- There's a small circle of people who truly
- understand what it takes to lead a health
- system right now. The decisions are enormous.
- The variables are constantly shifting, and the stakes
- of getting a call wrong don't stay contained.
- That's not a complaint. It's a structural reality.
- And it's why the most effective c suite
- leaders are deliberate about where they go to
- pressure test their thinking.
- Becker's fourteenth annual CEO and CFO roundtable
- brings together more than 1,500
- executive level attendees
- and nearly 400 speakers for four days built
- around growth, financial sustainability,
- workforce strategy, and the leadership questions that don't
- have easy answers.
- Join us November second through the fifth in
- Chicago.
- For the agenda and event details,
- visit beckershospitalreview.com
- and click on the events tab in the
- upper right.
- This is Scott Becker with the Becker's Healthcare
- Podcast.
- I'm thrilled today to be joined by an
- absolutely brilliant
- entrepreneur
- health care founder.
- We're joined today by Nish Kandwala.
- And Nish is the CEO and the cofounder
- of Bunker Hill Health.
- We're gonna talk about what Bunker Hill does,
- about Nish's Stanford
- background, and and a lot more.
- Nish, can you take a moment and tell
- us a little bit about yourself, a little
- bit about Bunker Hill Health, and how you
- got to the spot of founding this company,
- that's been funded by some of the most
- well known venture capital funds in the universe.
- Talk about how you got here and about
- yourself and about Bunker Hill Health.
- Sounds wonderful.
- Thanks for having me, Scott. For the audience,
- my name is Nish. I am the CEO
- and cofounder of Bunker Hill, as Scott mentioned.
- I was previously a student at Stanford working
- in the AI for medicine labs over there,
- where
- a lot of my work was actually focused
- on building different AI algorithms for different use
- cases in health care. We worked very closely
- with physicians
- and the school of medicine, and we would
- build some just absolutely wonderful algorithms
- that would drive better patient care.
- And
- at some point, we had this realization that
- we had built so many algorithms
- that seemed to work,
- but we hadn't actually gotten any of them
- deployed.
- That it seemed like there was a lot
- of buzz around AI, but when the Jabram
- hit the road, we didn't actually see many
- of them eventually get deployed.
- And so we got really worried about this
- because the whole point of this is to
- not just publish papers, it's to actually
- drive better patient care, drive better operational efficiencies,
- and we were just not seeing that translation
- happen.
- And we also saw
- that this problem was only gonna get worse
- with large language models. When I was a
- student, large language models weren't a thing, so
- it actually took us three to six months
- to build a model
- for a given use case. But now with
- large language models, you could build those models
- even faster. You could build those use cases
- even faster. But that problem of a lot
- of potential but not so much actually live
- in production use, that problem only got exacerbated.
- So we spun out Bunker Hill with the
- sole goal of bringing
- AI from
- people's might ideas and bringing them into a
- clinical practice and into production use.
- Since then, we are now live with over
- 30 different health systems and have raised around
- $60,000,000
- from some of the best venture capitalists like
- Sequoia Capital and Khosla Ventures.
- Take a second, Don. Give the audience a
- sense. You're working with 60 different health systems.
- Mhmm. Give us a sense of what Bunker
- Hill Health actually does. What do you folks
- do so people could just get that much
- of a clearer sense to that? Of course.
- So we are an AI agent platform, and
- it's a way for clinical and operational teams
- within health systems to build AI agents that
- end up doing the work end to end.
- So today, you know, you everyone has a
- lot of ideas around how they might want
- to use chat g p t, for example,
- in their day to day work within the
- health system, whether that's for something like enabling
- care pathways on the clinical side to something
- like helping with,
- triaging patients in a referral queue for patient
- access to something on the prior auth side
- to something on the registry data abstraction side.
