ICTD & Healthcare – Interventions in Assam, India
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ICTD & Healthcare – Interventions in Assam, India


Just quickly,
I’m Keyur Sorathia, I’m faculty member
at IIT Guwahati. I work in the area of HCI,
specifically in ICT4D, and we have just started working
in virtual reality and input interaction method. So given the time frame we have,
ten minutes, I’ll quickly brief you about
some of the projects that we are doing currently in Assam. And I’ll be majorly talking
about two projects, which are very recent
project we have started. The first one is Pragati, which
is basically trying to train community health
workers in Assam. And the second one is Swasthyaa,
which is basically an ICT supported complete ecosystem for
Tuberculosis. Okay, these projects are funded
by government of India and they are collaborating with WHO,
Government of Assam and National Health Commission. I’ll very briefly touch
upon what we are doing, and maybe if someone
has some questions, we can certainly talk
about them later. Okay, just a lot of people may
not sure that they are available health workers but just to
give you a quick idea about, Health workers are sort of
people who communicate health education and act as a mediator
between the doctors and the community members. So sometimes, they become
health professionals and support them in escorting to
health centers and so on. So currently, what we see is
there are a lot of challenges, loopholes into the health
worker recruit system. So first is they’re not
sufficiently trained. And even if they are trained,
the methods are very passive in nature, which includes
a variable method that someone, A trainer comes and
speaks about it and then expects all the health
workers to learn by themselves. So what happens, and there
are other problems that I’m not going to the detail of it. But because of this these
problems, the health workers are not able to gain health
education or training, which they are supposed to communicate
to the community members. And because of this, the healthy
communities, as in this results basically in unhealthy practices
in the rural communities. So what we have done is,
we have basically created, designed the Low-cost Head
Mounted Display based immersive virtual interface that is
to train health workers. So, one of the good part of this
project, this is not a passive set of modules, just audiovisual
modules where health workers would come and learn, or
listen to the modules. But, yet,
what we are trying to do, it is giving them certain task
that they would be performing in various set of activities. So we can see over here. So you can see but the health
workers would actually take the baby out of the womb, perform those activities unto
the virtual environment. The second, where the health
worker would clean the new born, and health workers would make
the pregnant women sleep on the chair, and so on. So those kinds of activities
they will be performing. And the idea is that to have
hands on practice-based approach rather than just
a passive module. So the other interesting part of
this project is they wanted to scale the project test. The solution works with a Head
Mounted Display, as well as in a 2D mobile application, as well
as in a 360-degree environment. So we wanted to ensure that such
solutions are not limited to training center, Which ideally would have
the cheap Head Mounted Displays, which the government of
Assam has agreed to provide. But it also goes
on to the field, where health worker can take
it onto the field, show it to the community members,
And act as a third person, specifically in the issues
related to family planning, sex, and pregnancy,
which they are very shy about. So can mobile phone
become a third person where information
related to sex and so on could be communicated
to rural communities? Through these mobile
interventions. And also, it works when
health worker is at home, they can also learn themselves,
sitting at their home. Okay, there are other part, I’m
not going to the videos right now, but the other part
of the solution is about a peer learning platform, Which
is the only audio and visual based peer learning platform,
where health workers, all across Assam can update whatever
their learnings are, Whatever the information that they
want to share it with people. So it’s the only audio-visual
interface where health workers would record an audio and
upload it over the system. Uploading is not based on
their own network data, but network which is supported on
the health centers itself. Yeah. So currently, what we are,
what stage we are in, we have done some studies
with 60 health workers, so this is one snapshot of when
we were doing evaluation. So we have done studies with
some 60 health workers and the results are encouraging. So we are doing the next
study in January, which is with 500
health workers, and trying to see if we can
increase the learnability and memorability of the health
contents, Specifically in mental and child health and see if it
can create an impact of it. So this is the first project. We are still on the project,
we still have one more year, one and
a half year on the project, then hoping to see
some good results. But the idea is that we deploy
the solution across this, which would have more than
50,000 health workers. Okay, the second one is the
earlier speaker had knowledge about TB. The second one is a project that
we are doing on called faster which is again complete
tuberculosis care we call it. So very quickly, the kind of
