February 6, 2025 • 43 mins
“There’s no digital feedback loop in health care, you have it in Tesla, in Netflix, in Amazon — but not in the industry that impacts every life,” Terry Myerson, CEO of Truveta, explains to Bloomberg Intelligence in this episode of the Vanguards of Health Care podcast. Myerson joins BI analyst Jonathan Palmer to discuss Truveta’s mission to aggregate and analyze health data across 30 major US health systems, covering one-third of Americans. He details the company’s work in regulatory-grade safety and efficacy research, the launch of the Truveta Genome Project with partners like Regeneron and Illumina, and the power of AI-driven insights to accelerate medical discovery. The conversation explores Truveta’s efforts to address data fragmentation, privacy, and interoperability challenges that must be solved to revolutionize patient care and life sciences.
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Episode Transcript
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Speaker 1 (00:18):
Welcome to another episode of Bloomberg Intelligence's Bandguards of Healthcare podcast,
where we speak to leaders at the forefront of change
in the healthcare industry. My name is Jonathan Palmer, and
I'm a healthcare analyst at Bloomberg Intelligence, the in house
research arm of Bloomberg. I'm very happy to welcome Terry Myerson,
CEO of Truvetta, to the program today. He brings a
(00:38):
fascinating background in healthcare technology, having previously served as a
longtime EVP at Microsoft. Prior to founding Truvetta, he did
stints in the venture and private equity worlds with Carlisle
and Mardona and Truvetta is In his first startup, he
founded inters, a web analytics company that was acquired by
Microsoft in the late nineties. Welcome to the podcast, Terry.
Speaker 2 (01:00):
Thank you for having me.
Speaker 1 (01:01):
Well, I'm excited to dive in. Why don't we start
with what Truevetta does and where you fit in this
very large landscape of analytics providers and data providers in
the healthcare space.
Speaker 2 (01:12):
Sure, I think the best way to answer that would
be the sort of tell a little bit of the
origin story of the whole company itself. And that's you know,
it's twenty twenty and the pandemic has started to emerge.
And one of my old colleagues was chief information officer
at Providence health Care, and during my venture stint post Microsoft,
(01:32):
I've had developed this fascination between data science and life
sciences and he knew about that, and he invited me
to be part of the virtual team to help Evenance
respond to the pandemic. And what became so clear in
that moment was Providence didn't have the tools to ask
and answer questions about this new disease. And at that
(01:52):
same time, a leading life science firm that had a
therapy that there was a theory that it was relevant
to this disease approached evidence seeking data, and Providence didn't
have the cultural or technical capabilities to respond quickly to
this life science request. We had the leader of the
World Health Organization tweeting that rock hydroxiclorican secure for COVID
(02:13):
and President Trump saying are saying that's not President Trump
saying that it was. And so it was clear there
was not the trusted data to ask and answer the
questions many sides of this industry needed. Public health didn't
have the answers they needed life sciences that the innovators
behind these therapeutics didn't have data they needed about their
(02:34):
own products. The health care and the treating physicians didn't know.
And so it was in this moment that Trebetta was born. Uh,
there was this idea that had been circulated amongst health
system leaders for actually a couple of years that putting
their data together so they could ask ask an answer
questions was a great idea. But there was this moment
(02:54):
of the pandemic that sort ofants took idea to action,
and you know, through the spring summer of twenty twenty,
there was a lot of work across many health systems
and many lawyers conceive, you know what, how could this
work on a full cliant way. And the company was
born in the fall of twenty twenty with four health systems,
(03:18):
Providence Health, Advocate Health, Trinity Health, and Tenant Health. And
they said, the world needs us to exist. We just this,
this has to happen. Let's do it. And you know,
since that time, the company has grown to be working
with thirty of the country's largest health systems. So you know,
it's it's a tremendous amount of patient care being provided
(03:39):
by these thirty health systems. It's eighteen percent of every
clinical care encounter every day in the United States, covering
about one third of all Americans. But then, you know,
say that we have all this data, we have all
this patient care being informed. And then in the fall
of twenty twenty one, Patrick Corbell, the cheap safety officer
(03:59):
of Pfizer, reached out to Treuvetta he's now actually on
our board, and said, I need a great data source
to help track the safety of the COVID vaccine. That
visor was rolling out across the country, and we were
very fortunate to be able to partner with Pfizer to
support their safety efforts, which was like a being on
the front lines of a but it was very the
(04:21):
scientific rigor with which that team pushed us the response times.
It really pushed us to develop the platform that Patrick
and his team could use and the systems developed to
be this regulatory grade safety and efficacy research platform to
be used by one hundred different customers outside of the
(04:43):
healthcare systems too. That all being said, you know, this
exciting announcement we had a couple weeks ago. There's always
been something missing from our data that you know, we
have all this medical outcomes data, we have all this
data on what's working, what's not, is it safe, is
it efficacious? Relative things, but the root cause of what
might be causing that variance in safety and efficacy. For that,
(05:06):
you need genetic data and multiomes data, and that data
is not collected during care at any scale. And we
launched this product called program called the Trubetta Genome Projects
to now hopefully genetic sequence I mean, from an aspiration standpoint,
all one hundred and twenty million of those Americans that
(05:28):
are getting care from our l systems, and so we
can really learn the biology of disease, learn the root causes,
so we can take care of patiently, and we can
get to the we can detect earlier, delay the onset,
you know, create the therapeutics necessary to change the course
of care. I mean, I'm so optimistic about the impact
of this on the cost of care, the quality of care,
(05:50):
to the access to care, the pace of innovation.
Speaker 1 (05:54):
That's there's a lot to unpack there. So maybe I
want to come back to the genome broad because it's
one of the key interests that I have, But I
want to start back to walking in the four walls
of maybe providence. And how surprised were you on a
scale of one to ten that they couldn't answer the
fundamental questions that they were trying to answer. I mean,
I guess with your technology background, were you shell shocked?
(06:15):
Were you modestly surprised? How surprised were you?
Speaker 2 (06:18):
So what I had watched from within Microsoft over the
last decade is just what we were broadly calling this
digital transformation. Which was at the source of it was
this idea that there was going to be companies which
digitally transformed themselves. And so Tesla is an example of
a company and automotive that was, you know, they were
collecting daily telemetry on every vehicle, you know, so that
(06:41):
they could continuously learn and improve their products. And that
continuous improvement in the collecting more data they just continuously
and it's going to create this competitive advantage over time
you would draw on an out, you know, versus other
car manufacturers that had none of that digital telemetry at
digital learning or digital feedback loop. You had the same
thing with me with Netflix where you had you know,
(07:02):
I remember going to Blockbuster to renting a video, taking
it home and if any movie studio wanted to learn
who was watching their movies or how long they're being watched,
what movies they're being watched in conjunction with the cohorts
to their customers. Well, you had to hire a market
research firm to stand outside a blockbuster or interview, and
(07:22):
you were getting this, you know, relatively, whereas Netflix knows
all of our clicks and they know who's clicking, who
in the family's clicking, the demographics people clicking, what's you know,
what we like, groups of people that like and dislike
various things, what type of movies to invest in, and
it gives them the digital feedback loop. It gives them
this competitive advantage. You can say the same thing happened
(07:43):
at shopping with Amazon or you know, finance with Bloomberg. Well,
what was so surprising to me was how there wasn't
just no digital feedback loop in healthcare. When the life
science firms now I realize, I mean, they they'll ship
their products into distribution, it gets deployed into the customer.
(08:04):
There's no data coming back to them or how we're
wet it's being used with which outcomes. You know, they
can run a clinical trial that takes a couple of years,
tens and millions of dollars, and you know, you negotiate
with the regulator what you're going to measure in that
clinical trial. But there's no continuous iteration at learning and
so much potential to improve. Likewise, on the healthcare side,
(08:24):
there's no digital feedback loop. You're trained, you're you're held
accountable based upon a whole variety of things in the
healthcare industry. You have your standards of care, you have.
Speaker 3 (08:35):
Your liability, but you know, with empathy, you deploy the
the practices you are comfortable recommending to your patient, and
where's the feedback on what.
Speaker 2 (08:49):
You know? It's an analogy. I heard the JPM Morgan
Healthcare conference from the CEO of to It Is Surgical
really struck me. You talked about turning surgery from an
apprint to start to a data science and just kind
of struck me like, there's this apprenticeship that healthcare professionals
have to go through. They learn, and they're professionals at
their art, but how do we accelerate that learning? So
(09:11):
it was just eye opening to me to see how
healthcare it was not Tesla, was not Netflix, was not Amazon.
It was something that lacked that entire learning loop, and
from that my passion developed for building Truvetta with the
healthcare systems and with the life of science customers, we
(09:32):
can just improve the pace of the innovation and the
quality of all of our work and take better care
of patients and solve one of the country's biggest economic challenges.
Speaker 1 (09:42):
Absolutely, If I think about the hurdles to starting Trueveta
versus say, collecting the data a Netflix, right, there's a couple.
