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September 25, 2025 • 48 mins

“Doctors don’t want pharma reps to buy them dinner, right? They don’t. They want help in the five minutes that really matter,” Viz.ai’s CEO Chris Mansi and Salesforce’s Frank Defesche explain in this Vanguards of Health Care podcast episode. Mansi and Defesche sit down with Bloomberg Intelligence analyst Matt Henriksson to talk about Viz.ai and its agentic AI platform that connects medical scans and images to the right diagnosis and treatment guidelines. Also tune in to learn how the Viz.ai platform aims to partner with Salesforce’s life-science division to improve pharmaceutical point-of-care workflow while providing more personalized care.

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Episode Transcript

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Speaker 1 (00:17):
Welcome to another episode of the Vanguards of Healthcare series.
My name is Matt Hendrickson, the medical technology analyst at
Bloomberg Intelligence, which is the in house equity research platform
of Bloomberg LP. We are pleased to have with us
today Chris Mansei, founder and CEO of viz Ai, and
Frank Deffesh, SVP and General Manager of Salesforce Life Sciences.

(00:40):
Viz Ai is a privately held healthcare company that uses
AI algorithms to better identify patients with suspected disease and
form critical decisions at the point of care and optimize
care pathway. Chris and Frank, thank you both for joining
us today.

Speaker 2 (00:54):
Thanks for having us Matt.

Speaker 1 (00:56):
Chris, while we start with an overview of your career
and the steps that led you to found in Visai.

Speaker 3 (01:03):
Absolutely yes.

Speaker 4 (01:04):
So. Prior to founding viz I was a neurosurgeon in
the UK and every day, pretty much like every doctor,
I saw firsthand the problem we ended up solving. You know,
I can remember a particular patient, a young lady who
was in fourteen knocked down by a car. Had what's
known as a cute subdual hemor, which where blood pours
into the outside of the brain. And my job as

(01:27):
the neurosurgeon is reasonably simple. I have to make a
big cut, remove the skull, relieve the pressure. But time
really matters because every minute, approximately two million brain cells
are dying, right, so.

Speaker 3 (01:39):
You need to move fast.

Speaker 4 (01:41):
The reason why I remember that case so vividly is
we have a photograph of the team afterwards celebrating how
well the case had gone. We set a record for
skin to skin time, so we were pleased, and then unfortunately,
twelve hours later, that young lady had died. And when
we look back into the reason why she died, it
wasn't what had happened in the operating room that had

(02:02):
gone well. It was the four to five hours it
took together there. It was the broken workflow. And so
I came out to Stanford in twenty fourteen with multiple
stories like that in my head, thinking how could you
solve that problem broadly? And back then in twenty fourteen

(02:23):
twenty fifteen, there was a new technology called deep learning,
which could read images. You know, the time I could
tell the difference between a cat and a dog, right,
and we realized that it could do just as well
reading medical images. But importantly, it wasn't about reading the
medical images. It was about using that read to automatically
trigger better workflow, better clinical care, so that that lady

(02:48):
would immediately be alerted to the newer surgeon who could
make a faster decision and bring them to the oh
much much faster. I pitched this idea for what became
VIZ in a Stanford class and Eric Schmidt with the professor,
so he seed founded the company and we you know,
went through many, many, many stages, you know, FDA clearance, reimbursement, sales, growth,

(03:11):
and now we're the largest clinical II company in the
United States. We have over fifty now sixty actually diseases
live on the platform in over eighteen hundred hospitals, covering
approximately two hundred and thirty million lives. And you know,
also work with many of the big farmer companies around

(03:31):
the world, helping them with their market development to make
sure more patients are going to benefit from the treatments
that we have.

Speaker 5 (03:38):
Wow.

Speaker 1 (03:39):
And so my quick follow up question there is how
was it having a class with Eric as your professor?

Speaker 3 (03:44):
Fantastic?

Speaker 4 (03:45):
Yeah, you know, the professors that staffored were all good,
but having the operators teach you like Eric were just
phenomenal because everything they were teaching you they had real examples.
So they weren't just telling you what might appear in
textbooks that were good framework. They were telling you the
reality of how they were applied.

Speaker 1 (04:03):
Yeah, and I mean also from his perspective too, it
sounded a lot like Shark Tank. And they gave him
a good investment opportunity with viz Ai. And so, you know,
Frank also with you, what was your path to salesforce?

Speaker 2 (04:18):
Yeah, so.

Speaker 5 (04:22):
You know, I've been software since since the beginning of
my career, and I was I was the black sheep
because I come from a family wherever I'm the only
one that's a mister defeche. Everybody else is a doctor,
whether they were practicing positions or practicing musicians turned business
uh into the pharmaceutical industry. So father, uncle, cut brothers.

(04:44):
Everyone's either farmer or a doctor. I'm I'm the black
sheep of the family.

Speaker 2 (04:49):
Now.

