:22 - 6:25and I had a lot more
hair back then as well. -
6:25 - 6:28But to me, the most significant
thing that I saw in 1995 -
6:28 - 6:30was this.
-
6:30 - 6:33Hard for us to imagine, but in 1995,
-
6:33 - 6:36this was the ultimate elite mobile device.
-
6:36 - 6:39Right? It wasn't an iPhone.
It wasn't a Galaxy phone. -
6:39 - 6:40It was a Palm Pilot.
-
6:40 - 6:44And when I saw the Palm Pilot
for the first time, I thought, -
6:44 - 6:46why can't we put the forms
on these Palm Pilots -
6:46 - 6:49and go out into the field
just carrying one Palm Pilot, -
6:49 - 6:52which can hold the capacity
of tens of thousands -
6:52 - 6:54of paper forms? Why don't
we try to do that? -
6:54 - 6:57Because if we can do that,
if we can actually just -
6:57 - 7:00collect the data
electronically, digitally, -
7:00 - 7:01from the very beginning,
-
7:01 - 7:04we can just put a shortcut right
through that whole process -
7:04 - 7:08of typing,
-
7:08 - 7:10of having somebody type
that stuff into the computer. -
7:10 - 7:12We can skip straight to the analysis
-
7:12 - 7:15and then straight to the use
of the data to actually save lives. -
7:15 - 7:17So that's actually what I began to do.
-
7:17 - 7:21Working at CDC, I began
to travel to different programs -
7:21 - 7:25around the world and to train
them in using Palm Pilots -
7:25 - 7:27to do data collection
instead of using paper. -
7:27 - 7:29And it actually worked great.
-
7:29 - 7:32It worked exactly as well
as anybody would have predicted. -
7:32 - 7:34What do you know? Digital data collection
-
7:34 - 7:36is actually more efficient
than collecting on paper. -
7:36 - 7:39While I was doing it,
my business partner, Rose, -
7:39 - 7:42who's here with her husband,
Matthew, here in the audience, -
7:42 - 7:45Rose was out doing similar stuff
for the American Red Cross. -
7:45 - 7:47The problem was,
after a few years of doing that, -
7:47 - 7:50I realized I had done
-- I had been to maybe -
7:50 - 7:52six or seven programs, and I thought,
-
7:52 - 7:55you know, if I keep this up at this pace,
-
7:55 - 7:57over my whole career,
maybe I'm going to go -
7:57 - 7:59to maybe 20 or 30 programs.
-
7:59 - 8:02But the problem is, 20 or 30 programs,
-
8:02 - 8:05like, training 20 or 30 programs
to use this technology, -
8:05 - 8:07that is a tiny drop in the bucket.
-
8:07 - 8:11The demand for this, the need
for data to run better programs, -
8:11 - 8:14just within health, not to mention
all of the other fields -
8:14 - 8:16in developing countries, is enormous.
-
8:16 - 8:20There are millions and millions
and millions of programs, -
8:20 - 8:22millions of clinics
that need to track drugs, -
8:22 - 8:24millions of vaccine programs.
-
8:24 - 8:26There are schools that need
to track attendance. -
8:26 - 8:28There are all these different things
-
8:28 - 8:30for us to get the data that we need to do.
-
8:30 - 8:34And I realized, if I kept
up the way that I was doing, -
8:34 - 8:38I was basically hardly
going to make any impact -
8:38 - 8:39by the end of my career.
-
8:39 - 8:42And so I began to wrack my brain
-
8:42 - 8:43trying to think about, you know,
-
8:43 - 8:44what was the process that I was doing,
-
8:44 - 8:47how was I training folks,
and what were the bottlenecks -
8:47 - 8:50and what were the obstacles
to doing it faster -
8:50 - 8:52and to doing it more efficiently?