- There are so many of these use cases,
- but we have found that health systems struggle
- to actually get those ideas about how
- technologies like chat g p t can enable
- with those use cases actually live into production
- use. So what we have built is this
- platform. We call it Carebricks,
- and it's a way for clinical and operational
- teams to work with us or to build
- them by themselves by taking our platform and
- building AI agents that end up doing the
- work end to end. Each of these use
- cases that I mentioned and many more,
- you can probably distill them down to three
- components.
- There is one component where you need to
- get access to data from multiple systems of
- record.
- Think
- EHR. Think ERP. Think other sources of records
- like clinical guidelines, payer policies, things like that.
- That's one component.
- Another component is that of actually running the
- large language models on top of,
- that data, and then you have a final
- component of actually taking actions
- on top of the output of AI. This
- is things like writing back to the EHR,
- things like sending patients' messages or physicians' messages.
- And so this platform
- allows
- clinical and operational leaders to arrange
- and sort of and
- assemble
- these components,
- these bricks as we call them, a bit
- that's where the name Carebricks comes from, to
- assemble these bricks
- in a way that we can help automate
- many of those clinical and operational use cases.
- And take a moment, Nish, on how do
- systems work with you?
- Who internally
- works with your platform?
- How much help do they get from your
- team?
- How does this work in practice?
- Does the team internally have to be all
- software engineers?
- Or or or how does this work with
- the people at the system
- so to make this usable for the system
- without them having to all be computer engineers?
- Or do they?
- Yeah. No. That's a great question. So we
- think that this is actually a great time
- to make an operational change as well. So
- we partner when we work with the health
- system, we are typically working with,
- someone like a chief AI officer or a
- chief medical officer where we partner very closely
- with them to have Carebricks become their AI
- platform.
- And we create this governance structure when when
- when there are new use cases
- that are coming from the grassroots or when
- we introduce new use cases that we have
- found, that receive a lot of love at
- other health systems.
- The chief AI officer, the chief medical officer
- is able to do a very quick sniff
- test and see if that is of interest
- to their organization.
- And if that use case does pass that
- sniff test,
- then the chief AI officer
- introduces
- us to the problem owners, those use case
- owners at their health system. And then our
- forward deployed team works very closely
- with the subject matter experts, those problem owners,
- to rapidly build AI agents that help them
- for their particular problem. And so to give
- you a very concrete example,
- from the first time we meet with a
- problem owner or subject matter expert at a
- hospital
- to going live, it takes about two to
- six weeks end to end, for a net
- new use case. And so we don't expect
- the health system to have any engineers
- responsible for this. We just, expect that we
- will have a collaboration
- where our team can rapidly build these AI
- agents on our platform for those use cases.
- And what does your forward deployed team look
- like? Yeah. Because that's so critical, that intersection
- with the with the client hospital. Because we
- see so many people that have great technology.
- But if you don't have that deployment team
- that's really working hand in hand, it it's
- almost what you talked about originally when you
- were back in the day, a graduate student
- at Stanford. Yeah. And people had ideas, but
- they weren't really getting put to work. Talk
- about that integration with the forward deployed team
- to make this
- as useful as possible.
- Yeah. I think our forward deployed team is
- incredibly curious
- and is very willing to be collaborative.
- And so to give you an idea, our
- VP of forward deployed,
- he has a background in consulting as well
- as in start ups. He studied applied math
- at Brown University.
- And when he was the only forward deployed,
- lead that we had,
- he would, you know, fly out to the
- health systems that we partner with, meet with
- the stakeholders, the problem owners. And just if
- they are already doing a process, but just
- manually,
- we would observe what they're doing, shadow them,
- and try to basically mimic that workflow on
- our platform, obviously, in a more automated way.
- Or if it's a process
- that they haven't the health health system hasn't
- implemented yet but is interested in doing so,
- then it's a long form conversation that we
- have and then very close iteration and collaboration
- on it as we go live with that
- use case.
- How much is the use of AI accelerating
- in health systems today? What do you sort
- of see now,
- use cases? And over the next three to
- five years, where you sort of seeing the
- most interest
- in in in how are things accelerating or
- cascading?