Speaker 5 (04:50):
I actually started my software career in Austin, Texas, working
on on premise software, and that ended up. You know,
I really liked software, and so I decided to move
to San Francisco because that was mecca for software back
in nineteen ninety nine. In two thousand, started working at
Sebel Systems, which is an on bremise CRM system back

(05:12):
in the day, and a company was using an interim
solution while they were doing this big, big, heavy implementation.
And I got access to this interim solution, and that
solution was Salesforce. It was a thirty person dot com
startup right and I was able to add a field
to a screen. And that was a moment that I'll

(05:33):
never forget because I felt like probably like a doctor,
where I was like, I can perform miracles that used
to be something that engineers needed to do. So I
quit right there, right then, because I knew that that
was a much better way. If that's possible, if you
can democratize building and tailoring software and do it in

(05:53):
a way where you know, a big company can have
as great as an application an eCos system of technology
as the largest enterprise, that's a good thing. So I
joined Salesforce in two thousand spent eight years at Salesforce.
During that time there, I met someone that became sort
of a mentor of mine, and a couple of years

(06:16):
later joined him in his startup, which was a company
called Viva Systems. Initially it was called Verticals on Demand,
but then it became Viva Systems, which was all about
bringing cloud software to the life sciences industry. Worked there
for sixteen years, and it's initially started by building a
pharma specific CRM on the Salesforce platform, and then they

(06:39):
built their own platform and did other things like clinical
and regulatory and content management reviews. And then they made
the decision to replatform off of Salesforce CRM onto their
own proprietary platform, and I was put in charge of
that as the GM of what they call Vault CRM,
and that gave me a lot of insight of what

(07:01):
the industry really needed. And the industry needed value, they
needed innovation, they needed competition. I believe competition drives innovation,
and that's what brought me to Salesforce. The AI advancements
that Salesforce was doing I felt could really unlock a
lot of improvement of how life sciences supports both patients

(07:25):
and providers.

Speaker 2 (07:26):
So that brought me.

Speaker 5 (07:26):
Back to Salesforce in the fall of twenty twenty three
and we've been doing amazing work ever since. And about
a year ago I met doctor Mansey here and we've
we're been doing some amazing work together with this.

Speaker 1 (07:38):
Well, let's you know, jump into vis a little bit
more and you know, Chris, you kind of gave us
a good overview of the algorithms and everything, but let's
dive a little bit deeper because you know, when I'm
anytime I'm thinking of machine learning AI algorithms, I always
want to know what the input data that is going in,
whether it's imaging other other sources, and then what does

(08:00):
that's the final output that's really going to be the
differentiating feature to help these doctors find the right care
for these patients.

Speaker 4 (08:09):
Yeah, so today the input data is very much multi
modal to imaging data. That's brain scans, heart scans, right,
it's the electronic health record data for contextual information, it's
ambient listening to listen to the doctor patient consult and
so you know the clinical context of the patient, the

(08:29):
current problem, and the investigation, the imaging, and that allows
you to put together a few things. So first of all,
it acts as a safety net.

Speaker 3 (08:39):
So in all of our.

Speaker 4 (08:42):
Nearly two thousand installations, their hub and spoke hospital networks
where you have a specialist hospital, think Mount Sinai in
New York. But then there's six sometimes as many as
seventy spoke hospital, smaller hospitals that maybe don't have as
much specialist expertise, but patients show up to and they

(09:03):
do the tests and have the data. We'll ingest that
data and in real time be looking for features consistent
with say lung cancer or stroke or a brain aneurysm.
There are things that are going to need treatment and
that if they get treatment that Lady I spoke about,
the outcomes likely to be good. If they don't get

(09:24):
treatment in a timely way or in a guideline consistent way,
the outlet comes likely to be bad. Right, So we
want to make a big difference a safety net. Find
The output is find more patients for that particular specialist.
Put that patient out in Queens in front of the
Manhattan based newer surgeon. So that's number one. Number two

(09:46):
is make their job easier. So that's a huge amount
of data, particularly thinking about like a cancer patient, some
with lung cancer, it's like maybe nine hundred pages of
electronic health record that you have to go through to
find out the previous history, the clinical context, and what's

(10:06):
currently happening. So we use a large language model to
summarize that fine tune too that particular disease, so the
relevant genetic information and the relevant stage grade, except all
of those things in front of the doctor together with
an AI read of the imaging, and then provide the
ambient services so they when they talk to the patient
that the notes can be document automated. We have a

(10:29):
bunch of other features right that we provide hospitals, whether
it's building support or support with administrative tasks like collecting
quality data. But the core of it is find more patients,
help the doctors to treat them, and for farmer, what
we're doing, particularly through a partnership with Salesforce Life Science,

(10:51):
is we are ensuring first of all that more patients
with target disease are getting to those specialists, so there's
a chance they'll get treated, and we're streamlined the workflow
and providing insights based on that aggregated data on what
gaps in the workflow a pharmaceutical company might be able
to help a hospital system or a provider to solve that.

(11:15):
Data can be real time, it can be involve communication
back and forth on the viz platform to Salesforce Life
Science so that the farmer rep can hear about what
the physician's looking for. And so there's two real use cases, right,
Medicines and team sport the physician side, the hospital side,
and then the life science side.

