Transforming Healthcare with AI: Hans Mize on Best Practices and Partner Ecosystems

Hey everybody. Welcome back to Ingram Micro One, and I'm talking with my friend Hans here, who is a solution provider with a specialty in healthcare. How you doing buddy?

Doing well. Been a really, really great conference. There's just been an awesome amount of innovation in healthcare lately, and I just walk people through a little bit what's going on in that sector and your role in it, and what are the opportunities?

Yeah, we're finding, um, there's a lot going on with an interest in ai, you know, trying to find ways to, you know, automate manual processes and we've delved into a very, very niche aspect of that industry with, uh, organ uh, procurement organizations and tissue banks, tissue processors. And, uh, there is a tremendous amount of opportunity both for AI and just automation across everything they do within their supply chain. Hmm.

There's also a lot of regulations in healthcare. I don't think that's, uh, much of a surprise to anybody, but we're trying to apply AI to that. How do we walk the nuances of that?

Where is concerns about privacy maybe and the data, but we also need to come up with some automation. That seems like a challenge. Yeah, it is.

And uh, you know, some of the things we're finding out, I mean, obviously we've got certain compliance and accreditations and we have to get organizationally SOC two and hipaa, and then you've got the, uh, PII information that they ho hold and that ties into their own processes. So we have to be respectful of the confidentiality of not only the information that they have on the patient records, but also the confidentiality of their own internal processes and how they use it. 'cause in some cases it's, it's a competitive advantage in some cases.

It's, uh, very, very, um, intrinsic to how they operate that they actually don't want it to release. And they're all very sensitive to the aspects of the, the regulations, you know, for that confidential information processes. And, uh, I mean, right now it's just respect and, and, and, you know, with the, the technology to be able to, to do what they need to do, but at the same time, not allow it to limit us.

When I talk to people, there are two challenges, but the first one, everybody seems to know. It's like AI will occasionally hallucinate. So how do I kind of work around that or, or account for that factor when I'm building out something in a healthcare scenario?

Yeah, that is, uh, it's really interesting because the way that we are actually applying AI for our clients in this industry is that, uh, that does exist. And the challenge is that, uh, the data, um, as much as you need it, and as much as it feeds into the AI engine, uh, it's basically the lifeblood of the AI engine as well, because the AI model has to be trained off of that data. And we've got all the regulations that you just mentioned that prohibits some of, you know, that data in, in terms of how we use it to aggregate it with other clients at a very aggregated le a aggregated level.

And we don't have that opportunity. So the, the, the model has to be trained and the more data that we can get from the organization we're working with, you know, we can work around that. That's number one.

Second part is, is that no single AI model will provide the outcome that we need for our clients. So we're having to stack the technology with other types of ML oriented tech, um, code, other types of AI oriented models that do very specific functions to work in tandem to provide an outcome. So I've kind of got a layered approach where some of the AI models are validating the output of the other AI models to get, make sure that whatever is being generated actually is supposed to be what it's correct.

Yeah. At a very simplistic level. Yes.

The other side of the coin too is that a lot of the healthcare processes are what we would call deterministic. They're supposed to be done the same way every time. Mm-hmm.

Uh, AI models are probabilistic and hardly ever do anything the same way twice. So how do I connect something that is probabilistic into a deterministic workflow and kind of meld that together? Yeah.

And that, and I, I go back to what I just said earlier, right? There's, um, um, there's the prompt engineering, there's the AI models, there's the AI model stacked and layered on the other AI models. We've got a vector database that's built into there as well.

The understanding of their business process and what the outcome is. Um, so the model in order to to, to train it, to have a predetermined outcome every single time, the more data that we can feed into it, the more scenarios that we actually get from it that we can hone in and refine on, we'll start providing that very, very precise answer. And it's one of those where it's a process of refinement processes or an iterative process.

So the first time you build it out, it's not gonna be perfect. And so the more data that we can actually feed into it, the more input we can get from the end users. Uh, we actually start honing in on that, that precise answer.

Now, everybody watching this is probably having the same question. Where did you find the people with the skills to go do that? Because most of the folks are saying, I love this AI stuff, but you know, they all wanna work for Nvidia or something.

So how do you get those guys to come work for you? Yeah. Um, trying to answer this the powerful way.

So there is a constraint in the marketplace for skilled AI software engineers, and I have to compete with, you know, the Facebooks and the, you know, all the, the big, you know, hyperscalers for that talent. Uh, in my particular case, I got very, very lucky because the AI engineer that we hired married my, my youngest daughter and I provided an opportunity and there was an opportunity you probably wouldn't get at the other places because he, uh, has an opportunity here to actually kind of define our direction and to be able to be very creative with the AI and in a, in a and applied sense. Um, but going beyond, you know, my small team of him and, and a couple of others, um, it, yeah, yeah, it, it naturally is, is very difficult.

