2016 Public Health Grand Rounds 03/04

PUBLIC HEALTH GRAND ROUNDS Linking Research to Community Health Improvement Jointly sponsored by the Department of Public Health Sciences & URMC Center for Community Health

- Good?
So welcome everybody, thanks for coming
to Public Health Grand Rounds today.
We present Public Health Grand Rounds
as a joint effort between the Center for Community Health
and the Public Health Sciences department,
we try to focus on projects that bring community health,
public health and community to basic research
that's being done within U of R to highlight partnerships
and really cool innovations that address population health!
And today is no exception to that.
I wanna make note, though, that the next
Public Health Grand Rounds will be two weeks from now,
with Dr. White, and there is a room change,
it's match day, so this room will not be available,
we'll be meeting at Whipple, which is in the Medical Center,
in two weeks, for that lecture.
So today, we are pleased to have Solomon Abiola presenting.
Solomon received his BSE from Princeton University,
in Mechanical and Aerospace Engineering,
and a MS from Carnegie Mellon University
in Biomedical Engineering.
He then worked at the University of Rochester
Medical Center, where he wrote and lead an NCF grant
using Node, a mobile health application
he developed for the Ebola outbreak in Nigeria.
With his mentor, Dr. Ray Dorsey,
he also worked on the development of mPower,
an Apple application to empower
Parkinson's disease patients.
Prior to starting his PhD, Solomon did an internship
with Merck, developing innovative early-stage
medical devices.
Solomon is a joint PhD student between the TBS program
and Computer Sciences.
His work focuses on novel applications of technology
towards preventive health, using techniques from big data
and artificial intelligence.
So really interesting, and like nothing we've heard before
in public health, so thank you, and join me in welcoming
Solomon Abiola.
(clapping)
Okay, thanks for the introduction.
So today we're gonna basically talk about research
that me and my team have done in mobile health
for the Ebola outbreak, and how this can be applied
to other infectious disease outbreaks
such as the Zika virus outbreak,
and a Lassa fever outbreak that's actually occurring
in Nigeria right now.
So let's give you an outline, we're gonna talk about
the rationale behind this, then what we did with Ebola,
and applications to Zika.
So before we start, I'd like to show you
what's known as the Gartner Hype cycle,
they release this every year, and it focuses on,
you know, you hear a lot about all these technologies,
Internet of Things, big data, artificial intelligence,
and if you look at where we have these two orange circles
right now, this is where we have mobile health monitoring
and big data.
So, you hear a lot about these terms, and right now
they're kind of what they call the disillusionment section,
and what that means is that you're seeing a lot of them,
but the question is, will these technologies
really turn out to be what we think they will become?
So, why are we doing, basically, mobile health
for infectious diseases?
If you look at a lot of what was done in the 20th century,
and we still do this today, a lot of what we do is
paper reporting, we send out health workers into the field,
we gather data that way, it's not entirely real-time.
But using mobile health technologies,
we can do continuous monitoring,
and do objective disease tracking,
and so that's what we set out to do
with the Node mobile application.
So, mobile health can be used for all sorts of things
from diagnostic monitoring, as was mentioned with,
some of you might be aware of the mPower
Parkinson's disease study, for chronic diseases,
you can use this for disease modeling,
as I'll be focusing on today, and you can also use it for
other ranges of tracking various disease progressions
throughout a population.
So, why are we looking at Ebola?
Most of the money that was invested into the Ebola outbreak
was spent on nongovernmental resources.
So this means for things like health workers,
you know, setting up databases, and so forth,
but these things are not lasting,
which means that West Africa as a whole
could suffer another outbreak,
and we might all wonder to ourselves,
"Weren't we there in 2014?
"What happened to all those resources?"
If you look at this chart, right here for even Liberia,
you have over $1 billion, almost $2 billion
spent on nongovernment resources,
and a measly $63 million spent on just actually building
infrastructure.