There's the privacy aspect. There's the fact. I mean, if
I own a Tesla, let's say I'm probably just driving
my Tesla and maybe one other car, you get most
of the data in there. But with healthcare had this
fragmented landscape of providers. And then I guess the third
(10:04):
piece is that and I've heard you talk about it,
the structured versus unstructured data. So how do you How
does Truvetta kind of tackle those three big problems through
the lens of providing a data back to life sciences customers.
Speaker 2 (10:17):
Well, I think I break the challenges into three. I mean,
the first one is the regulatory challenges around security, and
second one is the fragmented nature of the data, and
the third is the unstructured nature of the data. You know,
security and privacy is a it's just hard work. It's
hard work. It's third party audits of your work. It
is I mean, there's a AI in there to both
(10:40):
de identify data and to challenge the identification of the data.
And it's it's hard work, and it's just investment in
R and D dollars and to go build world class
security and privacy systems for this data. It's not easy,
but that's part of what Tchervetta does is investing the
R and D dollars to protect the security and privacy
(11:02):
of the data. The fragmentation is based upon building a consortium.
We have a consortium of the now covers a thousand hospitals,
twenty thousand sites of care. We're building partnerships with payers
to get their data, to fill in sight, fill in
data that's not done in our providers, and to build
these full longitudinal journeys. And then likewise the unstructured nature
(11:25):
of the data with chat GPT, which came out in
the middle of Trevetta's journey, everyone's very well, very well
aware of these technologies, but you know, we've been building
these machine learning AI based tools to bring structure in
a regulatory grade way to this unstructured data so you
can ask and answer questions quickly. Now, as part of
the challenge, you know, going back to the pandemic is
(11:46):
if you want to understand these adverse events, or if
you want to understand efficacy, you got to look at
these words that are drafted by doctors in the clinical notes,
or look at these images you know, of the heart
and look at information around the heart say is it
there or is it not? And it just now is
the time. We have the cloud computing, we have the
what we now call AI technologies to do this and
(12:07):
you know, with that just hard R and D work,
we can and commitment to third party audits, we can
address the security and privacy. Fragmentation is a business model.
I believe problem of building the consort with payers and
providers provide the data and then unstructured again is a
technology problem.
Speaker 1 (12:27):
So maybe if you go back, what's been harder. Has
it been building the back end technology or or signing
up the consortium of providers you know, to be part
of the network. Just wondering which one looks harder from
your perspective.
Speaker 2 (12:42):
I think that challenges on three dimensions. This there's the consortium,
the business model that allows you know, there's mission alignment
amongst all these parties. We all want to take better
care of the patients. But creating the business model economic
incentives such that people can prioritize the work amongst all
the other things they've got going on, that's that's hard
(13:03):
and important work. The technology is critical. Without the technology,
this data is not useful. I mean, it's crude oil
versus gasoline. I don't know if that's a kind of
a bad analogy, but you know, we can't heat our
homes and we can't drive our cars with crude oil.
You know, regulatory grade, safety and efficacy research cannot be
(13:23):
done without you know, really high quality processing of this data.
You know. And the third thing I would say is
the user experience behind this. This is not this is
a massive amount of data. So how do you make
it useful to all the different people that would like
to have access to this data, whether it's a data
(13:45):
scientist or an executive. You know, we've talked to life science.
There's the physician, there's the there's the patient, and so
I would say there's also this user experience or usability
challenge here to make this clean process data useful to
everyone in the very scenarios they would have, whether it's
like managing care quality, tracking population health, or try and
(14:05):
understand the safety of the vaccine or whatever it may be,
there's different user experience challenges here. Well.
Speaker 1 (14:11):
Thinking about those two more broadly, you know, how does
it How does the customer base or the usage base
lineup today?
Speaker 2 (14:17):
Is it?
Speaker 1 (14:17):
Is it more on the life sciences side or more
on the provider side evenly split?
Speaker 2 (14:21):
How does it?
Speaker 1 (14:22):
How does it look in terms of who's kind of
using that You're about a platform right now, I would say.
Speaker 2 (14:27):
It's probably one third one third, one third between healthcare systems,
academic research and LIFSKA. We have an academic program with
do CAN, Stanford and Dartmouth and several other reading institutions
to do research using this data as well.
Speaker 1 (14:40):
Where is that the ideal? Mixing your mind? Is that
where you want it to be? I mean that, you know,
thinking out loud as a as a financial analyst, you know,
the deeper pockets are probably on the life sciences side.
Speaker 2 (14:51):
You know, I think in terms of building trust inside
trust behind the data. What's just Bloomberg as an analogy, Bloomberg,
I don't know what your revenue is split is the customers.
But the data Bloomberg provides is trusted. One of the
things that enables it to be so trusted it's ubigulously available.
It's available to me as a consumer, it's available to
JP Morgan. And that ubiquitous access for us all to
(15:15):
have a ground truth to the data, I think is
critical to Bloomberg being that trusted data source. In finance,
I think it was an analogy here. You don't want
to just be available with the customer that's paying you more.
I think you want to have some ubiquitous availability so
(15:35):
you can build trust in the data, and I think
that unlocks value for everybody, and then how you ultimately
build your business from each customer segment will be different.
But if you know one customer saying well I see
this in the data, another customer saying well, I can't
see that, so I'm going to get my own data
to do it, You're not going to build the trust.
And I do think part of that efficiency and are
(15:57):
in healthcare overall, I do think trust is data is
one of the things we need to bring. I mean
there's still values debates that come once you have trust
in the data, but having a common understanding of the
ground truth about what's working, what's safe, what is the
cost of that intervention, I think will be unlocked incredible
opportunities to then have values debates.
Speaker 1 (16:19):
What has surprise to you, I guess in terms of
your ability to you know, what could be this data
across all these different systems and patients. I mean, is
there something that you were you as you know, not
not necessarily as the CEO of Truetta, but you know,
just as a human being. We're surprised about what you
could do with this this data.
Speaker 2 (16:36):
We all know people that are on GLP one medication
and losing weights, and there was no data comparing Ozembic
and Mojarro. And then one of our researchers spent one month,
you know, creering the data, writing up a study which
made it into jama and it was like it was
one month of work and it was like the first
(16:58):
ever compared to effect this o Zempic and Mujarro and
you know, makes the Today's Show. It was like national
news that we were able to do this, but it
was this was done with a query against the data
set just makes a whole lot of sense. The you know,
over a year later, there was a clinical trial completed
that cost tens of millions of dollars with less than
one tenth the patients, so one tenth that represented them
(17:20):
in this and was completely statistically equivalent. And it's just
interesting to me. I look at the efficiency we could
bring to the learning, and with good learning, we can
accelerate everything. And so that was just one of these
moments of obviously I was when the clinical trial results
came out, I was a little nervous it was going
to be different. Of course.
Speaker 1 (17:40):
I mean it's funny that you know, a lot of
times those head to head trials don't even happen, right,
of course, there's an economic incentive to not do it
for Willie and Novo, you know, not to pick on them,
but that's just the way the industry works.
Speaker 2 (17:53):
Which is one of those things is like to see
that those head to head trials are not done those
comparative to effectiveness. But when it comes to treating a
patient and you're making choices, you're making choices for that
specific patient. So understanding what is the best, you know,
in terms of efficacious, maybe cost availability care for that patient.
(18:13):
We need to be able to compare the options including
doing nothing.
Speaker 1 (18:19):
Now that makes sense. How should I think about the
competitive landscape that you operate in? I mean, I've been
in healthcare for two decades give or take. You know,
I remember IMS. You know there's a whole host of
what's called it newer age analytics companies. How does the
petitive market landscape look for you? Guys? You know, how
(18:39):
big is it? You know, how much are you going
head to head? How often are you going head to
head to some of these newer providers versus legacy providers?
What does it look like to you?
Speaker 2 (18:50):
It's a complicated question. You bring up IMS IMS. My
understand QBA right, My QB AS business is primarily in
the called commercial analytics. You know, understanding which doctor is
prescribed which drugs so you can send the sales are
up in there with the right number of football tickets
to incent them to prescribe a different drug. I don't
(19:11):
understand that side of the business, and Truetta does not
participate in that. We have an ethic policy that none
of this data de identified, but none of it's to
be used to targeted advertising to patients or physicians, and
so we don't participate in that sort of targeted advertising world.
You know, within clinical domain where you're trying to understand
(19:33):
the safety and efficacy, you know, it's a broad landscape
you're looking at. There's CROs that are conducting clinical trials.
You have clinical decisions support tools of a variety of
shapes and sizes, but you just it's you could take
each one of those things and try and think about
them as market participants in this space. You have you know,
(19:56):
within the life sciences, you have budgets for helping new
therapies and your budgets for accelerating accelerating therapy adoption. You know,
Truvetta primarily is focused on, you know, helping life science
partners with accelerating therapy adoption until the Truvetta Genome Project
and now we're focused on helping them develop new therapies.
(20:19):
On the healthcare side, you know, it's population health really
primarily right now, and there's a set of population health tools,
but obviously you know, our ambitions are to help with
clinical decisions support eventually as well, and so.