- So I think of use cases actually in
- the form of, like, a Maslow's hierarchy of
- needs at a health system,
- where you have your base foundational needs that
- need to be met. And then as you
- go towards the top of the pyramid, you
- hit those enlightened use cases that are very
- cool, that are very awesome. They make a
- lot of sense from an ROI perspective,
- but they are not foundational in the nature.
- So, like, an example of a foundational use
- case would be, oh, wow. With the new
- HR one bill,
- the definition of who is considered a Medicaid
- patient is about to change,
- and our hospital
- might now have a lower percentage of Medicaid
- patients,
- than what is required to be eligible for
- three forty b discounts.
- And suddenly, you are at risk as a
- hospital of losing tens, if not hundreds, of
- millions of dollars,
- that are afforded by the three forty b
- program. And so that is like a foundational
- need. It is very difficult to care about
- other things when you have such a foundational
- need that is unmet. So that will be
- a great use case to anchor our collaboration
- on where you could have an AI agent,
- for example,
- looking at every patient that is underinsured or
- uninsured,
- seeing which of those patients would actually be
- eligible for Medicaid,
- and getting them enrolled with the help of
- some assistance,
- for example. So I think of these use
- cases as these are, like, the foundational use
- cases at the base of the Maslow's hierarchy
- pyramid, and then you have use cases at
- the very top that are, like, you know,
- incredibly frontier care pathways that you have in
- mind. Like, at the for example, at the
- Cleveland Clinic, an example of a frontier use
- case is one where they are doing trials
- to understand if patients with moderate aortic stenosis
- would benefit from from earlier intervention, for example.
- And so they are very interested in a
- care pathway specifically
- for a frontier care pathway like that, and
- that would be an AI agent that helps
- with patient navigation and follow-up, for example. And
- so I think of it in terms of
- when we partner with the health system, we
- want to get a few use cases that
- cover the foundation, a few use cases that
- span across that pyramid going all the way
- up to the sort of, the top of
- the the top of the pyramid into the
- enlightened use cases.
- Thank you. Absolutely fascinating. They go up the
- Maslow's hierarchy type of perspective, breaking it down
- to what's foundational versus what's frontier, what's absolutely
- needed versus what's nice to have or next
- level. Exactly. Talk a little bit about what
- are you most focused on and excited about
- this next coming year
- both in in your work with your health
- systems, your work with your team, your work
- with your
- venture capital funds? What what are you most
- excited about and focused on as you as
- you work for the next six to twelve
- months?
- I think it's two things. The first one
- is actually something that I hope we can
- develop as an ecosystem
- as a whole,
- and that is how do you actually
- look at AI as an a as a
- as an enterprise strategy? What I mean by
- this is, you know, as we think about
- how we even want to make a business
- model around
- our platform,
- you know, one approach would have been to
- sort of charge use case by use case.
- But I can tell you that it creates
- a lot of friction when every time there's
- a new use case in mind,
- you have to go back to the legal
- team, negotiate a price point, get that down,
- and that sort of creates a,
- you know, loss of momentum over several months
- as an example. So that's, like, you know,
- a business model that is not really what
- we really appreciate because it really reduces
- the the sort of how much you can
- use AI for different use cases. So where
- we are converging towards and we were where
- we would love to develop this ecosystem towards
- is thinking of AI similar to how you
- think of electricity.
- Just like when you use electricity,
- you pay for watts,
- for every unit of consumption, in a similar
- way, we hope that
- the ecosystem start starts pricing AI similar to
- electricity
- where you pay for tokens of consumption
- instead of,
- instead of on a per use case basis.
- What that will result in is this rapid
- explosion of usage of AI within a health
- system where you can go from, you know,
- zero to twenty, thirty plus use cases without
- having to constantly think about, oh, wow. We
- need to go back to the legal team
- again and again. So that's the first thing
- that I'm really focused on, which is how
- can we shape this ecosystem to view AI
- similar to electricity as opposed to a host
- of point solutions.
