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Hi everyone!

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Welcome to Hard Problems, Smart
Solutions, the Newfire Podcast.

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I'm Will Crawford, Head of
Advisory Services and CTO at

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Newfire Global Partners, and I'll
be your host for this episode.

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Today I'm talking with Dr. Paulo Pinho.

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Over his career, Paulo has straddled
the line between care delivery,

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payer technology and entrepreneurship

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including stints as Chief Medical
Officer of Discern Health, and his

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VP and Medical Director of Innovation
for Diameter Health and Availity.

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He's also a practicing physician.

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Today we're talking about the
evolution of healthcare standards

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and the importance of clinical
informatics for healthcare innovation.

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We'll explore how advances in
standards have enabled new kinds of

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applications, including ones that
leverage AI and some of the challenges

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that come with those changes.

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We're also going to talk about
the transition from clinical

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medicine to the corporate world.

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And how as a non-clinician
you can work more effectively

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with your clinical partners.

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Dr. Pinho, welcome to the podcast.

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Thanks, Will.

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I appreciate being here and, uh, looking
forward to, to digging in for sure.

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So before we dive into, uh, specific
topics, could you just tell us a little

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bit about your journey in healthcare?

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Uh,

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Yeah, so a, a little bit
of an unorthodox journey.

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I, um, you know, started out
in, um, in clinical practice

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right outta medical school.

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I'm a primary care, uh,
physician, board certified in

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internal medicine and pediatrics.

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Um, and worked in both disciplines,
had my own practice that I was able

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to build up and, you know, started to
experience many of the challenges that

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exist in healthcare delivery, um, from
a, a data standpoint, from a quality

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of care standpoint, the defining what
quality care was for individuals.

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Ended up selling my practice to a hospital
system and, uh, it was early on in the

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consolidation of hospital practices and
eventually I, um, uh, actually made the

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move to more executive health roles,
uh, initially with, uh, Prudential,

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where a lot of my work focused on things
like thoughtful benefits, benefits

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design, um, really creating sort of
an engaged and empowered workforce,

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you know, using, uh, benefits design
as the foundation for that and, you

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know, really using it to drive employee
engagement, et cetera, and then moved out

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into the healthcare data standards world,
predictive analytics world and AI world.

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So it took me a while to kind of realize
the scale and the opportunities that

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exist, uh, when you use technology to
enable the way healthcare is delivered.

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And I think that now more than ever,
it's, you know, becoming a need.

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I, I think you have to mitigate some
of that supply demand mismatch with

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thoughtful, uh, technology solutions.

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And so I'm excited to be part of this, you
know, new wave of tech-enabled healthcare.

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So for people listening who you know,
don't come from that clinical informatics

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background, uh, could you just talk a
little bit about why data standardization

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for these applications is so complicated?

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Yeah, so I think dating back to the
meaningful use days, it's uh, it's

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really been a significant challenge.

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Data in healthcare by definition
is multi-source and multi-format.

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It's been increase, there's an
increasing amount of medical data

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that's created for each individual with
nearly a thousand percent increase in,

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in data capture on individuals over
the course of the last 10, 10 years.

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Uh, and we also know that medicine
is becoming increasingly complex

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to the point that the, this full
body of medical knowledge is,

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uh, increasing at rapid paces.

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I mean, we know that, uh, medical
knowledge doubling, you know, as of

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2020 was at 73 days, and so it's only
shorter at this stage of the game.

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We know that there has been sort of
a challenge in healthcare in terms of

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evolving off of, uh, legacy systems.

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Uh, we know that despite the fact that
meaningful use was, uh, intended to drive

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data interoperability, it really created
a bunch of different formats of data could

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be captured within provider's offices.

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There were, uh, different
electronic health record platforms.

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Each had their different way
of sort of collecting data.

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Uh, each had their different key pieces
of information and sort of, and, and

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its storage in different areas of
the electronic health record chart.

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And then, you know, you could have
two separate implementations of the

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same electronic health record in
two different hospitals that were

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across the street from one another.

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And the way they captured
data was very, very different.

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So, um, despite the fact that
meaningful use intended to sort of

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wrangle some of this in, it really
created a bunch of different languages.

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And then on top of that, a bunch
of different dialects as well.

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Um, and we know that the language
of clinical data capture was very

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different when you're capturing data
for clinical intent versus when you're

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capturing data for billing purposes.

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Uh, we know that providers, some of
the data that we, we capture when we

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see patients tends to be, you know,
very structured codified data that

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we pulled out from pull down menus.

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Um, but in other times you really
can't tell a full narrative story,

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um, unless you actually type in pros.

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And so some of that's made data
standardization really, uh, complex.

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I think on top of that, we're
starting to see data capture in a

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bunch of different formats that we
never thought would be, uh, data,

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you know, methods of data capture.

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Uh, we know that data is captured,
you know, outside of electronic health

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records that's really meaningful to
healthcare delivery, for example.

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There's an increasing the amount
of wearables that that, that are

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out there, and we're seeing people
diagnosed with atrial fibrillation

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for the first time, you know, from
their Apple watch, or with sleep wake

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disturbances from a wearable ring.

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We know that there's social data aspects,
uh, that that, that, you know, can't

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necessarily be capturing the same way

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from electronic health records.

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And so ultimately, you know, the types of
data that really have become meaningful

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to today's healthcare delivery in a,
in a personalized fashion, um, have

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become too difficult to consolidate.

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There is a challenge in sort of
the volume, velocity, variety of

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clinical data and nonclinical data
that drive healthcare outcomes.

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Um, and, and that's I think probably
where the biggest challenges lie.

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And there is, there are increasing
challenges when it comes to, uh, data

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privacy, data security, um, et cetera.

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And so, you know, while there's a lot
of opportunity with the, the sheer

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amounts of data that we're collecting
in healthcare, I think the challenges

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and the complexities of the different
ways that data is, is, is collected,

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the different languages and dialects have
really made it so that interoperability,

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despite its most pure intent, has have,
has really been elusive for the healthcare

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industry at the stage of the game.

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So the mechanics are there, but the
sort of semantic agreement on what

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gets exchanged over those pipes,
there's still a lot of work to do.

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When you talk about medical knowledge
doubling every 73 days, just tell us

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a little bit more about what knowledge

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that is.

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We see this right now.

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I mean, I, I remember practicing
medicine while I was in residency.

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I would say that, when I first
started in residency, HIV, for

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example, was an inpatient disease.

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We managed, I would say that probably 70,
80% of the patients that I was taking care

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of on the wards, um, even in the pediatric
wards were HIV positive individuals

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that had an opportunistic infection.

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By the time I graduated residency, HIV had
shifted to largely in outpatient disease.

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Um, you know, focused on giving
people the right highly-active

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anti-retroviral therapy, um, to prevent
some of these opportunistic illnesses.

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And so, um, we didn't
have inpatient disease.

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It was a complete shift.

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I mean, I would say that if I
had 20% or less of my patients

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having, you know, HIV, um, that was
probably a, a large representation

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by the time presidency was over.

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We're seeing this with cancer care.

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You know, when you look at how cancer has
has been treated up until now, it's really

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been this sort of race against time.

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You know, we're providing the body with
a poison in the hopes that killing the

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cells that are responsible for this
tumor happens at a rate of speed that's

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quicker than killing the cells that you
know are keeping a human being alive.

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Um, and you know, now with more targeted
therapies that are, you know, molecular

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based and focused on cell surface
receptors and really personalized to

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the type of cancer that someone has,

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you know, we're really able to sort of
augment not only the quantity of life for

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individuals, um, who have these cancers,
but also the quality of life because

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that side effect burden just isn't there.

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And so, you know, I think every era
is defined by a clinical illness

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or a set of clinical illnesses.

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I would say that my residency training
was defined by sort of that rapid

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evolution in, in, in HIV treatment.

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And I think future generations is, are
gonna be the cancer personalization

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and the cardiovascular disease
personalization that exists.

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And these things are
happening very rapidly.

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I mean, when we look at cancer care,
you know, I think that we're gonna have

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a generation of clinicians, 10, 15, 20
years from now, they're gonna look back

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and say, wait, let me get this straight.

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This is how you used to treat cancer?

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You used to give people poison
and hope that it wouldn't kill

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them and kill the, kill the tumor.

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This is the kind of, you know, information
that's doubling and doing so very rapidly.

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It's not limited to cancer or HIV.

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We see it in cardiovascular disease
and, you know, we see the ability

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to personalize medicine, to do
it in minimally invasive ways.

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It's changing the access opportunities
and the quality of care that we deliver,

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um, in ways that we never thought
would be possible 10, 15, 20 years ago.

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So I, I think that's a great example.

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I mean, I, I have some people in my
life who have benefited from some of

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those targeted cancer therapies, and
it's, it's really been remarkable and

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it's been very grateful for that because
it would've been a very different

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story, even a, even a decade ago.

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But bringing those two themes together,
I, there's a lot more information around

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the patient and then there's a lot more
knowledge to apply that information to.