Speaker 1 (11:31):
Yeah, and I'm sure we'll jump into the life science
side a little bit more as we talk about the
just announced partnership, but just with the hospital side, a
couple things that came to my attention, and yep, with
my medtech background, especially with stroke care, there was always
that hub and spoke strategy of how to get the
right stroke patient to the right comprehensive stroke center. It

(11:56):
sounds similar to that, but also might be a little
bit different because that is usually taking you know, a
stroke being more acute. Does it actually help with the
patients that are in such an acute condition, like a
schemic stroke.

Speaker 4 (12:09):
It helps hugely with acute conditions. We have our first
ever FDA indication. In fact, we were the first company
to get an FDA clearance for schemic stroke back in
twenty eighteen, and we got that on the back of
demonstrating that we could reduce time to treatment in that
study by fifty two minutes.

Speaker 3 (12:28):
It ranges all the way up to.

Speaker 1 (12:30):
Which, like you said here, if you're losing so many
millions of cells every minute, fifty two minutes is a
huge deal.

Speaker 3 (12:36):
It's a year of disability you're saving average.

Speaker 4 (12:38):
Yeah, that's also why we've got the reimbursement, because you
were able to show that outcome improvement. So in the
acute setting, it's it's very effective, but it's also very
effective in the chronic disease setting. I'll give you an
example of a moderately rare cardiology disease called hypertrophic cardimiopathy.
We hear about this when you know an athlete suddenly

(13:01):
drops down debt.

Speaker 2 (13:02):
Right.

Speaker 4 (13:02):
It's it's a genetic disease that often is undiagnosed, but
almost everybody has had an EKG or ECG at some
point in their lives, and this path is consistent with
the disease present there. So what we've been able to
do with that disease is take the time to diagnosis
down from five years to five weeks. So instead of
five hours to five minutes, it's five years to five weeks.

(13:25):
A different scale but still very meaningful.

Speaker 1 (13:27):
Yeah. Well, well let's just let's just jump through that
five years to five weeks for example, so is that
five years? Was it the traditional method for diagnosing the disease,
it would just take five years for them to have
enough data to confirm that you have HCM, or is
it something different?

Speaker 3 (13:44):
Okay, so I mean very different.

Speaker 4 (13:45):
So it's simply that the only doctors who might pick
up a pattern of HCM in an ECG is the
one or two specialists in a health system that specializes
in HCM.

Speaker 1 (13:59):
Okay.

Speaker 4 (14:00):
In the cardiologist who treats heart attacks, for example, is
unlikely to pick up that particular pattern just because they're
not seeing it every day. And so it's a niche
pattern that only a few human experts are able to see.
And ECGs are happening all the time for many, many
different reasons, and so the vast majority of the time

(14:21):
it's an incidental finding that we're finding and sending to
that AHTM specialist who can then say, yes, I agree
with the AI, let's work this patient up for diagnosis
and treatment.

Speaker 1 (14:33):
Gotcha, gotcha. And so this is kind of the power
of big data, and as you collect more data, you
have a more powerful algorithm. But one of the things
that I'm curious about is with FDA approved algorithms like visai,
how much can be adapted in the algorithm will still

(14:54):
be part of that same FDA approval letter.

Speaker 3 (14:59):
Right.

Speaker 4 (14:59):
So there's two parts to every regulatory environment in a company.
There's the FDA clearance, which is dictating your marketing claims,
your indications for use, right, and you'll do a study
for that and you'll be able to make claims based
on that. But then there's the Equality Management System, which
for software right is regulating through a verification of validation

(15:19):
process update to the platform. And so those updates of
the platform are often happening, you know, and so and
like med device, they're happening pretty regularly, like every month,
sometimes much more, and so you're updating that. So only
when you're making a change to indications for use or
some performance based claim that you would go back to
the FDA and do a special five teen k okay.

Speaker 1 (15:42):
So basically, so basically, the more data that you were
able to collect for you know, especially for like a
certain HCM, the better the algorithm should get because you
have more data being analyzed and being able to find
those fine tune points and that should all then you know,
eventually be able to make the algorithm stronger without having
to go through the FDA for you know, going through
that time consuming point or as early as twenty eighteen.

(16:05):
How has that commercial launch gone so far? How is
that You've talked about a little bit about you know,
being able to get reimbursement pretty easily. How is that
different from any other potential different kind of AI algorithms
or machine learning software that's out there.

Speaker 4 (16:22):
Yeah, I'd say it definitely wasn't easy. Convers this was easy,
including the FDA clearance and the commercialization and the reimbursement was.

Speaker 3 (16:30):
Hard to get it and it is very hard for
anyone to.

Speaker 4 (16:33):
Get So your question was how did it go from
FDA clearance to commercial roll out?

Speaker 1 (16:39):
Yeah, because I mean it seems like it's pretty you know,
and like I said, this feels like something where you know,
AI has been a buzzword for the last eighteen months,
but you've been using AI power tools for seven years now.
How has the adoption and the commercial launch been between
you know, the first five years in the last two years.
Any difference in strategy, any difference in saying, hey, you know,

(17:00):
we we knew AI was powerful back then, we know
it now come join us now, Just a how is
that long. Yeah, just anything about that commercial one, I
tell you.

