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Imaging, data reporting and the cardiology user experience

How can cardiovascular imaging departments leverage user experience design to address the challenges of fragmented data?

Hello and welcome to our program today.

We're discussing clinical excellence

and cardiovascular imaging. I'm

your host, Jerry. Big naughty.

Today's topic is about the importance of

the user experience in the adoption

of structured reporting, as well as in addressing

the challenges of turning fragmented

data captured in the cardiovascular

service line

to drive outcomes and insights.

Our guests on this program include Dr

Jennifer Hall. Dr. Hall is

a PhD and chief of data

science and the co director of the

Institute for Precision Cardiovascular

Medicine at the American Heart Association.

She leads the association's groundbreaking

initiatives in cardiovascular medicine.

Welcome, Dr Hall.

Thank you, Jerry.

Also joining us is Dr

Scott Flam, an M D. Who's head

of the section of cardiovascular imaging

at the Cleveland Clinic Imaging

Institute.

He's certified in cardiovascular

magnetic residents and in protecting

human research subjects in biomedical

and genetic research.

His specialty interests include cardiovascular

MRI, cardiovascular CT,

ischemic heart disease and congenital

heart disease.

Thank you for being with us today, Dr Flam.

Pleasure to be here. Cherry. Thank you,

Dr Flam. Obviously, a person with your

background and expertise has a keen

interest in cardiovascular imaging.

Let's talk about the importance of the

user experience in C V

imaging. Well, gosh, that's I

think, a pretty easy one in that,

uh, let's go back to I'm going

to date myself here, Jerry, and say that I'm

one of those people who used to have a Palm Pilot

many years ago. And it was a way we

could store information in our portable

phone roll, a Dex sort of thing,

but just absolutely paled

compared to either an

iPhone or an android phone that we have

today that allows us to do so much more

because the interfaces are so much

more intuitive and simple and efficient

to use. And the same thing is true with

the tools that we use in cardiovascular

imaging. Uh, I'm

particularly a dept and

have a focus on cardiovascular CT

and MRI,

and we've got these great scanners that

put out great information. But when we get

to the point of processing the information,

integrating that, converting that into

quantitative information, it

can sometimes be a little bit complex

and a little bit more difficult

to make that happen. So we really want to make

this as efficient as possible

and simple as possible. and so often,

really, the mantra should be, you know,

truly keep it simple. It's that

the simplest interface possible

to allow us to put out all

this information is really the most useful

because people will use it that well, what about

things like auto population of data?

How important is that? Let

me give an example of an

area that I think is really terrific.

And I think our echocardiography furs

have really done a great job with this

and that, uh, the

the analysts, the technologists who

the stenographers who acquire the information

are able to put the information

together and

really almost auto populate.

It sends the data from

their input devices directly to

a report. And so the

report is partially auto populated

by the time the physician

gets to it and starts getting around to it,

and we have only a very little bit

of that In other parts of cardiovascular

imaging, CT and MRI are just really

not like that at all. We're

working on it, and we've got a number of

vendors that are trying hard to make that

more integrated. It's just that there are a lot

of complicated steps along the way, So I think

we need to follow the pathway that

are echocardiography. Folks have done

and expanded into all of the

other imaging CT and MRI

and including the Cath lab and the

electrophysiology lab.

So it's clear from what you've said that workflow

is important and

and structure reporting,

uh, in cardiovascular imaging.

Dr Hall, what are the main elements that promote

clinical workflow excellence

and through that could help drive adoption

of these systems? I think

Scott said it best, and that is just

keeping things as simple

as possible and sometimes

keeping things simple

sounds easy, but understanding

just simple things about

where to put the button that they click. How many

clicks it's going to take? Is the

data auto populated so

they it just automatically

happens,

and we make the clinicians job

easier. Technology is meant to make

things simpler

that we have to remember. It's meant to fade

into the background and simply allow the

clinician to do their job well.

Trying to make it simple sometimes

makes us call upon the latest

technology and recently developed

tools,

and I'm particularly

interested in your take on it as a data scientist,

How you see artificial intelligence

driven tools and capabilities,

improving the clinical workflow efficiency

as well as the impact on the adoption

of structured reporting. It's exciting.

The field is certainly exciting. It moves

so rapidly that you know

just what was happening last week is

outdated. Today,

the point to remember about

AI and machine learning is it's there to

again help the clinician.

It's not meant to be the final be all

answer,

Um, but

use that technology and then

put it in the background and only

put out there what the clinician needs

to see.

So what I mean by that is data Scientists

may have

very challenging algorithms

and a lot of math behind

the ML, um, and AI.

But what the clinician needs to see

is simply the output, you know, where

do I click

to get X? And if they want more information,

the documentation is there and

transparent. But it is not

the first thing that they see. We need to be able

to make it simple to utilize,

you know, ai and machine learning to make their lives

simpler and to improve patient lives

as well. You mentioned the clinician perspective.

Let's turn to Dr Flam for that.