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So how are providers keeping
their head above water?

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Yeah, I think, um, medical education has
definitely needs to, to pivot and I think

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it already has started pivoting, obviously
providing sort of an evidence-based

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strategy for literature review.

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For how, and we saw this during the
COVID Pandemic, there were tons of

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articles that were released and some of
them were sort of in holding patterns,

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and we needed that data as clinicians
to be able to make informed decisions

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about our patients and how do we
do that in a way that's thoughtful?

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How do we do it to make sure that
what we are releasing to clinicians

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who are using these to kind of make
decisions about their patients is done

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so in an informed and an appropriate
way and that it's not quackery.

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And so I think that part of our medical
education system now has evolved to

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really be very critical of medical
literature and sort of some of the

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technology adoption that's happened.

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Um, and I think that was really
ushered in by the COVID Pandemic.

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Um, it's been ushered in by the
need to personalize, you know,

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healthcare for individuals.

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Physicians, nurses, nurse practitioners,
physician assistants, they all recognize

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at the stage of the game that, you know,
having that information accessible in

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the quickest possible way is important.

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So you see people using
smartphones on a regular basis,

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or tablets on a regular basis.

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You see electronic health records
embedded with solutions that allow

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providers to look for critical studies
that may help sort of guide care.

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You see clinical decision support systems
that are being embedded in electronic

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health record workflows that are guiding
clinicians to make the right decision

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and really sort of absolving them of
some of the administrative hurdles

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of the daily practice of medicine.

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And I think you see the advent of
solutions that are coming up that

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you know, obviously need to be done.

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So with a lot of responsible use
of comfort level with artificial

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intelligence, comfort level with things
like large language models, comfort level

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with things like ambient listening and how
that factors into the way data is captured

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from a patient at the point of care.

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And so I think there's whereas medicine
training tended to be very apprenticeship

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based in my, in my days of training,
I think there's a lot more flexibility

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built into the way residents are trained
in today's day and age to not only sort

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of retain some of that apprenticeship
training, but also give people the

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flexibility to go out and sort of
experiment with new technologies of data

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capture, new technology, of, of grabbing
insights about diseases and treatments.

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And so I'm, I'm excited at the
evolution that we've seen in

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medical education for that reason.

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So I wanna go back to

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that concept of comfort
level, especially with AI.

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Medicine is a very historically
rigorous evidence-based culture.

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AI models, you can't always
tell what's going on inside.

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And yet there's so much value in
things like documents summation.

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I remember having people talking to
me 10, 15 years ago about the need to

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pull in a medical student to do a chart
biopsy on a new patient who came in

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and go reading through every previous
node, and I, I, I sort of became very

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enamored with that for his chart biopsy.

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And I was saying it too often for a
while, AI can do the chart biopsy.

224
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Mm-hmm.

225
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It could probably do the chart
biopsy as well as many medical

226
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students as the provider who's
making a decision about care.

227
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How do you get comfortable?

228
00:13:11,175 --> 00:13:13,574
Yeah, so I wanna approach it
from a couple different angles.

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There's a story that I, I routinely
tell that, and there's actually a really

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good article by Reinhardt, uh, that was
written in 2020, and it was really about

231
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the stethoscope and how the stethoscope
was ushered in and became something that

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physicians had comfort level with, and so
it was invented in the early 18 hundreds.

233
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And at this point, uh, as we think about
its adoption within the United States,

234
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American physicians really had to attend
lectures and, and clinics in Paris to

235
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really learn from some of the fathers
of the stethoscope and relaying it.

236
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René Laënnec was actually
the person who invented it.

237
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And the challenge was, is that it
was limited to very elite physicians

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who happened to work in academically
affiliated medical centers.

239
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And so it wasn't every doc who was seeing
every patient that was able to see it.

240
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Adoption was slow, and I think
there was a lot of reasons

241
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why adoption was challenged.

242
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There was a lack of formal education.

243
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You know, there wasn't really a
lot of bedside training that was

244
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available with the stethoscope.

245
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There was a complexity that existed
in the way interpretation happened.

246
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And how to sort of, what do we do with
this atory information that's coming?

247
00:14:16,475 --> 00:14:20,195
There was a hesitancy to create
this barrier between the patient

248
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and a and a clinician by putting
an instrument in between them.

249
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And then there wasn't really a lot of
opportunities for continuing medical

250
00:14:26,315 --> 00:14:31,235
education that physicians could join
into after they've left medical school.

251
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And so a lot of the learning tended to
stop, you know, when they left medical

252
00:14:34,955 --> 00:14:40,230
school and that was, so, it was probably
a little less than a full century

253
00:14:40,230 --> 00:14:45,390
before adoption was so commonplace and
it would be hard, you'd be hard pressed

254
00:14:45,390 --> 00:14:48,480
to find a physician that graduates
from medical school that doesn't

255
00:14:48,480 --> 00:14:50,700
own a stethoscope, hasn't used one.

256
00:14:50,760 --> 00:14:53,880
And granted, there's technology
that's evolved to replace a lot

257
00:14:53,880 --> 00:14:56,850
of the things that we did with
stethoscopes and do so better.

258
00:14:57,060 --> 00:15:00,660
But it's such a central concept
to how we trained as physicians.

259
00:15:01,200 --> 00:15:04,110
And I think we're going through
some of the same challenges with

260
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artificial intelligence right now.

261
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There's a lot of information that's
coming at us, a lot of noise that

262
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exists, a lot of bias, a lot of
reluctance to adopt beause of the fact

263
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that we're fearful that it creates
a, a wedge between us and patients.

264
00:15:19,320 --> 00:15:24,120
I think there's a lot of value to provide
that, that AI provides in doing some of

265
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the things that tend to be very root.

266
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Quite honestly, physicians don't
have the time to deal with.

267
00:15:29,370 --> 00:15:33,360
If you go to an emergency department and
you ask the average doc, how many pages

268
00:15:33,360 --> 00:15:37,440
do they sift through of a person's past
medical history before they abandon and

269
00:15:37,440 --> 00:15:39,280
ship, and then go in and ask the patients,

270
00:15:39,500 --> 00:15:42,465
I think it's, the number is
somewhere around like seven pages.

271
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Right?

272
00:15:42,645 --> 00:15:46,155
It just, there's so much information
that's gathered in that medical chart.

273
00:15:46,515 --> 00:15:50,235
Some, a lot of which is repetitious,
a lot of which may not even be correct

274
00:15:50,235 --> 00:15:51,615
in terms of the way it's captured.

275
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AI is a way of kind of sifting
through that and making it

276
00:15:54,825 --> 00:15:55,935
so that it's streamlined.

277
00:15:56,235 --> 00:15:58,605
There's so much nuance
that isn't captured.

278
00:15:58,605 --> 00:16:02,325
Like I, I used to think about the way
I used to write my clinical notes.

279
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I would go in and have an interview
with a patient and have a, it was a

280
00:16:05,405 --> 00:16:10,295
conversation and then translating that
conversation into something that would

281
00:16:10,505 --> 00:16:15,844
be necessary to, to document code and
bill, I'd lost so much of the nuance that

282
00:16:15,844 --> 00:16:20,464
was so critical for differentiating some
of the gray zones in people's diseases,

283
00:16:20,734 --> 00:16:22,625
and that information was lost forever.

284
00:16:22,625 --> 00:16:27,125
And so things like ambient listening,
while they may be something of

285
00:16:27,214 --> 00:16:30,005
uncomfort in the room at the
point that it's being captured.

286
00:16:30,344 --> 00:16:34,485
Oftentimes provide much more granularity
and much more nuance that's really

287
00:16:34,485 --> 00:16:37,365
important to ultimately making
the right decision for patients,

288
00:16:37,635 --> 00:16:40,395
making the right diagnosis, and
prescribing the right treatment.

289
00:16:40,490 --> 00:16:45,314
I, I do think that there is going to
be an adoption issue and a comfort

290
00:16:45,314 --> 00:16:49,755
level that people are gonna have to get
through, but there's no way that it's

291
00:16:49,755 --> 00:16:51,170
sustainable at this stage of the game.

292
00:16:51,720 --> 00:16:55,830
Where a clinician sees a patient, you
know, and for every hour that they

293
00:16:55,830 --> 00:17:00,600
spend seeing a patient, they document
for another half hour after that.

294
00:17:00,660 --> 00:17:00,930
Right.

295
00:17:00,930 --> 00:17:03,900
That's just not sustainable when
we're, it's sort of seeing the

296
00:17:03,900 --> 00:17:07,349
dwindling numbers of providers and
increased numbers of patients that

297
00:17:07,349 --> 00:17:08,910
are increasingly becoming complex.

298
00:17:08,910 --> 00:17:13,829
In terms of how we adopt it's
really the thoughtful learnings from

299
00:17:13,829 --> 00:17:17,880
technologies like the stethoscope that
we think about how we as a medical

300
00:17:17,880 --> 00:17:20,790
discipline start to adopt technology
in a way that's that's comfortable.

