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This is Laura Dierda with the Becker's Healthcare
podcast. I'm thrilled today to be joined by
Tur Guy Ayer, professor in the school of
industrial and systems engineering at Georgia Tech. Tur
Guy, it's a pleasure to have you on
the podcast today.
It's my pleasure. Thank you for having me.
Now I'm looking forward to our conversation and
really digging into some of the cool things
you're doing, within your research at Georgia Tech.
But before we dive in, can you tell
us a little bit more about yourself and
your background?
Absolutely.
So I was trained as a decision scientist.
I did my t PhD
thesis on breast cancer diagnosis using artificial neural
networks.
And I work extensively with radiologists and some
other physicians. Even though I was trained as
an engineer,
I was trained almost like a pseudo doctor.
And as for my background, I was raised
to be a physician by my parents. They
really wanted to want wanted me to be
a physician.
And up until
junior high, I was thinking of being a
physician.
But my sister is a veteran medicine,
doctor, and and I was helping her when
I was in high school, and I fainted
as soon as I saw some blood.
And it made it obvious that I will
make a good physician. So I decided to
study engineering
because I was good at math. I I
liked, you know, analytical thinking.
And I had a lot of, you know,
I I I had a lot of trials
and errors, in my in my studies. I
start as a double e,
major and then switched to computer science
in my junior year
in college.
And only my senior year, I,
figured out a discipline called operations research within
industrial engineering,
which is a science of
decision making through mathematical modeling and and data
driven approaches.
So I ended up majoring in operations research
and did a PhD
in operations research slash industrial engineering focusing primarily
on on health care applications.
Fascinating. That's, you know, such a a cool
journey into the career you have today and
and really meaningful research you're doing. Can you
share a bit about that research and why
it's important?
Absolutely.
So I would summarize my research as,
data driven mathematical
slash computational modeling for important medical decision making
and health policy analysis.
Oftentimes, in clinical decision making
and health policy making, decisions are too complex,
and you need a framework for the systematic
decision making
while incorporating
data to capture important analysis.
And and such decisions become too complex for
human beings. To me, it's more like making
multiplications on the order of millions.
When you ask such a question to a
computer, it's pretty straightforward.
But as humans, if we try to do
these computations in our head, it becomes pretty
challenging.
So I help that building
frameworks, mathematical
computational frameworks
using utilizing data,
to help it solving such important,
pressing clinical and policy problems.
That's fascinating to hear. And, you know, really,
such a unique angle to be part of
the health care space as you mentioned using
your mathematics background and certainly,
you know, your your talent for,
computation to get this into,
the health care field.
Now I'm curious. So what challenges in health
care are you aiming to address through this
research?
Absolutely.
So in health care, the goals health care
research, the gold standard is randomized controlled trials.
In randomized controlled trials,
researchers do AB testing, meaning that, you know,
if there is a new intervention, new drug,
new technology,
clinical trials are designed to to assess if
the new technology, new intervention, let's say, a,
is better than the status quo b.
And in such randomized controlled trials, they are
typically expensive, very expensive, millions of dollars. They
are time consuming.
And and also it takes a lot of
efforts,
to conduct these clinical trials, recruit patients into
those trials. And typically, they are short term.
Typically, you know, the trials
last for about three to five years.
And
by the time the trials are completed, we
have answers to the question of whether a
is better than b. But oftentimes, in in
in practice, we are looking at multiple options,
not just a versus b. We are looking
at a versus b versus c versus d.
And and sometimes these competitors are more than
ten, fifteen, 20.
And it's not plausible to
assess those mini interventions in a clinical trial
setting because, a, it will be extremely,
costly,
and, b, it will be very time consuming.
And, c, it perhaps wouldn't be feasible to
recruit,
thousands of patients into such a trial.
Because and and the last limitation of randomized
controlled trials is that, as I mentioned earlier,
their time span is typically short, and oftentimes,
you would like to understand longer term implications
of interventions.
And at that point,
our research chimes in and and helps with
understanding some longer impacts
of such
interventions.
We collect evidence. We basically analyze evidence coming
out of randomized controlled trials, published studies, meta
analysis, real world evidence,
and build virtual trials,
meaning that, you know, we replicate
these trials in computer settings.
We validate these computational models.
And once validated that, they they they become
a test bed for us to ask a
lot of interesting what if questions
in the short term. So we are no
longer limited to three years or five years.
We can expand the time horizon to twenty
years, thirty years lifetime.
We are no longer limited to, you know,
a versus b. We can compare multiple,
interventions.