-
8:52 - 8:55And unfortunately, after thinking
about this for some time, -
8:55 - 8:58I realized -- I identified
the main obstacle. -
8:58 - 9:00And the main obstacle, it turned out,
-
9:00 - 9:02and this is a sad realization,
-
9:02 - 9:04the main obstacle was me.
-
9:04 - 9:06So what do I mean by that?
-
9:06 - 9:09I had developed a process whereby
-
9:09 - 9:14I was the center of the universe
of this technology. -
9:14 - 9:17If you wanted to use this technology,
you had to get in touch with me. -
9:17 - 9:19That means you had to know I existed.
-
9:19 - 9:20Then you had to find
the money to pay for me -
9:20 - 9:22to fly out to your country
-
9:22 - 9:24and the money to pay for my hotel
-
9:24 - 9:26and my per diem and my daily rate.
-
9:26 - 9:29So you could be talking about 10,000
or 20,000 or 30,000 dollars -
9:29 - 9:32if I actually had the time
or it fit my schedule -
9:32 - 9:34and I wasn't on vacation.
-
9:34 - 9:37The point is that anything,
any system that depends -
9:37 - 9:40on a single human being
or two or three or five human beings, -
9:40 - 9:41it just doesn't scale.
-
9:41 - 9:44And this is a problem
for which we need to scale -
9:44 - 9:46this technology and we need
to scale it now. -
9:46 - 9:49And so I began to think of ways
in which I could basically -
9:49 - 9:51take myself out of the picture.
-
9:51 - 9:55And, you know, I was thinking,
-
9:55 - 9:57how could I take myself out of the picture
-
9:57 - 9:59for quite some time.
-
9:59 - 10:01You know, I'd been trained
that the way that -
10:01 - 10:04you distribute technology
within international development -
10:04 - 10:06is always consultant-based.
-
10:06 - 10:09It's always guys that look
pretty much like me -
10:09 - 10:12flying from countries that look
pretty much like this -
10:12 - 10:15to other countries with people
with darker skin. -
10:15 - 10:17And you go out there, and you
spend money on airfare -
10:17 - 10:20and you spend time and you spend per diem
-
10:21 - 10:23and you spend [on a] hotel
and you spend all that stuff. -
10:23 - 10:24As far as I knew, that was the only way
-
10:25 - 10:28you could distribute technology,
and I couldn't figure out a way around it. -
10:28 - 10:30But the miracle that happened,
-
10:30 - 10:33I'm going to call it Hotmail for short.
-
10:33 - 10:36Now you may not think
of Hotmail as being miraculous, -
10:36 - 10:38but for me it was miraculous,
because I noticed, -
10:38 - 10:41just as I was wrestling with this problem,
-
10:41 - 10:44I was working in sub-Saharan
Africa mostly at the time. -
10:44 - 10:47I noticed that every sub-Saharan
African health worker -
10:47 - 10:51that I was working with had
a Hotmail account. -
10:51 - 10:53And I thought, it struck me,
-
10:53 - 10:56wait a minute, I know
that the Hotmail people -
10:56 - 10:58surely didn't fly to the Ministry
of Health of Kenya -
10:58 - 11:01to train people in how to use Hotmail.
-
11:01 - 11:04So these guys are distributing technology.
-
11:04 - 11:06They're getting software
capacity out there -
11:06 - 11:08but they're not actually
flying around the world. -
11:08 - 11:09I need to think about this some more.
-
11:09 - 11:12While I was thinking about it,
people started using -
11:12 - 11:15even more things just
like this, just as we were. -
11:15 - 11:16They started using LinkedIn and Flickr
-
11:16 - 11:19and Gmail and Google
Maps, all these things. -
11:19 - 11:21Of course, all of these
things are cloud-based -
11:21 - 11:23and don't require any training.
-
11:23 - 11:25They don't require any programmers.
-
11:25 - 11:27They don't require
any consultants, because -
11:27 - 11:29the business model
for all these businesses -
11:29 - 11:32requires that something be so
simple we can use it ourselves -
11:32 - 11:33with little or no training.