Speaker 1 (20:35):
Kind of everyone it's a big market.
Speaker 2 (20:37):
I got it.
Speaker 1 (20:38):
Uh, maybe we'll go back to the genome project, because
I spend a lot of time in that world of
life science technology and have followed Alumina and the other
sequencing companies for years. I know Allumina is one of
your partners in this, and I actually saw your name
up in their their side show at JP Morgan. So
congrats there. No, they invested, they were just part of
the investment ground, right, Yeah, it's.
Speaker 2 (21:01):
Very very excited to work with them.
Speaker 1 (21:03):
So maybe at a high level, well, how does your
project maybe differ from some of the things that have
come before, you know, I think of like all of
us or some of the work that Genomics England has done.
How's how's the Truvetta project going to be different?
Speaker 2 (21:17):
So my understanding of all of us is they have
the US government's invested three over three billion dollars and
they have two hundred and fifty thousand data points. So
the Trivetta Genome project is receiving zero dollars of government money,
and you know where Generon is committed to support the
first you know, completely finance the first ten million data points.
(21:40):
And so the scale of the Triuvetta Genome project fifty
times larger than the all of us, the government funding
is one hundred percent smaller. And you know the quality
of the phenotypic data or the medical records, you know,
with all this work we've been doing for the last
five years to build the consortium, build the regulatory Grade
(22:04):
D identification and security, and the structure the unstructured data,
that's what gets linked with the genetic data, which you
know Regeneron is sequencing, and so I think it's it's
higher scale. You know, phenotypic data, you know, we think
is of the highest quality. And you know, there's we
(22:28):
would welcome the government government sure, but you know, the
but there's no goal.
Speaker 1 (22:34):
Maybe there's a good analogy there to the Human Genome Project,
right because the government costs three billion, and Selera and
Craig Venor it costs three hundred million. So maybe that's
the analogy you want to have.
Speaker 2 (22:47):
Here having a business model where that all of the
participants in the ecosystem. You know, again, I think it
starts with mission alignment, like you know, does does prioritize
using this work, support our mission, taking care of our patients,
you know, developing new therapies, and then it's about finding
these the model by which people can prioritize the work
(23:12):
because they have an economic incentive, and that's what we've
been working so hard on, you know, with you know, well,
you know, seventeen different healthcare systems have invested capital and
then they're now they're investing their time and they can
attribute a geno project work Alumina and Regeneron of you know,
committed time and capital to this project and because it
(23:34):
fits their mission, fits their vision of how the world
should be, and of course there's economic incentives in them
for their for them.
Speaker 1 (23:41):
Also, how does the economic actentive work for just Regenerond
maybe specifically, you know, since they're doing all the sequencing
work and obviously they're a therapeutics company, do they have
first rights to using some of that data for therapeutics
or anything like that.
Speaker 2 (23:56):
Well, so they are investor in the company, So they
republic about investing one hundred and twenty million dollars in
the company, and so they they have no governance of
the company. They have no access to any customer confidential information,
but they are they do own you know, a portion
of the company. Uh, and then they do not have
(24:18):
any first rights to the data other than like other
companies have to license the data in that sense. So
certainly Regeneron has the data potentially at the same time
as someone else. But Regeneron, by virtue of financing the
sequencing and the biobanking, you know, with us and with
the they that data is available to license for everyone else,
(24:44):
including the academic researchers, including you know, all the companies
that I think should be using this data for their
drug discovery or safety and efficacy work. And so it's
not any sort of first right or delayed access. It's
just Regeneron has it. Fathers have to license.
Speaker 1 (25:01):
It, license it there after got it. So you mentioned
the ten million, I guess maybe first cohort, I guess
that's ten million people who are going to be sequenced.
Are those going to be people who present with an
illness or are they going to be sequenced at birth?
What does the workflow look like?
Speaker 2 (25:19):
The workflow starts with patients will have to There'll be
an incremental consent, a new consent, okay, probably when they
go to depends on when that consent is presented to them.
But think about when you go to get your blood
drawn for whatever reason during care, you know, whether be
to get your A and C tested or something else,
and there'll be a incremental consent that presents do you
(25:40):
want to be part of this tribeta genome project. It's
like organ donation. There's no incremental prick of your skin,
there's no extra blood being drawn from you. You're basically consenting.
You are consenting to the leftover biospecimens after your clinical
test being used for this anonymous genetic research. If the
patient consents, then after the lab is completed, the blood
(26:01):
will get shipped to a sequencing facility where it will
get a whole xome sequence okay, and then if there
are any leftovers after that, it will be banked for
other multio mix research, which is where some luminous innovations
start to come in roteomics or transcriptomex or other some
(26:24):
of the innovations. And though all of that multio mixed
data gets associated with the phenotypic data of the patient
for you know, developing new therapies and safety and efficacy
research to everyone you know that would like access.
Speaker 1 (26:44):
To it, and will the patients provider have access to
that as well.
Speaker 2 (26:50):
It's a complicated question, complicated question answer. So the the
what we're starting out with is research grade sequencing and
so the data will not be used for treatment of
the patient. That's why I use the organ donation analogy.
Speaker 1 (27:07):
Of okay, that that verifies it.
Speaker 2 (27:09):
You know, like it's kind of like you're you're donating you.
You're helping the world get smarter, to take care of
you in the future, take care of your family, take
care of your communities. But you're not. This anonymous research
is not about you. It's about and so in the
future could we could we change? But right now we're
very focused on creating this data to understand the biology
(27:30):
of disease to help everyone get treated in the future,
because we just don't know the answer to some of
these questions. You know, why do people smoke and never
get lung cancer? And why do some people never smoke
and get lung cancer? Like there is a there's something
I don't know this, but there's a theory that there's
something in our genetics that is leading us in that direction.
(27:51):
And why does everyone need to get a kolonoscopy? Why
are we spending billions of dollars on kolonoscopies? And why
does everyone have to drink that awful stuff when we
don't know who of us actually is likely to get
colon cancer. We have our family histories, but like with
this knowledge, with this knowledge, we really can start to
(28:14):
both you know, develop those new therapies but also guide
care in a better way. And that's what we're trying
to do, is discover this biology of humanity and use
that knowledge of all of us to improve care for
all of us and develop new therapies for all of us.
It's not about treating you today, and that's the what
(28:34):
the consent well as clearly as we can convey to
the customer. But it's not so that's the it's a
research program.
Speaker 1 (28:43):
Under said now that makes a lot of sense. Interestingly enough,
I saw a headline that somebody did a study somewhere
that they were able to use an AI model to
predict who was likely to get colon cancer far ahead
of you know, when somebody would typically present with symptoms
or even go in for their first call and ask
could be let's say at age forty five.
Speaker 2 (29:04):
You know, so we have what's so making is we
have the technology now to do this for so many
different diseases. What we don't have is the trusted data
and trust. Here it gets back to that ubiquitous access
and some of this trusted data so we can actually
make decisions to make these are life changing decisions with
and then distribution for getting this knowledge back into the
(29:25):
healthcare system. And Trevetta aspires to build that trusted data
set and help that knowledge get back into the healthcare systems.
Speaker 1 (29:34):
So what is the what is the pitch that you
typically use to sign up a new healthcare system to
be part of the consort um? You know, what do
you lead as the tip of the spear? What's the
offer or that what's resonated so far when you've gone
and said, hey, we'd like to add you to the network,
or maybe people are coming to you. I don't know
how about the process works. It really starts with mission alignment.
Speaker 2 (29:57):
I mean, do we you know, doing saving lives with
data mission that was coined in twenty twenty. It really
kind of resonates that so much of health care is
not data driven today, and that's just a belief. You know,
we're not talking about data about you know, there's a
(30:19):
bunch of data and healthcare about utilization of the emergency rooms.
You know, the cost of the bedpaan effort is there's
a bunch of cost ex cost utilization data. There's a
bunch of that data, but the care, the care itself,
and that data that unstructured, fragmented, you know, lockdown data.
(30:39):
There's knowledge in there. And I do think the mission
alignment values a belief that this is the vision of
how we think care should be done differently. That's at
the core of all of our healthcare partnerships and that's
really you know, that's where the complexity is, you know,
in terms of like they have a trusted relationship with
their patients, you know, to commitment to their privacy and
(31:02):
quality of care and access of care. And so that's
that is where the core discussion is about are we
going to make this investment in the future of care
and lean into this idea that the world needs this
data set to exist. And it is that mission alignment
discussion that the that's at the core. The cost of
(31:23):
being part of Treveta, the reimbursement that you get for
being part of Traubada, that's that is completely secondary to
the is this on mission? Is this part of the
is this something we needed to to take better care
of our communities. That's where all the dialogue is. That's
where the choice is. It's this tension between a relationship
with an individual patient and the relationship with the overall
communities you serve, and those are complicated values discussions for
(31:48):
all our members understood.
