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Their IT asset management team has helped health systems save millions in licensing costs, eliminate security vulnerabilities, and optimize IT investments. Whether you're modernizing your infrastructure, implementing AI solutions, Or strengthening your cybersecurity posture. SHI expertise helps CIOs and their teams focus on what matters most.

Enabling quality patient care through technology. See how other health systems have transformed their IT operations at ThisWeekHealth. com slash SHI.

Now onto our interview

(Interview 1) Welcome to another this week, health Solutions Showcase. Today we are excited to feature SHI International, a leader in empowering healthcare organizations through innovative AI and cybersecurity solutions.

Joining us are two distinguished technology leaders from SHI lee Ziliak Field Chief Technology Officer for Advanced Growth Technology at SHI who will share insights on i's AI lab capabilities, partnerships, use cases, and the broad range of services they deliver.

And also Kris Nessa, CTO for SHI healthcare who will validate how SH i's AI expertise is helping healthcare providers navigate and accelerate. Their AI journeys with real world success stories. Together, they're gonna show how SHI's AI and cyber labs provide a risk-free environment to innovate, validate, and deploy AI solutions at scale, all while maintaining a strong focus on measurable outcomes, security, and operational excellence.

Lee Ziliak: Yeah, great to be here.

Sarah Richardson: Oh my gosh, I love this topic because you have empowered organizations to innovate and validate AI solutions in a risk-free environment, ensuring they achieve measurable ROI before full scale deployment, all with trusted guidance and expertise from SHI.

If either of you wanna jump in first and share more, love to hear it.

Kris Nessa: What's funny, and thank you for asking that, Sarah, I actually had pulled over our CEO Ty Lee at the SHI ribbon cutting the other week.

I had a brief moment. We were walking the hall together, just the two of us. And I complimented her. I said, I want you to know, I think you're doing the right thing at the right time. And she ironically was a little nervous. She's like, oh my gosh. She really thinks so. Like she's still trying to find that balance maybe herself.

So Cerner being electronic health record company all of a sudden spun up remote hosting and. Manage capital of an IT. Like you see EHR companies doing that nowadays or trying to do it. But back in the day when Cerner did it, it was a game changer. And Cerner could do it cheaper, faster, more secure, and better than a lot of hospitals could do it at that time.

Before you ever sign a check to buy any of that infrastructure, can you imagine spending 7, 8, 9 figures on this stuff and bringing it home and then not knowing what to do, or you bought the wrong stuff? Like we're here for that playground for you to find and get the most value before you actually spend that money.

And I think it's just genius. Honestly,

Sarah Richardson: it is genius that safe space is gonna be huge because you don't know exactly what you're paying for yet in some spaces. And so, Lee, this is such a sweet spot for you when you think about where organizations can position the lab to address their concerns, have insights, and really streamline.

Both the operations, but to Nessa's point manage the cost.

Almost two years now, but we had our official ribbon cutting on our on-premise lab about three weeks ago. And so what that lab does is exactly what we talked about, right? It gives you the customer a safe place to experiment with your AI use cases. It is really. Proving ground that we leverage to make sure that your use case is valid, that you have the data to support the use case, that the models work the way that you will expect them to work, right?

Or we will expect them to work. And to her point, I. You can do this without a nine figure investment. So that is the function behind the lab. And like I said, we've been doing that for a few years and the lab is equipped with about 60 GPUs right now. So, again a variety of GPUs, but also a variety of platforms from different OEMs.

I gotta go from ideation, I gotta spin up the expertise. If I don't have it, I have to go out and hire it. I have to put a project together. I have to buy hardware to run it on, and then I have to deployment and that can take literally months to years. And so, another thing that we do differently than a lot of companies and even a lot of consulting companies, is we'll help you iterate through that in six weeks.

If you've made a nine figure investment, then that's pretty painful. So that's what we're there to help with. We help our customers do that across a wide variety of use cases and verticals.

I.

Sarah Richardson: one of the case studies that you highlighted from this lab was entity matching using generative AI.

Can you share a bit about that value that was delivered and what it was like to work with that organization?

Lee Ziliak: So this is a really great example of what I'll call the business side of the world, right?

So when people think healthcare and clinical work, they're thinking, oh, well. We're looking at images and we're helping do diagnostics and all that other stuff, and that is very important and we can certainly help with that, right? The flip side of that is you gotta keep the lights on, right?

And you have to do that as efficiently as possible. And the, so this is a great use case where we took what was a very highly manual process. And leveraging AI provided automation to help that, that workflow speed up. I'm not familiar exactly with the savings amount, but it was pretty significant in terms of just manual effort and whether that translates to savings or not, it translates into other productivity, right?

And this is a great use case example of that.

Kris Nessa: we talk a lot even about AI and a lot of the first use cases that are prevalent out there is like the clinical transcription and dictation and things like that everybody's talking about.