And and that gives us a lot of
flexibility to, again, ask a lot of interesting,
pressing policy or clinical level questions.
That's amazing to hear. You know? And certainly,
really cool to see that evolution of how
you can approach clinical trials in a really
meaningful way, especially
knowing how important they are in advancing medicine
and the treatment that is provided for patients
across the board in a variety of, settings,
as well as, you know, just the the
demographics of the patients can be so different.
How do you approach conducting research to ensure
that it has the kind of real world
impact on patient care? I know a lot
of the things we've been talking about,
you know, are looking at the design of
the trials and those kinds of things. And
so how do you see that impact?
So there are a couple of ways that
we ensure that our findings are practically useful.
One, we
collaborate extensively with it, end end users, stakeholders,
typically physicians, policymakers,
depending on the nature of the project, and
that is useful for multiple purposes. One, we
ensure that we capture important clinical nuances,
medical nuances that we may not be aware
of. So we have the clinical slash policy
expertise.
And two,
because it's a co collaborative
development, collaborative research,
those decision makers, end users are engaged early
on in the process,
and we deploy our solutions to those end
users.
And once they believe in the value, once
they see what these solutions tools could do
for them, they are typically the champions to
take take these solutions to the next step
and, you know, bring it to clinical implementation
or use it as a policy policy tool,
etcetera.
And the last thing that we do is
we ensure that our findings
are valid.
Before we deploy any solutions,
we do a lot of stress testing
compared against any available
real world data.
So it's
just to ensure that, again, the results are
clinically meaningful, and we also have the binds
from these end users.
That's helpful to hear. Thank you so much
for digging a little bit deeper there. Now
I'm curious. What are some of the big
obstacles that you face while you're,
doing this type of research and trying to
make sure that it's deployed in a meaningful
way?
Data challenges
is is a big issue. Oftentimes, health care
data is fragmented,
and they are kept in silos.
You don't oftentimes, you don't have national level
databases,
access to that data. You know, even even
if it is local level data may be
a challenge. We have to go through a
little bit pool processes, and
and in the end, it may not work
out. So as an example, you know, we
have recently approved
to have access to a data
all by,
kept by CDC.
And because of these communication freezes CDC is
going through, we basically even though our our
data access request has been approved after several
months of back and forth communications
and documentation, you know, report preparation, etcetera,
basically, this is this is on freeze now.
We won't be able to access this data
until this uncertainty is resolved.
So accessing the data,
compiling, augmenting datasets, and and also
validating, you know, these
including missing data, validating these datasets is is
one of the challenges. And and,
sometimes communication
gaps with the end users is a challenge.
Yeah. I would I would list these two
as the biggest challenges.
That's helpful to know. And, you know, certainly,
it seems like,
having that reliable data is a challenge for
a lot of health care organizations, as you
said, and and really truly something that, can
make a big difference,
within
the the research and development in,
many fronts, but but certainly with the work
that you're doing as well. I I think,
you know, when you look at that communication
aspect of it,
too, you know, is there anything you found
that has worked particularly well in,
communicating
some of the research and the data, to
those who, you know,
need to know about it or or would
most benefit from it,
or is that still very much a work
in progress?
Absolutely. So in one of our research projects,
we work with radiologists,
breast imaging radiologists,
from from one of the hospitals in Wisconsin.
And we were basically the key question research
question we were asking was whether
machine learning models, artificial intelligence models was useful
in diagnosing breast cancer based on mammographic images.
So in other words, radiologists look at suspicious
mammograms,
or or they look at a set of
mammograms if if and if they notice any
suspicion such as, you know, some some mass
some messes, calcifications,
distortions,
asymmetry, etcetera,
then they may refer them to a biopsy,
to
see if if patients have a tumor, a
malignant tumor, and and they need breast cancer
treatment.
And it turns out that this decision making
process is is very difficult and in more
process. There are, like, you know, over 50
risk factors that physicians need to consider.
And
and you need to again, to me, this
is like making multiplications on the order of
millions. And it turns out that about ninety
eight percent of the biopsy cases
are, false positives, meaning that out of ninety
eight percent of all mammograms referred to biopsies,
they are in reality, you know, just okay
mammograms. Right? Nothing is wrong with them. But
think about the psychology of a patient.
Right, when you tell them that they have
a suspicious mammogram and they need to schedule
a biopsy,
those biopsies are typically not scheduled for tomorrow.
Right? You need to wait for two, three,
four weeks, sometimes months,
to get these biopsies done
and on top of it further wait to
hear about the results. So you go through
a lot of stress. Right? You know, you
you go through a lot of stress. Your
your family go goes through a lot of
stress.
