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Welcome to This Week Health. My name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of This Week Health, where we are dedicated to transforming healthcare, one connection at a time.

Now, onto our interview

And they said we just signed on with Artisight. So you guys have a lot of momentum going on right now.

So let's talk about this for starters.

What's happening in the hardware world? We'll move through the software AI models because I think that's what

differentiates you guys. But let's start with hardware. A lot of the customers were coming to us, historically we had this hardware agnostic approach where we could use any camera, any speaker.

All on this? All on a single device. With a full NVIDIA GPU inside it, so we need no cloud compute to do any of the processing for any of our algorithms.

So you don't have to stream, you can actually do the inference engine is in the box. So

for all of our algorithms, we have about 32 different patient room algorithms right now.

They all live inside that device. Nothing ever leaves the patient room. That's pretty amazing. Tell

me a little bit about the camera. Cause you do work with a lot of different ones, but my guess is you probably picked one here that. That gives them the ability to do some things.

Or if a nurse happens to be doing a patient . Turn at the same time, you're never going to have that blind spot. So it's really two cameras is more important than the quality of a single camera.

of the things that's interesting about this. Having been a CIO and done construction projects and upgrades, we have to shut those rooms down when we do those kinds of things.

That's one of the big initiatives that we thought about when we were developing this device is to make it a lot easier to install into the patient rooms. About three different clamps that can actually just clamp it onto the TV or hook onto the mount for the TV. That allows you to install this securely without drilling a hole into a wall.

From our perspective, we want to make sure cameras can get installed as fast as possible. Helps our revenue growth, but also, from the hospital's perspective, they don't want to pay deployment teams for six months when they can do it in two weeks. So what do I need? I need power and Ethernet, essentially?

It's a single cable, so it's all PoE powered. So it does take a PoE power supply, so you have to make sure you have the appropriate power. If you don't have that in your network infrastructure, it can use a 120 volt power support, source. But you gotta remember, this is a full NVIDIA GPU inside it. So this is taking power here to run all of the processing.

Why do we have a TV in front of us?

Everyone else wants one. Everyone else in this space uses HDMI CEC to control the TVs, to change the inputs back and forth between patient content and teleconsult. That's fine if you're doing a pilot. It will work in small volumes, but Northwestern Medicine. Does 5, 000 two way video calls a day now. CEC might work 98, 99 percent of the time.

So what you're seeing here now is because we did all of that work, it comes with a lot of other cool benefits. Putting different sidebars on the TV. Putting patient entertainment on the TV. Clearing the patient's Netflix account the second the discharge order goes in. There's all sorts of automations you can build once you control the window.

So help me to understand that. I'm buying a standard LG, I'm not buying a specific LG TV. You just have the OS that gets installed on the TV.

It's specific hospital grade TVs. It can't be anyone. It has to be their hospitality grade TVs. But anything going back about five, six years, for both LG and Samsung, we now do all the firmware patching for these devices.

We take full maintenance control of the TVs for the hospital staff. That's pretty amazing.

It's like, we have AI or AI is in the name or whatever. It's like we do it. And essentially all they are is glorified algorithms. I love our conversations. Talk to me a little bit about what you guys are doing in the area of AI and integrating it with workflow and really supporting the clinicians.

So about now we have roughly 17, 000 patient rooms and about 1, 000 operating rooms online. We'll probably triple that this year. So with that footprint across healthcare in the United States, it really represents a very diverse environment for us to train algorithms. You cannot develop algorithms to a single problem in a lab, try to turn it on in the hospital.

So Artisight benefits tremendously from all of the work coming out of the hyperscalers now to open source major algorithms. This is really the year of multi modal models. And by that I mean it's not just computer vision or not just text analysis or generation by a large language model. It is really a foundational model that can ingest multiple different data sources across different modalities like vision, audio, text, or indoor positioning information.

And that's what allows us to continuously train algorithms and update them, even as workflows in the hospitals change. That sounds very nebulous, a great example. We've now released, because we have all of this hardware in the hospital, and we have control of the TVs, full ambient nursing documentation.

That audio gets converted to text. The text then teaches, through a large language model, a computer vision algorithm. The computer vision algorithm now understands what it looks like when a nurse turns a patient to their right side. So every time a nurse or a physician or a respiratory therapist or occupational therapist goes into a room and vocalizes what they're doing, it's simultaneously teaching our computer vision to monitor that task, document that task, and offload that responsibility from the clinicians.

Practicing in a hospital, used to be like, you hired a physician and they got used to Epic and then they went to another hospital and was on something else, they'd be like, Oh, I learned this or whatever it happens to be. I could see that happening now.

You touch on a kind of famous quote we have at Artisight we really do envision a future where there are no keyboards and no mice in the hospital.

It really could be done as a fully ambient experience. And to your point, we do see turnover rates at our client sites typically cut in half within the first six months. People don't want to leave this technology for the same reason that they're in love with ambient documentation. There's very few solutions out there that ask doctors and nurses to do fewer of the tasks they don't enjoy doing.

And if you take those away from them, you can solve a whole bunch of healthcare problems. So

as it scales, you're going to get more information. It's going to get smarter. It's going to be able to recognize more and more things.

Just by watching the surgical video in real time with, again, a GPU in that environment. All of the manual documentation, task tracking, notifications will move to Ambient in the future. And because, like you said, this essentially becomes a positive feedback loop. The more we can provide the hospitals, the more hospitals like it, the more it gets accepted by the doctors and nurses, the more training data, the better and more sophisticated these algorithms get, until we're essentially solving, theoretically, every problem.

Fantastic.

Andrew, always great to catch up with you. Thanks for coming by.

Thanks