Speaker 1 (31:51):
So if I think about, you know, being in the
four walls of a pride or today, I could think
of so many different use cases for data, right, and
I can improve workflows so that you know, we make
sure so and so gets their follow up appointment. I
could think about all the research topics that you brought
up before, you know, around discovery and innovation, But I
(32:13):
was thinking about clinical decision making and and are you
helping aid your providers with the critical decision making now
to say, I don't know, you know, maybe somebody's going
to get treated with X Y Z hip or NEEE
replacement or a stentor or whatever it might be. Is
the Trivetta data helping inform the cuindition which path they
(32:35):
should go down from a workflow perspective or is that
an aspiration in the future.
Speaker 2 (32:41):
Well, today we're seeing population health work done. We're actually seeing, uh,
there are two stents available for Ferra l Artery disease.
You know, we've heard a rumor that one causes more
major bleeds than the other. Let's look at the data
and let's let's inform our guidelines or per interesting decisions
within our healthcare systems based upon what we see in
(33:03):
the data. I mean, those kinds of questions today for
Travetta cannot be answered. So being able to provide a
tool where a healthcare a system can say, wow, we
feel we're kind of like this this step we are
using causing major bleeding. It's kind of a rumor in
the rumor, but let's go look at the data and
use that to that kind But in terms of getting
(33:24):
in front of the treating clinician for an individual patient,
you know, we're not there today. We obviously aspired to
get there, but right now it's population, it's not really
really you know, it's interesting, that's not classic research. That's
more of a healthcare operations you know, care quality.
Speaker 1 (33:43):
Looking at outcomes and scores and all that sort of stuff,
and eoritions.
Speaker 2 (33:46):
Definitely active there, but we're not in the treatment room today.
Speaker 1 (33:51):
So maybe as a follow up to that, you know,
if I think about your product roadmap over the next
couple of years, you know, for for whatever whatever, you
could share what you're working on today that we'll see
in the next year or two outside of the genome project.
Maybe that's too aspirational, and we're.
Speaker 2 (34:07):
So focused on regulatory grade, safety and efficacy research. Uh
and you know, bringing to life the Truvetta genome projects,
supplementing all of this incredible the clean medical outcomes you
know with multio mixed data. I mean, that is our focus.
(34:27):
You can say I was trained at Microsoft and focused, well,
I see the vision and I can, I can, I can,
I can look out beyond. But it's not We're very
We've We've got a lot of work to do and
it's challenging.
Speaker 1 (34:43):
So well, maybe thinking about that a little bit and
teasing that out, what what's going to be the hardest
to execute on for that near term vision your mind?
Speaker 2 (34:53):
You know, it's it's a Truvetta is such a team.
You know, it's a clout. You know, there's just your
a team. There's the several hundred Trevettans that are part
of our company. But like look Trevetta, the Trevetta team
includes thirty healthcare systems and people that are working hard
every day to use this data to improve care. But
(35:13):
also you know, now they're working on this consent and
they're working on you know, how do we route leftover
biospecimens and so they and then you have you know,
Regenerons now part of this team, a luminous part of
this team. You have Pfizer's part of this team. You know,
how do we work together to improve care? I mean
it's the maintaining this mission and economic alignment, so the
(35:37):
whole thing works, and that's the hard part, I guess,
but that's the I think it's with the everyone working
together is where we could do some amazing stuff.
Speaker 1 (35:50):
So thinking about that as well, when you're when you're
implementing Trevetta into a provider, how long does it take
to get somebody stood up? I guess on the on
your platform. So if I'm I don't know, it doesn't
matter whether they're Epic or Cerner or any of that
nuance as well.
Speaker 2 (36:08):
No, it doesn't matter. I mean, it's uh, we have
folks on Epics, Cerner, all Scripts and Meditech. We've got
the variety of the name it. And of course not
all the data is within the HR. Images are outside
the HR. Is outside the HR. And see it's a
variety of systems beyond the HR. But the core, the
core EHR, there's there's diversity there and it's months, it's
(36:31):
not weeks. Uh, and really how many months is measured
by priority on our side in their side I think
would be the stairway.
Speaker 1 (36:41):
And you just mentioned it and you mentioned it, you know,
prior to the discussion. But bringing in other types of data,
you know, and I think specifically you talk more about
the claims data from the payers. How has that process
gone and and how excited are the claims provider the
payers the manage care industry know what's their incentive to
provide the data? Are they also looking for that feedback
(37:04):
loop and.
Speaker 2 (37:05):
Doing mission and economic alignment? I mean having a clear
understanding of the logitudinal journey of the patients help them
achieve what they're trying to do to improve the health
of their patients that they're members. But you know, there
is compensation when you're de identified data as used by
others stame model and you know, and there's clear value here,
(37:32):
there's clear value in seeing those portions of the patient
journey outside of a given healthcare system. You know, you
have there's whether it be when you're on vacation or
just you know, here in Seattle, you know Common Spirit
Health to be Virginia Mason. I have care there, Providence
(37:52):
Health and Swedish Health. I have care there, but I
also have care at University of Washington, and I only
see that through the claims data, and so having that
data is critical. Of course, it's all the identified. You
can't see me, but you know, but conceptually, conceptually having
that complete patient journey requires claims data to be added
(38:13):
to the corpus as well.
Speaker 1 (38:14):
What about some of the models that have kind of
arisen outside of the traditional healthcare system, and one of
them is kind of maybe in your backyard like Amazon,
you know, others who have got these DTC platforms. Is
that a whole in your platform? And I'm thinking about
some people you know, going to I don't know, I
(38:35):
don't want to name names, but you know, going to
get GLP ones that are compounded that sort of thing online.
Speaker 2 (38:41):
Well, it's not a whole. It's not a whole, I
would say, because when we're thinking about that, these two
scenarios we're focused on. One is safety and efficacy research,
and so if an intervention is only available through those channels,
we would not see it. But if the intervention is
available through traditional healthcare systems, and we do see it,
and then we can look at the safety and we
(39:02):
can look at the efficacy data higher quality than the
highest quality way, and likewise with the muldiomics data, we
can correlate it to those metical outcomes. We can work
on developing new therapies. And so for those scenarios that
we're focused on, which is the safety and efficacy research
and R and D, those alternative channels are not necessary.
(39:25):
I mean, if I was doing if I was going back,
if I was trying to sell a drug or intervention,
then yes, I would want to know what doctors participate
in that channel and how much they sell in that
channel and whether they're selling my competitive But we don't
do that play in that game. We're of our regulatory
grade safety and efficacy research and then you know the
discovery of new interventions and so that's our focus.
Speaker 1 (39:48):
Maybe through that lens. You've got a rich background in technology,
what gets you excited about the next three to five years,
ten years? You tell me what the right timeframe is.
Speaker 2 (39:59):
I mean this most this multiomic data is just absolutely
fascin meaning it's so interesting to me, like the clinical
notes and processing the clinical nodes and digital pathology slides,
their scale, their complexity, that's been an incredible challenge for
the last five years. But now we're talking about gigabytes
of data per patient in this like ACTG or whatever. See.
(40:20):
I mean, without incredible AI and cloud computing that we
have now, like you could never make sense of this
and so finding those needles in the haystacks and finding
those correlations for the colon cancer. It's so exciting to
think about having all this clean data together with today's
raw technologies, with these innovative innovators and all these organizations
(40:41):
having access to it. It just feels like an incredibly
right place, right time moment. There's all kinds of work
to activate it. But it five year, five years ago,
this data wouldn't have been useless. It's just too complex,
too useful, too expensive to learn against it. But now
we have these tools to learn against it, you know,
in an economically efficient way. And even like the the
(41:05):
vocabulary and the skill sets of the people on the
healthcare and the life science side are getting there where
I think we're going to be able to put this
data to work like we never could before. And that's exciting.
Speaker 1 (41:17):
Well, that's an exciting segue into maybe our wrap up
question here, and we're going to move away from Truevetta,
but maybe to your own personal journey. And I like
to follow up with our guests about something that drives
them in their day to day and that could be
something you learned a business or your personal life, a
book you've read. You know what, when you think about
what drives you on your day to day journey, how
(41:37):
would you tell one of your team members or somebody
maybe who's looking to join your team, you know what
informs Terry on what he's doing day to day.
Speaker 2 (41:45):
Oh gosh, it's such a hard question. I mean, it's funny.
During the course of this conversation, I've talked about the
mission of our healthcare system members and now they really
want to take care of their communities. You know. It's
their commitment to their mission that I think motivates them
to be active members. And it's like, I don't know
this for me, just going all in behind this mission
(42:05):
of saving lives with data, and I really it's like
it's not a this isn't a half in thing. It's
like you're all in, all your mind, all your soul,
and I just think so much good we can do,
incredibly innovating, incredible innovators on all sides. That just I'm
all in, and I think you just have to be
all in and it's an exciting time.