Right. And we always speak about allowing our clinicians to operate at top of licensure. But there's actually, to Lee's point, there is the flip side of operational top of license and top of position as well because we're dealing with staffing shortages across the board in any of our friends networks and all of our provider organizations.

sudden, like expedite a lot of manual process and start to automate things with ML and AI and allow them to spread their wings and do other important things that the organization needs is a huge cost savings and a huge benefit as well. And I think we do need to talk about that a little more too, other than just the clinical side of the house.

Sarah Richardson: Well, for sure, because I'm curious when you think about your engineering team specifically and the technical expertise that y'all bring to the table. Tell me more about how that's a differentiator for healthcare clients that are exploring AI solutions and very specifically within that clinical setting.

They have a flat budget. They maybe had to cut budget, had to cut staff in their IT departments, and a lot of CIOs and our friends are like I don't have anything to be innovative anymore. I don't have a budget, I don't have humans. And so what I really like, what we've done is we've stood up. What I used to have there are software engineers who can code an app.

We can code a fire app, we can do anything. And we have AI and ML experts as well. We have data scientists. We have the team that's sitting here to help you do that, even if you cut them a while ago. So why not bring an idea here and you could do it cheaper. You can actually prove out if your idea is something you really wanna do or if you should scrap it yourself.

Let's go over it, see if it's ready, and let's get it ready to try it. On the different OEM things that we have in the lab, we actually were able to tell them. Your data is not ready to go do any ML or AI on. We actually saved them and stopped them from spending nine figures with us and told them their data wasn't ready.

Go home, do A, B, and C and let's come back and revisit this a year from now because we truly value like wanting to do the right thing for you and truly ensuring you're getting the value out of what it is you're about to spend, and then the journey you're about to go on.

So even if you have data scientists and ML lops engineers and all that stuff, right? You focus them on your core business, right? Which is probably the diagnostics piece of things. So there's this massive backlog of use cases that we can help knock down as well. But most people don't have those people on staff.

And the reason is, one, they're very expensive, and two, they're very hard to find. So if you had them and got rid of them, or even if you do have them or if you've never had them there's just such a huge demand for this skillset that we're very happy to be able to bring this to the table.

They didn't have the corpus of data to support their use case, even though they said they did. And again. I'm confident we saved somebody their job, but it was literally a nine figure investment that they were ready to make. And so, but they'll

Kris Nessa: be back.

Lee Ziliak: Oh, they'll be back. In fact, we're helping them curate that data.

The purpose of the lab. If you're, I can't get this across enough. It is a safe place to do your experiments. Yeah. It's not free, but it's a lot cheaper than nine figures. So, and that's the purpose of it, to help our customers iterate through that stuff quickly.

Sarah Richardson: So once they go back and they do data cleanup and they have the normalization, and they have the ability to actually have some sets that are going to bring them forward into the next phase of their AI implementation, how do you help them also with data privacy and security in the development of their solutions?

Not just the AI piece, but the entire architecture stack. And so security I've been saying this for a hundred years 'cause I'm not old. Security is everybody's business and it's very important especially in the healthcare space. With all the different regulations and potential problems that you can get into that we look at these workloads holistically and help our customers secure those workloads.

And that's, there's a million different ways to do it, but, at a very simple level it's end to end encryption. It is monitoring the environment, it is taking that telemetry and watching where the data is being used, how it's being used, et cetera. So. The solution that we will provide include in includes and encompasses all that.

Lee Ziliak: Yeah, it's not really moments. What I will say is, it's an interactive process from the inception, right? So we, part of that six week POC is sitting down and understanding that workload and that involves talking with our customers. Then we'll go out and choose the right model and work with our customers as we start to do.

The testing and validation of that model. And then we will work with them in the final sprint to go over that model and how it executes and how it runs. And if they're fine with taking the ball from there we can hand that MVP over. But the next phase, which is an additional, sprint or sprints would be let's roll this into production.

Sarah Richardson: And Nessa, you mentioned some of the clinical efficiencies that are available out there and really thinking about that.

I'd love to hear more though on about your thoughts, specifically on improving patient care and the operational efficiencies for healthcare providers. Like when you think about the ability to truly be innovative in utilizing these solutions, what does that mean for patient care?

Kris Nessa: Ooh, great question. I've always that person that thinks the word innovative is like a mindset and like a mantra in a way of living truly for like an organization and or a team.

Prior authorization, adjudication returns, DNFBs, just everything over there on that revenue side because they're short staffed over there too. There are a lot of discussions right now on imaging, whether it be academic medical centers, whether it be true huge imaging centers, or even like at a pediatric level.

Imaging is a big thing. Our AI lab is built on some of the preeminent OE EM second help. With like AI ML for imaging. And so that's a use case that's coming up a lot right now is we have staff shortages when it comes to radiology technicians and people reading it. But if you pause and you take your own patient perspective, when we go in for anything, for radiology, oncology or whatever, none of us wanna wait more than even four hours to hear a result, let alone we're usually waiting a week.

Also the whole digital front door. Patient engagement is top of mind. We have a few customers that we're talking to at this moment with their front door patient engagement chatbot or interactions. Some of them had maybe developed their own gone to market. They've kind of been out there on their website, used a little, not too much, but now they're totally rethinking it.