301
00:17:22,710 --> 00:17:26,879
Thinking a a little bit more about some
of the, you know, the natural language

302
00:17:26,879 --> 00:17:30,810
processing element you were just talking
about clinical documentation, do a little

303
00:17:30,810 --> 00:17:34,830
experiment, like if you get a letter back
from a, on a patient you sent out for

304
00:17:34,830 --> 00:17:39,450
a consult, and the first line is, thank
you for this very interesting consult.

305
00:17:39,794 --> 00:17:41,264
What do you read between the lines there?

306
00:17:42,764 --> 00:17:43,094
I've

307
00:17:43,094 --> 00:17:46,094
had that statement represent a
whole bunch of different things.

308
00:17:46,155 --> 00:17:49,905
One thing is, I don't know why you
sent this to us, because you probably

309
00:17:49,905 --> 00:17:51,195
could have figured it out on your own.

310
00:17:51,225 --> 00:17:55,304
You're a primary care doc and you
practiced internal medicine and

311
00:17:55,304 --> 00:17:56,534
you went to residency with me.

312
00:17:56,940 --> 00:17:59,520
I'm calling it interesting, but
it's a little tongue in cheek and

313
00:17:59,520 --> 00:18:01,470
this is probably something you
could have figured out on your own.

314
00:18:01,950 --> 00:18:05,340
I've been in academic medical centers
where there's some sincerity to that.

315
00:18:05,370 --> 00:18:08,610
There's there there's some, it's
a convoluted case that involved

316
00:18:08,880 --> 00:18:10,740
a lot of information gathering.

317
00:18:10,740 --> 00:18:14,700
Perhaps I, as the primary care doc
have gathered some of that information

318
00:18:14,700 --> 00:18:18,090
and synthesized it in a way that I
was able to send it to a specialist

319
00:18:18,090 --> 00:18:21,370
and the specialist pieced it all
together and because of their area of

320
00:18:21,370 --> 00:18:23,980
expertise came to an easier diagnosis.

321
00:18:24,100 --> 00:18:25,360
I've seen both of that, right?

322
00:18:25,389 --> 00:18:28,990
Both of those circumstances where on
one hand it's, hey, I just realized

323
00:18:28,990 --> 00:18:31,780
that the reason why you sent me
this patient is because I'm an ENT

324
00:18:31,780 --> 00:18:33,639
doc and this person had a nose.

325
00:18:33,850 --> 00:18:36,550
And then other times it's, hey,
this is a really complex case,

326
00:18:36,550 --> 00:18:39,340
you synthesized this very nicely
and I got a lot of information.

327
00:18:39,730 --> 00:18:43,750
But I think like I think when
you look at AI from a medical

328
00:18:43,750 --> 00:18:48,405
decisioning standpoint, some of that
information gets glossed over, right?

329
00:18:48,405 --> 00:18:53,025
What's really of essence is, hey, this
is a 53-year-old male with past medical

330
00:18:53,025 --> 00:18:55,425
history, significant for A, B, C.

331
00:18:55,785 --> 00:18:59,985
They're on these medications, these are
their allergies, these are social vices.

332
00:19:00,255 --> 00:19:03,315
They presented to you with
a complaint of X and this is

333
00:19:03,315 --> 00:19:04,755
what you saw on physical exam.

334
00:19:05,145 --> 00:19:07,305
I substantiated by finding this.

335
00:19:07,335 --> 00:19:10,035
I did a couple of extra tests
and in the end I've come to the

336
00:19:10,035 --> 00:19:13,155
conclusion that this is what the
diagnostic, the diagnosis is, right?

337
00:19:13,395 --> 00:19:17,524
It really, I think, a good consult
letter not only starts with those sort

338
00:19:17,524 --> 00:19:21,514
of entry statements and sort of exit
statements, but they really have a lot

339
00:19:21,514 --> 00:19:25,084
of information about the Socratic method
that medicine has practiced in, right?

340
00:19:25,355 --> 00:19:29,615
How we gather information from a
subjective objective, make an assessment,

341
00:19:29,645 --> 00:19:31,504
make a plan for what this patient has.

342
00:19:31,745 --> 00:19:34,534
And that's where I think AI has
become really good at pulling out of

343
00:19:34,715 --> 00:19:37,655
these consult notes to really help
make informed clinical decisions.

344
00:19:37,895 --> 00:19:40,745
And that was the theme that I was hoping
that we could dig in on a little bit

345
00:19:40,834 --> 00:19:46,140
because there is so much context and
things that are written and things

346
00:19:46,140 --> 00:19:48,000
that are not written in these notes.

347
00:19:48,300 --> 00:19:52,260
And you know, I remember working with
people years ago where simply pulling

348
00:19:52,260 --> 00:19:57,480
out someone's smoking status from
a, and this was before that was a

349
00:19:57,480 --> 00:20:01,230
required field as part of meaningful
use, was incredibly different.

350
00:20:01,290 --> 00:20:07,950
Difficult 'cause you only had the free
text node and non-smoker quit last

351
00:20:07,950 --> 00:20:10,650
year is very different than non-smoker.

352
00:20:11,070 --> 00:20:15,930
Which is very different than
12-pack-a-day smoker quit 10 years ago.

353
00:20:16,170 --> 00:20:22,110
And pulling that into a format where you
could make a, actually make a decision

354
00:20:22,140 --> 00:20:24,240
is this patient eligible for a trial?

355
00:20:24,240 --> 00:20:26,520
Are they in a real world evidence cohort?

356
00:20:26,520 --> 00:20:30,030
That's interesting, is very
challenging and I, I think while

357
00:20:30,060 --> 00:20:33,510
we have a lot more coded data now,
we have a lot more complexity that

358
00:20:33,510 --> 00:20:34,560
we need to work through as well.

359
00:20:35,850 --> 00:20:38,820
You know, I think you look at SNOMED
for example, it's become really

360
00:20:38,820 --> 00:20:43,949
expansive and you could really capture
a lot of the pros documentation

361
00:20:43,949 --> 00:20:45,840
in, in, in structured format.

362
00:20:46,050 --> 00:20:48,179
It's still not perfect and
it's not a hundred percent.

363
00:20:48,540 --> 00:20:50,879
And there's, I think the other
thing is that as we think

364
00:20:50,879 --> 00:20:53,189
about why physicians document,

365
00:20:54,855 --> 00:20:57,795
yes, some of it is to create
a historical medical record,

366
00:20:58,095 --> 00:21:00,105
but that really drives care.

367
00:21:00,375 --> 00:21:02,565
But there's a whole lot of other
reasons why they document to

368
00:21:02,565 --> 00:21:04,065
protect themselves medical legally.

369
00:21:04,065 --> 00:21:07,395
They document to establish
patient satisfaction.

370
00:21:07,395 --> 00:21:09,825
They document for the purposes
of building and coding.

371
00:21:10,095 --> 00:21:12,915
And so sometimes it's really
difficult to sift through that.

372
00:21:13,095 --> 00:21:16,035
And I think you can look at the
situation where in a consult letter,

373
00:21:16,035 --> 00:21:18,615
there's an intro sentence and
you can decide, all right, what's

374
00:21:18,615 --> 00:21:19,845
ultimately the meaning of that.

375
00:21:19,875 --> 00:21:25,620
But I think if someone, for example,
if you wanna allude to the fact that

376
00:21:25,620 --> 00:21:29,730
someone's, you believe that someone's
an alcoholic without allowing it

377
00:21:29,730 --> 00:21:33,720
to be a risk factor for you from
a, from a medical legal standpoint,

378
00:21:33,720 --> 00:21:35,010
because you didn't address it.

379
00:21:35,070 --> 00:21:38,400
While you also don't want the
patient to see their record where

380
00:21:38,400 --> 00:21:41,670
you're insinuating that they're an
alcoholic, so that you'll lose trust

381
00:21:41,670 --> 00:21:44,580
with a patient who you're trying to
essentially get to the bottom of things.

382
00:21:44,940 --> 00:21:48,630
It's a really dicey way about how to
appropriately document in a clinical

383
00:21:48,630 --> 00:21:51,420
loop, right, and still capture the nuance.

384
00:21:51,420 --> 00:21:55,240
Still capture, still protect yourself
from a medical legal standpoint and

385
00:21:55,240 --> 00:21:59,170
still establish and continue to establish
that trust with the patient because

386
00:21:59,170 --> 00:22:02,710
they ultimately get to see what's
written in there and dissecting that,

387
00:22:02,710 --> 00:22:07,120
from an, from a an NLP standpoint, that
becomes a bit of a challenge, right?

388
00:22:07,120 --> 00:22:09,400
Because you don't know what
the, what were the motives for

389
00:22:09,400 --> 00:22:11,280
documentation in this particular note?

390
00:22:12,660 --> 00:22:17,350
For companies that are developing, whether
it's ambient documentation systems or

391
00:22:17,620 --> 00:22:23,745
other AI assistive technologies, where
should they be focused if in terms

392
00:22:23,745 --> 00:22:25,755
of creating trust with physicians?