Well, I I would just add on

to what Dr Hall was saying

and that this is really a matter of

pulling more information in

to the clinician with the

imaging information or whatever

else what other kind of diagnostic

information was pulled in but be

able to pull in the relevant clinical

information about that patient's so

often we may interpret

an image or piece of diagnostic

information and a relative vacuum.

Uh, and we really want to have that

be a whole to

be able to have the relevant information

regarding that particular patient

that is recent and relevant

and will have an impact on

the diagnostic information that we've

we've been presented. So

I think this is where AI and ML can

really be valuable as well,

by integrating closely with the electronic

medical record and the background information

about the patient.

So when it comes to optimizing the user

experience and and thereby

maximizing the adoption of

these systems, Dr Flam

return to Europe for that.

Some advice, I guess. What should

imaging it vendors be doing

to optimize the user experience and

maximize the adoption of their system?

I think The thing that I worry about

sometimes when vendors

put together products is that

they may focus on

what is the minimum viable

products. You know what is the minimum

thing that can be put out there, too?

Bring this information. And

while that's a practical way to go

and sometimes can allow us to get

the post processing tools

out into the

market into the hands of the clinicians

as quickly as possible, I

think that it's also really important not

to settle there but to be able

to put in the best

interface possible so that

it is simple and easy

to use, pulls in all this relevant information

about the patient and

really makes it so that

the user wants to use it.

Uh, and not simply,

just say, boy, this is something

I need to use to put this

report out to get to this conclusion

about this patient,

make it not there, make it so that they want

to use it, that it really drives their

adoption and drives the desirability

of using that particular product

or tool to help on the patient's

care pathway.

Dr. Hall, did you have anything to add to

that I would simply say

you know, the the analogy. We

use a lot of times as it's like building a house.

If you're going to build a house and

you have the window

person doing the plumbing,

that's never gonna work, and

it's not going to work efficiently. And so what

needs to happen is,

let's say the scientists don't always know

all the expertise that the clinician knows.

So these need things need to be built together.

You need the window person. You need the plumber,

but you need to focus in your areas. Then come

together and go

Wait a minute.

Test this for me. Tell me what we need.

Let's work together on building

the best product.

Cardiovascular departments generate a wealth

of data, and a significant part

of that data is structured on the service.

It looks like an analytics heaven,

but

often the data is stored in multiple places.

Makes it a challenge to derive meaningful,

outcome driven insights.

Dr. Hall, you're the data scientist.

What's your take on this?

We've seen a lot of great

progression in the last few years, and

I couldn't be happier about that because

in not just the clinical world but the academic

world People have their data stored in their

laptops, and they don't share it. In today's

environment, sharing is

the new, you know, catch

all. It is what everybody is trying to

do, because by doing that

you bring data discovery

and discovery for the

patient so much faster.

It's all accelerated. If you can share data,

build a statistical power

to really have the evidence behind things

that we're doing, and we see it all

the time. Data spread all over the place.

But it's not such

a big pill to swallow.

If you take it one step at a time and

start building pieces

within each system, each clinical

organizations, you have the right data

for the right treatment to the

clinician at the right time.

Dr. Flame, can you give us the clinical side

of that perspective?

Well, this is an area where I would hope

that AI and machine learning could be particularly

valuable because I certainly recognized

and agree with Dr Hall's comments

that we've got data in a variety

of different databases and repositories

and depositories throughout an

institution and across

institutions, and sharing that data

is difficult

and you know for many years

now, we've tried to do it with a

pretty manual and somewhat Clukey

kind of process in, uh, in

trying to match this data point to

another data point and using

our eyes and and refined

fear defined field names,

et cetera. But I would hope

that this is where AI and machine language

could come in and do all of that matching

for us provide all of the

cross references and the crosstalk

between and among those databases

so that we could have a common

database and repository of information

to help drive that clinical decision making.

When it comes to

trying to to quantify the value

proposition for a system like this, I'm kind

of curious what your suggestion

might be in terms of demonstrating the time

saved or the clinical

outcomes or data storage.

What would your tape beyond it, Dr Flam,

in terms of

being able to demonstrate the value

proposition. But we do have to

develop our demonstrate

to return on investment, develop

and show that value proposition

for all the things that we do. And I

think that there are many examples we could

toss out and give some broad uh,

breast stroke kind of

examples or comments, But

let me just give you a

finite example. I mean, if we're

looking at an electro physiologist

who is doing a procedure in

the e p lab, we

really want to collect all of

the information that has to

do with diagnostic and operations

and throughput in that

particular room and even collecting

all of the,

um, information

regarding resources that go into

that room, whether it be

pieces, whether it be devices

or the electricity and the water

that goes into that particular room. So we

can quantify all of this information

and an easy and reproducible

way, uh, in ways that

we haven't been able to do now we've

taken pieces of

that operation in a

in an E P lab, for example, and

we focused on, Well, how many

of these devices or catheters

do we use? And do we

have a just in time process to

get that in and kind of quantify that?