Speaker 3 (17:09):
Yeah.

Speaker 4 (17:10):
So it's funny for us to be considered like the
ogs or like the company. We're always seeing ourselves as
the innovators. But I'll take you back to twenty eighteen. We
get the clearance in February, and you know, you you've
got hospitals that are interested already because they've been involved
in the research studies. And our first two hospitals were Grady,

(17:30):
a public hospital in Atlanta, and earl Anger and Chattanooga, Tennessee. Right, so,
California based company with all of our R and D
and is well, and we go to the southeast of
the country. The reason being that's where the biggest problem was.
So we started with the areas where the need was
the biggest, and those therefore often you have the doctors
who are specializing in that particular disease. I think for viz.

(17:53):
The stories being a little bit unique. We've you're added
an average of three to four hundred hospitals every single year.

Speaker 3 (18:01):
That's not normal.

Speaker 4 (18:03):
It's normally very very hard to get an adoption curve
in health systems.

Speaker 3 (18:07):
They're slow to adopt.

Speaker 4 (18:09):
The reimbursement patterns take a while to be established. Like
for US, we got reimbursement for that first use case
in twenty twenty two years later, right, So it took
time and was very hard. And we got another reimbursement
this year for the ATM example, but that's five years later,
so you can see that it just takes time. And
so we were very much focused on that dual value

(18:31):
prop clinical benefit but also financial benefit and on partnerships
like the recent partnership with Salesforce is an example of that.
But going back in time, it was partnerships with the
device companies like Metronic, with the pharmaceutical companies like BMS,
because then as a team, you're kind of going to
market and producing a much more holistic solution that the
market will adopt and it helps with paying for the solution,

(18:54):
et cetera.

Speaker 1 (18:55):
Yeah, and it turns out to be a win win
for those partnerships as well, because I'm sure was able
to get more of their thrownbectomy devices into the hospital
because they're working with the Visai software.

Speaker 4 (19:07):
Exactly, more patients are getting appropriately treated. Like everybody is winning.

Speaker 1 (19:11):
We're talking about partnerships. So let's kind of just jump
over to Salesforce and Frank. You know, my experience with
Salesforce from you know, the finance side is I have
a client interaction, I would log it in. Then our
sales team and our traders would come back and say, hey,
Matt interacted with you three times this quarter. Let's bring

(19:33):
some trading dollars over to our trading desk. I'm assuming
it is more complicated than that in the life science division,
and I would love to hear more about what interactions
are recorded, what inputs you know, and then what is
who's benefiting from the output at the end of the day.

Speaker 5 (19:53):
Yeah, well, I mean something start just kind of really
high high altitude.

Speaker 1 (19:58):
You know.

Speaker 5 (19:58):
Salesforce has been pretty basive cloud enterprise software provider for
over twenty five years across industries, starting off with SFA
expanding the CRM, and about ten years ago they started
focusing in on industry specific capabilities and applications. So your

(20:18):
use case, you might have been using, yes it's Salesforce,
but you might have been using the financial services cloud,
which is a suite of financial services specific capabilities industry specific.
If you think about Salesforce is a very flexible platform.
People can build anything they want the world of Bill
versus bi is still there because you've got these amazing

(20:41):
platforms that you can build on. But when it gets
to industry specific, you're talking about not going wide, you're
talking about going deep specific. The nuances they really really matter.
You know, for example, sampling right sampling is a common
practice that some people you know, they know about, like
a pharmer rep will distribute samples to a healthcare provider

(21:05):
and get a signature or validate their state licensed number. Well,
sampling works differently in different places, whether it's or for
different therapeutic areas or you know in the state of Ohio,
there are different regulations governing that process. So those specific
specific details is what Salesforce Life Sciences is all about,

(21:25):
is making sure that we are building out of the box,
fit for purpose, best practice regulated compliant capabilities to service
and support the industry. So if I that's a little
bit about industries at Salesforce now sort of double clicking
into Salesforce Life Sciences, you know, we help pharma, biotech,

(21:48):
consumer health, animal health medtech organizations both the devices and
the diagnostics. We help them orchestrate and be more personalized
in their engagement across AHTPS patients pharmacies payers through the
use of connected data as where BIZ comes in and

(22:09):
that becomes contextual insights through our semantic layer, so that
users and agents and patients and providers can benefit from
them with making better decisions, being more compliant, better access
to information. And you know, the biggest low hanging fruit
that everybody loves is reducing administrative burden. I mean, CRM

(22:33):
has been around for a long time, and it's been
painful for a long time, and now we finally can
turn the tables and provide an engagement platform, whether it's
in the clinical context, the patient services context of medical
or commercial, that does work that for that user, and
that user is not just a sales rep. That user
is a patient that users as a provider. That's why