So one of the way things that we're trying to do to augment that is that there are AI tools that allow us to, uh, do some code development on the front end. It's not perfect, still has a ways, ways to go, but it does save us time. So we're trying to use automation and code development to be able to close the gap on some of that.

And then, like anybody else, you've gotta go out and hire the talent as well. So we gotta ensure we get the right talent. Of course, we're at an Ingram event.

How did you get connected to Ingram and what does Ingram do for you and as part of the building of this solution? So the story that, um, you know, most resonates is that we got into this about two years ago. So that's when I hired our, our, uh, senior AI engineer, uh, November 6th of, uh, 2023.

And he came on board, and Ingram actually had, and we started out with, uh, IBM's Watson X. They were actually sponsoring a level three workshop with IBM m in Chicago. So his first day on the job was on an airplane heading to Chicago to get his credentials on, on Watson nuts.

So he spent a week there going through the workshop. And that was really brokered by Ingram. You know, having the foresight to actually, you know, go out and say, how do we actually get our partners enabled?

How do we get them engaged? How do we get them them to a point where they can actually start talking ai? And it was a really good foundation because it allowed us a better understanding of how, uh, the technology was being positioned, you know, in, in terms of the go to market.

But we had to learn after that, how do you actually go in and start selling this? So we relied on, on the Ingram team, uh, to, to understand what types of proof of concepts, how do we actually go through a sales cycle, how do we actually engage with prospects who have a need? And then we had to hone our skills from there.

Of course, you also work with a lot of the vendor partners that Ingram represents. Um, I don't know if you can tell me in a lot of detail, but which of those vendor partners are kinda at the core of that solution for you guys right now? And what is it that you would wish that maybe more of those vendors would remember when dealing with solution providers such as yourself?

Yeah. It's right now with, you know, just the, the terminology of ai. Um, I think the large vendors, you know, the ones that are well known that make the news every day, they're sewing a lot of confusion.

Everybody's talking about the art of the possible as a reseller partner, as somebody who actually engages with a client, by the time we engage, they don't wanna hear about the art of the possible. They wanna see a solution that actually works. So there's a big gap between, you know, what, what the, uh, the vendors are providing and what the solution providers actually have to deliver.

So we rely on Ingram heavily, not necessarily with the technology partners that we've worked with IBM and Microsoft, and there'll be others in the future, but it's the relationships that we don't have with the other technology partners that Ingram does have. So for instance, if there is a reason for us to change some of the backend coding with a different LLM or a different AI type model that is, uh, specific to a certain vendor, we don't have the re relationship. Ingram will probably have that relationship and we have to leverage Ingram to help build our credentials and reputation to be able to open the door and get the right resources we need so we can continue our development to provide that solution to the client.

One of the things that I hear a lot about is every CEO has a bad case of fear of missing out and things that this AI stuff is all magically happening tomorrow. And then there's their staff and people who are a little more circumspect 'cause they understand what's required to actually implement. How do you as the solution provider kind of navigate that relationship and those conversations?

'cause essentially you're a diplomat between these Groups. Absolutely, yeah. And it's, um, that was, I would say situationally, that was probably the case two years ago when we started, um, AI was this concept.

And pretty much every executive team says, okay, we've gotta get AI in here. And our first probably, you know, a handful of phone calls or, or overshoot from our, our, our client base necessarily our prospect base was, Hey, can you help us with ai? And my response was, yeah, absolutely.

What would you like us to do? It says, well, that's why we're calling you. And it was this, this panacea that all of a sudden you, you basically, you install something, you implement it, and everything is gonna work to perfection.

It's just like this magic button you press. In reality, that's not the case on the staff level folks, the operating level folks, they're seeing AI as a threat. So it's basically our executive team wants to bring in AI and it's, it will basically replace my job.

And so you have this, uh, uh, diversity of thoughts and understanding of what AI is supposed to do. So we have to obviously educate the C-level folks that it is not that be all end all solution where you push a button, everything magically works, um, and it's trained on your data. And then we also simultaneously have to work with the operations folks and let them know that we're not here trying to put a system in to replace your job.

What we are trying to do is that, uh, you know, right now the focus and the benefit of AI is really, you know, time savings and productivity improvement. So we want the system to be able to do the heavy lifting for them and the process and use everything that AI can do based on the data that's being fed to it, to free up their time to work on the very true value add, you know, needle moving types of, of aspects of their job that's gonna really enhance the company's productivity. One of the funny things about healthcare, at least from my perspective, is it is always perceived as a sluggish kind of business because they're collecting a lot of data, tagging it and organizing it.