393
00:22:26,745 --> 00:22:27,015
Yeah.

394
00:22:27,015 --> 00:22:31,845
I mean, I, I think as I've educated in,
in informatics and what I think, I think a

395
00:22:31,845 --> 00:22:34,280
lot of the, a lot of the times physicians

396
00:22:35,520 --> 00:22:39,929
are afraid that the, these solutions
are coming to, to are being adopted, not

397
00:22:39,929 --> 00:22:44,370
necessarily to drive the improvement of
the patient experience or to diminish

398
00:22:44,370 --> 00:22:47,879
provider abrasion, rather to make
it so that workflows that are more

399
00:22:47,879 --> 00:22:51,600
administrative in healthcare that
lead to better billing and coding

400
00:22:51,600 --> 00:22:55,080
and reimbursement, things like that
are really the motivating factors.

401
00:22:55,110 --> 00:22:59,699
And while I think we always have to focus
on return on investment when it comes to

402
00:22:59,820 --> 00:23:04,145
a lot of these solutions, some of that
investment is in some of that return

403
00:23:04,145 --> 00:23:09,754
comes in the form of a life saved or
a medication error averted, physicians

404
00:23:09,754 --> 00:23:12,784
went to medical school and clinicians
went to their professional school.

405
00:23:13,095 --> 00:23:15,735
Because they really wanna make a
difference in, in people's lives.

406
00:23:15,765 --> 00:23:18,975
And I think we have to resonate with
them as we have these conversations with

407
00:23:19,065 --> 00:23:23,160
solutions that are meaningful and we have
to see whether or not it truly helps it.

408
00:23:23,165 --> 00:23:26,895
It helps diminish provider abrasion by
implementing some of these solutions.

409
00:23:27,105 --> 00:23:30,720
So we wanna make sure that we're
thoughtful about how, what are we

410
00:23:31,665 --> 00:23:35,445
have clinicians understand what the
motives for implementation are, that

411
00:23:35,445 --> 00:23:40,185
they're truly about helping improve the
physician-patient interaction and they're

412
00:23:40,185 --> 00:23:43,665
helping improve the way care is delivery,
and they're helping to reduce some of

413
00:23:43,665 --> 00:23:48,945
the challenges that we have as physicians
from a rote documentation or some of the

414
00:23:48,945 --> 00:23:50,925
administrative billing issues, et cetera.

415
00:23:50,985 --> 00:23:52,755
That's really what I
think physicians want.

416
00:23:54,795 --> 00:23:58,755
So a few minutes ago you mentioned
SNOMED, and that may not be a term

417
00:23:58,755 --> 00:24:01,785
that all the people listening to
the podcast are familiar with.

418
00:24:02,235 --> 00:24:09,210
So for people who are joining us to learn
a little more about the shape of this

419
00:24:09,210 --> 00:24:13,004
healthcare standards and data world,
can you just give us the two or three

420
00:24:13,004 --> 00:24:15,195
minute tour through modern coding system?

421
00:24:15,885 --> 00:24:19,725
Yeah, so I think a lot of times, you
know, as if we're thinking about the

422
00:24:19,725 --> 00:24:22,965
lay people, there's probably two codes
that they, two types of codes that

423
00:24:22,965 --> 00:24:25,965
they see in their medical billing.

424
00:24:25,965 --> 00:24:29,715
For example, and that's usually the
ICD-10, which is the International

425
00:24:29,715 --> 00:24:33,645
Classification of Diseases in the
CPT, which is a, a list of medical

426
00:24:33,645 --> 00:24:37,425
procedures that are done, or, uh, the
types and intensities of visits that are

427
00:24:37,425 --> 00:24:39,284
captured at at, at the point of care.

428
00:24:39,584 --> 00:24:44,615
Those are largely transacted to be
able to drive thoughtful billing.

429
00:24:45,034 --> 00:24:48,935
But electronic health records have
become so much more complex in

430
00:24:48,935 --> 00:24:52,024
terms of the types of data that
sort of, you know, go into them.

431
00:24:52,415 --> 00:24:55,415
And, you know, there's a, a set
of terminology called LOINC that's

432
00:24:55,415 --> 00:25:00,495
responsible for how we capture things like
blood pressure and how we capture parts of

433
00:25:00,495 --> 00:25:04,274
a complete blood count or a comprehensive
metabolic panel or a blood culture.

434
00:25:04,635 --> 00:25:08,985
There's parts of medications that actually
have a set of terminologies that are

435
00:25:08,985 --> 00:25:10,635
associated with how they're captured.

436
00:25:10,875 --> 00:25:14,525
There's a coding standard that
exists for vaccinations, and so a

437
00:25:14,575 --> 00:25:17,780
lot of our vaccine registries are
set up with that coding standard.

438
00:25:18,050 --> 00:25:23,000
And SNOMED is really a, a sort of a
catchall set of terminology that really

439
00:25:23,000 --> 00:25:27,620
captures not only disease states and
their procedures, but also qualifying

440
00:25:27,620 --> 00:25:31,940
statements that tend to have adjective
and adverb qualities associated with

441
00:25:31,940 --> 00:25:34,220
them so that we can get more nuanced.

442
00:25:34,520 --> 00:25:40,310
Understand that ICD-10 only classifies
things in more broad brushes.

443
00:25:40,580 --> 00:25:44,295
I think you can get more specific
information about diseases if

444
00:25:44,295 --> 00:25:48,315
you start bringing in these other
coding standards and really creating

445
00:25:48,315 --> 00:25:51,585
sort of an amalgam of these coding
standards to help tell a story.

446
00:25:51,915 --> 00:25:54,645
And that's, I think the best way to
represent it is that each of these

447
00:25:54,645 --> 00:25:58,965
coding standards really is, represents a
different part of speech, let's just say

448
00:25:58,965 --> 00:26:04,395
in a sentence so that the full compendium
of the medical information that's captured

449
00:26:04,395 --> 00:26:09,165
in that chart has been codified in a
way that can be transacted by machines.

450
00:26:10,274 --> 00:26:13,695
Well, and we want our audience to be
able to sleep tonight so we are not

451
00:26:13,695 --> 00:26:17,085
gonna talk about NDC codes and how
that works with electronic prescribing.

452
00:26:17,264 --> 00:26:18,524
No, no, we're not.

453
00:26:19,485 --> 00:26:22,754
So pulling all this stuff together,
are there categories of applications

454
00:26:22,754 --> 00:26:26,895
that you think are just more feasible
now, thanks to all this progress,

455
00:26:26,895 --> 00:26:29,264
both in standards adoption and in AI?

456
00:26:29,925 --> 00:26:30,855
Like what are you excited about?

457
00:26:31,485 --> 00:26:32,235
Absolutely.

458
00:26:32,235 --> 00:26:36,719
I think, you know, there's been really an
evolution of a lot of different things,

459
00:26:36,719 --> 00:26:40,500
and I think just even through my, my
career, you know, I can come up with

460
00:26:40,500 --> 00:26:45,050
sort of four quick success stories as
different phases in, in, in my informatics

461
00:26:45,050 --> 00:26:47,460
career that have been really impactful.

462
00:26:47,550 --> 00:26:51,540
You know, I think COVID had a lot
to do with how standard adoption and

463
00:26:51,540 --> 00:26:54,990
electronic data capture, we became
more thoughtful about how to do it.

464
00:26:54,990 --> 00:26:58,860
And so I had the opportunity in one
of my former roles to work for a

465
00:26:58,860 --> 00:27:02,910
company that, um, happened to work in
a state at the public health level.

466
00:27:03,270 --> 00:27:06,270
One of the challenges that existed
from an interoperability standpoint

467
00:27:06,270 --> 00:27:09,630
is that there were over a hundred
different ways of capturing

468
00:27:10,004 --> 00:27:16,335
a simple concept like COVID-19 PCR test
and 40 different ways of representing that

469
00:27:16,335 --> 00:27:18,254
patient had a result that was negative.

470
00:27:18,524 --> 00:27:22,635
And so if you look at a hundred
different ways of capturing the test

471
00:27:22,695 --> 00:27:26,700
and 40 different ways of capturing
its negative value, there's 6,000

472
00:27:26,700 --> 00:27:29,640
different permutations that could
exist of how that data's captured.

473
00:27:30,120 --> 00:27:35,310
And so you could see where if you create
data standardization, it really has a huge

474
00:27:35,310 --> 00:27:37,650
ROI impact on public health initiatives.

475
00:27:37,650 --> 00:27:39,390
And again, this was only in one state.

476
00:27:39,395 --> 00:27:43,405
Imagine what this is across, you
know, this is 40 different labs.

477
00:27:43,405 --> 00:27:46,585
Imagine what this is across 400
different labs in 10 states.

478
00:27:46,585 --> 00:27:46,855
Right?

479
00:27:47,095 --> 00:27:51,505
So this, this is a, a real problem
that I think technology was aimed

480
00:27:51,505 --> 00:27:53,005
at solving and did a great job.