Furthermore, not only does this affect
just the infrastructure of these health environments,
but also, it affects things such as initiatives,
reduced child mortality, you know, all these issues,
if you look at, in 2010, you have this decline,
Sierra Leone, Liberia, and Guinea,
they were making a lot of progress,
but now post-Ebola, it's expected you see a 200% increase,
just in maternal mortality rates,
because all those resources were being spent on Ebola.
If you think about it, that could the result of,
okay, there's an Ebola outbreak,
you're afraid to go to the hospital,
you're not receiving basic care
because the entire health system is now preoccupied
with an infectious disease outbreak.
So, as a mission of, we first developed Node
when I was a senior at Princeton University
and was focusing on influenza.
Since then, we evolved this into an application
that we could use for Ebola, and could be used
for other outbreaks, such as the Zika outbreak.
So, the mobile application, you can see
right here on the left, it simply asks people,
you know, do they use vaccines and so forth?
We ask these questions because we think about this
from a public health standpoint,
let's say there was a vaccine out there for a Zika virus.
Most people, we would assume, that okay,
everybody's gonna take up this vaccine,
politicians are gonna say, "We're gonna put all of our money
"to developing this vaccine, and then we're gonna release it
"on the population."
What you might not know is the population itself
is resistant to vaccination.
Or they might say, "Well, in our community,
"we don't wanna do these types of vaccines,"
and so forth, so you now wasted public health funding,
and your problem still continues to exist.
You saw this when the Ebola outbreak first happened.
The recommendation from, you know, those of us
in the Western Hemisphere was to say,
"Oh, they should do proper burial techniques."
This is without concern for the cultural environment
which they were operating, and it wasn't really
until we started to understand,
"Okay, in these environments, this is how burials are done,"
you can't just say, "Okay, we're gonna put them
"in a plastic bag and burn the body,"
because of various religious norms in these communities.
It wasn't until we understood that,
that then we were able to start making progress
with the Ebola outbreak.
So this map that you see right here on the right,
was after one week of tracking various seven participants
with the mobile application.
So what this allowed us to do was moved beyond
just your typical infectious disease models that might say,
okay, within two weeks we expect 400 people to have the flu,
and actually be able to drill down on a geospatial level
and say, well, these are the hot spot areas.
So this fits in with what you would've suspected
without even looking at this, this blue area
being the student center.
These other black circle regions
being where there's dormitory environments.
We expect there to be infectious disease outbreaks
in those regions, due to all the contact that takes place.
So when we prepared to develop Node for the Ebola outbreak,
we did some preparatory, to research testing,
and this is really to focus on the fact that
unlike prior research that's done tracking of diseases,
where people are given an actual device,
we were gonna do this in an environment
where we hadn't done this before,
and also, we're gonna do it device-agnostic,
so what that means is, people weren't gonna all have
iPhone 6s, and it was gonna be of these perfect
pilot studies.
These are people who might be using older smartphones,
using the latest Android smartphones and throughout.
And also, we're doing it in a resource-restricted
environment, where power is not guaranteed,
so we had to optimize the software for that environment.
So, when we're looking at this data,
what's someone like a public health worker, epidemiologist,
really gonna see?
We're not interested, per se, in tracking
each individual person's movements,
we're more interested in doing contact tracing,
and seeing where these contacts are occurring,
and be able to isolate areas where we think
an infectious disease might spring up.
So with two users here, you can see this,
the light blue and the dark blue,
the key thing was that both users were in contact
when they're in the Saunders Research Building,
in which case, then we suspect that,
okay, if we're gonna send resources,
we need to send it to Saunders Research Building.
That allows us to target where we're gonna send
our infectious disease workers, or epidemiologists,
we're saying, we have to do the entire medical center,
and we might only, in our country, have 15 health workers
who are able to sent out into those environments.
So, after we have this framework in place,
we applied for a NSF grant, and we set out to do
basically three things.
First, we wanted to validate, will this work
in these environments?
Secondly, what kind of behavioral trends
can be observe from the environments?
And lastly, is there an infrastructure
in place to support this?
Or is this just something where, you know,
you hear about a lot of wearable mobile health studies,
"Oh, it worked for two weeks, after that,
"there was no adherence, or, this is not sustainable."
