Hello and welcome to Listen Up People, a podcast
of the USC Suzanne Dworak-Peck School of Social
Work. I'm Dr. Eric Rice, professor and associate
dean for research. The impact of artificial intelligence
has exploded in recent years. Intellectual leaders
from the technology space like Bill Gates have
said, I quote, AI will lead to breakthrough treatments
for deadly diseases, innovative solutions for
climate change, and high quality education for
everyone. Not everyone agrees with him. Intellectuals
like Uval Noah Harari has said, quote, for the
first time, we have real competition on the planet.
We have been the most intelligent species by
far for tens of thousands of years. And now we
are creating something that can compete with
us in the very near future. So my guests today
are at the center of this space and the center
of this debate. They are working with AI to improve
health and well -being, yet they are deeply concerned
about the role that AI will have in our workplace
and in our daily lives. So I'd like to welcome
my guests today, Dr. John Oberg, a USC School
of Social Work DSW alumni, who is founder and
CEO of Precina Health, and Josiah Bryan. who
is the Chief Technology Officer and Lead AI Researcher
at Precina Health. It's great to have you both
here. Yeah, thanks, Eric. Thanks for having us.
We're really excited to be here. So John, I know
you got a doctorate degree here. I was here to
see it happen. So of all the places you could
have gone, why did you decide to pursue a doctorate
here at USC? My grandfather taught at USC. My
mom went to USC as an undergrad. I was there
as an undergrad. and then it was really the natural
place to come home. My undergrad degree, I left
after a couple of years and finished up at New
Mexico, and I told people I'd be back to finish
my degree. I didn't finish my undergrad degree
at USC, but I did come back when it was time
to get a doctorate. Nice. Why the DSW program?
I think that social workers are really misunderstood
in the world. I think people think about social
work and whatever experience they've had with
them, whether it's CPS or medical social work
or whatever experience someone's had, they just
kind of put social workers into that bucket.
And that's not how I saw social work. I saw social
work as the people that use systems thinking
to draw the really big picture and connect it
to the micro level. So in social work, we call
that the macro level, the meso level or the micro
level. But I think most disciplines don't so
deliberate. connect the individual to the system
with the community in the middle. And I think
that's such an important framework for us as
we deal with these really complicated problems
that seem intractable, but they're not intractable
problems when you can actually get your mental
model around how the system plays with the community,
plays with the individual. And that's a really
important part of how we've actually impacted
type two diabetes. Like our first research project
during our doctorate, you know, 98 % success
in reversing type two diabetes. I'll share with
you later in the podcast, some of our more recent
research, but it's because we're able to look
at that systems thinking and that social workers
have this amazing view of the broad world and
how it plays with the community and how it plays
with the individual. I really felt it was the
right place to go and then take the research
and apply it in a way that was really meaningful
in the world. And, you know, I think all of our
clinical results are speaking to that. Yeah.
I mean, it goes without saying I'm a huge social
work fan as well. I wouldn't be here doing this
if I wasn't, but it's great to hear the value
you place on this. Josiah, I want to ask you,
I know you're a tech guy. How did you first get
involved in AI research? I've always been fascinated
with the way the computers have the ability to
make our lives better and make our lives easier.
I've inherently very lazy. I started working
with my hands, taking apart radios, my dad's
old reel to reel player. Then when I discovered
that I could be creative with computers, I found
that I didn't want to do the hardware thinking
anymore. I wanted to work with software. And
very early on when I was about 12 years old,
my dad started taking me to the library and I
started reading graciously all the books I could.
One of them stood out to me was this book, I
haven't been able to find anymore, it's called
Sierra Circuit Cellar. In it, it had an article,
teach computers to think for themselves. Really,
it was just this, pick from a menu what you want
the computer to do and we'll paste together,
think it's a basic code and we'll do its thing.
But ever since then, that sparked something in
me at 12 years old thinking, wow, I can get my
computer to help make my life better with a little
bit of intelligence and think intelligently.
And that's put me on this lifelong trajectory
of learning about AI, experimenting with it,
trying to find ways to integrate it into my life,
and to make human lives better, connect us more
deeply as humans while still enjoying learning
about what makes us truly human so I can make
AI better. I am very, very sympathetic to this
perspective. I think... Some of the listeners
that we have may know this, but some of them
who are turning in to listen to the two of you
may not know this. I started a center called
the USC Center for AI and Society back in 2016.
