Making Healthcare Accessible to Everyone | Christopher Peng

Ladies and Gentlemen, let’s welcome
Christopher. Meet Asnakesh, a 32-year-old mother of six living in Goro a small rural village 400 kilometers outside Addis Ababa the capital of Ethiopia. On April 12 2018 her second youngest child a daughter, Kidist, age 4, became very sick. She’d been coughing feverish with the cold for three days prior. the nearest health post was over a hundred kilometers away or eight-hour journey by
donkey cart. As the day’s progressed and she didn’t get any better the villagers came together and pooled what little resources they had to send Asnakesh and Kidist to the health post After a grueling eight-hour journey they
finally arrived only to have to wait another six hours as there was a queue
leading out the door and only two nurses working the post. When it was finally her turn, a tired and overworked nurse briefly examined Kidist
and she told Asnakesh that Kidist was fine, it was just a cold, she’d be
better by the end of the week. With no other recourse Asnakesh
took Kidist on another eight-hour journey back home. Three days later on April 18th,
2018, Kidist passed away. Unfortunately, due to time constraints and pressures and an
overall lack of training, the nurses had failed to realize that Kidist’s fingertips and lips had started to turn blue and that she had serious trouble breathing all potential warning signs for pneumonia With appropriate testing she would have been correctly diagnosed and with the proper treatment very likely would have lived to tell the tale Instead, a tragically preventable death occurred. As uncomfortable it is to hear stories
like this, Kidist’s fate mirrors that of millions of other children all across sub-Saharan Africa and the world Pneumonia, in low-income countries, leads
the list in causes of death for children under five. And it, as well as the
majority of all the other diseases on that list, are highly treatable if caught
early and treated. If Asnakesh and Kidist were born in Boston where I was born or in Taipei where I live now this would never have happened. So why does such a massive health outcome disparity still exists in 2019 Today we’ll dive into this problem and for sake of scope well specifically look
at low-income sub-saharan African countries such as Ethiopia. Those that
require the most disruption. Why are adequate health care services not
accessible for everyone in these regions? Well, from a macro level there’s a multitude of reasons but the most important is the severe lack of trained medical personnel, medical professionals in rural areas. To get an understanding of what
we’re dealing with here’s some numbers. In Europe, there are over 30 doctors for
10,000 people. Taiwan there’s 22. In Ethiopia, there’s less than one. And with the majority of doctors residing in centralized urban locations this means that those rural outposts like the one that Asnakesh visited, simply have no access at all to any health care professionals And with a roughly
four-fifths of Ethiopia’s 105 million people residing outside of urban areas, that leaves over 80 million people with simply no access whatsoever to health
care. That’s 80 million people in one African country. So what can we do about that, well the easy answer is obviously, let’s train more doctors Well we have
friends are doing that right now. This is the Nordic Medical Centre. It is a prestigious hospital and teaching institution located in Addis and they’re also a
partner of ours. Each year we train a certain number of doctors or nurses and release them throughout the entire system However, together we’ve done the math and it’s not enough Even if we increased year-over-year we still wouldn’t have enough 40 years from now so what can we do if that’s not enough Today I’m going to talk a little bit about the
technological trends that I personally witnessed and I how I foresee that this
could be a way to a better healthcare future. How the synergy between AI-connected tools and technology and humans could possibly lead to a new
class of medical personnel. Ones that could be trained instantly and from practically anyone and these, in-turn, these medical personnel aided with AI-powered tools can then go into the environment treat, diagnose, and help
lower the overall burden. Utilized in the correct way with the correct systems in place this could possibly be the solution to the healthcare problem. But before we can start I have to take you back to 2014. I had just finished a
six-month volunteering stint in Northern Thailand and I joined HTC’s innovative
R&D department formerly known as Magic Labs At that time they were working on
the ten million dollar Qualcomm Tricorder XPRIZE, a global competition that uses monetary incentives to encourage teams from across the world to
take on the world’s next big challenges The Tricorder XPRIZE was inspired by
the science-fiction TV series Star Trek in which doctors would use this device
to instantly diagnose a host of diseases and then take appropriate action The competition’s rules were to try to create a device, portable, small, medical there’s
able to diagnose a range of diseases from skin cancer to pneumonia and to do
it in an easy user-friendly way. Through a combination of data analytics and decision trees we were able to diagnose the majority of these diseases and
through service design, user experience design, and interaction design we created
a step by step protocol that anyone could use. During that time period I couldn’t help but imagine the possibilities and the
impact that something like this could have on health care in the future. Just imagine a volunteer armed with these devices strategically deployed
throughout different areas in rural communities there they would be able to