So we set out to enroll up to 100 participants,
these participants came from the Nigerian Institute
of Medical Research, Lagos University Teaching Hospital,
which is basically like our system here,
and the University of Lagos, so we have
three participant groups around the Lagos region,
and we tracked these participants over a three-month period,
and they're paid $50 per month of participation.
So, bringing it back to why are we doing this?
At the time when we were doing Ebola contact tracing,
this is the model the CDC had in place,
which is very complex, basically,
someone would come in, they would say,
"Okay, we have our Ebola patient,
"we're providing treatment, but now what we wanna see is,
"well, where are his contacts?"
Now, how many of you in this room
would know where you've been for the last 21 days?
Nobody, right?
We're not keeping track of this.
Sure, we have, you know, maybe if you're posting on Facebook
and Twitter and so forth, you can infer that,
but you're not keeping a day-to-day log of these events.
Secondly, if you come in as an Ebola patient,
you might be in a comatose state,
in which case, well, how are we gonna get
the information from you?
We don't know, we don't know which areas are at risk,
we're just saying, "Well, this massive city
"with 21 million people, is at risk,"
and we don't know, really, where to deploy
our limited resources, even with international help.
And you saw this same problem in New York City
when we had our Ebola patient.
They had an entire building, a call center,
dedicated just to one person, so you can imagine
if we had 50 patients in New York City,
our own health system itself would actually be overwhelmed.
The other thing is that a lot of these health workers
are being sent house-to-house, and many of us here
are aware of hospital-acquired infections,
if you think about, that's another way to set up
such an environment.
You go to a house, you think that they don't have Ebola,
you come in contact with them,
you now carry that to a house that doesn't have Ebola,
and so actually now you become the carrier
for an infectious disease outbreak,
when you're trying to prevent that outbreak.
So, using the Node framework, we're doing
the same exact steps in real time,
but using smartphone applications.
So what that means is being able to systematically
follow up with these people in their communities.
And many of these people do have smartphones
in their houses as we showed earlier.
By 2017, about 30% of the African continent
will have smartphone technologies, and that's
talking about the latest technologies,
so they could have even older devices.
But basically, that allows us to do
instantaneous contact tracing,
so that means someone comes in, we have our patient zero,
we look up, okay, in the last 21 days,
what regions had there been contact?
We send our limited resources there,
we set up quarantines, and we deal with the environment.
So, here's a static image of the system that we developed
using our participants.
So the first step, basically, as I said was,
you come in, you locate this user.
You identify, okay, who have they been in contact with?
And now you're able to geospatially see
where those users have been, and be able to enact change,
that means, being able to anonymously follow up
with that person.
"Hey, have you shown any symptoms of Ebola?"
You can imagine this for Lassa fever, for Zika virus,
for other conditions.
And so actually, we're gonna show you,
right here, we still have a few participants in the study,
so this is beyond our three-month period,
but basically, you come in here, you say,
okay, you're doing 21-day contact tracing,
you could go over here and choose the user's been in contact
with somebody, so this brings up now an anonymous user,
so you don't need to know who this person is,
so it's protecting the anonymity of that user,
but it brings up who are the other three people
they're in contact with in the last 21 days?
So let's say this user has now shown up in your hospital,
they have Ebola, they're being provided treatment,
you now need to know, okay well,
where have these people been?
So you can select this person here.
And you can either look on the map,
or you can get real-time information,
let's see.
Okay, so you can get real-time information
as to where that person was, what where they doing,
and so forth, so that way you could determine,
okay, well maybe this person's no longer moving.
Well, have they passed out?
Are they in a coma?
Are they sick, and so forth, you can now follow up
with that person in real time.
So, this also allows us to collect this information
in real time, now use it for forecasting.
So, right now in the US, we are having a few cases of Zika
come in to the country, but the real question is,
let's say, would some place like Rochester be at risk?
We might be wondering, well, you know,
if right now we're like, "Eh, there will be no risk,"
or our infectious disease models will say,
"Well, by the end of this year, we expect 20,000 cases."
Okay, well, how useful is that to, let's say
a hospital system, or a state governor, or even a president?
