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Executive Interview: Shrinking the Gap Between Idea and Reality with Nishith Khandwala

Care teams spend less time on manual tasks and patients receive faster, more reliable care. Learn more at this week. health.com/bunker Hill.

I'm Bill Russell, creator of this Week Health, where our mission is to transform healthcare, one connection at a time. This is an executive interview

quick powerful Conversations with Leaders Driving Change. So let's get started.

So nice to be here. Thanks for having me, bill.

I am looking forward to this conversation. I love the stories and the founder stories, founder and CEO.

So you're a grad student at Stanford. Take us from that moment till Bunker Hill becomes a reality.

Nish Khandwala: Yeah,

Bill Russell: absolutely.

Nish Khandwala: This was many years ago, back in, around 20 17, 20 16 when deep learning was just getting started. I was a computer science graduate student. I was working in a few AI labs and I was in the computer science role.

I was not in the healthcare side at all, but we were approached by. Many physicians from the School of Medicine with different ideas for how AI could help their practice. And I remember this one day we had the director of preventive cardiology from Stanford approach our lab, and he had this idea, which blew my mind, and I was like, oh, this is finally a great use case for ai for the clinical side of healthcare.

Had come to Stanford Hospital four or five years ago for some entirely unrelated reason. Maybe they had come in for a lung cancer screening, maybe they were part of a car accident and had come to the emergency department, maybe they had a pneumonia and then visited a pulmonologist. And in the due course of care, they had a CT scan that was done on that scan.

And we thought that was. the best thing since sliced bread. I'd be very ecstatic to work on it. We had clinical buy-in. We obviously knew that from a machine learning standpoint, this didn't seem that difficult. And so we worked with the cardiology group, the radiology group, to build that algorithm, and it worked.

And we published a paper. We made some incredibly bold claims about how this is gonna save hundreds of thousands of lives. Potentially impact lives of millions of patients and at the very least, revolutionized cardiovascular medicine. Two years passed by, none of those claims came true, not even close.

This is why, computer science students like myself, would go to Facebook and optimize for ads as opposed to, work in healthcare. I was ready to do the same. I was like, okay, this isn't, this seems so weird that there's something that works. The CFO wants it. The cardiologist obviously wants it.

Why is this not getting adopted? I was ready to pack my bags, but then life had it another way. My dad had a heart attack. Thankfully he's fine now, but when they rushed him to the emergency room, they got a cardiac ct and. The cardiologist was telling me, here's what coronary calcium means.

Oh gosh.

Bill Russell: So you just apply your algorithm and he's been okay.

At many hospitals across the world, why was it not even being used at Stanford? And I couldn't give a good answer. I needed to really spend some time digging up that. And we spend the next couple of years just trying to understand the question from the point that someone has an idea for how AI could impact a clinical problem or an operational problem.

How quickly can we bring that. Sort of idea to reality and widespread adoption. And that's why we spun out this company, which whose entire goal is to decrease that time to as little as possible. We could have started a company whose entire job was to commercialize that one algorithm that we had worked on, but we saw just how many researchers like myself across different labs were all facing this common problem of will you build something, it works.

Bill Russell: first of all, amazing story, amazing use case and the innovation usually comes outta frustration, right? So we get we get tired of a situation existing and saying, this is where we're gonna go. When I think of the problem set that you're looking at. You have to get access to clinical data, potentially some demographic data, maybe some genomic data.

bottom line is you need to get access to data in order to do what you're talking about and then apply those algorithms and then somehow get hooked into the workflows. Is that generally?

Nish Khandwala: Yeah. That problem used to be a lot more difficult. But then generative AI came and it became easier and easier to build those workflows.

You don't even need to build a new algorithm for this. You could take an off the shelf large language model, like open AI's, GPT oh three, for example, and basically ask questions about that patient's data to the large language model and figure out if a patient is a good candidate or not. And you could potentially implement a system that try to screen for those types of patients.

it has become easier and easier over time to build AI enabled workflows, but what has not? come easier is how quickly can you operationalize those workflows? How quickly can you take those algorithms or workflows that you've conceived of, that you have built and actually implement those in clinical practice?