But we need to look at the totality

of the process and not just

those pieces, but how many people are in

the room, and what does that cost

us and how efficient are they?

And are are we making? Are we

optimizing all of these pieces

for the patient to be able to

get to the proper diagnosis fastest

to get to the proper therapeutic endpoint?

Uh, fastest, most efficient

and

best Most accurately, you

know, these are not easy pieces. We tend

to come at it from pieces and parts,

and we need to do it from a more holistic

process. But all of that information

together do you see a challenge

in articulating that value proposition?

Maybe there's a

bit of a language difference between

the I T department and cardiology.

Absolutely. We've

got our I T folks who

speak their language and they try

to understand the clinical language

and clinical processes. Um,

but in truth, they've

got their specialties, and

the physicians have their specialties.

We certainly have more physicians who

are becoming adept at

more of the administrative

and operational aspects of medicine.

But there's still a minority, and not

all of them understand those sorts

of things, and that's perfectly fine. We really

want many clinicians to absolutely

be at the top of their game, simply caring

for patients. But this

may be another place where AI and machine

language come in and help bridge

that gap so that even though

both sides of this equation that

we're talking about have incomplete

knowledge of the other,

AI and ML can perhaps help

bridge that and allow there to

be a better totality

between the two of

Dr Hall in the two groups.

Excuse me, Dr Hall, um, interested

in your insights as well as

it pertains to expressing the value

proposition.

Our experience has always been

that, in this case, the data

science programs that are very successful.

The I T programs that are successful

are the ones that keep the focus

on the value proposition

that is best clinical care

at the least amount of dollars

spent. Can you perhaps see

more patients? Can you diagnose them

sooner? Can you prevent things

from happening? And so it's again

getting the data in the right place

at the right time.

It's it's really

that simple, and it takes

a lot of work to get there. But if

the data science team focuses

on that specifically and both

ends the clinician side, the administrators,

the data science side keep that all

in the back of their mind is the main goal.

Then you'll have a better chance of success

for our viewers in a moment. We're going to open

the program up for your questions

before we do. I want to remind

viewers of our guests, Dr Jennifer

Hall of the American Heart Association, as

part of this program, and we thank you for

being here today.

Thank you, Jerry. It's always fun

to be on calls with you as well, Scott.

So always appreciate your insight. And

Dr Scott Flam, head of the section

of cardiovascular imaging at the Cleveland

Clinic's Imaging Institute. Thank

you for being here today to

thank you, Jerry, really a pleasure to be here

and to be here with Dr Call.

And now we're going to open it up for

live questions from our audience.

And just by way of reminder,

you can use the

the box on the right side of your screen

to enter a question

so you can type the question in there. It'll be

received by our background

crew, and they will pass

it on to me for

our Q and a session. We're going to add

to the panel of Dr Hall and Dr Flam

joining us from change. Healthcare

is I tie Galilee. I

tie is the director of cardiovascular product

management at change Healthcare. So I

tie. I'd like you to just say hi so we can

see your face on the screen.

Everyone, thanks Very

happy to be here.

It is joining us from Tel Aviv. So

this is truly an international

event that we're having here.

Let's start the questioning with, um,

the cloud. Let's let's

bring it to a question of the top

drivers in moving cardiovascular

imaging into the cloud. And

I'd like to start with you. Dr Hall,

What are the top drivers and moving, uh,

cardiovascular imaging to the cloud.

Dr. Hall, You may have

neglected to

another mute your microphone

and you need to roll your mouse down

to the bottom of your screen

and you'll see where you can turn your

camera back on and,

um, meet your microphone

while we wait for you to do that. I will pose

that same question

to Dr Flam Doctor Flam.

What do you consider the top drivers moving in

moving cardiovascular imaging to the cloud?

Well, gosh, there.

We do need to move

all this information to the cloud because

it's become so voluminous

and we need access to

it as quickly and

efficiently as possible and doing

so, uh, in our own institutions,

our own repositories, which

we talked about before. Often

being in disparate places and

having different access

routes, et cetera, is just

not as efficient as possible. But

at the same time, I think we have to recognize

that plenty of well, at

least the largest institutions still

have a desire to, for

the most part, hold on to the data themselves

and not put it out to the cloud for

cyber security reasons and

and others that just take a little bit of time

to get over. Um, you know, it's

been a number of years so far, Uh,

and we will get there.

But I think that it may take a little bit more

time just for

the the general comfort

to seep into all of

these institutions and our I t department's

and folks so that we all feel

comfortable. We're all on the same page.

I ty. I know you keep an eye on that market

as well, being in product management,

and so I'd like to pose that same question

from your perspective. What are the top drivers

and moving cardiovascular into the cloud?

Yeah. Thanks, Jerry. So I would like

to maybe go back to two

things that most of them

and Dr Hall talked about

a few minutes ago. The first

thing is, and I think Dr Flynn was just

mentioning that the challenge

of, uh keep deriving insights

from data that's being stored in multiple

separate places. And

I think the cloud again, it's just the technology.