To say, well, where are these cases gonna be?
You have, you know, $3 billion in federal funding
to the spent on this infectious disease,
we have no idea where the outbreak is coming from,
you don't have any ideas to what the mosquito
migration patterns are, or if there's any way
to forecast this.
So in this simple model, what we just did was
we ran a hypothetical pathogen through our historical data
for the Ebola outbreak, and so what that allowed us to do
is randomly select a user at the beginning
of our three-month period, give them some pathogen,
forecast that pathogen through the entire system
in real time, and then be able to identify,
well, where would we expect it to be hot spot areas?
So if you look on this map here,
the community that's in the center here
is the Medical Research Center.
The community on the left is the hospital system,
the community down the right is the university.
The key thing to note is that we can now observe from this
is that, looking at where there are
non-affected individuals, you may suspect,
well, probably the reason why the Medical Research Center
was not affected, was simply because those people you know
are commuting to work.
They drive there, they go to work, they come back home.
In which case, they're not really interacting
with the rest of the population system.
Whereas if you look at the individuals
who are in the hospital, and at the university,
they have more diverse patterns,
simply because, if you think about students,
we're moving around the community,
we're not just doing a nine-to-five job,
that you know you can kinda expect,
okay, they're gonna be here at these times of day.
It also allows you to now drill down and say,
okay well, where do you expect that outbreak to be,
and where can you send your resources again?
So now, moving along from Ebola to Zika virus,
the key question might be, well,
Zika's a vector-borne disease,
although we have start to seen sexually-transmitted cases,
this is still capable of being carried
by a mosquito pathogen.
And so how do we adjust our framework
to accommodate for this?
So in phase one, we have what we call the infection phase,
which is either it came in via mosquito, or sex,
and we ask via our (microphone cuts out) symptoms
questionnaire, that might be, you know,
did you get bitten in the last 24 hours, and so forth,
I'll show those on the following slides.
The other thing that we're adding is now saying,
okay, well we can take photos of people who have rash
and red eyes, and feed that now into a predictive model.
So, while people are still developing rapid blood
diagnostic tests for Zika virus and so forth,
we have to think again about the environments
in which we're dealing with.
Do we wait for it to reach a critical mass,
as we had to with Ebola, in order to determine,
what is the the rate of the transmission,
and then we had to develop the technologies
to identify Ebola, and so forth,
while people are actually dying,
or could we at least start to collect this information,
and now start to use real-time computer visual analysis
to determine, well this type of rash, we actually observed,
has a pattern for Zika virus.
It's different from the rashes we have
for other clinical conditions.
And then we start to use that information
for other infectious diseases in the future.
The next step is basically being able to say,
okay, these were suspected cases,
some of these cases are gonna come into the hospital.
Some of them are gonna say they have microcephaly,
some of them are gonna say they had a rash,
some of the might may that, you know,
maybe they were paralyzed, and so forth.
We can start to collect that data
and actually start to get information out of it,
and not just, okay, well this isolated hospital system
in Costa Rica is collecting its own information.
It's not sharing, it was the wrong hospital.
It's not sharing with a hospital in London,
in which case, we're not really working on this
from a global standpoint to unite our hospital systems.
And lastly, the last stage is being able to forecast
these disease outbreaks.
So, yes, let's say by the time of the Olympics,
where will those people be, once they leave the Olympics?
Will they be returning back to, let's say, New York City?
What cases have we observed now in New York City
as a result of them returning back from the Olympics?
Can we start to model from that information
which other cities in the US will be at risk,
therefore set up appropriate measures to handle it,
and not just let it balloon out of control,
and then try and retroactively fight the outbreak.
So, you might be wondering, well,
there are other mosquito-born pathogens,
such as malaria and so forth, in these environments,
so you have dengue, Zika, chikungunya,
all these diseases, right?
Well, some of them show similar symptoms.
We all have, okay, the fever state,
you even saw that with Ebola, you saw that Lassa fever,
it's our natural immune system response, to induce a fever.
So how are we gonna be able to differentiate
between that information?