(22:54):
we call it an engagement platform. So we build and
continuously innovate life sciences specific purpose built product capabilities. They're
not just you know, GXP compliant features and workflows, but
life sciences specific agendic AI innovations to use automation to
drive those productivity gains and deeper insights we leverage. You know,

(23:19):
I'm just going to draw back to Stanford again. I
think all roads it leads back to Stanford. You know,
we have leveraged the ten plus years of experience and
investments in AI Salesforce has made and continues to make,
starting all the way back at the start for us,
which was you know, an AI research lab that Salesforce created,
hiring a bunch of Stanford AI machine learning data science

(23:41):
experts back in twenty fourteen, twenty fifteen. And you know,
AI certainly has had its booms, right. I remember watching
the movie The Matrix in nineteen ninety nine and it
was all about AI, and and then you see your
predictive AI and then you hear generative I come onto
the scene in late twenty twenty three, and now we've

(24:01):
got agentic AI. So it really is an exciting area
now and we focus on that from patient services to
clinical recruitment and enrollment, ongoing clinical collaboration and intelligence because
that's that's really where AI and data data aggregation can
come in. And then obviously coming next month is our

(24:23):
most exciting release for HCP engagement. When some formally think
of as pharmac CRM, and we try not to use
that word CRM anymore because as that sort of past
historical connotation of a CRM as a system or record,
go in and record what you've done, because we're creating
and delivering a engagement operating system of action and insight.

(24:45):
So you know, we've done pretty well. We've we've started
this initiative about two years ago and already have customers
using US across patient and AHCP engagement from large global
leaders like like Pfizer and at V to CATA, fee
am Gen, two smaller enterprises and biotechs you know, for
for a business unit or product line they launched just

(25:07):
fifteen months ago, where we already have over eighty customers
relying on us to help them with their patient and
provider engagement, all for the greatest good of all, which
is improving patient outcomes.

Speaker 1 (25:20):
Yeah. No, it sounds like a lot of development going on,
but it also sounds like there's an inflection point that
took place over the last you know, fifteen eighteen months
with you know, the shift from that kind of traditional
CRM that you know, people like me are used to,
you know understanding, to this new you know, engagement model

(25:41):
that's more interactive. Is this more of that? You know,
just we're into that new phase of the AI and
you know, I can talk about the matrix for hours
and just you know that about that. But is it
the shift from that the that traditional AI to this
kind of new or were there were there other you know,

(26:03):
incremental you know, factors and interactions that you had with
the pharma and the biotech clients that allowed you to
get to that engage model of your art.

Speaker 2 (26:11):
Now, well, it's interesting.

Speaker 5 (26:14):
It's there's basically the three inflection points that happened all
sort of at the same time. You know, I think
I think life sciences is truly at a hinge moment
right the way that things were done before and the
way things will be done, you know, as we go
through this transition. To me, there's there's three things. One,

(26:36):
the industry is changing, right, You've got personalized medicine, You've
got you know, modern medicine is far more complicated. You've
got all these different proliferations of patient support programs, you've
got a CP burnout, you've got higher level of competition,
you've got pricing pressures, you've got you know, just the
world of life sciences is different. It's changed dramatically over

(26:57):
the last not just two years, but probably you know
it's always been changing, but a lot in the last
ten years. If you think about life sciences has been
traditionally more of a push model. Right, We're pushing out
information and hopefully it lands on, you know, in an
a CP's head when they're making that decision of the
point of care, whether it's what prescription or what what procedure.

(27:20):
So that's inflection point number one the industry itself, right.
Number two is AI. Right, I think that's that's an
obvious one. Everywhere you look, every conversation you have, whether
it's at work or.

Speaker 2 (27:31):
At home, you're talking about AI.

Speaker 5 (27:34):
I don't think that the world has gone from you know,
AI being sort of named you know, nascent, to everywhere.
It's front page news and it's back page news, and
it's in every single you know page in the newspaper.

Speaker 2 (27:46):
Now.

Speaker 5 (27:47):
So AI breakthroughs with generative AI and the agentic AI,
that was the inflection point number two. You think about it,
It's all that's happening is that things are accelerating. You know,
you had machine learning for a long time and then
you know outcomes chat TPT and generative AI, and all
of a sudden additional breakthroughs come to enable agentic AI.
So AIB number two the third one is data innovations. Right,

(28:13):
so you've got innovation that can now take data and
reason with it and consolidated and connected. And you know
the amount of data that's in healthcare dwarfs all other industries. Right,
there's so much data. I mean, earlier Chris was talking
about I don't know how many pages of the HR, right,

(28:34):
like all of this data, whether it's claims data, script data,
preference data, you know, patient population data. The amount of
data is just getting bigger and bigger and bigger, and
that creates a lot of complexity. And there's a cure
for that complexity with these data innovations. It starts off
with things like data bricks and snowflake, where you're you know,

(28:56):
these data lakes that could intelligently connect data. And now
you have these concepts like zero copy. So part of
Life Science's cloud is we get to use the foundational
data layer of a product called data cloud here at Salesforce,
which can use zero copy. And what zero copy means
is you might have data that you're generating yourself, or

(29:20):
you're pulling it from the web, or you're buying it
from third parties. It's your back office data. It's all
your different data systems. All of that might be put
in a snowflake environment, and last thing companies want to
do is move around that data.

