You Don't Have a Data Problem. You Have an Intelligence Problem - The Hive Health

Your organization doesn’t have a data problem. It has an intelligence problem: the gap between having information and being able to act on it with speed, clarity, and confidence. That gap shows up everywhere: hospitals, schools, manufacturers, and any team drowning in dashboards while leaders still wait on someone to “find the story.”

Rick Anderson (Chairman & CEO, The Hive Health) is back to show just how much AI has impacted one organization in 12 months. Enter Corby Furrer (Harvard AI fellow, builder since college) and Will Furrer (former NFL quarterback turned COO). Together, they've built what they call a "trade intelligence platform" - not another analytics tool, but a system that encodes economic expectations, reconciles them against purchasing reality in real-time, and tells people exactly what action to take when behavior drifts off course.

Intelligence isn't about regression models anymore. It's about knowing what "good looks like," verifying AI assumptions through human-in-the-loop, and translating observations into stories that change behavior when delivered by the people who speak the right language (physician to physician, engineer to engineer, teacher to teacher, not consultant to administrator). Sustainable change requires three legs: understanding the rules of the game, seeing what's actually happening (not what's supposed to happen), and coaching insights through stakeholders who can shift behavior.

AI scales when it creates shared clarity people can validate and act on repeatedly, not when it generates reports that collect dust behind the CEO's desk.

In this episode:

• Why your organization can have endless dashboards and still lack decision-grade intelligence. 
• What must be true for leaders to trust AI results enough to act on them. 
• How a data observation becomes a story for the change agent that actually drives behavior change. 
• How coding and product building changes when AI can generate code, and why knowing “what good looks like” matters.
• Why one-time improvements fade, and what it takes to build a repeatable system.

3 Big Takeaways from this Episode:

1. Decision-grade intelligence starts with clear expectations and a next action. The bridge from data to intelligence = what should happen, reconciling it against what is happening, and using that gap to drive the next corrective step. The takeaway is widely applicable: if you cannot state the intended economic or operational outcome, you cannot reliably diagnose variance or drive consistent performance.

2. If the improvement is not repeatable, it is not a solution. Build a system that codifies the work, monitors performance against targets, and keeps savings from reverting once the project ends. The real value in AI projects is durable behavior change and ongoing detection of the next opportunity, not a one-time finding.

3. Insights only matter when they are delivered to the change agent as a story that drives action. A data observation has to become a narrative that the person who can change the behavior will actually respond to. In the AI era, that elevates a specific skill stack: storytelling, curiosity, and building, because trust and adoption live or die in communication and execution, not in the existence of a model. 

Links & resources

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Transcript

TechEd Podcast Introduction: 0:09

Announcer, this is the TechEd podcast, where we feature leaders who are shaping, innovating and disrupting technical education and the workforce. These are the stories of organizations leading the charge to change education, to rethink the workforce and to embrace emerging technology. You'll find us here every Tuesday on our mission to secure the American Dream for the next generation of STEM and workforce talent. And now here's your host, Matt Kirchner,

Matt Kirchner: 0:38

securing the American Dream for the next generation of STEM and workforce talent. This is the TechEd podcast. We are the number one podcast in all of STEM and technical education, believe it or not, now ranked in the top 1% of 1% of technology podcasts around the globe. So great to have you with us. My name is Matt Kirkner, your host. We're gonna have a lot of fun on this podcast. We've talked in the past about AI and the implications in healthcare and certainly AI Artificial Intelligence transforming so many aspects of our entire economy, not the least being the healthcare space. We've had one of our guests that's rejoining us on to talk about that topic, actually, with a couple of different endeavors, a great friend of mine, by the way, Rick Anderson, he was on the ground floor at WebMD with Jeff Arnold as that company came of age. Of course, incredible success story and incredible success that Rick had while he was there. He's now joined, by the way, he's with the hive health. And the last time he was on with us, he talked all about the hive health now has expanded his team with some amazing individuals, experts in the area of AI. We're going to get into that. But let's start off before I introduce those two folks, I want to welcome back to the TechEd podcast. My friend Rick Anderson. Rick, great to see you.

Rick Anderson: 1:48

Thanks, Matt. Good to be here again. And if I, if my memory serves me correct, am I now in the three timers club?

Matt Kirchner: 1:54

You are, yeah. And I know you're, you're, you're trickily trying to, trying to beat out the all time leading scorer, which is Mike Chico, who's the president and CEO of FANUC America, the largest robotics company in the world, has been on with us four times, but Rick Anderson now three. So what you're just gonna have to keep turning over your leadership team or adding amazing leaders to your organization, so you can have an excuse to come back on the TechEd podcast and join us and talk about the great work that you're doing. You know, one of the things that because Rick is such a good friend, I can pick on him, he pretends to know a lot about artificial intelligence. I'm not sure that he does, but he's got two teammates now that are super, super experts in this endeavor. Let's start with this, Rick, and of course, I'm giving you a hard time, because we all know the work you're doing at the hive health is is incredible, and you're an incredible technology leader. But if folks didn't catch our last episode, and we talked about how you're using AI to transform healthcare. Just give us that kind of quick cliffs notes of what your company's all about in the work that you're doing.

Rick Anderson: 2:50

Sure, no, thank you, man, thanks for having us. You know, it's really funny. I'll start off with a humble admission of how little I knew about AI. What was about a year ago? I think we last visited around Christmas time last year, and the question was, how is AI impacting healthcare? How is it does it matter? Where does it matter? And I looked back, and I actually re listened to that interview we did, and how little I knew about how transformative AI would be in literally less than a year, it has completely changed our business. But to answer your question, sort of what we do every day and how we see the world is what the high health does, is we we help healthcare providers, and this is physician groups, ambulatory surgery centers, hospitals, health systems, really the gamut, improve the clinical supply costs, especially these, these high cost things we call physician preference items. These are things like lab products, orthopedic products, cardiovascular we've now expanded since we last talked, into things like gi gastrointestinal, Urology. The pricing in here is really opaque. It's hard to control. It's kind of the Wild West. A lot of unfair disadvantages are placed on these people trying to do a good job. But what we do is it's kind of unique. We we combine physician led supply chain expertise with now what we call a trade intelligence platform. And I'll let my teammates will and Corby talk more about that. What that does, though, and we've named it Honeycomb, kind of a cute play on the business name of the hive. So what honeycomb does is it finds pricing opportunities, it helps negotiate better contracts. And then what it does is it continually monitors purchasing over time, so that those savings are actually sticky, right? So, you know, the best thing we could do is help a hospital get a better price from a vendor. And in our past, pre AI, we would declare victory. We would ring the bell the client would save the money. We would be paid for our efforts. We would leave, and five minutes later, these vendors would come and drill holes in the boat and try and find ways to get better prices, higher prices pushed through the system. And so this platform we'll talk about really helps to sort of mine for those opportunities and then monetize the savings. And then, most importantly, you know, be able to continue to monitor that, to make sure that everything that we did, the hard work we did, actually sticks. And you

Matt Kirchner: 4:59

know, I want to point. Out, by the way, because I remember you mentioning this in his in our last episode. In as much as you know, what we call vendors or suppliers in the healthcare space can can sometimes be defined as the villains, you're also seeing some great feedback from some of these as you partner over time, because they're able to better service their clients as well. So so they're not always just money hungry companies that are trying to find every angle on price, right? In many cases, you're, you're partnering with them as well. Is that fair to say?