Speaker 1 (42:24):
Well, Terry, I get to help from your enthusiasm that
you were definitely all in on solving this problem. Thank
you so much for your time today. That's Terry Myerson,
CEO and co founder of Truetta. Thank you so much
for joining us on our latest episode, and please make
sure to click the follow button on your podcast apps
so you never miss a future discussion with the leaders
in healthcare innovation. I'm Jonathan Palmer, and you've been listening
(42:48):
to the Vanguards of Healthcare podcast by Bloomberg Intelligence. Until
next time, take.
Speaker 4 (42:53):
Caresses as bases uses
Speaker 1 (43:33):
Bases
Welcome to another episode of Bloomberg Intelligence's Bandguards of Healthcare podcast,
where we speak to leaders at the forefront of change
in the healthcare industry. My name is Jonathan Palmer, and
I'm a healthcare analyst at Bloomberg Intelligence, the in house
research arm of Bloomberg. I'm very happy to welcome Terry Myerson,
CEO of Truvetta, to the program today. He brings a
(00:38):
fascinating background in healthcare technology, having previously served as a
longtime EVP at Microsoft. Prior to founding Truvetta, he did
stints in the venture and private equity worlds with Carlisle
and Mardona and Truvetta is In his first startup, he
founded inters, a web analytics company that was acquired by
Microsoft in the late nineties. Welcome to the podcast, Terry.
Speaker 2 (01:00):
Thank you for having me.
Speaker 1 (01:01):
Well, I'm excited to dive in. Why don't we start
with what Truevetta does and where you fit in this
very large landscape of analytics providers and data providers in
the healthcare space.
Speaker 2 (01:12):
Sure, I think the best way to answer that would
be the sort of tell a little bit of the
origin story of the whole company itself. And that's you know,
it's twenty twenty and the pandemic has started to emerge.
And one of my old colleagues was chief information officer
at Providence health Care, and during my venture stint post Microsoft,
(01:32):
I've had developed this fascination between data science and life
sciences and he knew about that, and he invited me
to be part of the virtual team to help Evenance
respond to the pandemic. And what became so clear in
that moment was Providence didn't have the tools to ask
and answer questions about this new disease. And at that
(01:52):
same time, a leading life science firm that had a
therapy that there was a theory that it was relevant
to this disease approached evidence seeking data, and Providence didn't
have the cultural or technical capabilities to respond quickly to
this life science request. We had the leader of the
World Health Organization tweeting that rock hydroxiclorican secure for COVID
(02:13):
and President Trump saying are saying that's not President Trump
saying that it was. And so it was clear there
was not the trusted data to ask and answer the
questions many sides of this industry needed. Public health didn't
have the answers they needed life sciences that the innovators
behind these therapeutics didn't have data they needed about their
(02:34):
own products. The health care and the treating physicians didn't know.
And so it was in this moment that Trebetta was born. Uh,
there was this idea that had been circulated amongst health
system leaders for actually a couple of years that putting
their data together so they could ask ask an answer
questions was a great idea. But there was this moment
(02:54):
of the pandemic that sort ofants took idea to action,
and you know, through the spring summer of twenty twenty,
there was a lot of work across many health systems
and many lawyers conceive, you know what, how could this
work on a full cliant way. And the company was
born in the fall of twenty twenty with four health systems,
(03:18):
Providence Health, Advocate Health, Trinity Health, and Tenant Health. And
they said, the world needs us to exist. We just this,
this has to happen. Let's do it. And you know,
since that time, the company has grown to be working
with thirty of the country's largest health systems. So you know,
it's it's a tremendous amount of patient care being provided
(03:39):
by these thirty health systems. It's eighteen percent of every
clinical care encounter every day in the United States, covering
about one third of all Americans. But then, you know,
say that we have all this data, we have all
this patient care being informed. And then in the fall
of twenty twenty one, Patrick Corbell, the cheap safety officer
(03:59):
of Pfizer, reached out to Treuvetta he's now actually on
our board, and said, I need a great data source
to help track the safety of the COVID vaccine. That
visor was rolling out across the country, and we were
very fortunate to be able to partner with Pfizer to
support their safety efforts, which was like a being on
the front lines of a but it was very the
(04:21):
scientific rigor with which that team pushed us the response times.
It really pushed us to develop the platform that Patrick
and his team could use and the systems developed to
be this regulatory grade safety and efficacy research platform to
be used by one hundred different customers outside of the
(04:43):
healthcare systems too. That all being said, you know, this
exciting announcement we had a couple weeks ago. There's always
been something missing from our data that you know, we
have all this medical outcomes data, we have all this
data on what's working, what's not, is it safe, is
it efficacious? Relative things, but the root cause of what
might be causing that variance in safety and efficacy. For that,
(05:06):
you need genetic data and multiomes data, and that data
is not collected during care at any scale. And we
launched this product called program called the Trubetta Genome Projects
to now hopefully genetic sequence I mean, from an aspiration standpoint,
all one hundred and twenty million of those Americans that
(05:28):
are getting care from our l systems, and so we
can really learn the biology of disease, learn the root causes,
so we can take care of patiently, and we can
get to the we can detect earlier, delay the onset,
you know, create the therapeutics necessary to change the course
of care. I mean, I'm so optimistic about the impact
of this on the cost of care, the quality of care,
(05:50):
to the access to care, the pace of innovation.
Speaker 1 (05:54):
That's there's a lot to unpack there. So maybe I
want to come back to the genome broad because it's
one of the key interests that I have, But I
want to start back to walking in the four walls
of maybe providence. And how surprised were you on a
scale of one to ten that they couldn't answer the
fundamental questions that they were trying to answer. I mean,
I guess with your technology background, were you shell shocked?
(06:15):
Were you modestly surprised? How surprised were you?
Speaker 2 (06:18):
So what I had watched from within Microsoft over the
last decade is just what we were broadly calling this
digital transformation. Which was at the source of it was
this idea that there was going to be companies which
digitally transformed themselves. And so Tesla is an example of
a company and automotive that was, you know, they were
collecting daily telemetry on every vehicle, you know, so that
(06:41):
they could continuously learn and improve their products. And that
continuous improvement in the collecting more data they just continuously
and it's going to create this competitive advantage over time
you would draw on an out, you know, versus other
car manufacturers that had none of that digital telemetry at
digital learning or digital feedback loop. You had the same
thing with me with Netflix where you had you know,
(07:02):
I remember going to Blockbuster to renting a video, taking
it home and if any movie studio wanted to learn
who was watching their movies or how long they're being watched,
what movies they're being watched in conjunction with the cohorts
to their customers. Well, you had to hire a market
research firm to stand outside a blockbuster or interview, and
(07:22):
you were getting this, you know, relatively, whereas Netflix knows
all of our clicks and they know who's clicking, who
in the family's clicking, the demographics people clicking, what's you know,
what we like, groups of people that like and dislike
various things, what type of movies to invest in, and
it gives them the digital feedback loop. It gives them
this competitive advantage. You can say the same thing happened
(07:43):
at shopping with Amazon or you know, finance with Bloomberg. Well,
what was so surprising to me was how there wasn't
just no digital feedback loop in healthcare. When the life
science firms now I realize, I mean, they they'll ship
their products into distribution, it gets deployed into the customer.
(08:04):
There's no data coming back to them or how we're
wet it's being used with which outcomes. You know, they
can run a clinical trial that takes a couple of years,
tens and millions of dollars, and you know, you negotiate
with the regulator what you're going to measure in that
clinical trial. But there's no continuous iteration at learning and
so much potential to improve. Likewise, on the healthcare side,
(08:24):
there's no digital feedback loop. You're trained, you're you're held
accountable based upon a whole variety of things in the
healthcare industry. You have your standards of care, you have.
Speaker 3 (08:35):
Your liability, but you know, with empathy, you deploy the
the practices you are comfortable recommending to your patient, and
where's the feedback on what.
Speaker 2 (08:49):
You know? It's an analogy. I heard the JPM Morgan
Healthcare conference from the CEO of to It Is Surgical
really struck me. You talked about turning surgery from an
apprint to start to a data science and just kind
of struck me like, there's this apprenticeship that healthcare professionals
have to go through. They learn, and they're professionals at
their art, but how do we accelerate that learning? So
(09:11):
it was just eye opening to me to see how
healthcare it was not Tesla, was not Netflix, was not Amazon.
It was something that lacked that entire learning loop, and
from that my passion developed for building Truvetta with the
healthcare systems and with the life of science customers, we
(09:32):
can just improve the pace of the innovation and the
quality of all of our work and take better care
of patients and solve one of the country's biggest economic challenges.
Speaker 1 (09:42):
Absolutely, If I think about the hurdles to starting Trueveta
versus say, collecting the data a Netflix, right, there's a couple.
There's the privacy aspect. There's the fact. I mean, if
I own a Tesla, let's say I'm probably just driving
my Tesla and maybe one other car, you get most
of the data in there. But with healthcare had this
fragmented landscape of providers. And then I guess the third
(10:04):
piece is that and I've heard you talk about it,
the structured versus unstructured data. So how do you How
does Truvetta kind of tackle those three big problems through
the lens of providing a data back to life sciences customers.