Speaker 2 (29:35):
A bunch of times. It's expensive and you create multiple
versions of the truth.

Speaker 5 (29:39):
So zero copy allows us to take all of that
data from all these formally dispared, disconnected systems, consolidate them,
and put them right into the flow of work in
an application. So, just to summarize, because I know I
gave a really long answer, you've got a changing industry,
You've got AI, You've got data inv and those three

(30:01):
things coming together brings us to this moment in time
where things are going to be very different in the
future than they.

Speaker 2 (30:09):
Were in the past. And I think we all.

Speaker 5 (30:11):
Have some insight on what that future is going to
be because we're you know, futures kind of arrived already,
but who knows what it's going to be like five
years from now.

Speaker 1 (30:20):
Yeah, And I tell you what, the changing industry is
definitely something because you know, I'm thinking about sales reps
and basically you would almost be able to just you know,
give them a nice dinner and then they would buy
your prescription basically through you. And you can't do that anymore.
So you need to have some sort of you know,
other data and results to kind of justify well having that.

Speaker 5 (30:45):
Data needs to be actionable twenty four to seven and
not just buy a rep or a medical science ligais
on if you think about AHTP. So I'll take my
uncle and my brother right. They're both practicing, and my
uncle wants to get his information, which is data from

(31:05):
pharma company in person, so he will call the farmer
rep leave a voicemail still like he's old school. I
don't think I've got a voicemail in a while, although
I think I left Chris a voicemail about a.

Speaker 2 (31:17):
Few days ago. I hadn't done that in a while.

Speaker 5 (31:19):
But he will leave a voicemail and say, come nine
point thirty, you know, whether you bring coffee or bring bagels,
that doesn't matter anymore.

Speaker 2 (31:28):
But he wants to do it in person. Now.

Speaker 5 (31:30):
In contrast, my brother will want to get that information
to get that support at two in the morning, and
he'll want to have that He wants that rep right
there at his house two in the morning. That's not
really possible. So this data and AI innovations allows that
HCP to get the information they want. No matter what

(31:52):
it is, no matter what time of day, no matter
what channel. If you think about the process has been
so broken. You got an HCP, it has some interaction
with a website, learning information and maybe an agent on that,
and then the next day they're meeting with a rep.
They're starting that conversation all over again. Hasn't been personalized.

(32:12):
And now through data and AI, we can connect that
experience because that's one person, they don't have multiple journeys.

Speaker 1 (32:20):
Yeah, I think that now bridges us into the partnership
between viz Ai and Salesforce. And you know, I'll just
start with the question of how is that integration additive
to what Salesforce and the Salesforce Life Sciences is currently providing.

(32:41):
And maybe I'm answering the question, but it sounds like
that that sales that sounds like the sales rep game
they call it two AM could potentially benefit from that partnership.

Speaker 5 (32:53):
Well, so you know, Pharma, I'm just gonna use Farmer
for this part. You know, it doesn't always have historically
hasn't had a great understanding of what's happening at the
point of care. Okay, right, but new solutions like like
viz and this is why we're so enamored with our

(33:16):
partnership with this. You know, can deepen pharma's understanding with
access to real time ACP workflows in those decisions, which
again historically pharma hasn't had access to all Right, So
what do you do with that data? Well, that data
becomes signals, signals to marketing teams and brand managers that

(33:36):
are managing launches because they understand a little bit about
what those ACPs need or more about their patient populations.

Speaker 2 (33:44):
Or it's informing a rep.

Speaker 5 (33:47):
Let's say I'm a I'm a rep. I'm Frank the rep.
There's doctor Mansey. I'm supposed to meet with him at
eleven am tomorrow.

Speaker 2 (33:56):
I want to know.

Speaker 5 (33:58):
The more I know about Chris, and the more I
know about what Chris did that morning or yesterday, the
better off I am. The more I know about my
custom more, the better I can support them. So if
I know that doctor Mansy's run these particular scans and
has these particular workflows going on, I can meet Chris
with the information where he wants it now, wants help.

(34:20):
That means I'm more orchestrated. That means I'm more personalized
and personal personalized engagement. You know, that is something that
I think business is to keep part of It allows
that rep or that agent to be more personalized with
that because it hasn't been. And that's why you've seen
over the last ten years, you know, more and more

(34:41):
ah cps not wanting to have a rep to come
into their office and clinic not opening those emails because
it's not it's not contextual, it's not relevant to what
they want, and that now can change. So our partnership
is about connecting point of care with port of promotion
and education.

Speaker 1 (35:00):
Very interesting and Chris, I mean, how does the VISAI
model differ from kind of the standalone version that you
have with the hospitals or is it the exact same
and you can kind of just implement that into the
Salesforce platform.