Rick Anderson: 5:25

Yeah, we typically like to see what our industry sort of calls an all play model. That is that, you know, many companies have great technology, and you know, if you just look at price alone, you could say, well, we go with vendor A, but then vendor B, C, D and on really don't have a right to play. And that doesn't seem fair. And so a lot of what we do is a little bit more consultative and kind of with an ear to the ground of listing and learning as opposed to declaring. And so it helps us really improve that relationship between that vendor pool. You know, some of our initiatives have 30 vendors. Well, if you just said, Well, let's go with the one with the lowest price, you're not going to see the technology and the important things, the things that are important to the physicians, come to light. And so we really like to bring it sort of as busy said, like an all player, fair approach.

Matt Kirchner: 6:09

And in this case, the beneficiaries are patients. The beneficiaries are some of these small healthcare systems. In many cases, not every case that can continue to compete, continue to remain viable and really change lives in the long term, which is exactly what healthcare should be all about. So credit to you for your leadership on that endeavor. Credit to you for the implications that AI has brought to that space and for your ability to leverage it. We're going to go deep into that with our next two guests. I want to give you an opportunity to reflect on those two gentlemen in just a moment, we're going to be introducing will Fuhrer, who is the Chief Operating Officer now of the hive health and Corby fear, who is the Chief Technology Officer? I know these two gentlemen have brought tremendous insights, tremendous talent and tremendous progress to your organization and the work you're doing. Rick, before we do that, what would you say the most important context is that our listeners should have as we kind of look under the hood of your organization, and go a little deeper on the technology.

Rick Anderson: 7:03

I think if I were to sum it up, I would say the problem with healthcare today is that we we don't have a data problem. There's lots of data we have a data intelligence problem. And what amazes us to this day is that the technology platform we built, and we did it wisely with a an AI advisory board of our peers, of our supply chain and C suite leaders to say what's important. How should we build this? Why does this matter? But having that data intelligence problem really kind of serves up an opportunity for us to really be different and just to revisit what we talked about a minute ago. Yeah, it's very humbling to think we had this figured out a year ago. We were three years into our business. You know, revenues were growing significantly. Client growth was pretty amazing. And we kind of thought we had it all figured out. And then you realize, you know, if you listen to your customers and you and you put an ear to the ground and say, what's important, what matters, you realize there's a lot of problems. So we just really, we went in, all in on AI. We were growing, we were doing well, but we realized that building this trade intelligence platform was was everything to us, because really, one big part about our industry is that you have a client that will hire someone like us, and as we mentioned earlier, pay us for our work. They get their savings, and then problems start to occur. And so we wanted to build a longer relationship with the clients. And so now all of our agreements are typically three years. We like to stick around. We like to be able to offer our insights and opinions, and so elongating that relationship was really important to us. I think

Matt Kirchner: 8:27

that's a great lesson, Rick, for just any about any business organization in this point in time and this point in history. First of all, you mentioned we thought we had it all figured out a year ago, certainly when you and I had our podcast a year ago, you were doing so many great things. We went deep on a lot of the great innovation that was taking place. Here we are a year later, and you're saying, Look, our market space maybe hasn't totally transformed, but because of the advancements in technology and AI, we have to constantly re examine our value proposition, constantly re examine our market position, constantly re examine and have conversations with our customers and clients to make sure that we're positioned not just for the last 12 months, but for the next 12 months. 12 months. I can tell you, you know, we used to do five year strategic plans in business, and we could kind of march through those five years. And there weren't a lot of surprises necessarily. Maybe something came out of left field. Today, the world of business is moving so fast, really, really important, that we're continually re examining how we're going to market, how we're building our team. We'll get into that in a moment. Super important, the other message that I want to make sure that our educators are hearing, because I think if there's one thing that healthcare and education have in common that maybe folks don't realize Rick talks about, there's no shortage of data. We don't have a data problem. We have an intelligence problem. Education is the same way. We don't have a data problem. We have so much data about our students, where they're from, what their extracurricular activities are, what their grades look like, you know, what their home situation is. I mean, there's just tremendous amounts of data that we have about our students in the same way that you have tremendous amounts of data about the spend in the healthcare system and the benefits they're providing to their constituents and their stakeholders. So lots of corollaries there. So as our. Listeners are listening to this episode, whether you're in education, whether you're in business, whether you're a student, I want to make sure that we're drawing those parallels between the work that's going on at the hive health, the work that's going on in healthcare, and certainly the work that is going on in other market spaces. To that end, I want to introduce to our audience Corby Fuhrer, Corby, as I mentioned before, Chief Technology Officer of the hive health. First of all, Corby, thanks for coming on. Great to have you on the TechEd podcast. Thanks for having me, Corby. I mean, incredible, incredible background. You know, anytime you bring up Harvard trained AI fellow, got to be a brilliant, brilliant individual. You've had major hackathon recognition. We'll get into that a little bit. You're part on prod, alongside a lot of the breakout AI builders, if I'm not mistaken, that was Harvard, Stanford and MIT that were involved in some of that work. So let's walk us through kind of the key chapters of your life. If people are watching on YouTube, they know that you're you're not an 80 year old guy, you're a relatively young individual. Early in your career, doing some amazing stuff. How did you get to where you are today? Talk about your background a little bit.

Will Furrer: 11:02

I've always just liked building things, and so I've always just, I've always just built stuff. And, you know, I remember the first time I used gpts, like API, I think I was like, a sophomore in college or something like that, like before it was available, kind of online. And the first time I use it. It was just so interesting to me. I was just like, I have to know how this works. Like, I always like taking stuff apart and kind of figuring out how it works. And that really just kicked off a learning journey that just never stopped. And so I just kept building things with it. Just kept experimenting. I ended up, you know, with a group of people among, you know, three great schools, a lot of bright minds experimenting with it, and we would all just share techniques and and sit around and talk about, you know, what works and what doesn't. And that's kind of how I arrived here.

Matt Kirchner: 11:49

So it's an amazing story, an amazing background, but going back to some of those original gpts, I'm, you know, I'm just finishing the book The thinking machine, which is the book all about the background of Nvidia and Jensen Wong and kind of the story of that whole company and becoming one of the, if not the most valuable companies on the planet, and just this incredible fascination that the team at Nvidia had early on with generative, pre trained transformers, and going from, you know, kind of, if this, then that type of question asking, using software to now using neural networks and understanding, you know, different layers of information, how quickly they can gather and predict the next word and give us answers. You know, looking back to those early days of gpts, was it chat GPT that got you fascinated originally, or what were you hooked on when you got started?

