- This is Chris SOA with the Becker's Healthcare Podcast.
I'm thrilled to be joined by Dr.
Hamel gda, president of the UPMC Heart Vascular Institute.
Dr. gta, thank you for joining us today.
- Oh, thank you so much, Chris, for having me.
- Of course. Could you please introduce yourself
and tell us about the Heart Vascular
Institute in particular?
- Yeah, I am, uh, interventional cardiologist
and so I'm, I'm used to doing less invasive, uh, heart
and vascular procedures on patients that require, uh,
a whole host of different levels of management.
Uh, but I oversee a very specific sector of the UPMC,
heart and Vascular Institute here in central Pennsylvania.
And, uh, we're located with our hub in Harrisburg,
but we have seven hospitals that kind of serve the hub,
uh, in about a 50 mile radius around Harrisburg proper.
In addition to that, we have 20 plus cardiology offices in
that area, and we're part of the larger UPMC system,
which is of course based in Pittsburgh, Pennsylvania.
Uh, but we have kind of tentacles that reach north, south,
east, and west all throughout the state of Pennsylvania.
So we're very large healthcare system, one
of the largest in the United States.
- Doctor, thanks for detailing that. I should say.
Uh, today we have you on talk about cardiovascular disease
and how that sorts with ai.
Alright. My first question would be why is early detection
of cardiovascular disease so important
and can you briefly explain how AI helps
cardiologists with early detection?
- Yeah, I think we're, we're kind of framing the discussion
to talk more about
what would be considered preventative healthcare,
and of course a problem is best extinguished
if you never have it.
So we here at UPMC are committed to working continually
to eliminate or reduce the risk
of preventable harm to our patients.
Um, we definitely take a proactive approach across our
healthcare system that focuses on creating a care plan
with the best possible outcomes for all patients.
Cardiovascular diseases clearly a very large
threat to human health.
It's the leading cause of death nationwide.
And of course in the central Pennsylvania region
that we service, um,
heart disease deaths nationwide increased
and have been increasing on an annual basis since
the late 20 teens.
Um, so we need better strategies to diagnose
and treat cardiovascular disease in the future.
I think artificial intelligence detection algorithms are
going to have a lot of potential to solve this problem.
And so really the whole concept here is to use the data
that we have in the electronic medical record,
identify patients at risk
of certain cardiovascular conditions, inaugurate
and advance their care within the system
to prevent them from having burdensome outcomes.
- Thank you for that Dr. Gada.
Next, uh, I would ask you,
can you tell us about the AI tools that you're using at UPMC
and explain how they're used?
- Yeah. We've used several different, um, AI tools,
if you will, and a lot of them are using our electronic
medical record and all of the power that it gives us
to really look into our individual health profiles as far
as our patients are concerned
and discern any risks that they have that would lead them
to develop more severe conditions.
And some of that is just disease detection, if you will.
And so, uh, I work a lot
with valvular heart disease patients.
Patients that have problems with their heart valves
and detecting these problems before they cause symptoms
or in patients who have milder symptoms is something
that's not overtly done with clinical visits.
You know, primary care providers,
cardiologists are really strapped
for time in these visits slots that are literally like 15
to 20 minutes long.
And so having a very powerful electronic medical record like
we have that is able
to garner information from cardiac ultrasounds
that the patient may have had other types of ultrasounds
or non uh, invasive assessments, we take that information
and we're able to basically qualify
or quantify the severity of certain valve diseases.
And so then we're able to get those patients seen by one
of our providers earlier than they otherwise would have
and advance therapeutic options in a way
that they wouldn't have had, you know, the advantage
to partake in if it wasn't for that AI algorithm kind
of kicking in for this type of disease detection.
- Gotcha. Uh, looking a little further into this, Dr.
Gada, could you tell us about some of the patient
outcomes you've seen while using
these machine learning models?
- I think what we've noticed, Chris, is that we've been able
to avoid emergent
and urgent hospitalizations in,
in a really, you know, great way.
And so when we started using the electronic medical record
like this, and this is now over five years
that we've been doing, you probably have more experience
with electronic medical records
and artificial intelligence therein than any other health
system in the state of Pennsylvania that we've been able
to avoid some harmful consequences such
as sudden emergency department visits,
patients developing critical conditions
that would've been prevented had they been detected
earlier in milder forms.
And so being able to stave off those emergent
life-threatening presentations is kind
of the most important, I think, benefit
that we've garnered from our artificial
intelligence algorithms here.
Uh, but in addition to that, you know, kind of
making the patient aware that we are always on top
of their health and are using all the tools that we can
to make sure that they get the best longitudinal follow up.
You know, they don't necessarily need to see us in person
in order for us to be able to track what's going on
with some aspects of their health.
And so that lag between getting diagnostic information
from a test and having that conveyed to a patient
and then a follow-up visit
where the patient hears more about it, we can make
that much more efficient if we've got flags in their chart
and in the electronic medical record that alert us to
critical, severe, moderate conditions that they develop
and then we can kind of troubleshoot those right away
and save the patient and the system a lot of time
by getting those patients seen earlier
and getting them triaged more effectively.
- Yeah, as you said, doctor, hopefully patients well,
I imagine patients are very grateful when they're able to,
uh, learn about what is needed
for their treatment sooner rather than later.
Uh, the last thing I'll leave you with Dr.
Gada is what in your field you think deserves the brightest
spotlight than it's getting at the moment?
- I, I think it would be that, and,
and I think that the pathways of care that have kind
of been historic and have led to a lot
of inefficiency in healthcare
and maybe a lot of frustration both from providers as well
as the patients that we can stem a lot of,
that we can cure a lot of that, alleviate all that burden
and create more efficient way of, of providing healthcare
by invoking these artificial intelligence, uh,
type approaches, especially those
that are embedded in our electronic medical record
for early detection of critical diseases
or burdensome conditions
and being able to get patients through the system quicker.
And so I do think that we need to put more emphasis on that.
And honestly, I think
that our payment policy structure should be engineered
to provide, you know, further, I would say impetus
for physicians
and for healthcare systems to partake in that.
Um, this is something
that I don't think is being done adequately at the moment.
And so there needs to be adequate incentive
for healthcare systems to invest time
and for physicians to invest time in order to
inaugurate these types of, of technologies
and again, make, uh, better healthcare,
more efficient healthcare for our patient populations.
- Dr. Got it. Thank you for being so generous
with your time and insights today.
We cannot wait to share them with our audience
and we look forward to connecting with you soon.
- Thanks so much, Chris. I really enjoyed this.
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