And like John, I'm really convinced that really
good social work -driven systemic thinking about
complex problems can help us to come up with
real solutions. But like you, Josiah, I really
think that AI is this incredible opportunity
for building tools that can be as maybe complex
as the problems that we're dealing with and have
some real opportunities to move the needle on
some stuff that we've had trouble moving the
needle on. And so I guess with that in mind,
I'll let the spotlight go back to the two of
you, which is to say, John, you know, maybe you
could just give us an overview of Christina Health
and what are the goals? of the company and what
kind of work are you doing there? Yeah, we're
a medical practice. So it's, you know, you bring
on two guys, you know, my master's degree is
in policy. And then I have a second specialization
in the management of technology where we researched,
you know, the history of technology and what's
happened in terms of pace and speed. That was
one of my capstone projects there. But we opened
up a medical practice because you've got this
really nasty problem with type two diabetes that
started with my mother -in -law getting sick,
ended up in the hospital. And I got really frustrated
that she was in the hospital in my mind unnecessarily
if she had just taken the medication the right
way. I've met her doctor. I have the highest
respect for her doctor. She's an amazing primary
care physician. And so I called my friends at
USC basically and said, I'm coming back to get
my doctorate. I want to solve type two diabetes.
And I think the nicest way to say it is that
the people at USC kind of patted me on the head
and said, hey, We've been dealing with diabetes
for the last 60 years as a world, and we haven't
solved it. That's cute. Come on back. We'll take
your tuition money. We'll educate you. But I
don't know if you're going to solve the problem.
And so in our first pilot study, we took 50 patients
who were super sick, average A1C of 9 .6 if you're
a clinician, and we reduced that average A1C
to 6 .4 in 12 weeks. And so most doctors would
tell you if you can reduce A1C by one percentage
point in a year, you have a billion dollar drug.
And using just a different set of behavioral
interventions, medical interventions, using devices,
pharmacology, but also most importantly, behavior
change, we reduced people's A1C by 3 .2 % in
12 weeks, which is super impressive. But then
we kept people in clinical control for the next
two years in 83 % of our cases. And I think people
were just, their jaws hit the ground. How did
you do that with this group that was low income?
They had low access to care. And most providers
who had worked with them said they've had uncontrolled
diabetes for years. And some doctors were calling
these patients lazy or uneducated. It was really
judgmental and it was horrifying for us to see.
And so we said no. it's not the right, it wasn't
the right intervention. So we stripped down the
medical practice to kind of its most basic units
of clinical intervention, of administration.
We really took apart the, like including the
paperwork you sign when you walk in, like everything
got ripped apart. And we created what we call
the systemic. therapeutic alignment and rapid
titration model that looks at all of the medicine,
all of the mental health issues, all of the behavioral
non -clinical issues, all the social determinants
of health, all of the health beliefs. But then
we layer across a patient's readiness for change.
And then we make these really, really, really
small steps to help patients get better a little
bit at a time. And we use AI to do things like
taking in thousands of patients, you know, pages
of patient charting, and then highlighting for
the physician, like which ones are most important
so that our physicians can do crazy things like
reading the chart before they meet with a patient.
And I think a lot of, you know, providers want
to do that. But when you have a thousand pages
of history, and I'm being literal, I'm not figurative
in some cases, right? So we can use AI to do
that. We can use AI for things that I would call
fairly vanilla, like dictation and all those
types of things. Sure. More importantly, we can
take 1 ,000 pages of history for 20 years, and
we can find patterns where it says to the provider,
hey, have you considered looking at this issue
that shows up like arthroscorosis, but actually...
over in this system hasn't been identified, does
that require a lab draw or not? Or, hey, in this
case, they've been pulled off of this particular
drug, and SGLT2 is one that was recent, and it's
actually no longer clinically indicated they
get pulled off. They should actually be put on
and actually have as much as they can of that
drug, and that... guidance is only four weeks
old. And then what we can do is say, hey, this
doctor wants to see the data in this way externally.