It's an enabler if you use it correctly.

And if you are able to build

cloud native applications in

the right way and and gain the trust

of different users, you're

actually able to unlock that challenge

and and get all this data in a single

place. Of course, you need to have

the right boundaries

and check checks in place to

make sure that yeah, you keep the data

safe that you anonymized the data

that needs to be anonymized. So

definitely, you know, you need the expert

to handle those things, but the cloud

is a great technology

to enable to unlock this data

in this in separate places.

The second point, I would say when you talked about

and think. Both Dr Flynn and Dr Hall

talked about the user experience

in getting users excited

to use the application.

I think again, technology could

be a great in a bar in that domain as well.

And what cloud can bring to the

table here is just the

ability to very frequently update

the user experience, to listen to customers

and actually

close the loop on feedback from the

field very quickly. So you don't need to

get a fix out there and her and wait for

each and every site to deploy it when

they have the time depends on the version. Depends

on you know, the it. You deploy

it in one place very quickly and everybody

can enjoy it. So I think again,

Cloud is not a magic thing. Consult

everything. It's a great technology

that we need to use in the right way

to to bring value. So I think that's

that's what we've seen, you know, talking

to, you know, people in the fielding

and key opinion leaders like Dr

Lam and Dr Hall.

And as a matter of fact, Dr Hall joins

us once again. Thank you, Dr Hall, for

getting past some of the technical issues that

we just experienced. I want to talk

about moving cardiovascular

to be part of a more unified

enterprise imaging

approach rather than just a standalone department.

I wonder if you could talk about the advantages of

that enterprise imaging approach, Dr Hall

And what might be the drivers in

doing that?

Yeah, thanks, Jerry. I think any time

that you can combine data

and people across disciplines

together,

you will have a better result.

I think people thinking, um,

you know, there's been a lot of evidence behind

that across different disciplines and bringing

their excellence to bear in those sorts

of ways helps drive our field

forward. So bringing those experts and imaging

with experts in public health or

are different types of health care together

is really going to bring our fields together.

And so I think, exactly as you're

saying, that will be something

that allows us to solve

the dark matter or unsolved problems

in our field.

I see you smiling, Dr Flam.

Did you want to add something to that? I just

didn't think that I would ever hear the phrase

dark matter in this conversation

today,

I always surprise you, Scott.

Delightful, Delightful. Here

is a question submitted from Menace to

Ari how important this

is for Dr Flam. By the way,

how important is it to have multi modality

imaging in one place? That

is from a storage standpoint and

view, like an image viewer that

is reviewing ultrasound images

along with C T and M R M

R images side by side

in the same viewer, in the same software,

along with reporting capabilities?

And how much time frame

should we expect to finish such cases

reviewing and reporting. But

that's a big unpack that in the order in which

you see fit, that's the That's

a big question. Uh, certainly,

in an ideal world, we would have

tools that allow us to visualize.

See, uh, analyze,

do some post processing, uh,

in in one place. For these various

modalities spread across C T

m r I ultrasound, nuclear,

uh,

the cath lab images and geography.

That would be terrific. I'm

unaware of any tools. Single tools

that do that today and part

of the issue with it is that there are

some different approaches and perspectives

that come to the analysis

of each of those kinds of image

sets that may not have a perfect

overlap. Now, let's not say that I

don't think that there could be tools

developed that would have the flexibility

to highlight certain things

while we analyze certain kinds

of images and then de

emphasize and highlight others when

we move to other sets of images. Right?

I think that's certainly possible. We're just not quite

there yet. And they certainly look forward

to that happening,

you know, as to how important it is

to have those tools together. I think it depends

on what we're looking at. There are some

diagnoses or sets of

images that I have, where I don't need

anything else. I may be looking at Justin

M. R I or just just

an echo or just a

CT scan, and I don't happen.

I don't have a an absolute requirement

to have any other image with me.

And so in in that setting,

one tool is fine.

But others I'd like

to have a full breath of tools

and imaging

openings available so

that I can cross, compare

and understand and correlate what's going

on with other image modalities with the one

I'm looking at, um, today,

I would say that it's a minority of the

time, but I may be biased

in that I don't have that tool available

with all those things available. And if

I did, maybe I

would want to use it more frequently. So

that's that's where we get to that

answer as far as how long

it takes reporting to be. Gosh, we'd certainly

like it to be as as

quick as possible. Uh,

depending on what goes into it. Some

studies are reasonably fast,

whether they're normal or have only minor

abnormalities. Uh, may

not take very long to get reporting

done, but then, in other

situations, we may have things that

are incredibly complex, such as

complex congenital heart disease. Um,

I'm not sure we're gonna get to rapid

reporting for those kinds of cases anytime

soon. Uh, will

simply take longer because there

is such a pain. Ah, pally of information

available that needs to be integrated

and come to a

multiplicity of conclusions. Not

just a single occam's razor kind

of conclusion.