Bill Russell: So you have a platform to essentially operationalize these breakthroughs. is it distinct from the EHR and how does it function with the EHR? So

Nish Khandwala: I think the EHR is a system of record that's very different from what we are trying to do, right? The EHR as we think of it is the way where it's the gold standard for truth.

I'll take another example. Wegovy was recently FDA approved for fatty liver patients the patients with fatty liver disease. Previously if a patient was diagnosed with fatty liver disease, you as a physician didn't have much of a optionality than to just tell the patient, Hey, take better care of yourself.

Now. Suddenly there's a treatment that's available to you. So if you are someone who treats a patient for fatty liver disease, the first thing that comes to your mind is, oh, could I basically comb through all the patients I've seen in the past and see which of these patients would be eligible for wegovy?

So these are the kinds of use cases that get us really excited. It's like, how quickly can you go from some kind of idea that you had that follows this pattern of clinical reasoning and action and implement that rapidly in your EHR. It doesn't matter. It's just we are not trying to replace an EHR by any means.

It's mostly a workflow layer on top of that.

Bill Russell: Because it's a layer on top of that, it gives you, I would imagine an awful lot of flexibility. the problems you can solve and the direction you can take it. I mean, it might be system of record, EHR data that you're pulling in, but you could be pulling in all sorts of data from various sources, I would imagine.

And then finally, the third source of information is any sort of specific rule set or SOPs that an institution might have for themselves. For example, cleveland Clinic has realized that aortic patients with moderate aortic stenosis could sometimes benefit from a valve replacement surgery.

Now, that's not technically in the guidelines, so they have a very specific sort of pathway for it for their group of patients. That's an internal SOP that they have. And that's something that we could also ingest and read and incorporate that. So we don't look at just the HR, it's EHR, the internet and any sort of internal documentation that a health system might have.

Bill Russell: who's the, Who's the champion within a health system? That you talk to and they go, we've gotta have this.

I mean, is it the researcher? Is it the specialist? Is it somebody is in charge of quality?

Nish Khandwala: That's a great question. I think we are as much of a. Tech innovation or a tech platform as much as an operational one. Now think of a chief operating officer or a chief medical officer, or now in many cases, a Chief AI officer at a health system.

They get inundated by people from within the system coming to them and saying, Hey, I think this is a problem for me. I would love to have additional headcount for this. Or in the case of the chief AI officer, it's like, Hey, I have this point. Solution could be on board that or procure that. They get bombarded with these sort of requests and this platform is intended for them.

Finding patients in a referral queue that need to be triaged up or helping with prior authorizations for specialty drugs or helping navigate patients with actionable findings. So we see in most cases that we've had success. It's typically someone at the enterprise level who says, Hey, this is going to be our AI strategy.

Bill Russell: So you're working with some impressive health systems. What kind of impact are they seeing in practice?

Nish Khandwala: So I'll share a couple of examples at University of Texas Medical Branch in Galveston, UTMB. We have over 15 use cases that are currently live now. That was just incredible. We started with one and there was just so much rapid adoption because when the Chief AI officer there.

Adopted this as their enterprise AI strategy. So many people from within the health system raised their hand and said, Hey, we have a use case for this. We have a use case for this. And it ranged all the way from cookie cutter use cases, like prior authorization for specialty drugs on the pharmacy side.

And we have found patients in the orders of thousands that have a high risk for cardiac disease, but no existing care from a cardiologist. They have never seen one never seen a cardiologist before. They're not on the right doses of statins or they're not on statin entirely and could really benefit from seeing a cardiologist.

Like one very recently we had an instance where. police officer had a high calcium score around 400. We notified that police officer he had come in for some entirely unrelated reason. That patient came back to the hospital, saw a cardiologist, and turns out that this patient had chest pain, but they had previously dismissed it as just heartburn.