-
11:33 - 11:36You just have to hear about it
and go to the website. -
11:36 - 11:40And so I thought, what would happen
if we built software -
11:40 - 11:42to do what I'd been consulting in?
-
11:42 - 11:44Instead of training people how
-
11:44 - 11:47to put forms onto mobile devices,
-
11:47 - 11:49let's create software that lets
them do it themselves -
11:49 - 11:51with no training
and without me being involved? -
11:51 - 11:53And that's exactly what we did.
-
11:53 - 11:56So we created software called Magpi,
-
11:56 - 11:58which has an online form creator.
-
11:58 - 11:59No one has to speak to me.
-
11:59 - 12:02You just have to hear about it
and go to the website. -
12:02 - 12:05You can create forms, and once
you've created the forms, -
12:05 - 12:07you push them to a variety
of common mobile phones. -
12:07 - 12:10Obviously nowadays,
we've moved past Palm Pilots -
12:10 - 12:11to mobile phones.
-
12:11 - 12:12And it doesn't have to be a smartphone.
-
12:12 - 12:15It can be a basic phone
like the phone on the right there, -
12:15 - 12:16you know, the basic kind of Symbian phone
-
12:16 - 12:19that's very common
in developing countries. -
12:19 - 12:23And the great part about this
is, it's just like Hotmail. -
12:23 - 12:25It's cloud-based, and it
doesn't require any training, -
12:25 - 12:27programming, consultants.
-
12:27 - 12:29But there are some additional
benefits as well. -
12:29 - 12:31Now we knew, when we built this system,
-
12:31 - 12:33the whole point of it, just
like with the Palm Pilots, -
12:33 - 12:36was that you'd have to, you'd be able to
-
12:36 - 12:39collect the data and immediately
upload the data and get your data set. -
12:39 - 12:42But what we found, of course,
since it's already on a computer, -
12:42 - 12:44we can deliver instant maps
and analysis and graphing. -
12:45 - 12:47We can take a process that took two years
-
12:47 - 12:50and compress that down to the space
of five minutes. -
12:50 - 12:52Unbelievable improvements in efficiency.
-
12:52 - 12:57Cloud-based, no training,
no consultants, no me. -
12:57 - 13:00And I told you that in the first few years
-
13:00 - 13:02of trying to do this
the old-fashioned way, -
13:02 - 13:03going out to each country,
-
13:03 - 13:06we reached about, I don't know,
-
13:06 - 13:08probably trained about 1,000 people.
-
13:08 - 13:10What happened after we did this?
-
13:10 - 13:12In the second three years,
we had 14,000 people -
13:12 - 13:15find the website, sign up, and start
using it to collect data, -
13:15 - 13:17data for disaster response,
-
13:17 - 13:22Canadian pig farmers tracking
pig disease and pig herds, -
13:22 - 13:24people tracking drug supplies.
-
13:24 - 13:26One of my favorite examples, the IRC,
-
13:26 - 13:28International Rescue Committee,
-
13:28 - 13:31they have a program where
semi-literate midwives -
13:31 - 13:33using $10 mobile phones
-
13:33 - 13:35send a text message using our software
-
13:35 - 13:38once a week with the number of births
-
13:38 - 13:40and the number of deaths, which gives IRC
-
13:40 - 13:43something that no one in global
health has ever had: -
13:43 - 13:46a near real-time system
of counting babies, -
13:46 - 13:48of knowing how many kids are born,
-
13:48 - 13:50of knowing how many children there are
-
13:50 - 13:53in Sierra Leone, which is the country
where this is happening, -
13:53 - 13:55and knowing how many children die.