Questions submitted by Ken Martin

is one that I'm gonna pose to Dr

Hall. But I welcome, uh,

either of our other Panelists to chime in if they'd

like to participate in this. But the particular

question from Ken comes in as

a question about tracking patient

consent.

What are the best practices to track

patient consent

to use their imaging as part of a patient?

Population studies where, UM,

artificial intelligence and machine learning

are being used for further insights

and value?

Dr. Hall.

Yeah, thanks, Jerry, that

thanks for the question. That's really an

important question today. There are several,

um, software programs that are beginning

to do this a little bit better.

Um, there's nothing perfect

out there yet today and

and there, you know.

You know, I guess it's just it's a

really difficult question to answer because

it depends on your institution,

um, and the studies that these

individuals are part of usually

as part of the study, I would

suggest going back, Um, if

you know that there are particular

studies research studies

being done at your institution, of which

they would be a part of getting in

touch with that principle investigator

and or study coordinator, and working

through them directly

is the most direct route to go.

Um, and then certainly making

sure you have that consent to do that.

Um, is the right answer,

Dr. Flam, did you have anything that you wanted

to add on that?

I think I can only add

that it is a complex

issue. And

I'm gonna leave it up to the folks

at the American Heart Association

with incredible

resources and digital

experience to lead us through

this issue.

Very well.

Another question we have dealing

with best practices deals with

what imaging leaders can do.

Uh, what kind of best practices they might follow

to maximize the clinical outcomes

and clinical efficiency?

And what role does a top notch user experience

Play in that? We'd like to

start with that one.

I'm looking for hands. Anyone, Bueller.

Well, I can certainly start,

um,

as as far as

maximizing efficiency.

I mean, I think we get

back to some of the issues that

we've already talked about and that is having

image display tools

available,

having image analysis,

having some cross talk, cross

referencing between the different

imaging tools,

as well as integration with

our electronic medical record to pull

the kind of information that we need

that is relevant current,

uh, available and

and potentially impactful uh,

that goes into the imaging diagnoses.

And so

if we have all that information, preferably

cloud available, so it's available

quickly. Uh, we can integrate

that information, uh,

as rapidly as possible and

get to a relevant,

uh, and we hope, accurate diagnosis

and then getting to patient outcomes.

We have a much harder time with that

with imaging, because

imaging doesn't lend itself

as well

to a direct relationship

to outcomes, it's often quite

critical along the pathway.

But it's more difficult to come out

with a direct relationship between

an imaging result and an outcome.

And we tend to associate

that more with a particular drug therapy

or device therapy or surgical procedure.

Uh, and and so that gets more complicated.

However, we have an opportunity talking

about AI and machine learning and deep

learning and pulling all this information

together,

I think we're gonna have a much brighter

future, an opportunity to

be able to highlight and recognize

how important imaging is

in a patient's care pathway and

providing better outcomes for those patients.

Very good

do either of our other Panelists want to

chime in on that one?

I would only add that

are the American

Heart Association has funded a number of

data scientists

that have done great work in the area

of machine learning and

artificial intelligence

with imaging and

using that for predictive modeling.

Um, and I think the field

is is certainly moving

in that direction. And, uh,

we have a long ways to go. But I think

even sharing best practices, which

is something that has grown organically

out of our funded scientists, that they've gotten

together and

share best practices together

on many of the pipelines

that they're building and a lot of the practices

that they're doing, so more of that and open

data sharing. I think,

um is the way to go.

I'd like to bring I Tiger Lily back into

this conversation because I think this next

question

may fall in your bailiwick. I tie. Since

you work in product management.

The questioner many, She says

that

because of the complexity of

software positions and clinicians

spend a significant amount of their time doing

administrative tasks. And

the question is whether you believe that there's a need

to take care of such things

through software using automation

so that the physicians, the clinicians,

can concentrate more towards patient

care

Your thoughts I chi.

So I think

the answer is definitely yes

again at the end of the day. Like I think both

doctor from Doctor hell mentioned

the idea is for the system to help

the clinician do their job

and and serve that, you know, to the patient

care walkthrough. So I would say

it's also a little bit connected to the previous

questions in my mind, because we're looking

at What can we do to help?

At the end of the day, the physicians to focus

on the patients and forget about other

things so it could be taking all

the high volume, repetitive tasks

that are happening every

day and try to automate them. We use

AI machine learning to do that. One

example would be

a lot of our customers indicate

the challenge with

image quality in measurement

variability between different modalities

between different technicians with different

skill sets and experiences. And that's that's

something that AI has proven

to be able to reduce, variability

significantly

and actually also improve efficiency because

if you don't have to take all the images at the time

of the study on the mortality, But can

look at what the A. I can just fix

the one that you don't agree with. You just save

80% of your measurement work.

So that's an example for both. I think

another thing that we see a lot is the whole,

uh, charge.

Capture inventory management.