- That's number one. And number two is actually
- around this the speed at which AI itself
- is improving and what capabilities it affords actually
- unlocks a whole host of use cases that
- previously were just not possible. So what I
- am focused on internally within the company is
- over the course of the last six months,
- AI has progressed so much with new technologies
- or new capabilities like computer use. How do
- we capture those advanced capabilities in unlocking even
- more use cases
- with our for our partners and unlocking even
- more value creation with them. And so those
- are the two things that I'm really focused
- on. One is viewing AI's electricity,
- and two is how do we keep up
- with the pace of the broader,
- inherent AI development that's going on.
- You've had a chance over the last few
- years to work with some of the highest
- end venture capital names in the world,
- Sequoia,
- Khosla Ventures, and so forth.
- Give people just some quick insight as to
- what it's like to work with those
- funding sources, those people. What is it like?
- I mean, you're dealing with really bright people.
- Yes. How do you connect with them?
- Any thoughts or advice about
- people working with funds like that? Yeah. No.
- No. That's a that's a great point. So
- even at Sequoia, we work with Alfred Lin,
- who is the head of the Sequoia
- team. And at and at Khosla, we work
- with Vinod Khosla himself. He is,
- obviously,
- an entrepreneur of of our generation.
- And
- so what I'm the reason I shared this
- is because
- those two guys, they have nothing left to
- prove.
- So when they think about investing in the
- future, investing in companies,
- they are not trying to think of, oh,
- how can I make a quick buck? They
- are thinking about this in the sense of
- how can we invest in ideas, in companies,
- in founders, in teams
- that can really change the shape of a
- ginormous industry.
- And that's how they are looking at us.
- So when they invested in us, when we
- were chatting with them and we continue to
- work with them,
- where they were coming from is, look, today,
- health care is usually criticized to be the
- last industry to adopt new technologies,
- and we have always criticized health care to
- be one where things are not nimble, things
- take a lot of time. And how they
- see Bunker Hill, how they see our platform
- Carebricks,
- is actually a way to make health care
- more iterable.
- How can you make it such that the
- cost of iteration
- reduces down close to zero as possible? How
- can you make it such that the speed
- of iteration,
- in health care increases as much as possible
- so that when you have a new idea,
- it does not take half a decade or
- a decade to get that into widespread use,
- that you can make it a lot more,
- sooner and you can do it in a
- way that feels very nimble and that feels
- very,
- very fast and safe. And so they think
- of that as if Bunker Hill can do
- that, it unlocks a whole host of value
- creation for the entire world. And so that's
- why they started working with us and we
- continue to work very closely with them.
- And what you know, unlike
- potentially other investors that that we that we
- know,
- where they encourage us to to do is
- what they encourage us to do is to
- actually swing big, which is don't make compromises
- just so that you can have a a
- quick win or, you know, a short term
- win as opposed to compromising for the larger
- vision. Take bigger risks. Go bigger.
- We are here, you know, we are here
- to support your grand vision. And so I
- think I feel very privileged in the sense
- that these investors are these partners we know
- than Alfred are really able to push us
- to think big. And, of course, you know,
- they just come with tons of experience about
- how to build
- enormous companies. Like, Sequoia obviously invested in Apple,
- Google, Airbnb, DoorDash, and Khosla was one of
- the first investors in OpenAI.
- They have seen how big companies get built,
- and and they they really help us make
- sure that we can learn from how those
- companies got built.
- That's fascinating. I mean, it's it's so many
- people at the end of the day, I
- count myself as one of them, are more
- incrementalist
- than thinking big people. Mhmm. How does that
- translate from I mean, you're you're obviously
- brilliant person,
- straight a's at Stanford in a graduate program,
- you know, double Stanford graduate.