Speaker 2 (10:17):
Well, I think I break the challenges into three. I mean,
the first one is the regulatory challenges around security, and
second one is the fragmented nature of the data, and
the third is the unstructured nature of the data. You know,
security and privacy is a it's just hard work. It's
hard work. It's third party audits of your work. It
is I mean, there's a AI in there to both
(10:40):
de identify data and to challenge the identification of the data.
And it's it's hard work, and it's just investment in
R and D dollars and to go build world class
security and privacy systems for this data. It's not easy,
but that's part of what Tchervetta does is investing the
R and D dollars to protect the security and privacy
(11:02):
of the data. The fragmentation is based upon building a consortium.
We have a consortium of the now covers a thousand hospitals,
twenty thousand sites of care. We're building partnerships with payers
to get their data, to fill in sight, fill in
data that's not done in our providers, and to build
these full longitudinal journeys. And then likewise the unstructured nature
(11:25):
of the data with chat GPT, which came out in
the middle of Trevetta's journey, everyone's very well, very well
aware of these technologies, but you know, we've been building
these machine learning AI based tools to bring structure in
a regulatory grade way to this unstructured data so you
can ask and answer questions quickly. Now, as part of
the challenge, you know, going back to the pandemic is
(11:46):
if you want to understand these adverse events, or if
you want to understand efficacy, you got to look at
these words that are drafted by doctors in the clinical notes,
or look at these images you know, of the heart
and look at information around the heart say is it
there or is it not? And it just now is
the time. We have the cloud computing, we have the
what we now call AI technologies to do this and
(12:07):
you know, with that just hard R and D work,
we can and commitment to third party audits, we can
address the security and privacy. Fragmentation is a business model.
I believe problem of building the consort with payers and
providers provide the data and then unstructured again is a
technology problem.
Speaker 1 (12:27):
So maybe if you go back, what's been harder. Has
it been building the back end technology or or signing
up the consortium of providers you know, to be part
of the network. Just wondering which one looks harder from
your perspective.
Speaker 2 (12:42):
I think that challenges on three dimensions. This there's the consortium,
the business model that allows you know, there's mission alignment
amongst all these parties. We all want to take better
care of the patients. But creating the business model economic
incentives such that people can prioritize the work amongst all
the other things they've got going on, that's that's hard
(13:03):
and important work. The technology is critical. Without the technology,
this data is not useful. I mean, it's crude oil
versus gasoline. I don't know if that's a kind of
a bad analogy, but you know, we can't heat our
homes and we can't drive our cars with crude oil.
You know, regulatory grade, safety and efficacy research cannot be
(13:23):
done without you know, really high quality processing of this data.
You know. And the third thing I would say is
the user experience behind this. This is not this is
a massive amount of data. So how do you make
it useful to all the different people that would like
to have access to this data, whether it's a data
(13:45):
scientist or an executive. You know, we've talked to life science.
There's the physician, there's the there's the patient, and so
I would say there's also this user experience or usability
challenge here to make this clean process data useful to
everyone in the very scenarios they would have, whether it's
like managing care quality, tracking population health, or try and
(14:05):
understand the safety of the vaccine or whatever it may be,
there's different user experience challenges here. Well.
Speaker 1 (14:11):
Thinking about those two more broadly, you know, how does
it How does the customer base or the usage base
lineup today?
Speaker 2 (14:17):
Is it?
Speaker 1 (14:17):
Is it more on the life sciences side or more
on the provider side evenly split?
Speaker 2 (14:21):
How does it?
Speaker 1 (14:22):
How does it look in terms of who's kind of
using that You're about a platform right now, I would say.
Speaker 2 (14:27):
It's probably one third one third, one third between healthcare systems,
academic research and LIFSKA. We have an academic program with
do CAN, Stanford and Dartmouth and several other reading institutions
to do research using this data as well.
Speaker 1 (14:40):
Where is that the ideal? Mixing your mind? Is that
where you want it to be? I mean that, you know,
thinking out loud as a as a financial analyst, you know,
the deeper pockets are probably on the life sciences side.
Speaker 2 (14:51):
You know, I think in terms of building trust inside
trust behind the data. What's just Bloomberg as an analogy, Bloomberg,
I don't know what your revenue is split is the customers.
But the data Bloomberg provides is trusted. One of the
things that enables it to be so trusted it's ubigulously available.
It's available to me as a consumer, it's available to
JP Morgan. And that ubiquitous access for us all to
(15:15):
have a ground truth to the data, I think is
critical to Bloomberg being that trusted data source. In finance,
I think it was an analogy here. You don't want
to just be available with the customer that's paying you more.
I think you want to have some ubiquitous availability so
(15:35):
you can build trust in the data, and I think
that unlocks value for everybody, and then how you ultimately
build your business from each customer segment will be different.
But if you know one customer saying well I see
this in the data, another customer saying well, I can't
see that, so I'm going to get my own data
to do it, You're not going to build the trust.
And I do think part of that efficiency and are
(15:57):
in healthcare overall, I do think trust is data is
one of the things we need to bring. I mean
there's still values debates that come once you have trust
in the data, but having a common understanding of the
ground truth about what's working, what's safe, what is the
cost of that intervention, I think will be unlocked incredible
opportunities to then have values debates.
Speaker 1 (16:19):
What has surprise to you, I guess in terms of
your ability to you know, what could be this data
across all these different systems and patients. I mean, is
there something that you were you as you know, not
not necessarily as the CEO of Truetta, but you know,
just as a human being. We're surprised about what you
could do with this this data.
Speaker 2 (16:36):
We all know people that are on GLP one medication
and losing weights, and there was no data comparing Ozembic
and Mojarro. And then one of our researchers spent one month,
you know, creering the data, writing up a study which
made it into jama and it was like it was
one month of work and it was like the first
(16:58):
ever compared to effect this o Zempic and Mujarro and
you know, makes the Today's Show. It was like national
news that we were able to do this, but it
was this was done with a query against the data
set just makes a whole lot of sense. The you know,
over a year later, there was a clinical trial completed
that cost tens of millions of dollars with less than
one tenth the patients, so one tenth that represented them
(17:20):
in this and was completely statistically equivalent. And it's just
interesting to me. I look at the efficiency we could
bring to the learning, and with good learning, we can
accelerate everything. And so that was just one of these
moments of obviously I was when the clinical trial results
came out, I was a little nervous it was going
to be different. Of course.
Speaker 1 (17:40):
I mean it's funny that you know, a lot of
times those head to head trials don't even happen, right,
of course, there's an economic incentive to not do it
for Willie and Novo, you know, not to pick on them,
but that's just the way the industry works.
Speaker 2 (17:53):
Which is one of those things is like to see
that those head to head trials are not done those
comparative to effectiveness. But when it comes to treating a
patient and you're making choices, you're making choices for that
specific patient. So understanding what is the best, you know,
in terms of efficacious, maybe cost availability care for that patient.
(18:13):
We need to be able to compare the options including
doing nothing.
Speaker 1 (18:19):
Now that makes sense. How should I think about the
competitive landscape that you operate in? I mean, I've been
in healthcare for two decades give or take. You know,
I remember IMS. You know there's a whole host of
what's called it newer age analytics companies. How does the
petitive market landscape look for you? Guys? You know, how
(18:39):
big is it? You know, how much are you going
head to head? How often are you going head to
head to some of these newer providers versus legacy providers?
What does it look like to you?
Speaker 2 (18:50):
It's a complicated question. You bring up IMS IMS. My
understand QBA right, My QB AS business is primarily in
the called commercial analytics. You know, understanding which doctor is
prescribed which drugs so you can send the sales are
up in there with the right number of football tickets
to incent them to prescribe a different drug. I don't
(19:11):
understand that side of the business, and Truetta does not
participate in that. We have an ethic policy that none
of this data de identified, but none of it's to
be used to targeted advertising to patients or physicians, and
so we don't participate in that sort of targeted advertising world.
You know, within clinical domain where you're trying to understand
(19:33):
the safety and efficacy, you know, it's a broad landscape
you're looking at. There's CROs that are conducting clinical trials.
You have clinical decisions support tools of a variety of
shapes and sizes, but you just it's you could take
each one of those things and try and think about
them as market participants in this space. You have you know,
(19:56):
within the life sciences, you have budgets for helping new
therapies and your budgets for accelerating accelerating therapy adoption. You know,
Truvetta primarily is focused on, you know, helping life science
partners with accelerating therapy adoption until the Truvetta Genome Project
and now we're focused on helping them develop new therapies.
(20:19):
On the healthcare side, you know, it's population health really
primarily right now, and there's a set of population health tools,
but obviously you know, our ambitions are to help with
clinical decisions support eventually as well, and so.
Speaker 1 (20:35):
Kind of everyone it's a big market.
Speaker 2 (20:37):
I got it.
Speaker 1 (20:38):
Uh, maybe we'll go back to the genome project, because
I spend a lot of time in that world of
life science technology and have followed Alumina and the other
sequencing companies for years. I know Allumina is one of
your partners in this, and I actually saw your name
up in their their side show at JP Morgan. So
congrats there. No, they invested, they were just part of
the investment ground, right, Yeah, it's.