So, this is actually from the World Health Organization,
where they were able to say that when someone comes in,
more than likely, if it's dengue fever,
there should be symptoms of bleeding.
If they come in and it's Zika, there should be rashes.
These are information that we can differentiate
between these diseases, but this is simply based
on our own clinical observations.
By doing a big data study, we might start to observe
other trends that can now be used
for symptomatic differentiation,
so that means it's now being able to say,
well maybe, if people have Zika virus,
they're having difficulty sleeping.
Well, we might not have suspected that initially,
but now we have data to back up that claim,
and now we can use that as our ability to say,
okay well, are you having difficulty sleeping?
Right now we would say, well, that doesn't make any sense,
but maybe it's in the data set.
And basically, that allows us to take in information
that we might not think is clinically relevant yet,
but be able to feed into a predictive model,
and be able to determine, this is what we're seeing
in terms of Zika cases, this is what we're seeing
in terms of dengue cases, and be able to, you know,
feed that into a public health system, and act upon it.
So lastly, I'm just showing a few mock-ups
of the application that we're developing now
for the Zika virus, and we're hoping to have this
for Android, in the next two to three weeks,
and ideally, for iOS built on the ResearchKit platform
before the Olympics.
The idea of being from the, let's say, US population,
that have people who are traveling to these countries,
that saw this application, do symptomatic tracking,
people in those countries also do symptomatic tracking,
and lastly, be able to feed this also
into a telemedicine platform.
So that means, let's say you go into one of these countries,
you can at least come in contact with someone who,
you know, can give you the correct information to say,
yes, go talk to this OB/GYN, or go talk
to this infectious disease doctor.
Because when you look at a lot of these
public health problems, it's not our inability
to treat them, it's really a lack of information,
that's getting down to the civilian level.
So, you saw it with Ebola, people were saying,
"Oh, well they can go bathe in bath salts,"
and, you know, all sorts of crazy things were coming out,
because there weren't cures in place,
there weren't vaccines, people didn't know what to do,
they were in a state of panic.
And you kinda see this with Zika virus, right?
You know, how many of you in the room know,
well, what is, let's say, Strong Memorial's Hospital's
position on Zika virus?
What would happen if you showed up in New York City,
and you have Zika virus, who do you call?
Who do you get in contact with?
We don't want it to be like, okay, this person died at home
from Zika virus, and we find out now it's actually
capable of killing people, and it's spreading
out of control, but be able to really act upon that data
in real time.
So, that's it!
Any questions?
Yes?
(woman speaking off microphone)
So, in terms of the uptake, there was about an uptake
of, let's say, greater than 50%,
primarily it's just because of, as I mentioned earlier,
some issues around the software,
being able to address different hardware platforms,
but most people, when we explain
why we were tracking the information,
didn't seem to have a concern with it.
I think the thing is like, when you compare a lot of the,
if you compare developing, to developed countries, right,
and developed country because we have our various
secret agencies, we have a higher concern over privacy
and tracking, than these other countries,
where they see it more as, "I'm benefiting
"from this service."
At the same time, though, in a developed country,
we all have, let's say, Facebook on our phone,
Google Maps, and so forth, all these technologies
are tracking your location.
And actually, if any of you have Android application,
you can go on Google, right now, go to their mapping page,
and it will show you your location history
since you've owned that phone.
So, and you're consenting to getting that information,
but why?
Because you feel like, "I get a GPS out of it."
I get something that can help me navigate through life.
So, it's trying to do the same thing with healthcare.
Yes, we're collecting this information,
but let's not just do it as researchers,
let's collect it in such a way that it gives value
back to the community in which we're collecting it.
You saw that with the Parkinson's study that we did.
The ability to say that, "Oh, you're collecting data
"as a researcher, but, I get some feedback
"that's showing me how my condition's progressing."
You think about with infectious diseases.
This app at least tells me who I can get in contact with,
it's providing information, because without it,
if there's an infectious disease outbreak,
I'm waiting for the media or some other source to tell me,
and before then, it might be too late.
You know, you're thinking, "Well, who do I get
"in contact with?