Corby Furrer: 12:33

Yeah, I mean, we were doing some some machine learning stuff in school. And obviously there's people, when people say, AI, there's a lot of different, like, types of AI. Obviously, what we're all familiar with is generative AI, but there's also machine learning. And then you've got, like, if you've ever watched the AlphaGo documentary, which is an awesome documentary about demossabis and Deep Mind early on, that's deep learning. And now we now we have what everybody's familiar with, which is, like, generative AI. I've definitely done some of the other stuff. But what I was really fascinated with was the use cases of generative AI, and that's, you know, primarily two things. Obviously, that's the response. Like, you type something in, it sends a response back. But there's also a lot of there's also another, I think, really important, derivative technology of this, which was a universal embeddings machine that's called Ada. So if you look up, like, open a eyes, Ada, that was the first time that you could send information in and have it put into a general representation that was so useful for querying, much like what Google did, but here it is, just accessible over the API to a college kid. So when we played with that, like that, that was kind of what I got really, really interested in it.

Unknown: 13:41

First, awesome. Yeah, very, very cool. I want to reflect a moment Corby on what you said there, because I think, and we make this point all the time, right? When people think about AI, most of them think that, you know, the gpts are AI, right? So they think about whether they're using cloud or perplexity, or they're using meta AI or copilot or chat GPT, or whatever platform they're they're into, and they think, Well, yeah, that's AI. I ask it a question. It gives me an answer. It can write computer code for me. It can And absolutely, gpts are a really, really important aspect, and probably the one that most people are familiar with. Here at the TechEd podcast, we talk all the time about physical AI, about the edge to cloud continuum, about sensors and devices on the edge that are now smart, can communicate with each other, have embedded intelligence, speaking with control systems, speaking with networks, speaking at the fog level and at the cloud level, watching all of this incredible work that we can do in quadropods and humanoid robotics and advanced manufacturing, certainly in healthcare. And then you talk about machine learning, and folks don't realize sometimes they go deep into things like, you know, supervised learning, unsupervised learning. Got classification learning, you've got reinforcement learning, I mean, all these fascinating aspects of artificial intelligence and so really, really important to remind our audience, as you did that, yeah, gpts are really cool, and they're driving a ton of this, but in a lot of cases, they're driving it into the physical world and making some really. Really interesting and fascinating use cases that we could probably do a whole episode about. We won't. We'll go back to your background, which our audience now knows is incredible. You're incredibly well trained. You're a very curious individual with with this incredible skill set that you have. It's a high demand skill set. So So you could have done anything. Here you are at the hive health doing this incredible innovation and healthcare. Why did you pick this vertical? Well, there were a couple of things that really attracted me to this vertical. I think that, like healthcare, I think healthcare needs a lot of help based on based on what I've seen inside of our data and from Rick's stories of interactions with prior customers, I think that there is a lot of good to be done in healthcare. And I wanted to do something that was more than just technology. I wanted to maximize for impact. And every single customer, you know, we go into, we help tremendously, like so much. And I think the other thing that was that was super, you know, important to me was a business that's incentive aligned with the customers. And if nothing else, like that is what the hive is. I mean, we only make money anytime we save customers money. And that business model was really attractive to me because it's, it's such a no brainer, right?

Will Furrer: 16:06

And if, if I want to impact, you know, 100 hospitals, 600 hospital systems, every single hospital in the country, that's only going to happen in a business where the ROI is exceptionally clear and where the business is exceptionally aligned with the customer incentive wise. So those were kind of the combination of things, the fact that there, there is a lot of impact to be had, and that I think hive has a business model capable of maximizing that impact, those were the two things that really brought me here. Rather than, you know, doing something like, you know, Palantir, or any of those other big tech firms, I want

Matt Kirchner: 16:40

our audience to make sure that that something you just said Corby landed, because I talk a lot of times about how when I graduated from business school, if I'm being honest, like your goal was to just go make money, it wasn't that you didn't want to do good, you know, certainly didn't want to do things that were were unethical or didn't help people, but, but, you know, for most of us, I think that this would be kind of early 90s or So. You wanted to be rich. You know, you saw the movie Wall Street. You saw some of the stuff that was going on in in New York. You saw some of the, you know, just some of the pop culture around successful business people. And that's what you wanted to be. We talked today about, yeah, I mean, everybody wants to make money. That's okay. It's all right to be financially successful. But for this generation of talent, your generation, and maybe the one that came before you. In so many ways, it's about so much more than making money, and in many cases, if you ask people to make a list of what's important to them in terms of their career, financial incentives and building net worth and so on, are on the list, but they're like number five, and ad or near the top is making a difference. And it's interesting to me that you led with that, and I want to make sure our audience heard that, that when we're building talent in our organizations, whether it's educational institutions, whether it's our businesses, this next generation, current generation of talent, yeah, it's about money, but it's more about making a difference, making the world a better place, working on something important, going home from work every day and feeling like you made a difference. And I want to make sure that that message landed and hit home with our audience, because I think you're just an absolutely perfect example of this again, while you're working on some really, really cool stuff. So let's talk about what's going on in the world of healthcare. You look at hospital supply chain issues you're learning from a guy like like Rick Anderson. Are there things that most people kind of misunderstood about the problem that you had to help them solve and what did you see there?

Will Furrer: 18:23

Yeah, well, I mean, I just think everyone, everyone is, was really concerned with, like, just seeing more and more data. And I think that our biggest insight so far, when when talking to customers, was like, Well, let's take a step back. Like you already have an ERP, you know, an EMR, EHR. There's 1000 acronyms that are holding data for a hospital right now. And we took a step back and said, Okay, it's cool that you have all of the data, but you know, what are you learning from it, and how fast are you learning from it? And then what actions are you taking after that? And I think getting with our product board, our tremendously talented product board, and talking with them about the current state of healthcare, like that was kind of the big leap was going from, hey, can you show me a new data point to, what do we even do with this data? And so that has been the biggest leap in terms of, kind of, like changing the mentality of supply chain or changing the mentality of people in the problem space, is like, show me more data points to Okay, here's all the data points. What, what needs to happen next? And that's the leap that we're trying to help people make. And I think that's kind of the biggest misconception in the industry right now, is that more data points are going to solve the problem. It's not