Speaker 2 (21:01):
Very very excited to work with them.
Speaker 1 (21:03):
So maybe at a high level, well, how does your
project maybe differ from some of the things that have
come before, you know, I think of like all of
us or some of the work that Genomics England has done.
How's how's the Truvetta project going to be different?
Speaker 2 (21:17):
So my understanding of all of us is they have
the US government's invested three over three billion dollars and
they have two hundred and fifty thousand data points. So
the Trivetta Genome project is receiving zero dollars of government money,
and you know where Generon is committed to support the
first you know, completely finance the first ten million data points.
(21:40):
And so the scale of the Triuvetta Genome project fifty
times larger than the all of us, the government funding
is one hundred percent smaller. And you know the quality
of the phenotypic data or the medical records, you know,
with all this work we've been doing for the last
five years to build the consortium, build the regulatory Grade
(22:04):
D identification and security, and the structure the unstructured data,
that's what gets linked with the genetic data, which you
know Regeneron is sequencing, and so I think it's it's
higher scale. You know, phenotypic data, you know, we think
is of the highest quality. And you know, there's we
(22:28):
would welcome the government government sure, but you know, the
but there's no goal.
Speaker 1 (22:34):
Maybe there's a good analogy there to the Human Genome Project,
right because the government costs three billion, and Selera and
Craig Venor it costs three hundred million. So maybe that's
the analogy you want to have.
Speaker 2 (22:47):
Here having a business model where that all of the
participants in the ecosystem. You know, again, I think it
starts with mission alignment, like you know, does does prioritize
using this work, support our mission, taking care of our patients,
you know, developing new therapies, and then it's about finding
these the model by which people can prioritize the work
(23:12):
because they have an economic incentive, and that's what we've
been working so hard on, you know, with you know, well,
you know, seventeen different healthcare systems have invested capital and
then they're now they're investing their time and they can
attribute a geno project work Alumina and Regeneron of you know,
committed time and capital to this project and because it
(23:34):
fits their mission, fits their vision of how the world
should be, and of course there's economic incentives in them
for their for them.
Speaker 1 (23:41):
Also, how does the economic actentive work for just Regenerond
maybe specifically, you know, since they're doing all the sequencing
work and obviously they're a therapeutics company, do they have
first rights to using some of that data for therapeutics
or anything like that.
Speaker 2 (23:56):
Well, so they are investor in the company, So they
republic about investing one hundred and twenty million dollars in
the company, and so they they have no governance of
the company. They have no access to any customer confidential information,
but they are they do own you know, a portion
of the company. Uh, and then they do not have
(24:18):
any first rights to the data other than like other
companies have to license the data in that sense. So
certainly Regeneron has the data potentially at the same time
as someone else. But Regeneron, by virtue of financing the
sequencing and the biobanking, you know, with us and with
the they that data is available to license for everyone else,
(24:44):
including the academic researchers, including you know, all the companies
that I think should be using this data for their
drug discovery or safety and efficacy work. And so it's
not any sort of first right or delayed access. It's
just Regeneron has it. Fathers have to license.
Speaker 1 (25:01):
It, license it there after got it. So you mentioned
the ten million, I guess maybe first cohort, I guess
that's ten million people who are going to be sequenced.
Are those going to be people who present with an
illness or are they going to be sequenced at birth?
What does the workflow look like?
Speaker 2 (25:19):
The workflow starts with patients will have to There'll be
an incremental consent, a new consent, okay, probably when they
go to depends on when that consent is presented to them.
But think about when you go to get your blood
drawn for whatever reason during care, you know, whether be
to get your A and C tested or something else,
and there'll be a incremental consent that presents do you
(25:40):
want to be part of this tribeta genome project. It's
like organ donation. There's no incremental prick of your skin,
there's no extra blood being drawn from you. You're basically consenting.
You are consenting to the leftover biospecimens after your clinical
test being used for this anonymous genetic research. If the
patient consents, then after the lab is completed, the blood
(26:01):
will get shipped to a sequencing facility where it will
get a whole xome sequence okay, and then if there
are any leftovers after that, it will be banked for
other multio mix research, which is where some luminous innovations
start to come in roteomics or transcriptomex or other some
(26:24):
of the innovations. And though all of that multio mixed
data gets associated with the phenotypic data of the patient
for you know, developing new therapies and safety and efficacy
research to everyone you know that would like access.
Speaker 1 (26:44):
To it, and will the patients provider have access to
that as well.
Speaker 2 (26:50):
It's a complicated question, complicated question answer. So the the
what we're starting out with is research grade sequencing and
so the data will not be used for treatment of
the patient. That's why I use the organ donation analogy.
Speaker 1 (27:07):
Of okay, that that verifies it.
Speaker 2 (27:09):
You know, like it's kind of like you're you're donating you.
You're helping the world get smarter, to take care of
you in the future, take care of your family, take
care of your communities. But you're not. This anonymous research
is not about you. It's about and so in the
future could we could we change? But right now we're
very focused on creating this data to understand the biology
(27:30):
of disease to help everyone get treated in the future,
because we just don't know the answer to some of
these questions. You know, why do people smoke and never
get lung cancer? And why do some people never smoke
and get lung cancer? Like there is a there's something
I don't know this, but there's a theory that there's
something in our genetics that is leading us in that direction.
(27:51):
And why does everyone need to get a kolonoscopy? Why
are we spending billions of dollars on kolonoscopies? And why
does everyone have to drink that awful stuff when we
don't know who of us actually is likely to get
colon cancer. We have our family histories, but like with
this knowledge, with this knowledge, we really can start to
(28:14):
both you know, develop those new therapies but also guide
care in a better way. And that's what we're trying
to do, is discover this biology of humanity and use
that knowledge of all of us to improve care for
all of us and develop new therapies for all of us.
It's not about treating you today, and that's the what
(28:34):
the consent well as clearly as we can convey to
the customer. But it's not so that's the it's a
research program.
Speaker 1 (28:43):
Under said now that makes a lot of sense. Interestingly enough,
I saw a headline that somebody did a study somewhere
that they were able to use an AI model to
predict who was likely to get colon cancer far ahead
of you know, when somebody would typically present with symptoms
or even go in for their first call and ask
could be let's say at age forty five.
Speaker 2 (29:04):
You know, so we have what's so making is we
have the technology now to do this for so many
different diseases. What we don't have is the trusted data
and trust. Here it gets back to that ubiquitous access
and some of this trusted data so we can actually
make decisions to make these are life changing decisions with
and then distribution for getting this knowledge back into the
(29:25):
healthcare system. And Trevetta aspires to build that trusted data
set and help that knowledge get back into the healthcare systems.
Speaker 1 (29:34):
So what is the what is the pitch that you
typically use to sign up a new healthcare system to
be part of the consort um? You know, what do
you lead as the tip of the spear? What's the
offer or that what's resonated so far when you've gone
and said, hey, we'd like to add you to the network,
or maybe people are coming to you. I don't know
how about the process works. It really starts with mission alignment.
Speaker 2 (29:57):
I mean, do we you know, doing saving lives with
data mission that was coined in twenty twenty. It really
kind of resonates that so much of health care is
not data driven today, and that's just a belief. You know,
we're not talking about data about you know, there's a
(30:19):
bunch of data and healthcare about utilization of the emergency rooms.
You know, the cost of the bedpaan effort is there's
a bunch of cost ex cost utilization data. There's a
bunch of that data, but the care, the care itself,
and that data that unstructured, fragmented, you know, lockdown data.
(30:39):
There's knowledge in there. And I do think the mission
alignment values a belief that this is the vision of
how we think care should be done differently. That's at
the core of all of our healthcare partnerships and that's
really you know, that's where the complexity is, you know,
in terms of like they have a trusted relationship with
their patients, you know, to commitment to their privacy and
(31:02):
quality of care and access of care. And so that's
that is where the core discussion is about are we
going to make this investment in the future of care
and lean into this idea that the world needs this
data set to exist. And it is that mission alignment
discussion that the that's at the core. The cost of
(31:23):
being part of Treveta, the reimbursement that you get for
being part of Traubada, that's that is completely secondary to
the is this on mission? Is this part of the
is this something we needed to to take better care
of our communities. That's where all the dialogue is. That's
where the choice is. It's this tension between a relationship
with an individual patient and the relationship with the overall
communities you serve, and those are complicated values discussions for
(31:48):
all our members understood.
Speaker 1 (31:51):
So if I think about, you know, being in the
four walls of a pride or today, I could think
of so many different use cases for data, right, and
I can improve workflows so that you know, we make
sure so and so gets their follow up appointment. I
could think about all the research topics that you brought
up before, you know, around discovery and innovation, But I
(32:13):
was thinking about clinical decision making and and are you
helping aid your providers with the critical decision making now
to say, I don't know, you know, maybe somebody's going
to get treated with X Y Z hip or NEEE
replacement or a stentor or whatever it might be. Is
the Trivetta data helping inform the cuindition which path they
(32:35):
should go down from a workflow perspective or is that
an aspiration in the future.