"I can push this button, and at least be
"connected to my local hospital system
"and be able to figure out what's going on."
Yes?
(woman speaking off microphone)
Okay, thanks.
So, the first question's basically like,
"How do we get the uptick in rural areas, and so forth?"
And so this is actually a question
when we were presenting this in Nigeria
to their private health sector,
there's two ways to look at it.
This type of technology's really preparing
for the future of healthcare.
So we're not interested in focusing on,
if you look at a lot of developing healthcare's
SMS notifications, you know, like,
you're focusing on the here and now,
but really being able to build an infrastructure
that the country's gonna start to integrate,
so you know the laws can come online, towards, let's say,
data privacy and so forth.
The second thing is, when you look
at a lot of these rural communities,
you see mobility patterns between,
and you see it even in the US, some of us live in the city,
and maybe our elderly population's living outside the city.
We still have communication with them.
We still send, you know, you're like,
okay, for Christmas, you buy your grandmother an iPad.
You do that in the US, and you know, Africa,
South America's no different.
How did they get those phones in the first place?
They're not technologists or anything like that,
but it's because you have these linkages
back to your rural or suburban populations,
and so that's really how you can disperse the technology,
because, they go into the city, which at the moment,
it's like, let's say, the source of knowledge
for that community.
You find out, okay, there's this opportunity,
you take that back with you to the village,
you share that with the people there.
So it's kind of using that, you know, corridor,
to transmit health information,
but doing so in a digital manner, so.
Yes?
- [Woman] So I think I missed a connection here,
but is there a place that you're personally
tying a person's name and ID with a phone?
Like, is it when they download the app
and register as their self, so that when they I show up
infected with Ebola, now they know my phone?
Or how does that connect?
- Okay, so, when I was showing, let's see.
Here.
Or we can even...
So when you sign up for the application,
what happens is, it doesn't actually,
you don't put any information in, like,
you don't put in a name, or anything.
It actually, in the background, generates
an anonymous user ID, which is done on the software level,
so even we as researchers don't, you know,
it's like a black box, basically.
It's not where you can say, "Oh, well we know
"what the IDs are and we can find some pattern
"and so forth from that."
So what happen is, when you show up to the hospital,
on the app, it shows what your anonymous user ID is,
that's what they'll be plugging in.
So now it would be up to you, if you, or depending
on that health system, if they're allowed to,
let's say, maybe they can access your patient record
by just asking your name, and so forth,
but this application is not linked
to any identifiable information in that manner.
- [Woman] So you'd have to give them your phone.
- Yeah, exactly.
Yeah, yeah.
Exactly.
So that's why, even with this model, you could,
let's say you're an epidemiologist and you notice
these three people you think are at risk,
you can send them a message, but you don't know
what their names are or any identifying information.
So it lets you get in contact with them
in a way that preserves their privacy.
Any other questions?
Yes.
(woman speaking off microphone)
Ah, yes.
So, we didn't show it like here on this page,
but if you look at, like, you know on your phone
where it shows you, you get like a notification,
like you have an incoming email or something,
or you have an incoming fold, like on the top of the banner.
We're using that exact same, what they call
push notification system.
So we could send you a message saying,
"Hey, we think you're at risk," or something like that,
"Please follow up with this hotline," or whatever,
and then they can start to engage in information transfer.
So that way it will fit into whatever
that health system's laws, in terms of contacting
individuals, and providing also just information
in terms of healthcare.
So basically, it's setting up like an anonymous way
to contact them, they still get some of the information
they need, and then you can feed that into a physician,
or feed that into a health worker,
or someone in that environment.
Yes?
(woman speaking off microphone)
Ah, yes.
So we're looking at it in terms of,
so let's say, 10 people from Rochester
fly to the Olympics.
They end up getting Zika, either sexually-transmitted
or mosquito-borne.
The question then is, if they start to show the symptoms,
where are they returning back to, like you said.
Do we need to notify, okay, these 10 people
are actually now gonna return back to Rochester,
so we need to be ready for them, or is it that
five of them are going back to New York City,
and, you know, going to other locations, and so forth.