Matt Kirchner: 19:37

just how much data you have, but what you're doing with the data, what, what insights you'll be able, you'll be able to create with that data. You know, as Rick knows, as our audience knows. Actually, I spent a week in China back in August. Part of that, part of that trip I haven't talked a lot about, is that an individual that was on that trip with us was a woman named Duan Peng, and Duan is the chief AI officer for Ashley. Furniture industries, traveling also with Cameron wanick, who's the Senior VP of supply chain, in learning about this is what we talked about extensively on that trip, which is that organizations collect all kinds of data. And our ed tech companies, separate from the TechEd podcast, we have tons and tons of data. We have a CRM and we have ERP systems. We have all kinds of data in disparate spreadsheets. We're pulling data from platforms like Wren that are informing us about customer activities and so on. And it's not just about the data you have, it's about the insights that you can create from that, in our case, creating data lakes and using the Dataverse and building MCP servers and then building AI agents that can go in and create insights for our team to understand what's happening in the marketplace and understanding how we can improve, and I think that is really quickly, whether people realize it or not, becoming the competitive advantage in so many businesses, certainly, certainly in healthcare as well. And so I think you make a great point Corby, which is it's not just about the data we have in many organizations. We have tremendous amounts of data. It's about what we do with that. So let's talk about that a little bit. Let's talk about this idea about intelligence. And it's not just analytics right now. It's not just the old way of going through and building regression models and understanding standard deviations and kind of all these traditional models that we would use to predict the future. It's now about intelligence, and we get that theoretically, but talk about what that means in practice. What is the output a hospital leader actually gets as they're working with your organization that they can then utilize to improve performance.

Will Furrer: 21:27

Yeah, I'm really glad you brought this up, because I honestly think we've kind of stumbled across like, a new type of platform that everybody in healthcare needs. And to the end of getting to intelligence from data. The only way that happens is if you have set up an expectation, and you're reconciling that expectation against the actual reality of the business. If you don't know what was supposed to happen, how are you supposed to understand how to correct what is happening? Right? And so it's about encoding, you know, your economic expectations for the business, and reconciling it against the reality. So when we talk about getting to intelligence, it's always trying to bring you from the data, you know, to your next action that gets you back to being great and that that would that is what we're calling a trade intelligence platform. You know, we believe that there are a lot of people in this industry who are kind of performing financial engineering around the hospitals, and we want to unwind that, make it explicitly clear what should be happening. And then, anytime we see something that's not supposed to be occurring, anytime we see an event like that, we can say, hey, this is what we intended to happen. And here's the next step you should take to get yourself back to that economic reality that you plan for yourself. And so to us, that's what the intelligence mean. Set up an expectation, reconcile it against the reality, and tell people how to get back to the best reality. So let's

Matt Kirchner: 22:58

unpack that a little bit. You mentioned the term financial engineering, and certainly I like the dichotomy that you kind of created there is we do get into some of these situations where, if it's all about the dollars, if it's all about the financial engineering, if it's all about leverage, we can get lost a little bit in what we're really trying to do as an organization. In the case of healthcare, obviously, improve patient care and improve lives. In many cases, should come first, not that we ignore the financial aspect of that, because without profitability, we can't have progress. We have to make sure those are working in tandem with each other. Give us a little bit of an example. I like how you kind of said, All right, we need to set an expectation, right? So what do we think is going to happen based upon the data that we have right now? And then build a model, build an AI model that helps predict the future, but then measures actual results against what our prediction was. Because the truth of the matter is, our predictions aren't going to be perfect every time, but as we inform our models over time, of where we miss, that model gets more and more effective over time. So so two follow up questions. The first one is, did I get that right? Is that, did I characterize that in the right way? And then secondly, is, there, is there maybe a working example, without giving up anything that you can't, where you could explain in general terms how that would work in practice?

Will Furrer: 24:06

Yeah, absolutely. And it's kind of funny that I've ended up on the sort of defensive side of this financial engineering, because my older brother actually works in what's called trade promotion optimization, right? So the we believe, you know, that vendors are taking part in trade promotion optimization, where they're actually looking statistically at your past history, and they're saying, you know, how can we create, you know, maximum benefit for ourselves with some sort of promotion, or, you know, some sort of new product line, or whatever they're introducing when they do this, you know, they're using a tremendous amount of, like I said, engineering, and so we want to provide a similar level of financial engineering to the other side of the table, which is where as you're getting new contracts, as you're getting new rules for your. Business, so to speak, in any any shape or form, whether it's a new formulary, or you're slightly pivoting the way you do a case, or you've just got a new rebate from a vendor, it could be any of these things. We want to help you, first of all, assess that against all of your history, right? See if it makes sense, right? That would kind of inform the negotiations. Hey, you know, we would hit this incentive, or we would miss this incentive. That's one thing, and then, more importantly, if you do decide that that is something that you that's a new rule that your business wants to adopt, we want to make it a living rule, right? So we don't just want to write it down in a notebook and put it somewhere. We want to encode it in a way that it can interact with your data as the future goes on, right? And so a really, a really simple example of this would be, you know, there's a Christmas sale this year, right? There's a Christmas promotion with, you know, some vendor like that. If you're buying stuff around Christmas time, obviously, you you want to take advantage of that. That's a business rule that you're allowed to adopt, and you should be able to take advantage of that. So again, we set up the expectation and we reconcile it against the reality. And if you're overpaying at Christmas time, we're going to help you reconcile that outcome against your expectation and bring you back up to better margins.

Unknown: 26:15

Brilliant analogy. Yeah, I love that. Love that story. And by the way, as a quick aside, if at some point you just get tired of working with Rick Anderson. I should be your first call. I mean, absolutely, absolutely brilliant individual here that we have on the TechEd podcast. And again, I'm kidding, of course, but let's, let's, let's dive into this. You know, I read an article over the weekend Corby in the Wall Street Journal. The whole idea is, we got all of these silk Silicon Valley you know, young tech people that are saying, oh my goodness, this is the last opportunity to build any real wealth. The American dream is dying. AI is taking all of the things that we spent all this time training on and understanding and learning about during our educational journey. Really frustrated me in some ways. It was a well written article. I'm not picking on the author, but the whole idea for me is, yeah, AI is changing the world. And if you had skills that were honed over the course of the last 10 years, then AI is now able to assist people that didn't have those skills with completing the same tasks we used to need somebody in that role to do. People have an obligation to change. They have an obligation to be lifelong learners and to pivot their careers into areas that are in demand, because there are plenty of them. But it is interesting to look at how AI is changing the world of coding. You know, not just over the course the last 15 years, but maybe over the course of the last 15 months or 15 weeks. I mean, it's just how quickly this is all changing. I would love to hear you talk a little bit about that. You know, writing code with AI now is way different than it was even a couple of years ago. You know, what has changed the most in your build cycle, whether that's speed or iteration or or testing, and then it is, we're moving faster and faster. Do you see some risks that we need to make sure we're avoiding? Yeah, that's that's probably an interesting article. I'm not sure if I've read the exact same one, but I know a lot of times they define the American dream as like, out earning your parents, and the probability of out earning your parents for our generation is lower than it has been historically. And so I think that people are feeling a greater pressure to make an impact when they're young, to change that trajectory, right? Which is why you see a lot of this narrative around sort of the last tech boom. But, you know, it is getting easier, easier and easier to build stuff, right? And so this is, this is eroding, you know, the value of an education actively. Because, you know, if you don't have to learn software engineering to be a software engineer, why would you go to school to learn software engineering? I believe that the defensible skill set going forward is simply going to be problem solving, curiosity and building like it's very simple, right? Code is code, but code has always been sort of just a barrier to an outcome, to a solution of sorts, and now it's just easier to write code than it ever has been. But that doesn't mean it's easier to solve a problem necessarily, right? It's just easier to get some kind of a result on the screen. And so yeah, the new risk that have shown up is, instead of you spending four hours writing a single file, it takes you 20 minutes to bring that file into existence and then an hour to clean it up, right? And so you do still see the time improvements, but the way that you're engineering is less so like this, right, and more like, Oh, here's this. Let me shave off this edge and shave off this edge, you know, and clean up these five things. That's what engineering looks like now. And so you have to know what good looks like, right? And so I still think learning it in the traditional way is important. But knowing what good looks like and what good problem solving looks like, those are kind of the important and defensible skills in terms of the new risks that have showed up. I mean, just bugs everywhere, right? So you got to be able to find them and clean them up really quick. So give me your three again. You said problem solving, curiosity and innovation. Were those three? Did I get it right? What were the three?

