Making AI Work: Operationalizing AI Across Healthcare, MedTech, and Life Sciences

Healthcare organizations recognize AI’s potential, but many struggle to move from strategy to practical implementation. In this episode, Richard “RJ” Kedziora, co-founder of Estenda Solutions, explains what it takes to operationalize AI successfully in healthcare.

Discover why education, workflow integration, change management, data interoperability, and continuous experimentation are essential for sustainable AI adoption. Learn how healthcare leaders can reduce staff hesitation, implement AI without disrupting patient care, and turn promising technology into scalable solutions that improve efficiency and health outcomes.

Key Takeaways:

• Prioritize education to reduce hesitancy around AI implementation.
• Integrate AI seamlessly into existing workflows for optimal efficiency.
• Manage change thoughtfully, addressing both technical and human needs.
• Ensure data interoperability for effective AI utilization.
• Foster a culture of experimentation and continuous improvement in AI applications.

Resources:

Estenda Solutions

Productive Harmony

PlaybookAIPartners - AI Mini Solution Locker

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Transcript

SPEAKER_00 0:00

But in starting Estenda back in 2003, it's crazy to talk about 23 years of doing this with the same same company. I found my passion clearly and loving it. We focused on healthcare because there was always that sense of giving back. And in 2003, we had different challenges. There was a lack of data. Healthcare has always been the first industry to embrace technology. You think your X-rays, your MRIs, there's a lot of technology involved. But data was always sort of a little behind the curve. But fast forward 20 plus years, the world's changing. Everybody's aware of AI. It's making a significant impact. And healthcare is, in fact, one of the leading industries in embracing these AI technologies today.

SPEAKER_03 1:41

Number one, our AI solution mini locker. This is a series of quick 20-minute courses that you can learn a new skill, you can learn about a new tool, you can learn how to solve a problem, whether that's email overload or learning about your digital navigator, teaching AI about your business so you have more refined or customized prompts, whether you're looking for a marketing strategy or creating a newsletter and saving up to 15 to 20 hours a week. So make sure and check that out. If you're not sure of what skill you want to learn, there's also the NoFumble AI 3 pack and the Playmaker 5 pack, which these are pre-bundled courses that help you whether you want to learn automations or social media, how to enhance that. They are bundled courses to help you accomplish that goal. Make sure and check it out. They are affordable and again, who can't beat 20 minutes of time to learn a new skill? Finally, I want to talk about the HUDL. The HUDDL is our community. And one of the things that I have found is to stay current on AI and to figure out all the tools and all of the things you need to know without overwhelm, community really is best. In our community, we do two live sessions a month talking about a tool or a current trend to help you stay on top of things. And we also do quarterly boot camps. This quarter, and we are talking, it is currently June 2026, we are doing a boot camp on the digital navigator, talking about how you can really teach AI about your business, really setting things up to get those customized responses. And I'm going to show you how you can save that 15 to 20 hours of time. Through July through August 15th, it is free. You can come and experience two live sessions as well as participate in the boot camp to get a flavor of what it's like and access to the AI solution mini locker. I will drop a link to that in the show notes, so check it out. Hopefully I will see you there. And now on with the show. Well, hello everyone, and welcome to the show, enabling healthcare, medtech, and life science organizations to operationalize AI from strategy to scalable execution. AI has the power to transform healthcare, medtech, and life sciences, but only when it moves beyond ideas into real workflows. So how can organizations turn AI strategy into scalable, responsible execution? To get into this topic today, I have Richard Kedziora, also known as RJ, on the show. RJ is the co-founder of Estenda Solutions and has over 25 years of software product design, development, and management experience. At Estenda, he focuses on people and process management and provides strategic technical direction, guidance, and innovative insights into creating cost-effective digital health solutions that make a difference in people's lives, helping them live longer and healthier. His work has won multiple awards, including a Hems Davies Award and recently the Health 2.0 Outstanding Leadership Award. Welcome to the show, RJ.

SPEAKER_00 4:54

Thank you for having me. Looking forward to that conversation.

SPEAKER_03 4:56

Absolutely. Well, let's get into it. So why don't you share with us your journey and how it's led you on the path today you were on with Estanda Solutions and AI.