challenges that the TB ecosystem has, And in their first is the
poor referral system, that means if I’m a citizen and I may
be a prospective TB patient. There is a process that
a health worker refers to the health system that I may
be a Tuberculosis patient. Second is tracking of initial
and retreatment defaulters. That means, if I am identified
as a TB patient, one option is that I don’t even enroll
into the government schemes. How do I track if I
don’t enroll, and eventually the person
would get into very serious stages of tuberculosis. Or I start consuming medicines,
but I don’t complete the course. So then again, I go from
category one to category two or three patients. So then how do we track
these initial and retreatment defaulters? Third is the DOTS consumption,
how do we real-time track DOTS consumption, 99DOTS has been
doing amazing work onto that. And there are two major other
things which are a part of project test. All the health centers,
even public and private, they’re completely disconnected. Specifically in Assam,
where most of the population, or migrate from one place to
another place for work. So if the health centers
are not connected, the patient is enrolled into
the program from the starting of the program itself. So, and most often, they don’t
even enroll into the program because they have to,
they are daily wage workers. And second is, all these
problems lead basically to any kind of preventive measures,
which are often delayed. By the time the health
administrator would know that the patient is not
consuming medicines or not even registered into the
DOTS program, it’s too delayed. And eventually, all the government schemes
have become ineffective. So what we have
currently proposed, which we are currently in
the development stage, So it’s a complete ecosystem
which will connect patients, family members,
all the health providers, Which includes medical officers,
TB control units, STS Senior Technical
Supervisors, health workers, DOT providers, all the officers
where the Top managing, the National Health Commission,
and so on. And also connect all
the private clinics and the government clinics. So they somewhat
knew we are doing, just to give you a very quick
overviews, We are using this multi-technology platform
which includes them. Mobile and IVR system based
on the technology literacy of the users, that means if
the health worker has specific technology literacy or the patient does not have at all
technology literacy, Then they can use different platform such
as IVR, giving a missed call or just calling and entering
a particular number to it. So, and also we have introduced
one incentive-based system, which means it’s not
a purely monitored incentive-based system. But the moment health workers
ensures that the patient is tracked, the health worker would
get 5 rupees of mobile recharge. And similarly, every 15 days and
every one month, if the medicine consumption of
the patient is appropriate, as in regular, Then the patient
would again get some, every 15 days, they will get
5 rupees of mobile recharge. So that is, in a way,
they are trying to incentive and see if that works out too, make sure that everyone
is on the platform. Very quickly, I’ll touch, as in
how the whole ecosystem works if some patient is,
if he’s a prospective patient. That means, he’s coughing or has
some symptoms of tuberculosis, he’s basically referred. He goes to the health center
where the health worker, or we call it
an ANM Auxiliary Nurse Midwife, refers her on
the digital system. And then asks the patient to
visit the lab technician where the check-up will be done
to see whether a patient is a prospective patient,
converts into patient or not. So one of the major drawback
in the current TB ecosystem is there is no data that how many
prospective patient actually converts into the patient. That’s very critical because
they want to measure what location these people come from
and what are the age group and other parameters. That becomes very critical for
TB ecosystems, so that is one we are measuring. Second, once if
the patient is identified, as in the prospective patient
is identified as a patient, he’s requested to enroll
into the DOT program again. Incentives comes over here the
moment the patient enrolls into the DOS program, to the patient
as well as the health worker. Then the medicine consumption
is typically dragged by the IVR system that we
are currently developing. And then all the patient family
members and medical officers and all the related stakeholder. So the TB ecosystem are there
into the loop knowing what the patient is doing exactly
at that point of the time. And yeah, and once the whole six
months patient system is over, then we hope that
the patient gets cured. So yeah, just completing. [LAUGH] I’m almost done, yeah. So we are hoping that we’ll
launch this in one district, which is, it’s a district of
3,000 tuberculosis patients, but hopefully, we
are starting with 500. The government wanted to
start with directly 3,000, which seems to be
a little bit impossible. But anyway, so
that’s the current ecosystem. Just one last slide that we
are also sort of discussing with the USC on collaborating on
how we can predict, whether the prospective, other patient
would be followed or not. And how we can predict,
whether the health workers would be motivated enough to be
a part of the ecosystem, because motivation is
another major challenge. Thank you. I’m sorry, I took too
many of your questions.>>[APPLAUSE]
>>Thank you sir.

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