Will Furrer: 29:52

Problem solving, curiosity, and I think most importantly, willingness to build. Got a willingness to build. You can get out there and actually. Build something right? A lot of people talk about solutioning, but very few people will actually sit down and do something about it. Awesome.

Matt Kirchner: 30:06

Yeah, I know that's terrific. You know, we had Barbara humpton, who's a former chief executive officer of Siemens. Everybody knows Siemens Corporation, Siemens USA, on the podcast little over a year ago, memory serves, and she's on to a new role now, but, but at the time, she said that her her quote, and it just stuck with me is, if you have curiosity and initiative, the world is yours. And I just think that's absolutely true, and that's that's kind of what we're touching on here. Be curious, be a problem solver. Be willing, not just to innovate in your brain, but to actually sit down and make something happen, build something real, whether that's in the digital world or the physical world. In as much as we're here securing the American Dream for the next generation of STEM and workforce talent, we can dwell on the definition just for a moment, which I think historically, has been out earning your parents. To me, it's really about it's a derivative of that. And you don't earn money for the sake of earning money. You should earn money because you want to create a future and a quality of life for yourself and a way of life for yourself that's that's comfortable and meaningful for you. And if we can do that for the next generation of Americans, regardless of income, I think in so many cases, with the advent of AI, that's the reality of that we're seeing on the ground. But certainly in my in my belief, as far as defining the American dream, if we define it that way, there's absolutely bright futures ahead for young people, including yourself. Corby, so one final question before we switch over to will we've talked about human in the loop. And, you know, the idea is, are we going to get to a point where AI is just doing AI for AI sake and and there aren't, you know, the people aren't involved in this at all. We've just got computers and AI models and data centers and what have you, kind of innovating on their own, without any help from us. This human in the loop. Part of it looks is really, really important, I think, and I think is a key part. If you, if you listen to anybody talk to anybody who's who's innovating in the AI space, there's always going to be people, or at least for the foreseeable future, people involved with that process. So what, where do you insist that humans validate the work that you're doing, and where do you let some of the automation just do its own thing?

Will Furrer: 32:05

Yeah, well, I mean, another thing that we're focused on is building important data sets and data sets that are hard to construct. And you know, Rick and team did a tremendous job building this as business of physicians. And when you talk about data sets that are hard to validate, hard to construct, hard for generative AI to work on getting medic like medicine Correct. That's that's a really hard thing to just find out there on the internet. It's not, it's not just out there, right? And so operationally, when we talk about human in the loop, it's verifying any kind of assumption that we make that like, we don't trust, right? And so it's very easy to look at where you use AI throughout the company and assess it and say, Is this a place where we actually need physician intelligence to physician verification? Is this something that, if it failed, you know, could cause a huge problem? It's pretty easy to identify where you need to have a human in the loop, and especially in medicine, it's so critical to have that kind of intelligence. And I think that's something that really differentiates our business,

Matt Kirchner: 33:10

differentiates your business, for sure. And that human element, I know is important to Rick, I know it's important to you, and I know it's important to will Fuhrer as well, Chief Operating Officer of the hive health. So I want to welcome now. Will Fuhrer to the TechEd podcast. First of all, will thanks for being here. First former NFL player, I think we've had on the podcast. You were an NFL quarterback. Can't have you on without you at least touching on that history in the world of football before we move into the world of technology. Tell us about that.

Unknown: 33:36

Yeah, it was great. Well, first of all, thanks for having me. This is really great. Love your format, love the content that you cover. Just love the diversity of guests. So thank you for having us. Really appreciate being here. Yeah, it was, it was really fun to be able to do, but there's just so many things I think that being in that space, professional athletes, professional sports, really prepares you for but what I think of most of the time were really the lessons that I learned and the things that I observed. And, you know, Corby was you, and you were just talking about curiosity. I mean, it, it's the most correlated thing to a great leader. It's the most correlated thing to success in life. And I was just thinking back to a time when we were sitting in a meeting room with the Broncos, and you know, John Elway looks over and he's like, Well, what do you think? Can he I'm like, what? Why are you? Why are you asking me, you're John Elway. Me, you know everything. And I think when you're able to be around people like John Elway, who is still a learner every single day of their career, despite the fact that they have achieved really every great success that you could ever want to achieve. I think it's a really great lesson to carry with you as you go through life, that the real people that you want to surround yourself with and build yourself to become like are those that remain curious and are just constantly asking questions and asking. Other people for their perspective. And I just think, you know, this was an example of him reaching down, you know, to someone many levels below him, and asking for input and asking for inclusion. So I just really think that that's such a great theme about what we do today. And that was just one of many lessons that I learned as I was able to be next to, you know, greatness, I myself, was not great, but I got to be next to a lot of greatness. You know,

Matt Kirchner: 35:24

I think you're probably being a little bit humble, because you seem like a great individual. I think our audience is going to learn that over the course of time. I'll admit, a lesson that I learned from from John railway is that the Green Bay Packers don't win every Super Bowl, because I remember back to when they matched up against the Broncos in the in the late 90s, if I'm not mistaken, and didn't come out on top. Of course, my dear, my dear, Green Bay Packers actually had an opportunity to cross paths with John Elway at a Colorado buffs game a number of years ago. And what a class act and an absolute, absolute, you know, stunningly talented individual, both on the field and off the field, and had has had an incredible career of leadership following his time playing in the NFL. So so awesome that I'm sure you got to get to integrate yourself and interact with so many incredible football players and so many people of great character, which I'm sure will in a lot of ways, has created the individual that you are today and now this amazing pivot from from the world of being a professional athlete to the world of technology, getting to work with someone like Rick day in and day out, and as he's told me, he's just got tremendous respect for you in the work that you're doing. So what a great partnership. But let's talk about these AI initiatives. Corby got a little bit into, in a good way, got into some of the details about how we're using AI in a company like the hive health you know, as the Chief Operating Officer, you're kind of thinking about different aspects of the organization, where do some of these initiatives typically fail, and what are the friction points that you're running into over the course of time, whether that's people or process or trusts or or incentives and so on. And then, how do you alleviate some of those challenges that I'm sure you're facing in your role?