481
00:27:53,455 --> 00:27:57,865
I worked with a group of students
addressing how to take medication

482
00:27:57,865 --> 00:28:02,095
instructions and parse and parse
them using not only medication

483
00:28:02,125 --> 00:28:05,665
normalization or, or data
normalization, but then deploying

484
00:28:05,665 --> 00:28:08,290
novel solutions like NLP and Gen AI.

485
00:28:08,639 --> 00:28:14,520
To really get better F1 scores for some of
these solutions, and, you know, the amount

486
00:28:14,550 --> 00:28:18,870
of medication instructions that could
be parsed once normalization is added

487
00:28:18,870 --> 00:28:23,879
to to, to these NLP Gen AI solutions,
uh, makes it so that the capture of

488
00:28:23,879 --> 00:28:25,980
the information is almost near perfect.

489
00:28:25,980 --> 00:28:30,900
So that's, it's speaks to the fact that
we can really reduce medical errors,

490
00:28:30,900 --> 00:28:34,770
which are substantial impact in, into
why people stay in hospitals longer,

491
00:28:34,770 --> 00:28:38,430
and a significant cause of morbidity
and mortality in amongst patients.

492
00:28:39,690 --> 00:28:44,130
And then in sort of my most recent role,
it's really identifying individuals

493
00:28:44,370 --> 00:28:47,520
and pulling together some of this
multi-source and multi-format data,

494
00:28:47,880 --> 00:28:52,410
really pulling in a combination of
social patient reported outcomes

495
00:28:52,410 --> 00:28:55,950
data, pulling in a lot of the medical
data from electronic health records.

496
00:28:56,190 --> 00:28:59,775
A lot of the billing data and sometimes
the incongruence between what's

497
00:28:59,775 --> 00:29:03,765
captured in a chart and what's what's
billed is actually very insightful.

498
00:29:03,765 --> 00:29:07,925
That sort of absence of congruence
is actually very helpful in how

499
00:29:07,925 --> 00:29:09,585
you build machine learning models.

500
00:29:09,825 --> 00:29:12,675
And so we looked at
individuals primarily older age

501
00:29:12,975 --> 00:29:17,235
with clear modifiable risk factors
like things like polypharmacy or

502
00:29:17,235 --> 00:29:20,564
the way their homes were set up
that were at risk for falls and

503
00:29:20,564 --> 00:29:25,095
actually started targeting thoughtful
interventions to prevent those falls.

504
00:29:25,274 --> 00:29:29,085
And this is really showing that having
a combination of data standardization

505
00:29:29,415 --> 00:29:34,125
as the foundation for more advanced
analytics, predictive modeling and AI

506
00:29:34,385 --> 00:29:39,605
is really important to how we make the
job of treating patients and doing so

507
00:29:39,605 --> 00:29:43,845
in a way that's contextually appropriate
and thoughtful and diminishing in the

508
00:29:43,845 --> 00:29:46,745
provider's burden is really very valuable.

509
00:29:46,745 --> 00:29:52,685
So I think those are really four
good examples of how automation

510
00:29:52,745 --> 00:29:56,014
clinical decision support, and sort
of advanced analytics can be used to

511
00:29:56,014 --> 00:29:57,455
help drive healthcare improvement.

512
00:29:59,465 --> 00:29:59,735
So

513
00:29:59,855 --> 00:30:01,405
actually going back to SNOMED.

514
00:30:02,645 --> 00:30:05,910
Just listening to this conversation
as we've been having it, there's

515
00:30:05,910 --> 00:30:08,070
always been an adoption barrier there.

516
00:30:08,070 --> 00:30:09,420
It's obviously a lot of work.

517
00:30:09,870 --> 00:30:14,130
It's a lot more work to enter structured
information and you've gotta create

518
00:30:14,130 --> 00:30:17,700
an incredible user experience to
be able to do that versus being

519
00:30:17,700 --> 00:30:19,665
able to write a narrative note.

520
00:30:21,155 --> 00:30:25,350
With these a newer AI tools and summation

521
00:30:26,070 --> 00:30:30,480
have we been solving the wrong problem
in terms of designing code systems?

522
00:30:32,170 --> 00:30:36,465
Early on, I think the purpose of
coding systems was really automate

523
00:30:36,465 --> 00:30:40,635
the way hospitals worked, automate
the communication between different

524
00:30:40,635 --> 00:30:43,305
areas of the hospital, drive billing.

525
00:30:43,695 --> 00:30:48,135
I even think, by the way, I think that's
a very insightful, insightful question.

526
00:30:48,585 --> 00:30:50,505
I don't think it's even
about coding systems.

527
00:30:50,505 --> 00:30:53,865
I even think that it's about the
reason why the electronic health

528
00:30:53,865 --> 00:30:56,085
record evolved the way it did.

529
00:30:56,205 --> 00:30:56,865
It wasn't.

530
00:30:57,300 --> 00:31:00,570
I, I don't think it was created with
a thought in mind that this is the

531
00:31:00,570 --> 00:31:06,330
best way to capture data, to create a
chart, a living chart for a patient.

532
00:31:06,450 --> 00:31:10,110
It was really about how do we
create something that lends itself

533
00:31:10,110 --> 00:31:11,610
to billing and coding, right?

534
00:31:11,640 --> 00:31:13,950
And not necessarily how we
create something that tells this

535
00:31:13,950 --> 00:31:15,390
person's full medical story.

536
00:31:15,720 --> 00:31:19,650
And so I think the way coding standards
did evolve, or at least the way they were

537
00:31:19,650 --> 00:31:24,645
utilized, was not necessarily for the
primary intent of patient care delivery.

538
00:31:24,675 --> 00:31:29,715
I think we've done a good job of
starting to pivot and really being more

539
00:31:29,715 --> 00:31:35,025
thoughtful about how we use these tools to
identify opportunities and gaps in care.

540
00:31:35,235 --> 00:31:38,835
But I think we've been reactive in the,
in the way we've gone about it, because

541
00:31:38,835 --> 00:31:43,395
again, they weren't set up in a way to
drive true chronicling of medical care.

542
00:31:43,635 --> 00:31:46,305
They were invented for other
purposes, billing, coding,

543
00:31:46,305 --> 00:31:47,985
medical, legal, et cetera.

544
00:31:47,985 --> 00:31:51,990
And I do think that the technological
evolution that we're seeing now

545
00:31:52,290 --> 00:31:53,820
probably is more thoughtful.

546
00:31:54,030 --> 00:31:57,870
I think the role of the physician
and the clinician informaticist

547
00:31:58,169 --> 00:32:01,710
has made it so that we're being
more protective of our turf.

548
00:32:01,800 --> 00:32:05,100
We're not a technology, we're
not a technology person first.

549
00:32:05,100 --> 00:32:06,450
We're a clinician first, right?

550
00:32:06,810 --> 00:32:10,500
And we're building thoughtful technology
solutions that help augment the way we

551
00:32:10,500 --> 00:32:11,879
can deliver better care for patients.

552
00:32:13,455 --> 00:32:16,545
So in a way we're, we're really
almost going back to the beginning.

553
00:32:16,545 --> 00:32:20,835
People like Octo Barnett and Larry
Weed, who were right at the beginning

554
00:32:20,835 --> 00:32:25,815
of this electronic record keeping
revolution back in the sixties and

555
00:32:25,815 --> 00:32:31,065
seventies and had a very clinical,
very patient oriented historian

556
00:32:31,065 --> 00:32:32,835
approach to designing these tools.

557
00:32:33,270 --> 00:32:37,275
And then the process automation
juggernaut took over from that.

558
00:32:39,255 --> 00:32:43,784
I think we, we deviated a little bit
from that purity of intent early on,

559
00:32:43,875 --> 00:32:49,185
and I think that by having clinicians
involved in the process now, where I

560
00:32:49,185 --> 00:32:50,745
think we're getting back to it for sure.

561
00:32:52,034 --> 00:32:57,915
Continuing on on this theme of clinical
adoption, have you seen any organizations

562
00:32:57,915 --> 00:33:02,445
that you've felt have done a, a really
good job and of thinking about the ROI

563
00:33:02,445 --> 00:33:06,720
on some of these clinical informatics
programs and interviewing the work of

564
00:33:06,720 --> 00:33:09,600
their CMIOs into their strategic plans.

565
00:33:10,650 --> 00:33:14,070
Um, I think that there's a
lot of very forward thinking.

566
00:33:14,130 --> 00:33:18,460
I think there's forward thinking hospitals
that have done really good work around

567
00:33:18,730 --> 00:33:22,155
how are we incorporating some of these
solutions in a way that's not just

568
00:33:22,155 --> 00:33:28,034
about checking a box and really bringing
in the CMIO decision making, um, into

569
00:33:28,034 --> 00:33:29,895
the whole, into the whole process?