And what we did is
we basically,
built a an artificial neural network model,
and we asked the key for the the
following key question. Right? Could an artificial
neural network model, a form of an, artificial
intelligence, machine learning models,
help with biopsy decision making
using findings or extracting data from mammographic images.
And it turned out that and and and
our results we published our results in in
a clinical journal, radiologist journal. We thought, you
know, physicians, radiologists
would be reacting to those findings because our
findings indicated that
our machine learning based solutions
could perform
better than majority of the radiologists
in our setting. Specifically,
seven out
of eight radiologists,
seven of them performed worse than the artificial
neural network model. So meaning that a AI
model
was more successful
in detecting abnormalities on mammographic images as compared
with those seven out of eight radiologists.
Whereas one radiologist performed significantly
better than the AI model.
That's
that radiologist who stood out had thirty two
years of domain experience, whereas the other seven
radiologists
who are outperformed by the AI model had
an experience ranging from two to ten years.
And we positioned our paper in a way
that we we we we basically,
you know, highlighted the key message that
AI based solutions may complement physicians, especially junior
physicians, to help it, you know, to to
act as a second reviewer or a second
reader,
especially in the resource limited settings.
The doubles double screening in radiology is very
common, meaning that, you know, a radiologist first
reads an image. And if the radiologist is
is unsure,
he or she consults with a second radiologist,
and then,
they discuss and make a decision together. But
in the resource limited settings, that's not feasible.
And we positioned our paper study in a
way that we we emphasized that in resource
limited settings,
AI based solutions could act as a second
screener, second reviewer.
And
to our surprise, pleasantly, our findings are very
well received by by the radiology community. Right?
Radiology community is not trained
as as AI experts.
But even though, you know, the findings
could have sort of, like, to to to
some extent threaten their their their their discipline,
they were very positive, very enthusiastic in embracing
our findings. And we they they ended up
accepting our paper,
to get published in in one of their
flagship journals in their communities.
This is just one example.
I can think of several such examples where
our findings were very well received by the,
by by the corresponding communities.
That's a really, powerful example, and thank you
so much for, explaining,
deeper there and,
telling us a story because I think, you
know, it's it's really cool to have that,
frame of reference.
Now have there been any surprising or unexpected
findings that you've had within your research?
Yes. Many of them. I'll just mention maybe
one or two.
In one project, we were looking at, hepatitis
c elimination.
Hepatitis c is an infectious disease, and it's
a prevalent disease. As as a matter of
fact, as of 02/2006,
the number of people dying from hepatitis c
in The US has surpassed those dying from
HIV.
Right? So when I when we think about
HIV, I don't think we need to make
a case that HIV,
has a has a big burden on the
society.
And it turns out that as of as
of, you know, as of 02/2006,
hepatitis c has even a bigger burden on
the society.
And
because it has a such a so it
has such a big disease burden, I was
I was interested in, you know,
doing some modeling analysis in that space. I
elaborated with epidemiologists and physicians and decision scientists
in this work,
from Emery Rowland School of Public Health, Harvard
Medical School, among others.
And the key research question in one of
the projects, the key research question we were
asking was the following.
Does it make sense to treat hepatitis c
in correctional facilities? Does it make sense to
provide access to vast access to hepatitis c
treatment in correctional facilities,
or should it be limited in some ways?
And the reason why this question was relevant
is that by the time
the initial hepatitis C treating drugs, direct acting
agents came to market around early 2000s,
many of the correctional health facilities were limiting
access to hepatitis C treatment,
because hepatitis c treatment
by then and and even still,
was was quite expensive.
By the time they came to market,
the single
pill cost per day was about $1,000
per pill, and patients were expected to be
on treatment for twelve weeks. So meaning that,
you know, by the time the treatment is
completed,
the per patient cost would be about $84,000.
And even though correctional facilities receive some discounts,
even after discounts,
a study has shown that
if correctional facilities provide a treatment to all
hepatitis c infected patients,
the total cost would be higher than the
entire health care spending budget allocated for correctional
facilities.
And and and inmates are not suffering only
from hepatitis c. Right? They could be infected
with with HIV. They may have chronic,
diseases such as cardiovascular
disease, diabetes, etcetera.
From a resource allocation perspective,
it it it was very clear that
correctional health systems wouldn't be able to provide
universal access to all inmates. And it was
a very controversial topic. As a matter of
fact, many prison many, many inmates are suing
state prison systems,
for for for not providing them access to
hepatitis c treatment.
And our key research question was the following.