Not to mention obviously the downstream ROI that a health system benefits from, in terms of more procedures. So on and so forth. Just the patient story itself there was incredible to observe

Bill Russell: The architecture sounds like a true platform. So you have the platform, then you have Care Bricks.

Describe for me what Care Bricks are. It just, it sounds modular like I Yes, it's a great name.

Nish Khandwala: That's the intention. So Care Bricks is how we implement these workflows. And as the name implies, as you astutely noted, it's a bunch of bricks put together like Lego bricks. At the core of it is our reasoning brick, which is a large language model accompanied by a library of FD acle tools that we have licensed from academic centers.

That automated action can be something like notifying a patient. Through text messaging through snail mail, through the EHR could be notifying, the physician could be writing back to the EHR by creating an order of some kind like a referral or even interacting with third party portals, like peer portals or pharmacy portals, for example.

And Care Bricks is the way to go from an idea to a automated workflow within a couple of hours. The buzzwordy way of saying this is this a platform to create AI agents? But I know AI agents nowadays mean everything under the sun. I don't start with that.

Bill Russell: yeah.

Nish Khandwala: Yeah, I think as it becomes easier and easier to build. As AI becomes more powerful, I think it's gonna help solve that problem even more. So, our North Star is from the time you have an idea to it being live, could you shorten that timeframe as much as possible? So, I would love to live in a world where someone at a hospital has an idea.

They could either create that workflow within a couple of hours. Or borrow an existing workflow that a different health system has already created on the platform and just start using it. Wouldn't that be great? Like, I live in San Francisco and you just see the pace at which these AI companies are moving, and then you attend any healthcare conference and you're like, wow, this just feels like

Bill Russell: Yeah. And really, I mean, what you guys are providing is the guardrails for it to go faster.

I sat with some CIOs and I was talking about all the. Things we're able to do in our business? Well, our business is not healthcare. It's media events, executive development and those kinds of things. I'm like, look, I can do this. I can do this. I'm using autonomous agents. I'm doing this, and I'm funneling all this information through.

It's almost like having a COO who sits inside of or data. Yeah. And is just giving me insights all day of, Hey have you considered this? And what about this tax strategy and stuff? And you're like, oh my gosh, it's like I just hired experts who are, giving me this feedback

But I mean, that is so scratching the surface of Yeah. What we're gonna be able to do with care.

Nish Khandwala: Yeah. People do share that. Hey, healthcare just doesn't move fast enough, and I haven't heard a first principal's reason for that yet. Yes, it's high stakes, but so is the finance industry.

They move pretty fast. Healthcare is obviously higher stakes. One could argue, but there's so much low hanging fruit like. Why would it be so challenging to conceive of a system that looked at every patient within the health system, looked at their records, scanned those records every day to see is there an untreated finding?

Again, obviously I'm biased here with the modular and a platform that you can rapidly implement these types of workflows and use cases.

Bill Russell: I wanna thank you for coming on. I want to thank you for sharing your story. Where can people get more information about Bunker Hill And start a conversation with you.

Nish Khandwala: Yeah. You can go to bunker Hill health.com. You'll find more information about about us there. You can also email me my, it's my first name, nsh@bunkerhillhealth.com. Would love to hear from your audience,

Bill Russell: NSH. I'd be remiss if I didn't ask bunker Hill.

Nish Khandwala: We are not a Boston company, even though people might think or find that as a reference, there was a TV show in 20 16 20 17, which I would not recommend you watch. Uh, It got canceled midway through the first season. That's how lame it was. The TV show was called Pure Genius. While it was a lame TV show it was centered around a hospital called Bunker Hill. The research that was done in the morning was used in clinical practice in the afternoon.

Bill Russell: And it is very memorable, so that's fair. I appreciate it. N looking forward to keeping this conversation going as I'd love to see the progress you guys make over the next year.

Thank

Nish Khandwala: you,

Thanks for joining us for this executive interview with me, bill Russell. Every healthcare leader needs a community they can lean on and learn from. Subscribe at this week, health.com/subscribe and share this conversation with your team. Together we're transforming healthcare.

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