570
00:33:30,254 --> 00:33:33,524
I think that there's a couple of
very thoughtful states that really

571
00:33:33,524 --> 00:33:38,340
accelerated the sort of modernization
strategies during the COVID pandemic

572
00:33:38,340 --> 00:33:40,560
and did so in a way that was thoughtful.

573
00:33:41,070 --> 00:33:45,210
I think you're starting to see a lot
of tech enabled organizations that kind

574
00:33:45,210 --> 00:33:50,520
of have come out that are more maybe
concierge based or even concierge light.

575
00:33:50,520 --> 00:33:53,550
They don't demand sort of a lot
of, a lot of membership dues for

576
00:33:53,550 --> 00:33:59,450
patients, but ultimately really
deliver hugely tech-enabled solutions.

577
00:33:59,810 --> 00:34:02,690
I think that there's, I, you know,
look, I, one of the, one of the

578
00:34:02,690 --> 00:34:06,720
indictments that I have of meaningful
use is that I think that there wasn't

579
00:34:06,720 --> 00:34:09,929
necessarily the right carrot and there
wasn't necessarily the right stick.

580
00:34:09,960 --> 00:34:10,320
Right?

581
00:34:10,409 --> 00:34:13,949
It became a really big challenge
to get providers to engage because

582
00:34:13,949 --> 00:34:16,800
of the fact that it was such a
laborious process to get people on

583
00:34:16,800 --> 00:34:20,909
board, and then the penalties for
non-engagement were actually piddly,

584
00:34:20,909 --> 00:34:23,970
especially if you didn't necessarily
have a big Medicare or Medicaid

585
00:34:23,970 --> 00:34:26,490
population in your patient portfolio.

586
00:34:26,845 --> 00:34:31,155
And so I think that people adopted
and there was a lot of checking

587
00:34:31,155 --> 00:34:32,805
boxes to say that they adopted.

588
00:34:32,805 --> 00:34:37,395
A lot of the initial meaningful use
requirements were self-report, and I think

589
00:34:37,635 --> 00:34:42,675
when you have that kind of self-report
and you don't have the right incentives

590
00:34:42,735 --> 00:34:47,085
to get people to do it or disincentives to
prevent people from not doing it, I don't

591
00:34:47,085 --> 00:34:48,555
think that there's thoughtful adoption.

592
00:34:48,650 --> 00:34:52,155
One of the challenges that's existed
is, is that while the capacity exists

593
00:34:52,155 --> 00:34:56,284
to allow people to do that the education
doesn't necessarily exist, and I

594
00:34:56,284 --> 00:35:00,480
think there are thoughtful hospital
systems, thoughtful payers, thoughtful

595
00:35:00,480 --> 00:35:05,520
providers that are really making
patient education and patient navigation

596
00:35:05,520 --> 00:35:07,230
about how to use those solutions

597
00:35:07,440 --> 00:35:12,120
a central part, you need to get, I mean,
I think the two biggest adopters that need

598
00:35:12,120 --> 00:35:16,260
to drive this are your medical staff and
your clinical staff and hospitals, and

599
00:35:16,260 --> 00:35:19,860
then you gotta get patients on board with
understanding about how to do to do this.

600
00:35:19,860 --> 00:35:24,060
It's such a complex care delivery model
that unless they're fully bought in,

601
00:35:24,060 --> 00:35:26,130
you could have patient right of access.

602
00:35:26,430 --> 00:35:27,510
No one's accessing it.

603
00:35:27,510 --> 00:35:31,575
And so I think, I think that's really
what's required to have success.

604
00:35:31,575 --> 00:35:35,475
It's really the buy-in of both
the clinician staff as well as the

605
00:35:35,475 --> 00:35:39,225
patients, regardless of whether
it's a payer provider, hospital

606
00:35:39,225 --> 00:35:42,645
system, a concierge practice, a
tech enabled practice, et cetera.

607
00:35:42,945 --> 00:35:44,505
Those are the things that
are absolutely required.

608
00:35:45,735 --> 00:35:46,155
I'm

609
00:35:46,425 --> 00:35:51,555
reminded of a conversation from 15,
20 years ago with the uh, CIO of a

610
00:35:52,215 --> 00:35:57,325
hospital system that had one of the
first patient portals and he told me two

611
00:35:57,345 --> 00:36:01,605
things that people use it for refills and
appointments, and it's paid for out of the

612
00:36:01,605 --> 00:36:05,175
marketing budget, not out of the clinical
budget, not out of the core IT budget.

613
00:36:05,715 --> 00:36:09,225
And really they saw it as a tool for
retaining their patient population.

614
00:36:10,965 --> 00:36:15,135
What are some of the levers that, like
that convenience of being able to make

615
00:36:15,135 --> 00:36:19,725
an appointment or send a message to your
clinician that will help drive patient

616
00:36:19,725 --> 00:36:21,465
adoption of these new care models.

617
00:36:22,455 --> 00:36:24,915
So I, I think a lot of
it is really access.

618
00:36:24,975 --> 00:36:28,515
You know, you look at things like
mental health, there's some states,

619
00:36:28,515 --> 00:36:32,234
and we're not talking about states
that are necessarily significantly poor

620
00:36:32,234 --> 00:36:36,194
states, and we're talking about affluent
states that still have challenge with

621
00:36:36,404 --> 00:36:40,875
people being able to access crisis
care from a mental health standpoint.

622
00:36:40,875 --> 00:36:44,145
There, there's such an issue
that in one of my previous

623
00:36:44,145 --> 00:36:46,484
roles, I actually did a study on

624
00:36:46,785 --> 00:36:50,805
the incidents of somatization, so
people presenting with physical

625
00:36:50,805 --> 00:36:56,355
complaints for psychiatric illness has
gone up because of the fact that they

626
00:36:56,565 --> 00:37:01,065
oftentimes can access mental health
or can't access the appropriate type

627
00:37:01,065 --> 00:37:04,935
of mental health, or there's social
stigma as associated with mental health.

628
00:37:05,355 --> 00:37:09,135
Or you even look at mental health
providers just being able to find a

629
00:37:09,135 --> 00:37:13,335
psychiatrist or a psychologist or a
social worker that's in your network

630
00:37:13,665 --> 00:37:17,595
that can see you any, anything
sooner than two months from now

631
00:37:17,834 --> 00:37:19,245
has been a significant issue.

632
00:37:19,245 --> 00:37:23,325
And you know, you can see where solutions
like telemedicine, you can see where the

633
00:37:23,325 --> 00:37:29,294
ability to ask patients' questions in a
way that's re recursive allows people,

634
00:37:29,294 --> 00:37:33,705
allows it to serve as a triage system
that can identify those people that

635
00:37:33,705 --> 00:37:36,734
truly need to be seen much more rapidly

636
00:37:36,765 --> 00:37:38,354
and those that perhaps can wait.

637
00:37:38,444 --> 00:37:41,805
You know, one of the stories I tell is
about a family member of mine that had

638
00:37:42,524 --> 00:37:47,145
respiratory complaints and ended up
getting admitted and nearly intubated

639
00:37:47,354 --> 00:37:50,504
because they went to an emergency
department at the same hospital three

640
00:37:50,504 --> 00:37:55,725
separate times complaining of shortness
of breath and on three separate times,

641
00:37:55,725 --> 00:38:00,915
got antibiotics as the prime modality of
treatment for a diagnosis of pneumonia.

642
00:38:01,275 --> 00:38:04,875
And it turns out that this person never
had pneumonia and if they just looked

643
00:38:04,875 --> 00:38:09,225
into their medical records and accessed
them in a way that was easier than

644
00:38:09,225 --> 00:38:13,185
having to look at pieces of paper or
duplicate charts, you know, where there

645
00:38:13,185 --> 00:38:18,424
was cut and paste, where you had an
active comprehensive problem list that

646
00:38:18,424 --> 00:38:20,705
was informatically coded into the chart

647
00:38:21,065 --> 00:38:25,384
you could really pull together information
and synthesize that information in a

648
00:38:25,384 --> 00:38:28,895
way to come up with a very thoughtful
differential diagnosis about what

649
00:38:28,895 --> 00:38:30,095
was going on with this patient.

650
00:38:30,095 --> 00:38:31,955
It turns out that this
patient didn't have pneumonia.

651
00:38:31,955 --> 00:38:36,004
They had pneumonitis, and if they just
went back to their gastroenterology

652
00:38:36,004 --> 00:38:38,825
records and they went back to their
neurology records, they would see

653
00:38:38,825 --> 00:38:42,694
that in addition to respiratory
complaints, they had GI complaints

654
00:38:42,694 --> 00:38:44,165
and they had neurologic complaints.

655
00:38:44,435 --> 00:38:47,160
And it turns out that this was the
first set of symptoms that this

656
00:38:47,160 --> 00:38:50,759
person was declaring themselves as
having a rheumatologic illness with.

657
00:38:51,029 --> 00:38:56,790
So I, I, I think the ability to put
information at the hands of clinicians in

658
00:38:56,790 --> 00:39:01,800
a thoughtful way, not putting the burden
on the patients to tell their story every

659
00:39:01,800 --> 00:39:06,060
time they go into the emergency department
or every time they see a new specialist.