It's more on the invasive side

of cardiovascular. But

typical, you know, invasive procedure

can have dozens of charge

codes and inventory items,

and we want to make sure that the physicians are actually

treating the patients.

Opening data is replacing the malfunction

involved and not dealing with

scanning inventory items and making sure

that you reduce stock or capture

the charges emitted it on something. So

definitely, we think software systems

have the role and and and also

the responsibility to do executive

and allow clinicians to focus on patient

care. The last thing that was me, I

would mention, is again. We see today

sometime in his connection between technology

and workflow,

because if sometimes the technology will

get you to do some

of your studies on the ideology side,

some of your studies, for example, cities and MRI,

some sometimes even nuclear medicine,

and in some of the studies on because the other side

but the workflow could spend

across both

the cardiac city. That's happening, maybe

on the theology system would need to

be used in a structural heart procedure.

For a prep, you need to see the classification

of the valve. And if you don't have the software

tools to allow you to connect the dots and

actually be able to run your

workflow,

forget about the different actions

and technologies around the world for in a seamless

way, then I think

your efficiency is automatically

being produced. I think that's

that's an important part of any system that gets

into to serve commissions.

I know that Dr Hall has something

she'd like to add Dr Hall

just quickly. I'll just let

you know that there and several of you already

know this. There's many companies out there

that are already optimizing work floor

workflow in the hospitals and using

machine learning and AI and predictive

modeling to figure out number of beds

and discharge

and many things. So that is coming

and it's coming your way to separate

workflow and use machine

learning and predictive modeling to help

you achieve those goals.

So you as the physician are not

handling those things

very good. And Dr Flame, I saw you

shaking your head affirmatively during many

of those answers. Well,

I just think that there are so

many pieces of the puzzle that can

be or should be automated.

I think of so many reports

that I put together with C T E R

M R I that I'd love

to have more information pulled

from the electronic medical record automatically

inserted into the report

or measurements

that would, um, adjust

some of the measurements that

I've already obtained or technologists

have obtained. We're putting into the report,

uh, so that

all needs to be done. Uh, we should have that

today, and we don't, but I'm looking

forward to it as soon as possible.

Very good. Uh, Peggy from,

uh, H C A. Healthcare says

imaging is typically seen as an independent

support service.

This results in fragmented leadership.

And so she's asking what's the best practice

for incorporating

imaging

within CV services?

Who would like to go first?

No, I think I think that's

a doctor hall question

that's outside of my area.

Well, I guess

I guess when it comes to that question I'm sorry,

Peggy, but we

We don't have a Panelist who can help

you with that. So we're gonna have to move on to

Laura's question. Laura

is asking,

uh, regarding the PACs integration.

There are some integrate herbal software

solutions that work as an extension

within hospitals, own packs.

And there are some internal solutions working

as separate desk apps

or web apps, depending on the provider.

What

is your opinion on those two different solutions,

huh?

Well, let me let me toss out

an answer. And then afterward, I would

really like to hear what it says

about this. Uh, but

we certainly have both

options available.

Uh, we have some tools

that are stand alone, some tools

that are integrated with packs and some tools

that are both.

And I

think that

they each have their pluses

and minuses, at least today.

And this we

may really be I

don't want to say stuck with it, but it may

simply be where we are today

that we don't have the full integration. I think

earlier on in in these

discussions, we talked about having

a single tool

or orp anomaly of

tools available that would allow us to see

different imaging modalities and

have different, uh, tools,

measurement options, processing ability

for that all integrated together.

And that might be the very best option for

us. Uh, but we don't have that

today. And I think in the current

environment,

I look at the tools that I have,

and if they're stand alone, I'm sort

of used to that. If it were integrated

into packs,

I'm not necessarily sure

that I would like that better. But I'd

like the option to be able to try that and

see if it worked better for

me if it were a

smoother, more efficient, more,

uh, more obvious and

intuitive to be able to use those tools

if they were integrated.

Well, I tie. He teed you up, so

I'm talking to you. Thanks,

Jerry. So definitely, I think

it's It goes back to the classic

battle between best of breed

and a single vendor.

If you want. You know all of your

applications to be from single providers,

that we have a unified, smooth user

experience. You have to sometimes compromise

on capabilities,

or if you want to choose for each and every

cast that you're performing, you want to choose

the best of breed, uh, cutting

edge solution. You oftentimes find yourself

with many two separate standalone

systems that you need to toggle between to

complete your work. And I think

from what we've seen that that's

how it's used to be a piece from what we

saw, you know, out there in the market.

But I think technology today is

at the point that people should

stop compromising, at least on a lot

of things. You can still have these standalone

systems today, but the the level

of integration

and the ability to embed systems. And

when we move from declines to

Web apps to zero footprint,

this becomes easier and easier to still

use those standards and best of breed solutions

and embed them very seriously into

into your workflow into your system. So

I think pax historically was

the backbone. See packs of imaging

workflows. I think those

integration capabilities should

allow us to maintain that backbone,

but bringing all these new capabilities

that us as a as a you

know, PACs vendors won't have

the resources to always provide

the best

and most cutting edge solutions,

but we should definitely be able to bring that in

and allow,

you know, conditions like Dr Flam you

to use them inside the workflow

in the most in this way.