This other external wants to see the data a different
way. Can we use AI to format exactly the way
they want, review it and send it out in a way
that's not administratively burdensome? And so
we use AI and Josiah, correct me if I'm wrong,
12 or 13 different discrete places in our medical
practice without being a tech company. My next
question to Josiah is like, John's described
a bunch of applications of AI. What are you doing
with AI? to make all this stuff, this amazing
stuff happen. We've put our trust in a platform
called Salesforce. It's a small little company.
I'm sure you might've heard of it, but they've
done a great job. They've done a great job making
it so that I can move faster by putting my trust
in the, keeping my data there and then building
on top of their platform. to that end there,
there's some things that they're incredibly good
at and some things that they just aren't going
to focus on because that doesn't make sense for
their market. And so we build into their platform
and provide our providers with things like a
provider coach. to help them reset emotionally
between calls and to coach them up in Prasena
way of thinking. And it all is oriented around
adding in emotional context, adding in wisdom
in some form or fashion into the AI systems that
we deploy into our practice. That's the provider
facing one. We're currently in beta testing with
a patient facing AI, which will enable them to
advocate for themselves and to know what questions
to ask their doctors for that Prasena, as well
as their other health. care providers. So those
are two of the AIs out of a large plethora of
AIs that we have deployed. Some are more boring
behind the scenes, such as the document filtration
AI, which is a workflow that goes through, takes
in and process the documents for our providers.
It lifts the load off of the human providers.
There's a number of agents like that behind the
scenes that help us accelerate and deliver more
human care at a lower cost and save more lives
and save and help more people. I want to make
it real for the listeners. So two really, really
use cases. We've essentially taken doctors and
nurse practitioners and physicians associates
and said, hey, look, you've got to learn an entirely
different way of doing medicine if you're going
to work with us, similar to a fellowship in terms
of the level of training that we give. And the
medical doctor, my partner, Dr. Dustin Williams
is a long -term medical educator, has written
medical curriculum for 80 different medical schools
across the world. So we know that we're asking
a lot of a provider to come in and work with
us, very similar to what he asked fellows to
do when he was training fellows. And so the AI
will actively help support us to deliver that
training in a lower cost way by having our providers
actually practice what we want them to say with
the patient, because it's so nuanced and the
AI never gets tired. So if somebody wants to
practice 20 times before work, they can do that
any hour of the day, the AI gives that feedback
to the provider. And if the provider wants to
share that with their manager, the manager can
come in and be even more new. So that's a really
helpful tool. On the flip side of that coin,
if a patient says to their provider, I know I
need to eat better, but I need a meal plan. It
used to take hours to create a meal plan. In
five or 10 minutes, our providers can talk to
a patient. in the video chat, create a meal plan,
and feed into the AI what food is in the patient's
house today, ask what the shopping list should
be based on the size of the family and how many
times that person wants to eat the same leftovers,
based on their own preferences for food, their
own allergies, the actual store that's nearby,
and Josiah has told me that very shortly we'll
be able to wire it up so we can actually send
that shopping list to that store for the patient
and have them just pick it up at the curb if
they want to. And that's all taking like five
minutes now with a well -trained provider versus
the hours of time it would take, or it just not
getting done at all and the patient not following
the plan because it's just too hard to follow
the plan. Very, very small movements sometimes
require a lot of planning. Well, yeah, I mean,
making a plan seems like not a hard thing to
do when you are the person who is instructing
somebody else on the plan. Enacting a plan when
it creates, you know, all of them, there's all
these logistic barriers in real life is really
hard. I mean, that makes perfect sense to me.
And I gotta say, Josiah, you'd said something
that I just wanna throw my two cents on, which
is, you'd said, oh, well, there's some of this
stuff that's not that exciting, that's the background
thing that is digesting all of these thousands
of pages of information. I actually think that
that stuff is incredibly exciting. I mean, it
doesn't sound sexy, right? I think sometimes,
you know, AIs that talk back to you or that allow
you to talk to them sound in some ways more like
the science fiction robots that we envisioned
when we were kids. But the fact that a real human
being doesn't have the time. to read 1 ,000 pages
of patient notes. But an AI might be able to
digest some of those patient notes for that provider
and put in front of that provider some possible
things to think about that then doesn't replace
the provider as the provider, but just makes
the provider better. at what the provider does.