660
00:39:06,390 --> 00:39:10,410
I mean, that's a huge absolution
for patients of having to do that

661
00:39:10,680 --> 00:39:12,240
every time they, they seek care.

662
00:39:12,420 --> 00:39:16,705
And think about it, that the, the more
complex you are as a patient, the more

663
00:39:16,705 --> 00:39:20,215
you're gonna likely have to become an
expert in your own diagnosis in order

664
00:39:20,215 --> 00:39:23,815
to basically transact your care because
your medical chart has grown to the

665
00:39:23,815 --> 00:39:27,565
point that there's thousands, tens of
thousands of pages, and you need to be

666
00:39:27,565 --> 00:39:30,805
able to synthesize and summarize that
so you know that the medications that

667
00:39:30,805 --> 00:39:34,675
you've been given have worked, and these
are the side effect profiles that you've

668
00:39:34,675 --> 00:39:38,035
had, and this is the medication that
didn't work, and this is what ended up

669
00:39:38,035 --> 00:39:39,384
happening when you took this medication.

670
00:39:39,565 --> 00:39:43,225
Like all of that information should
be in a way that's accessible so that

671
00:39:43,815 --> 00:39:47,775
it lends itself towards appropriate
clinical decisioning, be it automation

672
00:39:47,775 --> 00:39:53,775
by machines or improved rate of,
of speed by a clinician who's able

673
00:39:53,775 --> 00:39:57,765
to look at that information on one
screen and say, aha, this is what's

674
00:39:57,765 --> 00:39:58,845
going on with this individual.

675
00:39:58,845 --> 00:40:01,845
It looks like pneumonitis
because they have GI issues and

676
00:40:01,845 --> 00:40:03,045
they have neurologic issues.

677
00:40:04,605 --> 00:40:09,615
So that's a good segue into talking a
little bit more about equity and access.

678
00:40:10,095 --> 00:40:15,495
So, how both with improvements that
we have made around standardization

679
00:40:15,495 --> 00:40:19,485
of healthcare data and of course
these newer AI enabled technologies,

680
00:40:20,115 --> 00:40:23,895
how do you see that helping bridge
some of the gaps in healthcare access?

681
00:40:23,925 --> 00:40:26,505
Like we talked about it a little bit
in the mental health context, but

682
00:40:26,835 --> 00:40:28,015
I'm sure there's quite a few others.

683
00:40:29,004 --> 00:40:32,370
Yeah, I mean, I, I think it comes
with some pitfalls that I think

684
00:40:32,370 --> 00:40:33,690
we need to be concerned about.

685
00:40:33,750 --> 00:40:38,440
I think one of the things that I did
in a study at one of my, recent job

686
00:40:38,460 --> 00:40:45,180
was I, I looked at data quality as it
varied by demographic features, how as

687
00:40:45,180 --> 00:40:51,595
it varied by socioeconomic status, race,
ethnicity, languages spoken, et cetera.

688
00:40:52,410 --> 00:40:55,859
We have a significant challenge when
it comes to data equity, um, in the

689
00:40:55,859 --> 00:40:59,580
United States, and not everybody
has their data captured at the point

690
00:40:59,580 --> 00:41:02,129
of care in an equivalent fashion.

691
00:41:02,220 --> 00:41:06,149
And a lot of this has to do with ways
that people utilize the healthcare system.

692
00:41:06,480 --> 00:41:09,390
I think in areas where
there is provider mistrust

693
00:41:09,629 --> 00:41:12,150
I think the emergency department
tends to be a big source.

694
00:41:12,180 --> 00:41:16,740
I think you fail to create a longitudinal
record that tells a full story.

695
00:41:17,040 --> 00:41:21,060
Even for chronic illness it tends
to be an episodic care delivery

696
00:41:21,060 --> 00:41:22,680
model for these chronic illnesses.

697
00:41:22,950 --> 00:41:26,460
And so a lot of times you're not really
getting a robust data capture, and

698
00:41:26,670 --> 00:41:30,270
that's, it's one of the many reasons
why you see such social disparities

699
00:41:30,270 --> 00:41:33,450
in the treatment of chronic illness
is that some people receive their

700
00:41:33,450 --> 00:41:36,180
chronic illness care as a series of

701
00:41:36,315 --> 00:41:39,645
independent acute illness representations.

702
00:41:39,645 --> 00:41:42,255
They go to the emergency department
when they feel dizzy because their

703
00:41:42,255 --> 00:41:43,875
blood sugars are running 400.

704
00:41:44,295 --> 00:41:47,445
Instead of having proactive care
that drives, that helps them so

705
00:41:47,445 --> 00:41:49,125
that their sugars are never 400.

706
00:41:49,125 --> 00:41:53,145
So we need to identify that there's
a substantial data equity piece.

707
00:41:53,835 --> 00:41:57,465
When we're looking at build, you know,
machine learning models, one of the

708
00:41:57,465 --> 00:42:00,944
challenges that exist there is that,
you know, your features may be the

709
00:42:00,944 --> 00:42:04,665
same for two diverse populations, but
the way diagnoses are captured may

710
00:42:04,665 --> 00:42:08,265
be vastly different to the point that
we may underestimate the prevalence

711
00:42:08,265 --> 00:42:12,810
in a population of a certain disease
entity, or because of social stigma, it

712
00:42:12,855 --> 00:42:17,865
may actually present with a different
ICD-10 code than depression, right?

713
00:42:17,865 --> 00:42:23,805
They may have a series of somatic
presentations of, uh, mental illness

714
00:42:23,805 --> 00:42:28,935
that we should have built-in ways in
machine learning models that identify,

715
00:42:29,115 --> 00:42:33,825
hey, this person's now been seen for
palpitations, headache, abdominal pain,

716
00:42:33,915 --> 00:42:35,685
alternating diarrhea and constipation.

717
00:42:36,299 --> 00:42:38,850
Perhaps there's a mental health
issue that we need to address.

718
00:42:38,850 --> 00:42:43,170
Perhaps that should be built into the way
that we think about how this population

719
00:42:43,410 --> 00:42:47,700
in this geography or in this community
tends to represent the way it, you

720
00:42:47,700 --> 00:42:49,799
know, mental health gets represented.

721
00:42:50,220 --> 00:42:53,430
And so I think the data
equity piece is a big one.

722
00:42:53,640 --> 00:42:55,020
We need to understand that.

723
00:42:55,080 --> 00:42:59,919
We need to make sure that we're
considering the cultural context, we're

724
00:42:59,950 --> 00:43:02,140
considering broadened data sources.

725
00:43:02,470 --> 00:43:06,520
You know, not only some of the medical
data, but also some of the social data is

726
00:43:06,520 --> 00:43:08,379
really important for certain populations.

727
00:43:08,799 --> 00:43:12,250
Um, we wanna do even stratified
sampling within some of those

728
00:43:12,250 --> 00:43:16,120
populations to make sure that we're
doing things in a way that's fair

729
00:43:16,120 --> 00:43:17,950
from a, an algorithmic standpoint.

730
00:43:18,370 --> 00:43:21,160
And at times where there's
data that's missing, we need to

731
00:43:21,435 --> 00:43:25,755
understand disease prevalence in
certain populations and even do data

732
00:43:25,755 --> 00:43:29,715
augmentation until we get to a point
where data capture is seen as more

733
00:43:29,715 --> 00:43:31,206
equitable for certain communities.

734
00:43:32,126 --> 00:43:35,636
So you started your career on
the practice side and then made

735
00:43:36,101 --> 00:43:38,951
transition into clinical informatics,
but never really stepped away

736
00:43:38,951 --> 00:43:40,541
from the practice element either.

737
00:43:41,141 --> 00:43:48,371
So for other physicians who are interested
in getting more deeply involved with

738
00:43:48,671 --> 00:43:52,931
building digital healthcare technology
and wanna make that transition either

739
00:43:52,931 --> 00:43:57,821
partially or completely into the
healthcare technology world, what

740
00:43:57,941 --> 00:43:59,736
would you suggest they keep in mind?

741
00:44:01,001 --> 00:44:04,661
Medical education's evolved and has
needed to evolve because of how much

742
00:44:04,661 --> 00:44:06,521
technology that exists in healthcare.

743
00:44:06,971 --> 00:44:09,761
You know, I think that different
schools have evolved in different cases.

744
00:44:10,211 --> 00:44:13,481
Um, I would say that those students of
medicine that are kind of going through

745
00:44:13,506 --> 00:44:17,711
the, the, the programming now take the
opportunity to learn with people that

746
00:44:17,711 --> 00:44:19,121
are doing different things in healthcare.

747
00:44:19,481 --> 00:44:22,571
And they'll feel that because of the
fact that you went to medical school,

748
00:44:22,571 --> 00:44:25,961
that there's only one traditional way of
practicing medicine and delivering, you

749
00:44:25,961 --> 00:44:28,451
know, value from a healthcare standpoint.

