A17Z Podcast
Software Company in Healthcare
(Extracted)
https://en.wikipedia.org/wiki/Knome
JORGE CONDE -
KNOME
So in our
experience and know me, it was interesting because here, this is a company with
the sole purpose of the company was to provide software capability to analyze
genomic information. And so, you know, when you launched that, your
assumption is, well this could be used to power all kinds of applications.
It could be used for research, either in academia and industry. It can
be used for, you know, clinical diagnostics, flexible. We thought it was
very flexible and so challenge one is, you know, a solution looking
for a problem is always a very, very dangerous thing. I think that's
universally true. It's especially true in the healthcare space. And challenge
two was understanding exactly where in the case of the clinical setting
where this technology would be used in the workflow. So here we want it to go
after the clinical labs.
Speaker 2: That was your initial hypothesis.
Our initial hypothesis for an
application in a clinical setting, you have technicians and docs that are
inside of the laboratory setting, receiving samples, running a test, analyzing
the results of that test, generating a report that gets signed off by lab
director, that goes back to a physician.
Usually it's in the form of a
diagnosis, right? And it gets signed off and it goes to the physician. The
physician now takes that report and basically decides what to do based on that
information. So our assumption was, well, if you have the ability to
sequence DNA now in a way that you couldn't before, before you'd have to do all
of these specific tests, you have to know what the test, and then you'd test it
and then you'd get a report. You have to know what street lamp the keys were
under, right?
Speaker 2: Like they're in that case. Whereas
once you had the full genome, you would just sequence everything and
just run a bunch of software [careers.] So our thought going into this was,
well, that's an incredibly powerful tool for clinical labs because first of
all, you can sequence just once and analyze over time, right? You can
again get like a totally legitimate, right. And it turns out that there
was a lot of challenges with that assumption. The first one is every lab
is different.
A lot of them didn't have the
budget or the willingness to basically pay the upfront piece to buy the
capability to use this technology, or they didn't have the ability to sequence everything
upfront, even of all of the subsequent queries would be technically free later
the way they're reimbursed. Oh, how fascinating. Too expensive.
Basically it's too expense. So even the, theoretically there's an ROI, a
return on the investment of sequencing up front, just the way the industry is
structured, the way reimbursement flows, the way payments flow.
Speaker 2: It just didn't make sense for a lot of
labs to do this.
So how is that not just a complete
roadblock at that point it was a big roadblock. So that would, that
required us to do was to then focus on clinical labs that had the ability
to make certain investments in up front cost and those tended to be very
sophisticated labs that do a lot of research work in addition to patient care.
Then they tended to be on this sort of on the bleeding edge and they wanted
to incorporate new technology and they were great partners and all of that.
But then it goes back to your N of one problem.
So you sell something into that
lab and you go next door and next door. It has a totally different set of
capabilities, a totally different set of constraints, a totally different
set of expectations.
Speaker 2: And so therefore all of a sudden the
solution you created for lab a is not relevant or unattainable for lab B.
Now to just add to the stepping in it, you know,
when you're analyzing genomic data, there's a massive amount of computation
required. And so we went in there assuming, well this is easy, we're just gonna
shoot all of this up to the cloud, we'll run the analysis, we'll send the data
back to the lab to the lab, could verify it, generate a report, and off we
go. It turns out labs weren't comfortable sending data up into the cloud
full stop at that time, at that time, arguably even today, arguably even today
in 2019 but definitely at that time we probably should have known that
earlier. That would have changed how we thought about going into the clinical
lab space. How would you have done your homework?
Speaker 2: I mean, what would that have actually
looked like? It was frankly, I think just defining the specs of what would be
required to bring in our technology because I think people intuitively know
that genomic data is massive. But you know, I don't think they know sort of the
level of computation required to run the interpretation. Right? So like really
running the numbers, running the numbers for them. And by the way, we tried
everything. I mean we brought representatives from AWS that could show them
that they had a HIPAA compliant cloud, that they had received all the
certifications and it came back to risk aversion. So someone in the lab
director saying like, look, I'm sure all of that's true, but I'm not going
to risk sending all of this data up into the cloud. So that was a big, big
challenge for us. And it ended up being a major limitation for our ability to
expand into the clinical setting because of all of those barriers.
Speaker 2: So what did you do? We have to do a
plan a and a plan B. And so the plan a was we assumed that there would be a
couple of forward looking labs or forward thinking labs that would be willing
to work in a cloud environment much easier to deploy there. The plan B was we
had to create a box, we had a grid of box and the box had to have essentially
the competition. Yeah, we had a normal plan. Remember that? Oh my God. Because
they didn't want the data to go outside and it's for the reasons that
we'd expect, you know, there, there's regulatory, there's risk associated with
that today in 2019 in fact, the companies that have managed to use this
technology have taken the sort of full stack service approach. So that sort of high,
low strategy became the approach is get folks to deploy into the cloud when
they were willing to, and in the case where folks needed an appliance, we
basically had to go to labs that had enough of a sample volume that an
appliance made sense for them and make basically the case there from an
investment stand.
Speaker 1: So again, multiple choice variety and
like addressing in different ways. Okay.
Speaker 2: Pure software company in healthcare
is a really hard thing to do because on the one side you have this challenge
that it's hard to create a sort of a solution that's going to fit everyone and
therefore you need to have some level of services around that software that's
on one extreme. So when you need to have humans in the process or in the loop,
and in the other extreme of it, if it is pure software, then it's considered
the, it should be free. So it's very hard to abstract value.
Speaker 1: That's so interesting. Do you think
that's shifting at all with the kind of understanding of the importance of data
and some other,
Speaker 2: yeah, look, I would argue with
shifting on a couple of of axes. The first one is, is data is becoming more and
more valuable. Historically data was viewed as being either too small in terms
of its impact, too narrow, too dirty, et Cetera, et cetera. Too difficult.
Yeah, to unstructured, you know, so that historically had been the case. So if
you have ways to ingest data and clean it and make it meaningful, then I think
that is valued. Probably the most public one is would flat iron was able
to do and ultimately getting acquired by Roche for $2 billion. And
that's viewed as using an electronic medical record to capture patient
experiences, take that information and give researchers the ability to drive
valuable insights from that. That's a relatively new thing. So I think there is
the ability to create value there. So I think that's one axis. I think there's
a general shift in the model that having a tech enabled service can be a
valuable thing and if done well can be a scalable business. In other words,
if you know what you're trying to build and if the software layer reduces
sufficient friction in the system and allows you to add people not linearly as
you scale, right, but in a leverageable way, then all of a sudden you can have
tech enabled services that can grow and become large businesses.
Speaker 1: So leaning into what it is that
makes it difficult almost. And then scaling that, leveraging that. Exactly.
Finding ways to make that scalable. Yeah, no, that's not easy to do, but