Speaker 4 (35:14):
They work so synergistically together. That's one of the things
that's been really nice to see. So maybe I'll give
you an example how work. So the patient comes in
and has a chest CT scan for some reason, and
a vis algorithm identifies that there's a lung nodule, right,
So previously an diagnosed lung nodule, VIS is going to

(35:35):
organize the clinical workflow, triggering diagnostic tests, right, it might
be a gardened liquid biopsy, so we get the genetic
profile earlier and then before the treatment decision is made.
And this is kind of the magic and why point
of care is so important. Will integrate that information with
Salesforce to enable Salesforce life Science, to enable HDP education

(35:58):
both in VIS and in Salesforce platform, to orchestrate compliant
nexpec's action and sales workflows communication from that rep or
the MSL to the AHTP at the point of care
where the AHTP actually wants the information. So to your

(36:19):
point of you know, doctors don't want farmer reps to
buy them dinner, right, they don't. They want help in
the five minutes that really matter. And I think the
fact that this is real time point of care access
and which has previously been impossible, it's really the holy grail.
It's point of care access that skills trusted and actually

(36:40):
leverageable through our partnership, so that the clinical event which
we will then trigger next best clinical action, can also
provide farmer support and the next best farmer action leading
to measurable outcomes. That really is a phase shift in
what farmer can do today.

Speaker 1 (36:56):
Yeah, and it's almost just go back to the biopsy
and the chest CT scan, I mean, because maybe it's
just I haven't had a CT scan on my chest,
so I don't know the traditional timeframe. But I guess
if I had that chest CT scan without VISAI, does

(37:17):
the doctor have the results of that scan at the
time of the scan or do I have to set
up another appointment to hear those results?

Speaker 3 (37:26):
Right, it's even worse than that.

Speaker 4 (37:28):
So, you know, we know that for most diseases, guideline
directed care is happening around ten to twenty percent of
the time. So early days in a new treatment, single
digit percentages of patients are getting the treatment that they deserve,
and then as it mature, as it gets up to
twenty percent, I mean, eighty percent of the time, they're not.

(37:50):
Why is that? Well, if you map out the workflow,
for example, for this lung cancer patient, there's probably fifty
or so different steps with twenty or different htps that
all need to happen without the technology to get that
patient to treatment. Okay, With visit's like a super higher
We're going straight from that detection of the lung nodule

(38:13):
straight to guideline directed care. With lots of nudgers and
guidance along the way, like surfacing guidelines, surfacing education, surfacing
things from the farmer company, who might they might want
to present at that point in time. And so it
takes that ten to twenty percent, up to fifty percent,
up to eighty percent. So more patients are getting treated.
And it's important to know it's not necessarily that the

(38:36):
radiologist doesn't know how to pick out a lung nodule,
but they might have been looking for something completely different.
So all stroke scans have the chest as part of it, right,
but you're looking for a stroke in the brain, you're
not looking for lung cancer. It's getting this all the time.
We've actually found that a lot of the time the
finding is in the radiologists report, but it never got
routed to the right specialist and so it fell off there.

(39:00):
I personally had this that happened to me with a
spinal disc prolapse. Well, I've read the scan, I knew okay,
I didn't need surgery, so I kind of made my
own assessment. But eighteen months later I heard from the
hospital system, which I want name, Oh, you've got this finding.
You need to come in and see us, and it's like, well,

(39:21):
it's kind of crazy, right that that would happen, and
it's in one of.

Speaker 3 (39:24):
The best hospitals in the world.

Speaker 4 (39:25):
But it's just it's an important understanding for how things
are today to realize why this solution is so impactful.

Speaker 1 (39:33):
Yeah, and it's definitely important to see just at the moment,
you know, being able to get it earlier and at
that moment gets you know, keeps that ball rolling sooner
and sooner until you can get to a final solution.

Speaker 5 (39:45):
I just want to add something. Yeah, you know, just
listening to us talk about this, I think the one
common thing that I'm hearing is this is all about
making sure that all the right dots get connected right
because so many things. When you're sitting with mountains and
mountains of data, it's easy for not the right pieces
of data to get put together so the right decision

(40:08):
is made. And you know, just hearing Chris talk about it,
even reflecting on why, I say, like that is the
real power of change that is happening. His data can
be connected in real time so that right treatment decisions
are made and right education is time.

Speaker 1 (40:26):
Yeah, and a stick dinner is not going to do that. No, No,
So I mean walk us through now, because the partnership
has been announced, what are the steps between today and
getting it onto the actual salesforce platform and getting you know,
the farmer companies and the h GPS all on board

(40:47):
with this, with this partnership.

Speaker 5 (40:50):
Yeah, maybe I'll start and Christ to it. So, you know,
a partnership should never just be you know, something written
down yes to be put into action, and you have
to have an aligned vision. And I think Chris and
I have a very much aligned vision on you know,
new commercial models, new Tree point of care models. So

(41:13):
there's sort of that vision and that ethos that has
to be connected first. Then you get into the technical
nuts and bolts of things. Right, So this is making
sure that VIZ whether it's Viz data or Viz agents,
you know, Viz data getting into life sciences cloud or
Viz agents communicating with agents that sit within life science
as cloud, because it's no longer a world of hey,

(41:36):
let's integrate these two systems. Let's have the agents do
that integration and constantly can be communicating to each other.
And that typically requires an orchestration layer in between. But
you know, at the end of the day, it's we
are working with all of these pharma companies. You know,
every pharma company has a decision to make on their

(41:58):
next gen commercial and medical engagement platform. Right because of
AI sure, but also because of the salesforce and my
former company, that partnership was dissolved, and so everyone has
to either move to Viva's new CRUM application platform or to.