750
00:44:28,481 --> 00:44:31,061
I think there's a lot that
needs to be fixed in healthcare.

751
00:44:31,391 --> 00:44:34,571
Um, there's a lot of policy
issues that that PD addressed.

752
00:44:34,691 --> 00:44:37,271
Um, there's a lot of the different
ways we transac healthcare.

753
00:44:37,571 --> 00:44:40,841
There's a lot of gaps when it comes
to care and ation, and it's one of the

754
00:44:40,841 --> 00:44:44,141
things that I was kind of mentioning
is previously, uh, patients don't know

755
00:44:44,141 --> 00:44:47,471
how you use healthcare in the United
States, like I had the opportunity to

756
00:44:47,471 --> 00:44:52,391
work in an urgent care center like it,
it was a mixture of people that truly

757
00:44:52,391 --> 00:44:55,631
needed to be there because they had
something that acquired urgent care.

758
00:44:56,036 --> 00:45:00,176
And then I would say the other half of
the people were people that probably

759
00:45:00,176 --> 00:45:04,256
either to be an emergency department and
passed six of them along the way, uh, in

760
00:45:04,256 --> 00:45:05,876
order to get to this urgent care center.

761
00:45:06,236 --> 00:45:08,486
And there were, there were people
that would come in and they had

762
00:45:08,486 --> 00:45:11,186
six months worth of the complaints
that probably could be better dealt

763
00:45:11,186 --> 00:45:12,356
with with a primary care doctor.

764
00:45:12,986 --> 00:45:14,906
People don't know how to
use the healthcare system.

765
00:45:14,906 --> 00:45:19,226
I think that we as clinicians can
be empowered as of getting people

766
00:45:19,226 --> 00:45:22,616
to, you know, use it in, in a way
that's contextually appropriate.

767
00:45:23,126 --> 00:45:26,786
And then obviously, you know, in many
of the conversations that we had, you've

768
00:45:26,786 --> 00:45:31,166
had, there's a gap in, in, in terms
of the resources that are necessary

769
00:45:31,166 --> 00:45:36,116
to provide care to whatever complex
and ever aging, you know, population.

770
00:45:36,116 --> 00:45:39,566
And so technology's gonna have
be the way to, to mitigate that.

771
00:45:39,566 --> 00:45:46,346
And so really identifying that you can
provide value with your clinical know-how,

772
00:45:46,616 --> 00:45:51,536
in a way that's non-traditional is really
very, very valuable for, for, you know,

773
00:45:51,536 --> 00:45:53,126
clinicians of medicine to understand.

774
00:45:54,656 --> 00:45:59,006
And then the opposite of that, for
all the non-clinicians in the audience

775
00:45:59,066 --> 00:46:04,946
who want to get involved in healthcare
technology and improving the way that

776
00:46:04,946 --> 00:46:09,206
we deliver care, whether it's in,
in this country or globally, where

777
00:46:09,206 --> 00:46:12,986
should they focus, you know, as they
build their foundational knowledge of

778
00:46:13,361 --> 00:46:16,421
clinical informatics and
interoperability in healthcare, AI?

779
00:46:17,291 --> 00:46:21,801
Some of the more successful companies,
um, when, when it comes to tech, new

780
00:46:21,821 --> 00:46:25,361
technology companies, some of the more
successful companies in healthcare

781
00:46:25,931 --> 00:46:30,161
are first and foremost the health
solutions company, and they have the,

782
00:46:30,191 --> 00:46:33,201
the, the key clinical pieces in place.

783
00:46:33,791 --> 00:46:35,381
Healthcare is nuanced, right?

784
00:46:35,381 --> 00:46:40,421
It's, it's not like there's a binary
outcome for every compilation of tests.

785
00:46:40,811 --> 00:46:44,501
Not every test combination
adds up to the same diagnosis.

786
00:46:44,501 --> 00:46:49,241
There's a lot of many to one, one to many
relationships that exist in healthcare.

787
00:46:49,511 --> 00:46:52,721
There's a lot of interrelationships
that exist and there's no one

788
00:46:52,721 --> 00:46:54,281
way that a diagnosis presents.

789
00:46:54,281 --> 00:46:56,111
There's a lot of nuance
to how they presents.

790
00:46:56,141 --> 00:47:01,426
There's not a one size fits all treatment
when it comes to, you know, hypertension.

791
00:47:01,806 --> 00:47:07,181
We often look at people with hypertension
who have other comorbid illnesses, may

792
00:47:07,181 --> 00:47:11,036
respond to different anti-hypertensive
medications, may actually have those

793
00:47:11,036 --> 00:47:14,456
anti-hypertensive  medications treat
some of their other comorbid illnesses

794
00:47:14,666 --> 00:47:16,406
if the right medication is selected.

795
00:47:16,766 --> 00:47:22,436
So as people that are studying technology,
studying data science, studying machine

796
00:47:22,436 --> 00:47:27,386
learning, AI, et cetera, understand
that medicine is binary, it's nuanced.

797
00:47:27,446 --> 00:47:30,956
Um, there's a lot of drivers of medicine
that are not just simply, purely based

798
00:47:30,986 --> 00:47:34,586
on, you know, how someone presents what
diagnosis they have of what treatment

799
00:47:34,586 --> 00:47:38,606
they receive, but there's a whole lot of
interplay from social factors, et cetera.

800
00:47:39,056 --> 00:47:45,056
Having clinicians on board to learn,
knowing, knowing how to talk the talk and

801
00:47:45,056 --> 00:47:48,806
walk the walk of, of, of the clinician
is important to adoption, right?

802
00:47:48,806 --> 00:47:53,311
Because there are stakeholders that
oftentimes may get overlooked and these,

803
00:47:53,576 --> 00:47:57,416
these technology solutions get implemented
in these, you know, hospital systems and

804
00:47:57,416 --> 00:48:01,136
provider offices, et cetera, and they're
the ones that could be part of your

805
00:48:01,136 --> 00:48:03,066
greatest success on a go forward basis.

806
00:48:03,066 --> 00:48:09,791
So, you know, I would say understand
the limitations of AI, the biases

807
00:48:09,791 --> 00:48:14,171
that exist in AI for something as
convoluted as as the medical sciences

808
00:48:14,561 --> 00:48:16,751
and learn from those medical scientists.

809
00:48:16,781 --> 00:48:20,981
The healthiest organizations I've been
a part of, I've taught as much medicine

810
00:48:20,981 --> 00:48:22,661
as I've, as I've learned data science.

811
00:48:22,991 --> 00:48:25,631
I've taught as much medicine as
I've learned standards, right?

812
00:48:25,991 --> 00:48:30,251
Um, and I think that that bi-directional
exchange of information really makes

813
00:48:30,251 --> 00:48:34,431
growing organizations and really
deliver, delivers the best ROIs

814
00:48:35,561 --> 00:48:40,541
So, uh, if our listeners could take, uh,
one thing away from this conversation

815
00:48:40,541 --> 00:48:43,451
that we've had over the last hour or
so, what would you like that to be?

816
00:48:44,831 --> 00:48:46,271
So, I think the time is now.

817
00:48:46,541 --> 00:48:50,441
Setting aside adoption, setting aside,
you know, some of the challenges that

818
00:48:50,441 --> 00:48:55,601
exist from the bias standpoint, setting
aside some of the, uh, challenges that

819
00:48:55,841 --> 00:49:02,471
exists from patients and providers
and technology solutions, et cetera.

820
00:49:03,176 --> 00:49:07,406
We need to reform the way healthcare
is delivered in a way that's equitable,

821
00:49:07,406 --> 00:49:09,986
in a way that's thoughtful and
a way makes the right diagnosis,

822
00:49:09,986 --> 00:49:11,126
prescribes the right treatment.

823
00:49:11,546 --> 00:49:14,186
At the end of the day, technology
enablement are really related.

824
00:49:14,816 --> 00:49:21,326
Um, and having the right organizational
mixture of clinicians and technologists

825
00:49:21,536 --> 00:49:26,881
at a company is vital key to success,
not only of that company, but to the

826
00:49:26,881 --> 00:49:28,081
entire healthcare delivery model.

827
00:49:28,331 --> 00:49:28,991
Thank you very much.

828
00:49:30,431 --> 00:49:31,011
Dr. Pinho.

829
00:49:31,031 --> 00:49:33,161
Thank you so much for being
with us this afternoon.

830
00:49:33,551 --> 00:49:36,521
I think that this conversation is
gonna provide a lot of great context

831
00:49:36,521 --> 00:49:38,981
for listeners as they continue
their own journeys in health IT..

832
00:49:40,241 --> 00:49:43,451
This has been Hard Problems, Smart
Solutions, the Newfire podcast.

833
00:49:43,841 --> 00:49:46,571
Thanks for listening and come back
next time when we'll be diving

834
00:49:46,571 --> 00:49:50,051
into interoperability strategy and
digital health product management

835
00:49:50,291 --> 00:49:51,671
with podcaster Omar Muza.