- How do you train your mind and work
- with people to think big
- versus looking at the next thing? How do
- you how do you sort of combine those
- two thoughts,
- short and big, small and long term, big
- thinking?
- How how do you sort of do that
- as a a founder, entrepreneur,
- CEO, leader?
- How do you evolve to do that to
- do that well? Is is that something that
- you evolve into or that naturally you see,
- or how do you look at that? That
- is such a great question.
- You know, I I think that
- we see this in our ecosystem too. I
- think there's a school of thought where it's
- like AI has
- enormous potential
- for health care,
- but it also comes with a lot of
- risks.
- So we gotta get everything right
- on the first try, and we gotta make
- it such that the first time we do
- it, it's correct and we can execute on
- that. I think that that's a recipe for
- failure, especially
- when this is such a new industry and
- it's such a fast moving industry.
- Like, none of us here, no one in
- this world
- can claim that, oh, we have five years
- or ten years of experience
- deploying AI
- because that AI in its current form did
- not even exist
- more than a couple of years ago. And
- so I think it's important to almost take
- a step back and be self aware
- that we are not gonna get everything right
- as an industry
- on day one
- despite
- the potential for AI being so huge.
- And when you have that sort of awareness,
- what that almost translates to is that you've
- gotta set up yourself in a way
- where not everything feels like a one way
- door.
- Not everything feels like a three to five
- year commitment when you know it's risky. Not
- everything feels like a huge gaping hole in
- your p and l.
- It should
- feel like
- the stakes are low. It should feel
- like it's the cost of iteration is lower.
- And so
- where I like to sort of think about
- this, not just in terms of our platform,
- but also internally,
- is how can we make the cost of
- making mistakes as little as possible?
- How can we make the cost of iteration
- as little as possible? How can we increase
- the speed of iteration?
- And when you have that,
- what that results in is that you make
- very quick strides
- towards your ultimate vision.
- I think you'd you'd dream big, but then
- you know where you are today as an
- industry, and then you have a actual path
- that you can chart from where you currently
- are to where you want to be and
- where you see the potential being. And you
- make it such that you can take small
- steps quickly,
- iterate,
- backtrack if it doesn't work, and continue
- to move towards
- that end state. And so I think it
- ultimately just boils down to how can we
- make it such that you can iterate as
- rapidly as possible.
- No. And I and I love that perspective
- on thinking. So I'm I'm very much an
- incrementalist,
- an iterative type person,
- and and and, like, I you know, if
- I'm writing something, it's gonna go through 10
- drafts till I get it to where I
- want it to be. And one of the
- worst things that a hiring manager could do
- at one of the companies I work with
- is get scared of hiring because they've not
- hit it perfectly.
- Because, like, I don't want people to get
- gun shy because they missed on a hire,
- but they don't hire the next hire. That
- life and almost everything we do is a
- work in process. I mean, one of the
- things you point to is the epics of
- the world, the apples of the world, and
- one of the things they've gotten very right
- is its constant improvement. It it is minimum
- viable and then constant improvement.
- And what was Apple
- fifteen years ago is so different than their
- operating system today. And the same thing with
- Epic, it was very much an an incremental
- incremental type of effort going big, but realizing
- it's a work in process constantly and it
- never stops. And that seems to mirror some
- of the thinking that you have.
- Yeah. You'd look at the first iPhone that
- was released
- to the 17 Pro Max right now,
- and you see that there's just been, you
- know, decades of innovation that has been,
- that has been born through through its cycles
- of iteration.
- Really fascinating.
- Nish, I wanna thank you for joining us.
- Again, Nish Kanwala,
- CEO, cofounder of Bunker Hill Health,
- one of the smartest people that I get
- to visit with and talk to. What a
- pleasure to visit with you, Nish, and I
- look forward to visiting with you some more,
- and and continued good luck in your journey
- and what you're doing. Just fantastic. Thank you
- so much, Scott. Really nice.