Speaker 2 (42:19):
Life Science's cloud.

Speaker 5 (42:20):
So when you've got an entire industry that isn't evaluation
of making that change, that's the perfect opportunity to all, Right,
if we're going to make a change, let's change how
we do things, and let's really move the needle forward
in terms of how we can provide better, better engagement
with a CPS and better patient outcomes.

Speaker 2 (42:42):
So our partnership is.

Speaker 5 (42:43):
Around connecting that point of care to our ethos, which
is you know, service supporting patients and supporting the promotion
and education to AHCPS.

Speaker 4 (42:55):
Yeah, and it's a transformational moment for Farmer, particularly for
the commercial strategy, a huge opportunity to engage very differently
to how they previously engage in a much more effective way.
And so I think the way Frank and I think
about it is we want to partner with the cutting

(43:17):
edge farmer companies who want to create the future platform
right together so that it's really working both for the
doctors who are going to treat the patient, for the patients,
and for the pharmaceutical company, so it's more effective and
efficient than ever before. You know that old model where
farmer spending billions on tv ads after the fact, I

(43:39):
think lots of websites that no one goes to that
really makes sense and kind of there's no one who
thinks it does, but it's just what's traditionally happened. Now
we're offering the opportunity to partner on a new model
where you can be in the workflow in real time
when it matters most.

Speaker 1 (43:56):
Actually, because that's always interesting because it's always those commercials
are never directed at the physicians. They're always directed at
the patient, right, and the patient would go to the
doctor and say, hey, how about you know k truda
or you know ozempic or anything like that. But it
sounds like at the end of the day, viz Ai
and Salesforce are going to have the data and the

(44:17):
AI analysis done to be able to say, well, yeah,
those commercials are nice, but the clinical data that we're
seeing points to this and that instead, which kind of
goes to then you know, being able to get the
right patient to the right medicine at the end of
the day.

Speaker 5 (44:35):
Yeah, I mean these advertisements and these you know, just
broadcasted messages, you know, whether it's targeting consumer, a patient,
or a provider. I mean, you know, those are going
to be a thing in the past, I believe, because
if you can provide the right information at the right time,

(44:55):
you know, in the moment, then you I mean you
think about it, all these mess it becomes noise, yeah,
you know, and all the needs of a CPS or
you know, the voice of an ACP is not understood
by by a life sciences organization, just like the messages
of the life sciences organization are not landing with the

(45:16):
AHCP because there's too much noise. And so whether it's
advertisement or five different rep seeing calling on the same
ACP that all work at the same company, that don't
even know that their peers are talking to the same person. Like,
how poor of an experience could that be. So now
it's basically streamlining everything, and you know, it's kind of

(45:38):
getting back to basics of if you have a question,
ask it at that point or systems now know what
questions you should have and are starting to tell you, hey,
here's information even before you ask.

Speaker 4 (45:54):
Yeah, it's pretty obvious to I think both of us
and a lot of people in the space. The pharmaceutical
companies who can be agile early adopters will win their
own new model, and the fast and slow followers are
going to spend years and years and years trying to
catch up. And because of the way the pharmaceutical model works, right,

(46:15):
you're seeing the huge success right in what Lily's done, right,
getting that advantage in your commercial model, getting a more
steep up curve in branded in apuptake of your brand.
Those advantages accrue and compound, and so like we're seeing
the innovators in the space being extremely excited not just

(46:38):
in using our shared platform, but in wanting to do
us to do more, asking for more agents, for more intelligent,
for different functionality, and so it very much is a partnership.

Speaker 1 (46:48):
Yeah, and so I'll be keeping my eye on this
the next couple of years. And you know, if I
keep seeing fewer and fewer those commercials a football games,
I'll know what the reason is for it.

Speaker 4 (47:02):
It might live to be the administration banning it, right,
that's the other fact.

Speaker 1 (47:05):
That's yeah, Well, like I said, that's that. That could
be an episode for our public policy analysts next week.
But Chris and Frank, thank you both for joining us today.
I was a very enlightening episode about you know, the
potential of AI and healthcare and pharma.

Speaker 2 (47:25):
Thank you, thanks for having us math, and.

Speaker 1 (47:28):
Thank you to our listeners for tuning in today, and
we hope you joined us for future episodes. If you'd
like to stay up to date, you can click the
subscribe button on Spotify or your favorite streaming platform. Take Care.

Speaker 4 (48:07):
Users us

Speaker 2 (48:17):
Into bases
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Host

Jonathan Palmer

Jonathan Palmer

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