Alright, folks, it's time for you

to get out your crystal balls

And look five years down

the path. five years in the future.

What emerging technology do you see

most? Disrupting

the cardiovascular imaging market

In the next five years, I'm going

to start with Dr. Hall on this one.

Yeah, I

think. Did you get Did you get your crystal ball

from Amazon? Yeah.

I'm just now pulling it out

of of the drawer here. Jerry, Um,

I think it's really gonna be

driven by a number of

things. Um,

certainly technology and

the speed at which

at which it is moving

and computational power.

So the speed at which

clinicians can pull

up images, bring them all together

and analyze them with,

you know, other data at their

fingertips. Like Scott is saying,

the electronic medical record,

the ability to have

templates at the ready. So these

tools and reports

are

Kind of a thing of the past. They almost

automatically write themselves one

Scott is able to transcribe,

you know, his expertise

down um, in those

very difficult cases,

and I think he will see what we talked

about, which is the separation of workflow

from real clinician,

um, duties, which is really taking

care of the patient. And that

will be,

you know, driven by a lot of machine learning

on that workflow side, which

is something maybe we didn't predict many

years ago.

Dr. Flat, I'm gonna throw it over to you, get

the crystal ball out, take a peer into

it and tell me what's happening with emerging

technologies in the next five years. What's going to be

the big disruptor? Well, five years

is still pretty close, and

naturally, I'm biased because

I'm someone who focuses

more on C t and M R I.

And I will tell you, with both of those technologies,

I am really excited

what the near future is bringing.

When I look at what m

r. I is capable for the cardiovascular

realm what it's capable of doing, uh,

in looking at tissue characterization,

Uh, and more to the

point in the speed

at which it's being applied so

far for me. You know, a couple decades we've

had cardiac MRI examinations

that take a long time. It can be an

hour plus, uh, in many

places. But I think we are seeing

the advent of

examinations that

are Acquiring an incredible

breath of information with function

and tissue characterization and flow quantification,

uh, strain information.

All of this is going

to be acquired in 10 to 15 minutes,

and it will be so much faster, a better experience

for the patient and an incredible

wealth of information in this short period

of time.

And at the same time, I think about where

we are going in C t scanning

with these new, uh, crystals

that are, uh, photon counting

that are that are giving us greater

precision, greater detail,

both spatial resolution and tissue characterization

looking at arthroscopic

plaque in the coronary arteries. And

I think this sort of detailed information

combined with the AI and machine

learning that we've been talking about, is

going to get us to the point of finally

being

more predictive not just

for population science, but on

an individual patient basis,

really getting into the

people talk about precision medicine,

but into personalized medicine. And

I think we're going to get there, uh, soon

because we've got some great tools that are opening

some some great vistas for

us in patient care.

Interesting, I tie. Let me

allow you to peer into the crystal ball.

What do you see? So

first I want to join Dr Flan

with the whole shifting imaging mix.

I also believe that we're going to see more and more

cardiac cities and cardiac MRIs

being performed

on the expense of maybe

Auto Sounds and NM studies.

The second thing that I'm starting to

see is a better connectivity,

not just to the EMR but also

to data coming from outside of the hospital,

whether it's

consumer devices that

help is screening and closing the diagnostic

gap. So more patients actually

knows they have a condition earlier

in the process.

And the second I think also chronic care management,

home care management for people with

cardiac disease. And I think that

connection to the acute episode

is going to call stronger over time. So

those are the things I would,

you know,

with my money on

very good and you mentioned data.

So how important is data

consolidation?

Uh,

in the current environment, I

wonder if you could address that

I tie. I'll start with you, Go ahead.

So I think if we've learned

something today, is that data consolidation

is key for any future.

You know, success in cardiovascular

if you want to get insights If you want

to be able to improve patient care,

you have to start with being able to

understand the data you're getting and

turn that into insights

and be able to drive outcomes to understand

what is happening. So I think

one of the you know Dr Hall

talked about that. And Dr Frank talked about it several

times in this session that

one of the biggest challenges is

how do we get all this

data to preside in multiple places and actually

paint one full picture

that gives us, you know, understanding of the

patient health

and try to predict what it would look like

in the future. And by the way, we already

see some companies that are combining

SCG data within our data

and having some pretty good results

on predicting future onset

of cardiac disease such as heart

and problems, and even stole, so it's not

there yet, but I think

it's definitely a very important

key in enabling a lot of these things in the future.

We have another question from one of our viewers.

This is a question for Dr Hall.

Most of the software, the

question goes, most of the software provides very

loose

information in terms of patient risk assessment.