That's so exciting. I mean, it's hard to, I mean,
you know, it takes 30 seconds to explain it.
So it's not, it doesn't, it's not pithy in the
same way, but that's a game changer, right? It
is. It helps us be connect better as humans.
I think with some of the AI we've done with Dustin,
he said it takes his work, his prep time down
from 45 minutes to 15 minutes, but it enables
him that 45 minutes he gets back, he can then
invest that with the patient. Dustin's been doing
this for years. So that's 45 to 15 for a world
-class expert at the person that designed the
protocols. For other people, it's 90 minutes
to 15 minutes. It would be hard to overstate
the impact, but it's also hard to overstate the
importance of the human in the loop because,
again, let me use a real example. When Josiah
uses AI to code, he's a domain expert and knows
exactly how to use AI to help us move faster
in burning down our backlog. Amazing. If I were
to try to use AI to code, not a domain expert,
I would create additional work for quality assurance
from Josiah that would likely be faster for him
just to do the work himself than trying to oversee
the garbage I would create. Right, so technology
solves big problems or it can create big problems
too, right? So a car can get you from A to B
really quickly, but if you don't know how to
drive, it can cause an 18 car pileup too. 100%.
And I think that really kind of gets to the heart
of some of the debate that I was mentioning at
the beginning of our podcast, which is, it seems
really exciting and wonderful, all of these things
that AI can do, but people... you know, really
thoughtful people like Harari. I mean, he's not
a he's not a clown. Like, he's freaked out that,
you know, we've kind of created the our own demise.
I mean, and so when you're thinking about these
things, I mean, how are you as a company thinking
about making the decisions about what AI should
do, maybe what AI shouldn't do? Yeah, I mean,
I'll say where we start with everybody. Sometime
in the late 1800s, the guy that ran the patent
office for the US said it was time to shut the
whole thing down because everything that ever
could have been invented already had been invented.
That was in the late 1800s, that was said. Now
just think about the number of, we didn't have
air flight. commercially. In fact, you know,
Kitty Hawk was like 1914 or something like that.
Yeah, give or take. So I mean, and then we've
gone from like the Wright brothers to the moon
to now talking about Mars in the last hundred
and 25 years. And so the reality is that humans
don't love change. If you were to kind of look
at like a behavioral chart, like go look at some
of the peer reviewed disk models and how people,
you know, there's, there's a small percentage,
let's call it give or take less than 20 % heuristically
of the population in the U S that loves the idea
of change as a starting place. The other 80 %
not so excited, right? Right. And so, you know,
we're calculators bad for people in the, in the
large floors of rooms in corporate America where
they had 10 foot pieces of paper doing spreadsheets
by hand with slide rules. Yes, that was very
bad for that part of the industry. And people
had to be retrained to do other things. And there
were some individuals that were really negatively
impacted by that. And am I worried about how
my son is going to have to tool up for the job
world relative to how I did? It's going to have
to be totally different. Our people have to be
OK with the fact that things are changing. And
for us, the way medicine is being practiced is
changing. And that's OK. And we're going to lean
into that. But we're really, really thoughtful
about clinical outcomes first and foremost, measurable
clinical outcomes. And then we also want sustainability,
not just financial sustainability, but like behavioral
change, sustainability, financial sustainability
for sure. But, but how do you do all these things
so that the problem that's spinning out of control
with healthcare in America in particular, like
we can actually tell individuals and we say this
to our patients, the system may be working against
you. Everyone would say it's broken in some way,
shape or form. And there's enough things you
control. that if you want to focus on what you
can control, just accepting that the environment
sucks, you can do enough to get healthy today.