Speaker 2 (32:41):
Well, today we're seeing population health work done. We're actually seeing, uh,
there are two stents available for Ferra l Artery disease.
You know, we've heard a rumor that one causes more
major bleeds than the other. Let's look at the data
and let's let's inform our guidelines or per interesting decisions
within our healthcare systems based upon what we see in
(33:03):
the data. I mean, those kinds of questions today for
Travetta cannot be answered. So being able to provide a
tool where a healthcare a system can say, wow, we
feel we're kind of like this this step we are
using causing major bleeding. It's kind of a rumor in
the rumor, but let's go look at the data and
use that to that kind But in terms of getting
(33:24):
in front of the treating clinician for an individual patient,
you know, we're not there today. We obviously aspired to
get there, but right now it's population, it's not really
really you know, it's interesting, that's not classic research. That's
more of a healthcare operations you know, care quality.
Speaker 1 (33:43):
Looking at outcomes and scores and all that sort of stuff,
and eoritions.
Speaker 2 (33:46):
Definitely active there, but we're not in the treatment room today.
Speaker 1 (33:51):
So maybe as a follow up to that, you know,
if I think about your product roadmap over the next
couple of years, you know, for for whatever whatever, you
could share what you're working on today that we'll see
in the next year or two outside of the genome project.
Maybe that's too aspirational, and we're.
Speaker 2 (34:07):
So focused on regulatory grade, safety and efficacy research. Uh
and you know, bringing to life the Truvetta genome projects,
supplementing all of this incredible the clean medical outcomes you
know with multio mixed data. I mean, that is our focus.
(34:27):
You can say I was trained at Microsoft and focused, well,
I see the vision and I can, I can, I can,
I can look out beyond. But it's not We're very
We've We've got a lot of work to do and
it's challenging.
Speaker 1 (34:43):
So well, maybe thinking about that a little bit and
teasing that out, what what's going to be the hardest
to execute on for that near term vision your mind?
Speaker 2 (34:53):
You know, it's it's a Truvetta is such a team.
You know, it's a clout. You know, there's just your
a team. There's the several hundred Trevettans that are part
of our company. But like look Trevetta, the Trevetta team
includes thirty healthcare systems and people that are working hard
every day to use this data to improve care. But
(35:13):
also you know, now they're working on this consent and
they're working on you know, how do we route leftover
biospecimens and so they and then you have you know,
Regenerons now part of this team, a luminous part of
this team. You have Pfizer's part of this team. You know,
how do we work together to improve care? I mean
it's the maintaining this mission and economic alignment, so the
(35:37):
whole thing works, and that's the hard part, I guess,
but that's the I think it's with the everyone working
together is where we could do some amazing stuff.
Speaker 1 (35:50):
So thinking about that as well, when you're when you're
implementing Trevetta into a provider, how long does it take
to get somebody stood up? I guess on the on
your platform. So if I'm I don't know, it doesn't
matter whether they're Epic or Cerner or any of that
nuance as well.
Speaker 2 (36:08):
No, it doesn't matter. I mean, it's uh, we have
folks on Epics, Cerner, all Scripts and Meditech. We've got
the variety of the name it. And of course not
all the data is within the HR. Images are outside
the HR. Is outside the HR. And see it's a
variety of systems beyond the HR. But the core, the
core EHR, there's there's diversity there and it's months, it's
(36:31):
not weeks. Uh, and really how many months is measured
by priority on our side in their side I think
would be the stairway.
Speaker 1 (36:41):
And you just mentioned it and you mentioned it, you know,
prior to the discussion. But bringing in other types of data,
you know, and I think specifically you talk more about
the claims data from the payers. How has that process
gone and and how excited are the claims provider the
payers the manage care industry know what's their incentive to
provide the data? Are they also looking for that feedback
(37:04):
loop and.
Speaker 2 (37:05):
Doing mission and economic alignment? I mean having a clear
understanding of the logitudinal journey of the patients help them
achieve what they're trying to do to improve the health
of their patients that they're members. But you know, there
is compensation when you're de identified data as used by
others stame model and you know, and there's clear value here,
(37:32):
there's clear value in seeing those portions of the patient
journey outside of a given healthcare system. You know, you
have there's whether it be when you're on vacation or
just you know, here in Seattle, you know Common Spirit
Health to be Virginia Mason. I have care there, Providence
(37:52):
Health and Swedish Health. I have care there, but I
also have care at University of Washington, and I only
see that through the claims data, and so having that
data is critical. Of course, it's all the identified. You
can't see me, but you know, but conceptually, conceptually having
that complete patient journey requires claims data to be added
(38:13):
to the corpus as well.
Speaker 1 (38:14):
What about some of the models that have kind of
arisen outside of the traditional healthcare system, and one of
them is kind of maybe in your backyard like Amazon,
you know, others who have got these DTC platforms. Is
that a whole in your platform? And I'm thinking about
some people you know, going to I don't know, I
(38:35):
don't want to name names, but you know, going to
get GLP ones that are compounded that sort of thing online.
Speaker 2 (38:41):
Well, it's not a whole. It's not a whole, I
would say, because when we're thinking about that, these two
scenarios we're focused on. One is safety and efficacy research,
and so if an intervention is only available through those channels,
we would not see it. But if the intervention is
available through traditional healthcare systems, and we do see it,
and then we can look at the safety and we
(39:02):
can look at the efficacy data higher quality than the
highest quality way, and likewise with the muldiomics data, we
can correlate it to those metical outcomes. We can work
on developing new therapies. And so for those scenarios that
we're focused on, which is the safety and efficacy research
and R and D, those alternative channels are not necessary.
(39:25):
I mean, if I was doing if I was going back,
if I was trying to sell a drug or intervention,
then yes, I would want to know what doctors participate
in that channel and how much they sell in that
channel and whether they're selling my competitive But we don't
do that play in that game. We're of our regulatory
grade safety and efficacy research and then you know the
discovery of new interventions and so that's our focus.
Speaker 1 (39:48):
Maybe through that lens. You've got a rich background in technology,
what gets you excited about the next three to five years,
ten years? You tell me what the right timeframe is.
Speaker 2 (39:59):
I mean this most this multiomic data is just absolutely
fascin meaning it's so interesting to me, like the clinical
notes and processing the clinical nodes and digital pathology slides,
their scale, their complexity, that's been an incredible challenge for
the last five years. But now we're talking about gigabytes
of data per patient in this like ACTG or whatever. See.
(40:20):
I mean, without incredible AI and cloud computing that we
have now, like you could never make sense of this
and so finding those needles in the haystacks and finding
those correlations for the colon cancer. It's so exciting to
think about having all this clean data together with today's
raw technologies, with these innovative innovators and all these organizations
(40:41):
having access to it. It just feels like an incredibly
right place, right time moment. There's all kinds of work
to activate it. But it five year, five years ago,
this data wouldn't have been useless. It's just too complex,
too useful, too expensive to learn against it. But now
we have these tools to learn against it, you know,
in an economically efficient way. And even like the the
(41:05):
vocabulary and the skill sets of the people on the
healthcare and the life science side are getting there where
I think we're going to be able to put this
data to work like we never could before. And that's exciting.
Speaker 1 (41:17):
Well, that's an exciting segue into maybe our wrap up
question here, and we're going to move away from Truevetta,
but maybe to your own personal journey. And I like
to follow up with our guests about something that drives
them in their day to day and that could be
something you learned a business or your personal life, a
book you've read. You know what, when you think about
what drives you on your day to day journey, how
(41:37):
would you tell one of your team members or somebody
maybe who's looking to join your team, you know what
informs Terry on what he's doing day to day.
Speaker 2 (41:45):
Oh gosh, it's such a hard question. I mean, it's funny.
During the course of this conversation, I've talked about the
mission of our healthcare system members and now they really
want to take care of their communities. You know. It's
their commitment to their mission that I think motivates them
to be active members. And it's like, I don't know
this for me, just going all in behind this mission
(42:05):
of saving lives with data, and I really it's like
it's not a this isn't a half in thing. It's
like you're all in, all your mind, all your soul,
and I just think so much good we can do,
incredibly innovating, incredible innovators on all sides. That just I'm
all in, and I think you just have to be
all in and it's an exciting time.
Speaker 1 (42:24):
Well, Terry, I get to help from your enthusiasm that
you were definitely all in on solving this problem. Thank
you so much for your time today. That's Terry Myerson,
CEO and co founder of Truetta. Thank you so much
for joining us on our latest episode, and please make
sure to click the follow button on your podcast apps
so you never miss a future discussion with the leaders
in healthcare innovation. I'm Jonathan Palmer, and you've been listening
(42:48):
to the Vanguards of Healthcare podcast by Bloomberg Intelligence. Until
next time, take.
Speaker 4 (42:53):
Caresses as bases uses
Speaker 1 (43:33):
Bases
Host
Jonathan Palmer
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