Uh, lose, because the data is from

mostly single databases rather than aggregations

from data storage locations, data

silos or from other vendors. So

the question is, do you believe artificial

intelligence can

definitely helped to pull the key data elements

about the patient

so that it automatically creates a risk

assessment from all these data

that are scattered in different locations

and still adhere to current guidelines

and and have some sort of graphical format?

I would

I would back up a little bit and say

that AI is not a magic

bullet.

And neither is any statistical

method. In fact, some of the older

statistical methods have outshone

ai and many methods,

Um, and in many different

papers of of late,

the key to all of it is the data

upon which you build

the artificial intelligence

models. And so if you have

and uh,

person with the question is absolutely

correct, if they're built on small

research studies or single research

studies.

Um, those risk prediction

models will be relatively,

um,

minor. You know, they're not gonna They're not

gonna have a great effect. It's not until we

begin to share data and use

data that includes

multiple

individuals, people from underrepresented

groups and minorities as

well as women.

That will have better insights that

we can begin to really break down

what's happening. And then when we have

that large data set, then we can do

things with artificial intelligence and keep

in mind bias and things. Thanks

for the question. Yeah,

I've done a number of interviews regarding artificial intelligence

and machine learning, and the recurring

theme is always

you need the big what we used to say Big data.

You need lots of data for it.

In other words, you know, if you don't have

that base of data,

it just won't be as effective.

Here's a question from Laura.

Um, it's a question concerning how much

effort it takes from clinicians to

learn to operate new software. Is

it better designing the user interface

to look and follow the same logic as

existing ones?

Four.

Try to truly understand the user flows

in the inter place and optimize the experience.

Uh, and taking the risk

of the interface looking so different from the existing

ones. Well, something that looks different,

the intimidating who approach,

uh, in your opinion, even though

you know, if all the user testing in and heat maps

show evidence that they work better.

And since you're in product management,

I ty I'm putting this one on

your desk.

Yeah, thank you. So, by the way, I would definitely

love to hear Dr Flam after

the You know the clinician's perspective

on that because I can, you know,

bubble on the, uh

what what we've seen and what our

in theory, you can walk in with US

companies and UX people.

At the end of the day again, it's a balance

between how do we drive adoption? What is the adoption

period? Because

ultimately, of course, it's better to

have the work for terror

to the you extra to

to serve the work for best, even if it's very

different from today.

But there is a learning curve, and you

have to get above a certain threshold

to be able to get adoption.

And if you won't get above that threshold. It's

hard, tough, and traditions are very busy,

and I do think it's intimidating

to switch.

So I think if you want to do the switch, you have

to be very mindful of what it means

to clinicians. And you have to provide the right tools

in the right level of support

to get them over that barrier. I think in

the long run, it's probably the right

thing to do is to focus on what's best

for the workflow, but what we think

is the best user experience.

But we have again to be very mindful

of what is the journey from

today to that new user

experience? It's not gonna happen in

a single day and it's not gonna happen.

It's not gonna be pain free,

so we have to provide the right tools

to get them from Point A to

point B. Dr.

Flam Uh, if you'd like, I can recap

the question for you unless you know

I'm good, ready

to go on that one, and I think

it is really

an individualized answer. I

certainly have colleagues and no people

who continue to use one tool

because they just

don't want to change their dug in. They

know how to use that tool, and it provides

them enough information

and they just don't want to change their

kind of dug in.

And, uh, there are

other people like me who are sort of in

the middle, where

I'm perfectly comfortable learning

new tools and new interfaces

as long as it's not onerous.

Uh, if it's an onerous

proposition, then you know

who wants to do that. It becomes too difficult,

no matter how good the tool is. And

so you really need to design

tools that have a at least a reasonable

interface. I mean, certainly you'd like

it to be simple, intuitive,

obvious, uh, and something

that you can embrace very, very easily

and obviously, But, uh, most

Most of these

complex

tools available now to do

post processing of a variety

of imaging modalities have

a complexity to them because

the imaging that we're acquiring is complex,

with a variety of dimensions

to it. Uh, so it

is challenging. Uh, you know, I

think that

a reasonable person can move

to a another

tool walks tool set with

without it being too much of a process.

Um, but, uh, people

who are developing these tools do have

to be mindful and make sure

that the transition

process is one that is not

too difficult, uh, and

something that people really embrace

and would like to move forward with,

because the tool available is better.

Well, folks, I have to say that the

sun and the moon and the stars must have aligned

just perfectly because

we have run out of questions at the same

moment that we've run out of time. So

I'd like to thank our Panelists.

Dr Jennifer Hall

from the American Heart Association, Dr Scott

Flam from the Cleveland Clinic Imaging Center

and I tie Galilee of Change

Healthcare. Thank you very much. Uh,

for those of you watching, we will follow up

with the recording of this session

so that you can share it with others

for later viewing. If you're interested

in learning more about change healthcare,

cardiovascular solutions,

please head to info dot

change healthcare dot com

forward slash cardiology Again.

That's info dot change healthcare

dot com forward slash cardiology.

Thank you. We hope you have a great day

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