And yet that may be their secret sauce for AI because they did a lot of that heavy lifting of the data and worked already then a lot of other vertical industries have not. They, uh, I would agree that to a certain extent, uh, but there are, um, I would say upstream functions that take place that are still very manual and a lot of that tagging categorization of data, which actually makes our job easier, right? Because it's all, all well-defined.

Um, a lot of that has been done, but there's a lot of unstructured data that shows up in forms, in handwritten notes. Um, it shows up in jpeg images, OCR images that we have to translate in. And not only that, you can have forms that have the same information, but the forms are different and, and the context is missing from that.

So when we actually build out these AI solutions, we have to ingest those documents, we have to understand what is in those documents, we have to understand what the context of those documents are so that we can turn the information on those documents into relevant, very well-defined, categorized information to then let the AI model be able to provide the output that it wants. So to your point, a lot of what they do operationally, you know, for production, for, um, you know, um, patient outcome, yes, that is, but upstream from that, a lot of the information is very unstructured and increase the challenge for them, and there's a huge amount of opportunity as far as productivity gains from that as well. It almost sounds like, you know, you have a solution and you've kinda landed and now you're looking to expand.

So what are you thinking about as the next opportunity? Yeah, so we, we are, we're actually, uh, we, we've got a great client that we're working a, um, informing a strategic relationship with, and it's a tissue processor, so basically a organ donation, and then they actually take, um, uh, tissue, uh, donor tissue and then look at, uh, eligibility requirements. So they, they be able look at lifestyle, they be able to look at disease, they look at things of that nature and, uh, qualification criteria for the tissue.

They have their own manufacturing process and you know, as we spoke earlier, that's very well defined. The information is very well defined. How they capture the information and move the information through there is very well defined.

But on the front end of that, how they actually analyze the, the donor and how the tissue, the suitability for the tissue that they have to process for their, in, you know, inpatients, the hospitals, the doctors, the clinicians. Um, so we, you know, we've developed an AI application to be able to do that manual process on the front end, and then the automation that follows that, the donor traceability. So now that we've got the components being processed and tracking that all the way through to the final production of that tissue, so that it's either a skin graft or a bone graft or whatever that final product is, so they can inventory that and then push that out to the hospitals.

And then there's the entire supply chain and ecosystem where the supply demand match is very inefficient. So we're looking at the hospitals, the doctors, the clinicians, when they actually need something, how can we actually compress that cycle time so that they can get it from the tissue processors in a shorter period of time? And, and there there's additional opportunities beyond that as well.

So there's a plethora of, of things that we can do. Technology can help, but you've gotta have a good partnership with folks in the industry to do it. Yeah, I almost think like anywhere there's friction, it becomes an opportunity.

Absolutely. Oh, absolutely. So last question.

As you look into the coming year, what are you excited about? What are you thinking about and maybe what's keeping you up at night? Well, the geopolitical stuff's keeping me up at night, so not, not a whole lot I can do about that, but there are opportunities and I think, you know, we've had, uh, two years of maturation, uh, not only within my organization, but I think within, uh, just industry in general and understanding, you know, ai, AI has gone from this concept of AI is, you know, it's just this broad, uh, term that everything's kinda lumped into it.

Now we've got, you know, the uh, uh, generative ai, we've got agentic ai, uh, we're still focused on use cases, but I think a lot of the use cases will be addressed by agentic AI to a certain extent. And then you have the possibility of that automation of where you get the AI agents talking to each other and looking for those opportunities. But at the end of the day, when you look at it, right, we are still collecting data as part of that process.

We're trying to take the friction out of the, the supply chain. So the question I asked the CEO of our client is that if you were to look at your processes and you were to take all the friction out, what is the shortest, shortest amount of time that it would take to act, you know, to actually process the tissue that, that you work with? And he thought about this, I said, that should be our goal and that's what I'm excited about.

'cause I think the technology can get us closer to that goal because we can automate it, a lot of the processes in a very smart, intelligent fashion. Not necessarily to replace jobs, but again, take the folks that are very good at what they do and allow them more time to be able to do it better. Especially the stuff I don't enjoy doing in the first place.

Yeah. Hey guys, it takes a village to do AI and it kind of starts with the solution providers and includes the distributors and the vendors, and that's how it all comes together. Buddy, thanks for coming by.

Yeah, thanks for having me. And we'll be back in a minute.