John, this reminds me of the conversation that
you and I had a few months ago. We were having
dinner together and you were mentioning again,
a conversation that you're having with your children,
right? About AI. And I don't want to steal your
thunder because this was really your idea, but
maybe you can share a little bit of the insights
that you shared with me about how you're talking
to your kids about how AI is going to be impacting
them in the work world. Yeah, you're talking
about the conversation we had about knowledge
and wisdom, right? Yeah, exactly. Yeah, so what
computers do really well is they handle knowledge
really, really well. And when I say knowledge,
I'm talking about things that our brain handles
kind of in the cerebral cortex and how we process
information. And Josiah is going to have to kind
of make sure that he's keeping me saying the
words correctly here. But broadly speaking, the
way that a computer processes information is
really similar to our cerebral cortex. And that
means that a lot of the work we do there today
is going to be done by computers later on because
they're going to be faster, better, on for 24
hours, like they're just, and they're going to
make less mistakes. Like that's just the way
that computers are. However, there's two other
parts of our brain, the amygdala and the limbic
system that process information in a much different
way. It's not just different processors. It's
actually different biological architecture where
I'm not the expert at that. But what I do know
is that information in information out is different
in those three parts of the brain. And the computer
has the first one like nailed. And today, I've
not heard a lot of people talking about making
different architecture that solves for two and
three. I've heard lots of emulation conversations.
How do we emulate emotions? And that would be
kind of similar to someone who has clinical autism
level one, and they emulate emotions because
they've observed it and they can think about
it, but they're not really feeling in that same
way that you or I might feel. And computers can
do that, but we're trying to actually write ways
to get deeper and better emulators and try to
re -architect the software so that we can actually
have computers be more thoughtful about the way
they're interacting with the human. And then
teaching my kids, getting human experience, those
are the things that computers aren't going to
be able to take the place of in the workforce
of the future. So how do you find skills that
are going to be valuable? 20 years now, 30 years
now, and how do you build toward those? You can
create value today and create value in the future.
And then how do you learn how to learn? Because
you're going to have to be pliable as we go through
the next, the coming decades. When we were having
this conversation, you used this phrase that
I just loved. And so I'm going to, I'm going
to make sure that you get credit for this phrase.
You said it's all about wisdom work, right? So,
and, and, and I thought that that was such a
great way of explaining in a very shorthand way.
What you're talking about about these human experiences
that are not going to be about information and
information processing, but it's about something
fundamentally qualitatively different. I mean,
can you maybe tell us a little bit more about
like What does that even mean to you? I think
it's a great question. In wisdom work, I mean,
it's certainly not a technical or clinical term,
right? This is a shorthand way for me to talk
to my kids, right? You made up this phrase, but
I was like, this is beautiful. I love it. Yeah.
And what it really is, it's saying like all the
stuff that computers aren't going to be able
to handle well, right? So it's how do you do
the things that aren't going to be done well
by a computer and how do you lean into that?
And so that's going to be about, you know, dealing
with people. I think that's going to be a really
important part of the future. That could be managing
people. That could be clinical therapy or clinical
intervention a lot of different ways. My understanding
of the brain is that when you go to make decisions
in a rational, healthy kind of kind of non -abnormal
psychology kind of brain, that when you make
a decision, it's generally made emotionally first,
and then you justify that decision logically.
And so if someone makes a decision emotionally
and can't find the logic to back it up, we sometimes
describe that as buyer's remorse, right? And
then when somebody makes a decision logically,
but doesn't have the emotional will to kind of
commit to it, we have analytical paralysis, right?
And if you have neither, you just don't make
the decision. But if you have the decision made
emotionally and then you justify it with the
logic, that's where you get people that make
long -term decisions that are good. And hopefully
for my kids, they can learn how to do things
that have kind of delayed gratification, which
is an important skill set next to everything
else we're talking about, right? And that's a
whole other topic. But wisdom work is about understanding
how the brain works. outside of the cerebral
cortex and not just the black and white logic,
but the feelings behind it and what feelings
are driving that and getting really honest about
those feelings. And I tell my kids like, you
know, you can have your own feelings and those
are going to be valid based on your lived experiences.
You can have your own opinions. And I'm going
to have a different level of respect for your
opinions based on how you formed them. So if
you've had opinions that are formed because you've
taken both sides of the argument, you've know
how to passionately argue both sides. You understand
all the logic behind both sides. and then you
form your opinion based on that, I'll have deep
respect for your opinion. Even if I disagree
with it, I'll have a deep respect for you. Now,
if you have an opinion because you read something
on the internet and that's your opinion now,
it's still your opinion. I'm just not gonna respect
it as much because you haven't formed it well.