Will Furrer: 37:00

Yeah, well, that's great. That's a great question, something we love to talk about and are talking about all day, every day. And you know, look when, when we go into these hospitals, we want to understand how to align with them, and how to align align with their incentives. And so I think this all starts with making sure that the way that we're compensated actually makes sense to them. And I just think there are so many things in this space that have needed to evolve, and I think the way that you get compensated is one of them. And so we've really focused on becoming accountable to the savings and the benefits that we can bring. And what that means is, as we observe those savings, then that's when we would bill for the savings that we actually generate. The second thing that we want to do is we want to make sure that we position our hospital systems to actually defend against the sales engines that are and marketing engines that are actually out there with the vendors. The vendors are not bad. They are bringing tremendous innovation to healthcare, and they play a critical role. They are also, at the same time, very, very good at sales and they are very good at building relationships with physicians. And so any cost savings initiative that runs through our system, we need to be able to make sure that when our work is done that we can monitor the performance around the expectations that Corby talked about. And so we need to make sure that a system is actually left there in place that actually memorializes, codifies and actually builds durable savings for these hospital systems, because their financial model and financial plan has an expected outcome. And so what we want to make sure that we do is leave behind, if you will, a system that is powered by AI and has AI running in lots of different areas that can actually support what our customers are telling us they really need, and that is, please help me monitor this and help me make sure I'm hitting the targets that we have all been so ambitious that we put out there, and then furthermore making sure that we're helping them identify the new and the next opportunities that are out there. So this is also our power that I think we're putting ourselves in a position to actually bring to the Supply Chain Leaders, is to take them from being somewhat reactive to being proactive and way more strategic in the organization. Supply chain leads a lot. Supply chain has a very big role at the table. That role, though, is going to, we believe, going to grow and expand, because we're going to help them essentially provide preventative care for the way that things are being purchased throughout the hospital organization, and so that's really what we spend our time doing, and I think that the AI applies and fits very nicely into that, but that's what we want to spend our time doing, is understanding what our customers opportunities and challenges are and making sure that we're delivering solutions or. Around it that only comes by spending time with them and listening to them

Matt Kirchner: 40:03

some great themes and corollaries that are kind of running through that. That answer will, I would mention you talk about leaving behind this, you know, this AI engine, this platform, this system that allows your customers to continue and your clients to continue to benefit from the work that you've done, that it's not a one and done consulting arrangement that you're working with them over the course of time. You're talking a little bit about how you standardize your model with your customers, so that you create repeatability. You create that that long term relationship. Had a conversation with an education leader yesterday, and one of the things that that he said was, I knew you were going to be this was going to be a great conversation. He said, You're in our ed tech businesses, separate from the podcast, he said, you know, you're just all about aligning yourselves with what your customers think is important, and that's exactly what I just heard from you. It's about alignment, and it's about alignment in mission. It's about alignment in outcomes, and not just the outcomes going on in the healthcare market itself, but about but it's about the outcomes that take place from an economic standpoint, and in aligning your economic model with the success of your customers, to your point, you think we're going to see more of that. And I agree. The other thing, I think we're going to see more of and you're touching on it here. It's a conversation we went deep on with Todd wanak, the Chief Executive Officer of Ashley Furniture industries, when he was on a couple of months ago. And that's the whole idea of agentic AI agentic marketing. You know, these, these vendors, as we call them. I use the term supplier, but same, same point, they're getting more and more sophisticated, not just in terms of how they're selling and how they're selling, in your case, to physicians, but in how we can now use AI agents and understanding what folks are doing, maybe in their interaction with a with a generative, pre trained transformer, and creating their own AI agents on the sell side that present information to your clients. AI agent in a way that a lot of these conversations are going to become agentic, and we'll have a human in the loop, but the person might not necessarily be leading that dialog. It's really fascinating what's happening across the market, and how quickly it's happening to that point, being able to rely on data, being able to rely on artificial intelligence, being able to know that the insights that we're getting are actionable, are by and large accurate. Talk about that a little bit what must be true in a healthcare system in order for people to trust the results that artificial intelligence is providing for them.

Unknown: 42:21

Yeah, it's, it's a very interesting space where it sits now, Matt, and you know, there's a tremendous amount of spreadsheets that are running in the background. These spreadsheets are often provided, whether it is by the GPO to track compliance to their agreements, group, purchasing organization or or one of the manufacturers or vendors that are using it to actually track their spend towards, let's just say, a striker device. And so most of this is happening in a spreadsheet world. So what has to be true? I think there's a few things here that have to be true. One, you have to be aware of the business conditions that you're actually agreeing to. And if you would like to call those as the laws of nature, if you will. And so the ability to build, you know, like a prototype of what your world would look like. You would want to do the same things around the economic outcomes that you expect from buying these very expensive items. And so we have to establish what those like rules of nature are, gravity, etc. So need to make sure that you have a very clear understanding of that. The second thing is, you need to be able to understand and see very, very quickly what you are actually purchasing, what is actually going through the hospital system. So it's not just a matter of how much that spend is, because obviously you want to know that, but it is where it is being spent. Because where it is being spent should be informing you of many different things. Do we even have contracts for those band aids, if you will? Do we have preferred pricing for the item that someone wants to use. Why is a specific physician using a product that we don't even have contracts for? Like, where are they even basically getting it? You know? Is it coming in in a trunk and then ending up in a person? And those things can happen all the time, and then the hospital has to pay a premium price for that. Now, there's great reasons for all of those things. Sometimes it's a new innovation, and only a certain patient can actually use that kind of a device. And so those we understand, those things happen, but usually that level of innovation that is being slid into a procedure, unbeknownst to the hospital, is probably giving back any savings that they were able to achieve in some other way. So you have to be able to monitor those things, and then the final leg of the stool is a lot of this is change management. You have to be able to coach and bring those insights back to the organization, whether it's through the person that leads that service line or whether it's through the head of surgery, you have to be able to find a way. Way in supply chain or in materials or in value analysis, to have that conversation and create a further storytelling and conversation cycle inside the hospital, because those behaviors are not going to change if they come from supply chain. They do not speak the language of the physician that must come physician to physician or Chief of Surgery. So you must be able to marry these things up, the laws of nature with what's actually happening in the hospital, and then you must be able to thread that into active and proactive conversations to change behaviors. Because this is not a one and done. This is not every hospital system out there has a huge notebook from every consultancy behind the CEO or CFOs desk. They never open it. They never use it. They don't do anything with it. So taking all of those great suggestions and recommendations and game plans and making them come to life in some kind of a platform that's really the only way to bring some level of sustainable or durable change to this industry, which I believe is desperate for change, and I believe that change is only needed really, so that these hospitals can serve the communities where they are. And I think that there should be these hospitals shouldn't be under the financial constraints that they are right now, and we want to alleviate some of those constraints so they can keep serving people in all the areas across the country, not just on the coast.