-
13:55 - 13:57Physicians for Human Rights --
-
13:57 - 14:00this is moving a little bit
outside the health field — -
14:00 - 14:02they are gathering, they're
basically training people -
14:02 - 14:06to do rape exams in Congo,
where this is an epidemic, -
14:06 - 14:07a horrible epidemic,
-
14:07 - 14:10and they're using our software to document
-
14:10 - 14:13the evidence they find,
including photographically, -
14:13 - 14:17so that they can bring
the perpetrators to justice. -
14:17 - 14:20Camfed, another charity
based out of the U.K., -
14:20 - 14:24Camfed pays girls' families
to keep them in school. -
14:24 - 14:27They understand this
is the most significant intervention -
14:27 - 14:29they can make. They used
to track the dispersements, -
14:29 - 14:31the attendance, the grades, on paper.
-
14:31 - 14:33The turnaround time between a teacher
-
14:33 - 14:35writing down grades or attendance
-
14:35 - 14:37and getting that into a report
was about two to three years. -
14:37 - 14:40Now it's real time,
and because this is such -
14:40 - 14:42a low-cost system and based
in the cloud, it costs, -
14:42 - 14:46for the entire five countries
that Camfed runs this in -
14:46 - 14:48with tens of thousands of girls,
-
14:48 - 14:51the whole cost combined
is 10,000 dollars a year. -
14:51 - 14:53That's less than I used to get
-
14:53 - 14:58just traveling out for two weeks
to do a consultation. -
14:58 - 15:00So I told you before that
-
15:00 - 15:02when we were doing it
the old-fashioned way, I realized -
15:02 - 15:06all of our work was really adding
up to just a drop in the bucket -- -
15:06 - 15:0710, 20, 30 different programs.
-
15:07 - 15:10We've made a lot
of progress, but I recognize -
15:10 - 15:12that right now, even the work
that we've done -
15:12 - 15:14with 14,000 people using this,
-
15:14 - 15:17is still a drop in the bucket.
But something's changed. -
15:17 - 15:19And I think it should be obvious.
-
15:19 - 15:21What's changed now is,
-
15:21 - 15:24instead of having a program in which we're
scaling at such a slow rate -
15:24 - 15:27that we can never reach
all the people who need us, -
15:27 - 15:31we've made it unnecessary
for people to get reached by us. -
15:31 - 15:34We've created a tool that lets programs
-
15:34 - 15:37keep kids in school, track
the number of babies -
15:37 - 15:40that are born and the number
of babies that die, -
15:40 - 15:44to catch criminals
and successfully prosecute them, -
15:44 - 15:46to do all these different
things to learn more -
15:46 - 15:51about what's going on,
to understand more, to see more, -
15:51 - 15:55and to save lives and improve lives.
-
15:55 - 15:57Thank you.
-
15:57 - 16:01(Applause)
- Title:
- The surprising seeds of a big-data revolution in healthcare
- Speaker:
- Joel Selanikio
- Description:
-
Collecting global health data was an imperfect science: Workers tramped through villages to knock on doors and ask questions, wrote the answers on paper forms, then input the data -- and from this gappy information, countries would make huge decisions. Data geek Joel Selanikio talks through the sea change in collecting health data in the past decade -- starting with the Palm Pilot and Hotmail, and now moving into the cloud. (Filmed at TEDxAustin.)
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 16:18
Brian Greene edited English subtitles for The surprising seeds of a big-data revolution in healthcare | ||
Krystian Aparta commented on English subtitles for The surprising seeds of a big-data revolution in healthcare | ||
Krystian Aparta edited English subtitles for The surprising seeds of a big-data revolution in healthcare | ||
Krystian Aparta edited English subtitles for The surprising seeds of a big-data revolution in healthcare | ||
Krystian Aparta edited English subtitles for The surprising seeds of a big-data revolution in healthcare | ||
Krystian Aparta edited English subtitles for The surprising seeds of a big-data revolution in healthcare | ||
Thu-Huong Ha edited English subtitles for The surprising seeds of a big-data revolution in healthcare | ||
Thu-Huong Ha approved English subtitles for The surprising seeds of a big-data revolution in healthcare |
Krystian Aparta
The English transcript was updated on 1/11/2016.