The facts we can't change though. Like you don't
get two sets of facts. And you as a scientist
know this, like data is the data is the data.
I just saw something the other day, a marketing
company marketed something based on a study.
And I went back and read the study and the study
was clear. No conclusive evidence. This is true.
And the marketer was like, this thing studied
this thing. And it's like, that should be against
the law. Like it should, you should not, like
it is so misleading. I was, I was angry. I can
name the feeling I was angry. Well, those things,
those things infuriate me as well, too. And I
think that you're also tapping into something
really important that I think as scientists we
sometimes have a little bit of trouble with,
which is the idea that emotion and intuition.
These are a part of the human experience that
are important in how we make decisions and important
and that we use facts. Hopefully, we use logic,
but we also use intuition and feeling and that
is not going to be, at least as I can understand
it now, easily replaced by even the best of AIs.
And I know, Josiah, you weren't there with us
when we were having this conversation, but Certainly
you must be thinking about how AI is changing
what people are doing and what people are not
doing at work because you're actively trying
to do that. So I mean, what, when you're thinking
about at Prasena, what do you see as the great
benefits of using AI and maybe even some of the
challenges of where this could come in? Using
AI is going to unlock things that we don't know.
As humans, we are bad at predicting the future.
I'm fascinated by the drawings I've seen from
the 1930s and 40s of the optimistic 2020s and
the people riding around on the waves with their
video phones hanging out in front of them talking
to the people. You know, we can laugh at that
and see how cute it is and draw analogies between
that and the iPhones and FaceTime. But let's
face that, we're not very good at predicting
what's going to happen and the effects of that
technology on our interactions. AI in the short
term has really made a difference in how I work.
And I committed to making the AI that I make
have a positive impact, putting safeguard rails
around it, thinking about how people will feel
when they use my technology. And yes, I do use
that intentionally as a technologist, thinking
about people's feelings. We've done a disservice
in society and not emphasizing people's need
to have strong and positive labels for their
feelings, understanding the nuance of their feelings
and their emotions. My research has led me to
conclude that memories are formed first from
our limbic system. The limbic system encodes
our memories of how we feel about situations,
and then our minds go back to backfill those
memories with facts as best as we can fit them.
If we don't have a rational capacity for labeling
our feelings, we're going to have difficulties
processing those feelings as memories of the
future. But the point is, is that if I can focus
on how the technology that I make helps people
feel, makes people feel, I can make a better,
more grounded experience for them. So practically
speaking, that looks like when I'm making a patient
advocate app that helps them advocate for themselves,
know what questions to ask and respond to the
patient about their medical situation. I'm not
going to just throw in a large language model,
five page prompt, call it a day, you often have
a cocktail. I'm going to take some serious work
in putting guardrails around it, deconstructing
the experience, thinking about, can I take emotional
cues? Can I factor in their sociological background,
their family history? Can I put in place rules
around what the AI can say and can't say when
it can defer to the human provider? I'm going
to put a very nuanced... thought to the experience
so that their feelings are considered. And at
the end of the day, it's going to be a very simple
experience. It can be a chat window or a phone
call, but there is going to be much more work
put into considering the human experience rather
than just the technology or how easy or hard
it is for me to create. That's great. Go ahead,
John, please. My hope for this is that if we
get really good at prompting AI, it's going to
teach us to ask better questions of one another,
not just of the computer. You give a bad prompt
to an AI, you get a bad set of data out. And
if you're humble enough to recognize that, hey,
that's because of me, not because of the computer,
then you start to learn to ask better questions.
And so when you look at intent analysis or sentiment
analysis in computers, what it's doing is it's
actually looking at the surface level. great
therapist would say the problem the patient brings
you isn't the problem. It's usually several layers
deeper than that. And that's work that we can
maybe teach computers to do over time. But I
think better better serve would be having a computer,
you know, psychologically code the conversation
and then indicate to the human like, hey, there's
something deeper here. I can't get it. That's
your job. That's the wisdom work. Right. And
so that's, you know, in this yesterday show up
very clearly in a conversation where someone
was telling me about another person. And they're
like, you know, they're motivated by money. And
I said, I'm sorry. you know, respectfully and
expertly, I'm going to tell you that they're
not motivated by money. In fact, no one's motivated
by money. They're motivated by what that money
does for them or what it means to them. So maybe
it's ego. Maybe it's a scorecard. Maybe it's
what they can buy. Maybe it's security. Maybe
it's something else. But money is always the
secondary thing. And so if you don't understand
the underpinning motivation, you're going to
have a hard time connecting with that person
the way that you want to. That's an amazing point.