Matt Kirchner: 46:26

So let's hang for a moment on a couple of things that you just said, which I thought were, I mean, that whole answer was really fascinating. But we talk a lot about in any business or any organization that I'm involved with, we say, and this is an age old adage, what gets measured gets managed. What gets measured, improves the ability to measure some of this data, not just in the area where the healthcare system knows that it's using data and knows that it's driving continuous improvement, but in some of the one off areas, as you suggest, where the individual physicians are still able to kind of do their own thing, which to your point, we need some of that right. Innovation doesn't happen at scale until it happens you know, one person at a time. So, so super, super important, but at the same time, we don't want to have this dichotomy where we're improving, improving our experience in one area and then just and then just creating a bigger problem in another area. So being able to generate this data over time about where that spend is happening, how and why decisions are being made, not that you're going to totally change the entire culture of an organization, but just have a better, better insights into how decisions are being made, that part of it really, really interesting. And then you touch on this idea, and it hadn't occurred to me, but I can find so many corollaries to your example. You know, supply chain isn't going to speak in a way that a physician is necessarily going to respond to. Or I think about sometimes in a manufacturing organization, HR can human resources, can come up with really great ideas, but you're not always seeing that reflected in the activity of the operations team. Or in a school district, you could have a CTE director or a chief academic officer, perhaps, or a provost and a chancellor at a university that comes up with a great idea, if they're not able to communicate in a way that's meaningful to the person that needs to make change. We have a problem. So as we kind of continue down this path of artificial intelligence, I think what you said was really prescient in the whole idea that, yeah, we can create all this data, we can prove all kinds of things, we can come up with all kinds of insights, all of that has to be delivered to the change agent in a way that is meaningful to them and that gains their their their support. So I just thought that was a brilliant answer. Anything you would add to

Unknown: 48:24

that, well, it's all about storytelling, and that's what our customers tell us. They tell us, you know, they're swamped with data. They have no idea what it means. Really difficult time tracking it and understanding what impact it can have on their bottom line. But when you think about the but behind that. But the real challenge they tell us about is that they have a difficult time telling change, driving stories around the organization, and so making sure that you can go from a data observation to quickly telling a story, calling for a meeting, carrying the data over that you've observed to the request for the meeting, what we will specifically be talking about are the behaviors around what hips are actually being, you know, put in in the surgery room, those types of things, closing the loop on that, and helping these supply chain folks who know everything that is happening in the hospital. So does value analysis materials, and obviously the administration does as well, but helping them tell effective stories that will actually lead to change, I think, is one of the number one things that they share with us. And so I think those fundamentals, once you understand the laws of nature, once you understand what's being purchased and what you when you understand where you can affect change, that those are really the three pillars that I think we're actively trying to attack

Matt Kirchner: 49:42

and as our audience knows, I do a ton of keynoting. I think I've got a dozen stacked up in the next four or five months, that whole idea that you just mentioned is going to find its way to a bunch of presentations. You'll get all the credit, of course. But this idea of this age of AI being able to convert data and insights into meaningful storytelling that. Is just such a key, key area of where we're going with all of this technology as we march into the future of AI driven fill in the blank, whether that's healthcare, education, manufacturing, defense, energy, doesn't matter. It's got that same problem at its core, and we need to be really good storytellers. That's a skill that I think is important. Corby mentioned some other skills that are really important, and some other personality traits maybe better put things like curiosity, problem solving, being a builder, as you look to build the future of an organization like the hive health will and certainly the work that you're doing is so, so very important. Building a great team really, really important. You're doing that, and it's terrifically evident, are there things you would add to what we've already talked about in terms of the traits that we need to be able to build into this next generation of AI talent so that they can bring benefit to an organization like the hive help.

Unknown: 50:50

Yeah, I mean, we're just really focused on great people. So we want great people. We want diverse backgrounds, and we want people that recognize that, you know, storytelling and building and being curious are what this is all about. I can't tell you how fascinated I am with the business that Rick had before the day before, you know, I got here, the expertise of the people, the challenge, challenges within the industry are remarkable, and the problems that have been surfaced are amazing. The ability to engage and listen and learn are what we want the next set of talent that we bring into this organization to be able to really focus on because unwinding what a physician says, or unwinding what's happening in supply chain, it takes a very special person to be able to engage and listen. And so we're going to be looking for those curious folks that can come in and build and that are using, you know, modern skills, tools systems to be able to do that. So yeah, that's exactly what we're looking for, is a set of really talented listeners and folks that are curious. And I think if we can combine those with the team that we have with the experience that we have and the platform that we've built, I think we're going to be in a really great position for growth, and that's what we're we're managing through right now, is how quickly we are growing and how we can ensure that the business is actually operating in a predictable way. And a lot of the things that we're dealing with here are very unpredictable, and our job is to make them predictable and allow for our customers to predict what is happening within their business. So that's kind of our theme, besides just get stuff done. You know, the theme is develop things so that they can be a little more predictable.

Matt Kirchner: 52:33

You're fascinated by the business that that Rick built in the organization that Rick built before you got there. I'm fascinated by the organization that you're building after you got there, obviously a great team of people that you're building and will continue to build. And I want to talk about is we're focusing here on team building Corby in full disclosure, and our audience knows this. We are huge, huge advocates and invested, both in terms of in terms of our passion and our energy, and also financially, in a movement around discover AI, talking about hands on, learning around the edge to cloud continuum, teaching students that that AI is yes, it's chat, GPT, yes, it'll pre chain transformers, but it's also physical AI. If you watch Jenson Wong's presentation at CES a couple weeks ago, huge, huge innovation happening on the physical AI side, a lot of folks feel that that is going to be as important as some of the things we're doing with with gpts, and so we've been huge advocates for bringing this learning into our nation's K 12 systems, especially in high school, so that students have a perspective and a context for how AI manifests itself, not just in the digital world, but in the physical world, and where those two marry. I'm a huge, huge advocate for that, but would be interested in your thoughts as we continue our conversation here about, you know, you're certainly closer to high school than the other three of us in terms of in terms of your age and recent experience. What are those things that students need to be thinking about while they're still in high school if they want to have a viable career here in the age of artificial intelligence?