Top of mind right now.

Sarah Richardson: So if I bring all of this to you, I'm listening to this and I'm like, dang, I could not only have them help me clean up my data, create the right use cases, get my team trained up, et cetera. Can you just walk us through what that process and timeline looks like? Lee, you mentioned the six week POC, but I'm just gonna call you after this and say, here's all of my ideas and all my stuff and all my people.

What? What is that engagement actually going to look like for a person who wants to make traction in this area and knows they need you in the lab.

Lee Ziliak: So it kind of depends on where you are, right? If you have a hundred use cases we can sit down and Yeah we'll help you narrow it down, right? There's priorities.

So if you have a hundred use cases, we can help you work through that and figure out what the priority is and then start knocking him out one at a time. We can do more than one at a time, but generally that's the approach. So that we'll sit down and say, all right, here's your a hundred use cases.

So that's typically, a week or two to, if you're, if you've got a whole bunch of stuff, then we can help figure that out. And then we would go straight into the the the POC process, which is a six week engagement. It comes in different sprints. At the end of that, you have an MVP.

The engagement after that is scaling up into production, and that's basic, typically a six to 12 week engagement. And so, you can really have an operational system in probably worst case, 18 weeks, maybe a little bit more depending on if you need data cleaned up upfront.

So. That's kind of what the engagement looks like and how do you do that? Work with the context that you're familiar with, right? So reach out to Kris or reach out to your account team and they'll engage me or my team to come in and kind of help figure out where you are and where you need to help.

That's one of my slides. If if I were doing slideware for you, it's like, where are you on this journey, right? Are you, do you not know how to spell AI or are you very advanced? Right? And so. We've got all sorts of things to help meet you kind of where you are.

Sarah Richardson: It is interesting you mentioned though, the very advanced aspect of all of it because I have to believe that AI is moving so fast.

How are you helping organizations balance the AI structure, the governance, the ongoing maturation of the capabilities within an organization?

Lee Ziliak: So those are typically ongoing engagements with us. And when I say engagement, that doesn't always mean paid by the way. So, there are consultative only engagements like, AI workshops and briefings to kind of keep you in your team up to speed with alright, here's the GB 200 and why, how is that different from the H 200?

And do I need that? Right? So there's different briefings that we can potentially help with as well. There are. AI governance workshops that we offer for companies that need to, set up a governance team to help the pipeline of AI workflow that comes through there

Kris Nessa: Yeah, blog posts, webinars, SHI summits. We've got a lot. But again, it can also be just even as a CIO if you missed it or your IT director did just shoot me an email, gimme a call or call up your SHI account exec and we are always here to help you.

Sarah Richardson: What future developments can we expect from your lab?

And what are each of you most excited about now that you've had time to spend with clients in the environment and really seeing, I think, the art of the possible, which is one of the hallmarks of what y'all bring forward.

Lee Ziliak: So from a lab perspective, it is literally evergreen. If I told you what's in there today, it will change tomorrow.

So we can show alternatives and connectivity to our customers. So, the landscape there is just nonstop and it's a full-time job just about keeping up with it, much less getting it into the lab. What I think is the coolest thing about not just engagement with customers, but with my job that comes from engagement with customers is.

I've never said I'm the smartest guy in the room, but I got a pretty good idea of stuff that you can do with AI. What's really cool is when we sit down with our customers in one of these AI workshops, and I have 26 years in telecommunications and so I, I think I know that business pretty well and I can ideate on things you can do in healthcare or construction.

Sarah Richardson: That's a great perspective, Lee thank you. Nessa?

Kris Nessa: It'd probably be the same thing 'cause now I'm working with other people too that are excited to just. Solve a problem in the art of the possible, right? Like, like I said, I've got the perfect playground now sitting behind me, and I've got software engineers, and that's all I ever needed.

I've got plenty of ideas rolling around and either I'm willing to help a customer and give them more feedback and more ideas on top of it to take their business idea. But it's just like having the humans and the capital and the space and the freedom to do it and just play with it. And it's like. How do we move the industry forward?

My kids got a surgery next week, and when I set foot in that hospital, man, my heart's gonna be racing for my little kid. And I want everything to function and be ready and to go. And so like this is just at the forefront that we still have a long ways to go, but there's a lot of great people here to do the right thing and our heart's in the right place still.

Sarah Richardson: There definitely is. The team that you've assembled to work on these has the background that's needed to your point, to figure out how to make things happen and make them happen at scale, which sometimes is where we get lost inside of our own organizations, as most of us having worked in hospitals and healthcare settings before the bureaucracy gets in the way of just being able to have.

at scale. With help from their experts you can easily accelerate innovation and integrate leading AI solutions across your entire organization. I can't wait to see what else comes out of this lab and the real time application of the wins you are delivering across our industry.

Kris Nessa: to be here.

Great conversation. Thank you.

Lee Ziliak: Yes, thank you.

Sarah Richardson: Take care. Thanks for listening to our Solution Showcase with SHI. That's all for now.

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