I think we are running to the end of our time
together, although I could keep talking with
the two of you about how you're using AI and
your thoughts about AI for hours, given my own
interests in this area run really deep and also
you're just both a lot of fun to talk to. I want
to give you each a chance to sort of say if there's
anything that you want to end with, especially
about the work that you're doing at Prasena and
how AI is shaping that. Maybe you'll turn to
Josiah and then let John kind of close us out.
My view on AI, if I could say anything is...
see through the hype and look at it as a tool.
Inherently, it's about how we use the tool. I
would look like an idiot if I showed up to a
wedding with a hammer to cut a cake. I'd look
like an idiot if I showed up to a construction
site with a cake cutter to hang drywall. AI is
a tool, learn what it is, learn how it can assist
you and use the right tool for the right job.
And that's what I try to do with AI and tools
in Prasena to help our patients and help our
providers. I'll end up with our latest research
that hasn't yet been published, but our data
has been collected. We had 79 patients with an
average A1C of 11 .06 coming out of the hospital.
And in the first 90 days, dropped them to 7 .8,
and 180 days to 7 .2, with 30 % coming off of
insulin at the six -month mark. The clinical
results we're having, because we've leaned into
the tools, are really fun to talk about. And
it took us 10 years to get here. We've been playing
with AI for a decade. I think it's really, it's
like we're another overnight success 10 years
in the making. And so, you know, I think, I think
this is, it's a thing that's here to stay. And
it's something that's going to take time for
people to learn. And it's going to be frustrating
in moments. Certainly we talk about all these
great things we're doing when we're on podcasts
like this, but you know, we also, we don't talk
about the, all the, the long weekends that, you
know, Josiah, Dustin and I spent 16 hours on
a Saturday and Sunday banging our head into a
wall to get it wrong 32 times. And then we finally
get something that, that makes sense and is helpful.
So. it's a tool and it's a tool that's being
shaped and developed right now. So it's not a
mass generated hammer or a video game that's
been worked on for 10 years that has no bugs.
Like AI is this thing that's constantly being
relearned and every time a new model comes out
from any of the major producers, we have to evaluate
the whole model in light of what we know and
then maybe even unplug something and replug something.
So I'm constantly pushing on Josiah for more
modular, flexible development and to be able
to be more nimble more as we grow. And he of
course is like constantly pushing. back on me
about the unreasonable demands that I'm making
about technology. But the reality is what it's
going to lead to is better patient agency, better
healthcare outcomes, and people being able to
spend more time focusing on human connection
with each other if we want it to. And so my hope
is that we have a more connected world, not a
less connected world because of AI, if we choose
to use the tool that way. And I think that, you
know, in our, we're seeing that happen for real
inside of our practice, with our patients, with
our team, with each other. We're a much more
connected community than we were even a year
ago because we have more time. more space, more
energy, more to talk about. And so for anybody
out there, I'd really encourage you like use
AI to drive forward human connection, not to
isolate yourself, which is already too big a
problem, not to become more polar in the conversation,
but to come back and meet people where they are.
Like go meet your neighbor right where they are.
It is the most amazing human experience to do
that. Like that's what we're really excited about.
Like how do we do this for a billion people in
the next 50 years? That's our goal. Amazing.
Thank you, John and Josiah for joining us. on
such a great discussion. If you want to learn
more about the work of Prasena Health, you can
go to their website, prasena .com, P -R -E -C
-I -N -A .com. And if you have questions for
the guests on our show or you want to support
the transformative research and programs here
at USC, you can email us at listenuppeople at
usc .edu. And just one more time, thank you both
so much for joining me. Thanks, Eric. We'll be
back anytime. We'd love to geek out about this
stuff. 100%. you