Will Furrer: 53:59

Yeah, I think a couple of things. I mean, first of all, be aware of how AI is influencing you everywhere, right? The greatest example of this is the algorithm on your phone. You know, be aware of that. Try to get away from that so that you can do your own thing, because its goal is to keep you attached to your phone. And then, more practically, I think that the way we teach math, this is kind of my one education opinion I have. But the way we teach math in the American school system is largely based on calculus. I think that was kind of a world war two idea. You know, we needed to calculate the trajectory of missiles, and we needed to to kind of understand physics in that way. But now I think the law of nature, or the rule jungle, is probability, and this is something that I wasn't even really exposed to all that much until the very end of high school and the early days of college. But if you can get a grasp on thinking probabilistically, you will do great. You will be able to use these tools in an amazing way, because that's what they are. They are, you know. Probability based word calculators. And then the other thing would just be, go build stuff. It doesn't have to be aI related. Like, I love I've used to build rockets in high school. I would build, you know, fishing nets, rocking chairs, cutting boards. I would forge knives in the garage. I'd make fishing poles. Like, I'd just make anything I could. And I think that it's just great, a great skill to have in your back pocket that you can just look at stuff and make something.

Matt Kirchner: 55:22

Make something for sure absolutely. And I love both those, the probability aspect, the fact that you can look at something, look at stuff, and make something super, super important. It speaks to hands on learning, which we're huge advocates for here at the TechEd podcast, talking about it. And I'm glad you brought up the phone, because when we talk about teaching edge to cloud to students. The first example we use is Spotify, which is relatable to the most of us, and how it can perfectly predict the next song you want to hear at any given point in time. And so glad that you started there, because that's where we start. When we teach edge to cloud is with the with the device. If we can teach students that there's this algorithm running in the background, and understand their relationship to that and how it can affect the way they think about things, how they how they can affect behavior, how it can affect behaviors. Really, really important. If we can crack that code and help students understand the benefits of AI and the risks, perhaps we can also break Rick Anderson of his addiction to Tiktok, maybe, maybe not. But we can do we can do our best. Kidding again. One final question for both of you. We'll stick with Corby, and then we'll pitch it to to will it's a question that Rick has already answered. So we won't, we won't ask it of him, Corby, you don't have to go back quite as far as the rest of us to get to being your 15 year old self, your sophomore in high school self, but I want you to think back to that point in time. And if you could give that 15 year old young man, one piece of advice. What would that piece of advice be?

Will Furrer: 56:43

It would definitely be to keep playing with your Arduino. I stopped like, I started writing, doing like, web, a lot of web development and stuff like that. But I really, really enjoyed robotics early on, and I wish that was something that I had continued to do was right in those very low level languages, because it's a great way to learn, and it's very relevant.

Matt Kirchner: 57:04

Again, I love that answer, and I'll tell you why. When we talk about teaching edge to cloud that that is a perfect example, right? So, an Arduino, small, simple microprocessor, you know, programming in Python, inputs, outputs, if this, then that, and understanding some of that basic programming, in a lot of ways, is one of those building blocks of physical artificial intelligence. That is great advice. I will just tell you, as someone that spends all kinds of time in K 12 education, and pointing to platforms, for one example, like minds eye education, that's doing some really cool stuff with Arduinos, or maybe in some cases, Raspberry Pis as well. But those small, simple microprocessors and been the building blocks of coding and artificial intelligence. Great answer we're going to use that will you've come up with all kinds of things we're going to use over and over again here on the TechEd podcast and beyond. I want to pose the same question to you. Let's, let's talk about going back in time, pre NFL quarterback, pre technology career. You're a 15 year old sophomore in high school. Tell me a little bit about what you would give, what kind of advice you would give to your 15 year old self, if you could go back in time.

Unknown: 58:08

I was thinking about this when Corby was answering. I don't know if I have a 15 year old answer, but I would, for sure have a maybe first job answer. So I'll spin it just a little bit differently and and say that, you know, when you have a job, there's always time to do more and go meet someone else in the business. If you have a sales function in the job, you need to be spending time with the product team. And if you have a product job, you need to be spending time with the finance team, so you can understand how the product life cycle actually drives back to the financial performance of the company. And so that's what I would have challenged myself with when I was 2122 years old, and I was in the NFL, I would have been challenging myself to do other incremental learning and understand either more about the business of football, rather than just the football, or I would have been learning some other skill. And I just think that we try to constrain ourselves with paying attention to what our J, O, B asks us to do today, and we need to spend our time kind of thinking about the world in this way college. And again, we talk about the necessity of college. Who knows? But in college, you pay to go learn things, and when you get your first job, they're actually paying you now to learn things. And so the question is, are you going to be constrained with what you learn, or are you going to look at it like it's a huge canvas and you can learn anything that you want? And so I just always challenge the young people that I speak with is just to go learn something that they're not even telling you is your job, because that actually is your job, to learn as much as you can and provide a way to create value back to the business. So that's what I would tell myself, maybe at young 20s, not 15,

Matt Kirchner: 59:48

perfect answer. And I write a monthly magazine column every single month for Gartner business media have been doing that now for 21 years. It's kind of hard to believe my February column will is I'm becoming a polymath, and the whole idea. That you can't be stuck in a single discipline, that you have to understand how all regardless of what you're doing, whether you're in business, whether you're you're in a huge hospital system, whether you're practicing healthcare doesn't matter. You're a student. Your ability to understand not just a specific discipline, not just multiple disciplines, but how those disciplines converge, overlap, lap and impact each other, I think is going to be one of the absolute most important skills and traits as we move forward into the future of technology. So perfectly aligned in that answer. I love the way you answered that question, and I also like the idea of going to college, you're paying to learn, and then you get into the workforce and they're paying you to learn. That's exactly right. We are going to see all kinds of innovation, disruption and new learning models as we move forward and as we continue to be lifelong learners, which clearly everybody on this podcast episode is because technology is changing so fast that there's no way that we can remain relevant over time. What a great episode we've had with the leadership team here from the hive health, playing off of all the great things that we learned from Rick Anderson a year ago, seeing where this organization is going, seeing how AI is going to impact and revolutionize not just healthcare, but every single space within our economy. I've learned a tremendous amount. I want to thank all three of our guests, certainly, Rick Anderson, Chief Executive Officer, will Fuhrer, Chief Operating Officer and Corby Fuhrer, Chief Technology Officer of the hive health. We're going to link the show notes up, let's put it at furor F, u, r, r, e r. So you'll find those at TechEd podcast.com/fur, you'll check out all of the great resources that we talked about previous episodes of the podcast, resources that were mentioned. You'll find all that on the show notes. So check out the show notes there. When you're done, check us out on social media, as we've mentioned. Our YouTube channel has just been going crazy since we started posting these episodes on YouTube. We've got, believe it or not, episodes of well over 100,000 views, in some cases. So having lots of fun there. Check us out on Instagram. Check us out on Facebook. You'll find us on LinkedIn, wherever you go to learn about what's going on on the planet. You will find the TechEd podcast there. When you're there, reach out, say hello. We would love to hear from you. Can't wait to see you all on our next episode of The TechEd podcast next week. Until then, my name is Matt Kirkner, thank you for being with us. You.