So yes.
And then the other thing is being able to say that,
okay, on the mosquito side, you know that
this person said that they got bitten by a mosquito
and they're showing symptoms.
Well, we might not be able to track every mosquito
in real time, with our current technology,
but we can at least use, basically, people,
I mean, in a way, we're like, using us a sentinel or a bait,
or something, to say, "Okay, well I was in Georgia,
"I got bitten by a mosquito, I'm showing symptoms for it,"
now we can start to build models that say, well,
up to Atlanta, we've noticed there're a bunch
of mosquito bites in this region,
they all resulted in Zika.
Now we know the range of the actual mosquito itself,
and then we can use that information to say,
well, based on our weather forecast,
this is how far we expect the mosquito to migrate
further north in the country.
There's a question in the back, I think, yes?
(woman speaking off microphone)
Ah, yes it is.
So, when I was showing the slide for
differentiating between the three diseases,
that was very important with the current Zika outbreak,
and that was also important with Ebola,
because the reason why they didn't know
it was Ebola at first is 'cause they thought
it was Lassa fever, in their patient zero,
in which case, then, things got out of control
because they didn't know what they were dealing with.
You see the same thing if you have a malaria patient
that comes to the US.
We don't have malaria here, so,
you probably would actually receive better treatment
in West Africa, in their hospital system,
than you would in the US.
So we are looking at ways to differentiate
between the different disease types,
and I am working on, actually, a pilot project,
looking at using continuous temperature tracking,
as a way to say, maybe there's a temperature profile
associated with each one of these fever outbreaks.
So instead of just saying, "Oh, you have 105-degree fever,"
okay, that's a static value that,
every fever, of course, is gonna be higher
than your normal body temperature, but what is, you know,
maybe there's a pattern.
If you think about this, you can infer that around malaria.
It's a cyclic infectious disease, it has a cycle,
which means that the fevers themselves come in cycles,
which means that if you were to observe that over,
let's say, seven-day period, you'd just be able to look
at that data set and say, "Hey, it seems like
"you keep having a peak every six hours,
"we think you have malaria," and then you imagine that
for other infectious disease outbreaks.
Yes?
- [Man] So, what's your next, what's your biggest hurdle?
I know you're talking about how the African governments
are a little bit more open to security,
allowing this to take place.
Is there any potential in the United States,
and what is the biggest challenge?
- I think that it could work in the US,
I think the thing is that, as a civil population,
when it comes to healthcare, right,
we have the impression that we're invincible, right?
You saw that with Ebola.
"Oh, it won't come to the US, we'll be fine!"
Like, everything will be great.
And then, it showed up in Texas,
and we saw how it was handled, whereas if you look
at these developing countries, when it comes to healthcare,
because the consequences are so immediate and obvious,
people are more willing to say,
"Well, yeah, I wanna be part of some system
"that's gonna protect me," and so I think that that's,
the biggest hurdle is basically, you know,
trying to make the population say, okay,
there are these pathogens and even chronic diseases
and so forth, but just to be more health-aware
and just say, "Okay, there's ways for me to navigate
"a condition."
And I think that we don't have that right now,
I think it's partially because, you know,
as a hospital, even, we're trying to make it
more patient-centered, right?
But if you think about health right now,
it's more centered around the clinician, the hospital,
like, come in and seek care, but there's people right now
who have the flu who aren't seeking care,
there's someone who probably has Alzheimer's
who's not seeking care, you know,
they're out there in the population
and we're not tracking them, or managing that population,
and, you know, that's one of the things that telemedicine
is trying to address, it's to say, well,
could we at least know that they were there?
And then maybe we can adjust our health structure
to fit that.
Or can we use things like precision medicine from the NIH,
to say, "We know where these various American populations
"have the issues, let's develop programs for them."
Any other questions?
- [Woman] Well, I guess you get to gift of time today,
I'm sure someone will, later, if you have any questions,
you all should have received evaluation,
please complete the evaluation,
so we can use that information
to set the agenda moving forward,
so thank you very much!
- Thank you! (clapping)