help diagnose and treat simple diseases preventing them from ever becoming
life-threatening issues and by catching it early
they would also vastly reduce the downstream healthcare costs associated
with treating the later stage versions of these diseases. This is a type of
scalability that this kind of thing could bring, However, due to the limitations
still provided there was still a lot of way that we could actually solve this
problem because when we created this we could only diagnose a number of diseases
if we really wanted to take that next step. That step towards healthcare
accessibility for all, we would need something stronger, something smarter, something more adaptable something like an Artificial Intelligence Data Analytics Cloud I’ll call this AIDAC The AIDAC would be the brain, the data analytics
and management powerhouse that would take devices like the tricorder to a
whole other level Now, when the device meets a new disease
something that it wasn’t pre-programmed to do, it would be able to use this as an
opportunity to learn to improve Using supervised machine learning and taking in raw sensor data it can also build a training model based on what the
final outcome was, the outcome being the doctor’s diagnosis and once it has an
algorithm that works proven and tested it would be able to scale and send this
update over the air to every other device. Let me put that in perspective
what I’m saying is that once one device learns something every single other
device can gain from that same knowledge and this is the type of scalability
that AI and machine learning can bring Once we’ve done diagnosis, why stop there? Why don’t we tackle the treatment process as well? Through connectivity and
measuring along the connected cycle we are able to track a patient’s long-term
health outcomes based on symptoms and long-term result. This means using a
combination of unsupervised as well as reinforcement training modules we have
the opportunity to discover new treatment plans that no human being has
ever thought of before. So how does this type of AI human integration actually
look like? Well I think the tricorder is a great example an example of how the
right type of technology applied can make a huge difference and make anyone
into a “health professional” but there’s one thing that I haven’t
mentioned yet also the most important element. Which is by leveraging the
machines ability to be a machine, we also have the most underrated the most underappreciated element in the entire healthcare delivery cycle And that is
the Human Element. The Human Element of empathy, of compassion, of connection
and cultural understanding by leveraging the machine’s ability for calculation that means we can let the humans be humane building the much-needed trust and connection that we would actually need to actually implement in these
communities. These traditionally very difficult to work with environments and that is why, combined this kind of technology with the scalability
factor calculation factor and the human factor is able to unlock millions of
people to become these functioning medical personnel and these in turn
could be the way that we bring millions of people across sub-saharan Africa
access to health care. This is a future that I believe in. This is the future also that Kiipo, the company I co-founded with Jordan Masys, believes in as well At Kiipo, we believe that everyone has a
fundamental right to a healthy life And we’re building the different tools to
make that possible Currently, Kiipo is building our
health tech ecosystem And health analytics AI Officially named the Kiipo
Artificial Intelligence Data Analystic Cloud, that’s a long name we just call it
KAI for short. Kai is a continuously improving data analytics AI specifically focused on health care and it is the backbone of our entire Kiipo ecosystem. Although it’s very much in the early stages at this point, we’ve been in Ethiopia for
the last five years, and we’ve also been building the different systems and infrastructure to allow this to be possible Jordan is also a founding team
member of the Nordic Medical Centre and our team has built the first-ever
emergency medical services system in the country, In operation for the last three
years. Our goal is to push out from there to expand this across all of sub-saharan
Africa. And with that let me bring you back to Ethiopia this time the not too distant future as Asnakesh is now a grandmother of many but this time when her granddaughter gets sick she can take her to a nearby health
post only 20 minutes away and at the post the staff, a diverse mix of
volunteers and nurses-in-training, armed with the latest in KAI analytic skills are able to instantly diagnose and treat and take the appropriate action The world that she is now living in doesn’t have the same diseases as the ones she
grew up in. this could be our reality but I don’t know when we will ever reach
this or if we do because it’s not going to be easy it’s not given we will have a
long journey an arduous path there will be skeptic
and there will be naysayers and there will be different things that will come in our way but as long as we challenge the status quo if are able to change or
shift the way that we look at healthcare and what human health and vitality
really should be I am confident I am optimistic that we
can get there and I’m optimistic because I think together collectively we have
the opportunity and we can overcome this challenge and build this future. So today, I am asking everyone here with me to join us on that journey. Commit to making the most advanced healthcare technology accessible, not just the few but to all. let’s make this planet, this world, our only world a better, healthier place, together. Thank You!

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