As I mentioned, I'll be repeating myself. Does
it make sense to expand health care spending
for correctional facilities
such that
access to hepatitis c treatment could be expanded,
for inmates behind the bars.
And, this was a politically controversial question because,
you know, because you know, I mean, I
don't wanna get into too much into into
political discussion. But as you may imagine, some
some politicians are arguing that, you know, there
is no point of further expanding health care
spending,
for correctional facilities.
And what we did in our in in
our analysis is we have taken a radical
approach. Right? We have asked the following question.
Right?
We have taken a radical approach and asked,
what happens if we expand
treatment to hepatitis c
in correctional facilities from a societal perspective
for the for the for the community, right,
outside the bars.
And we have shown that
it makes
a lot of
economic sense to treat hepatitis c
while,
in the correctional facilities because the idea is
the average time spent in behind the virus
is less than five years.
Correctional facilities provide an ideal setting for identifying,
infected inmates,
treating them and linking them to care. Outside,
you know,
in the outside community, it's very harder to,
diagnose patients. The prevalence is very lower. And
also linking patients to care is more difficult.
In correctional settings, it's almost like a laboratory
environment, so linkage to care is way higher.
And through, again, validated
mathematical and computational models, we have shown that
from a resource allocation standpoint, if you are
going to spend as a society, if you'll
spend any resources on hepatitis c treatment and
elimination,
it makes a lot of sense,
rational economic sense to increase spending
for for
for reducing hepatitis c infections and treating hepatitis
c,
in the prison settings, community,
in the in the correctional health settings, again,
because of this intermingling happening upon release of
these inmates.
This was somewhat a contrary to finding in
the sense that
on one hand, you would think that, you
know, it may not make sense to further
increase health care spending for correctional stand you
know, settings. Again, this is not my standing,
but there are people who believe that way.
On the other hand, we have taken this
radical extreme approach and have shown that even
if you
don't care about the lives of these inmates,
which is obviously
unethical and doesn't make sense, but even if
you have taken such an extreme approach,
it still makes rational
economic sense to prioritize,
treatment access
in the correctional settings from a societal perspective,
because return on investment
in such cases is higher because, you know,
detecting those patients and treating these patients and
linking them to care is easier in correctional
settings.
So this is one example. I can I
can keep going? I can share more examples
if you'd like.
If I can share a second one and
stop that.
A second example is is in another project,
we looked at,
Medicare Advantage Plans. Medicare Advantage Plans are basically,
you know, Medicare plans
offered by private insurances. What what what happens
is Medicare sort of outsources
the provision health care of health care services
to to private entities, such as UnitedHealthcare may
be providing coverage for Medicare patients, and it's
part it becomes part of the Medicare Advantage
Plans.
And it is well established that it is
somewhat well understood in the published literature that,
Medicare Advantage Plans
tend to do risk selection, meaning that they
select profitable patients,
through some selection mechanisms.
And the well under there is this well
understood notion that, you know, they may be
spending
money
allocated
for high risk patients towards treatment of sorry.
Money allocated
for low risk patients towards treatments of high
risk patients, and it's referred to as cross
subsidization.
In in one of our research papers, we
have shown that
the other way around is also correct, meaning
that medic Medicare Advantage Plans tend to utilize
resources allocated for high risk patients
to affect low risk patients into their plans,
you know, and through mechanisms such as such
as,
reduced gym memberships. Right? You know, some Medicare
Advantage Plans offer reduced gym memberships.
And if you think about reduced gym memberships,
it sounds great. Right? You know, you would
think that, you know, this is a great
add on perk that they're offering, but when
you carefully think about it,
carefully think about who would be interested in,
gym membership
among elderly population. Right? We are talking about
Medicare patients, patients over age 65.
An individual suffering from multiple chronic conditions
and not more you know,
not easy to you know, not having a
hard time with movements, etcetera,
wouldn't be so much interested
in gym membership.
But healthy physically healthy,
physically active,
yeah, older individuals,
would be more attracted to gym membership, reduced
gym memberships.
Even though at first, it sounds like a
great perk,
in reality,
it is a selection mechanism. Right? It is
a it is a mechanism primarily designed for
attracting
relatively healthy healthier older adults into their population.
And we have shown that they are using
basically these resources allocated for these relatively healthier,
individuals,
towards treatment of sicker patients.
Wow. That's fascinating to hear. You know? And
really, really,
interesting to understand the applications of your research
and dig a little bit deeper here. Jorge,
thank you so much for joining us on
the podcast today. I I really appreciate your
time, and I look forward to, connecting with
you again soon. Thank you so much for
the opportunity.