SPEAKER_00 5:07

My journey actually starts decades ago. Interestingly enough, as I was a teenager in high school and even into college. I my thought was, I'm gonna go and get a PhD in AI. Like it's it's hard to believe people are like AI really existed way back in the 90s. Yes, it probably even in the 70s, AI was was a topic and it's really hit its stride though in the last few years. But you know, through the 90s, I I got going in various different consulting areas and accounting systems and and and really found my passion in in software development. But in starting Estenda back in 2003, which is crazy to talk about 23 years of of doing this with the same same company, I found my passion clearly and and loving it. Um we we focused on healthcare because there was always that sense of of giving back. And in in 2003, we had different challenges. There was a lack of data. Uh healthcare has always been the first industry to embrace technology. You think your x-rays, your MRIs, there's a lot of technology involved. But data was always sort of a little behind the curve. But fast forward 20 plus years, the world's changing. Uh everybody's aware of AI. It's making a significant impact. And healthcare is in fact one of the leading industries in embracing these AI technologies today. Still has lots of challenges, uh, but that they are on the forefront of adopting AI solutions.

SPEAKER_03 6:35

About getting your PhD in AI because you're right. It is all of a sudden, it's like just yet yesterday or last year, we may have heard about AI, but it certainly hasn't over the past, I think toward the end of the year into this year, it's it seems to me that it's really just picked up um like just in significant momentum and being a topic of conversation. So um it's hard to think that it's been around a while. And I kind of laugh. I mean, you've been on my healthcare podcast. People talk about 20 years of experience, but AI, most people don't have that because it's so it's it's been around, but I think people, it seems to me to maybe to sum it up, are people are embracing it more? So um I find that um find that interesting, but I'm glad you've been in it to win it, so to speak, for a while. Now, you also made a comment about healthcare and that healthcare seems to be one of the industries that's really embracing AI. Not to sound condescending, but healthcare sometimes is it always seems a little bit behind in embracing technology, at least from what I've seen. Tell me more about healthcare embracing AI today.

SPEAKER_00 7:39

It it's in two specific categories, and and even more so in the first one, the idea of ambient listening and taking notes. Uh, you know, so your healthcare practitioners, doctors, nurses, they didn't get into healthcare to stare at an electronic medical record while they're, you know, talking to a patient. That's not why they want to do this. And so we can take this technology, can listen to the conversation, can transcribe the notes and do a very good job of that, such that the healthcare professionals can focus on the patient, why they got into healthcare. So that's the first use case that's really been very quickly adopted by the industry. And it's been fascinating, even seeing that progress over the last couple of years as I've gone to various healthcare technologies. You'd have 10, seven different startups all with their ambient listening technology, and you talk to them, it's like, what's different about yours? And what's different about yours? And there was no difference. They were all just trying to make their mark in the world. And and now the EMRs are adapting that technology and bringing it into themselves. So it doesn't make some of it a challenge for so many smaller startups. How are how are they implementing the ambient listening? And you think about large language models, the OpenAI, the Chat GPTs, and and they are known for hallucination, um, making facts up and and not being a hundred percent accurate. And that is looked upon by a lot. You see criticism and in many industries, including healthcare, like it's gonna listen to this conversation, and how's it gonna transcribe an accurate note? It is true. The pushback that I don't think is recognized enough is that us, people, humans, we're not perfect either. So a very well-accepted use case in in the world of healthcare is scribes, is people listening to these conversations. But there are stats out there that the human scribes are not perfect. And if you implement a review process, they can those notes can be improved, they can be made better. Same thing with the large language technology, that we can then use those. Yes, it might make mistakes, but then we implement those review processes that we already have in place that can improve the quality of those notes and save time. Some of the most recent research is talking about doctors in situations saving as much as an hour to a day. That's a phenomenal amount of time. The other use cases are radiology reading images. Some of the earliest AI-approved technologies are in the area of radiology and looking at x-rays. And we are using it at Estendo on some projects for retinal, um, looking at retinopathy, images, diabetic retinopathy, um, doing go-no-go decision making. There's still very much a human in the loop um to validate what it's seeing and helping them in the process, but it's speeding up the ability to read those images. So very much an accepted use case. And then now, even from an admin perspective and billing, um, revenue cycle perspective. So uh what is interesting from that perspective is AI is increasing that billing that's being you know sent off to the insurance company. And what I find fascinating at some point here, we're gonna get an AI submitting the bill and the insurance company using an AI to be like, hey, is this valid? And you're gonna have these two AIs talking to each other, which is a whole nother world challenge, but that's where we're going.

SPEAKER_03 11:13

No, I find that interesting, you know, the retina, I think we probably talked about this last time too, but I've often wondered, because I know people can still be skeptical about that kind of thing, but you know, I think back about um a retina specialist, you know, you think about if you go into a retina, which I do, but you go in and you literally see your retina specialist for like three minutes if you're going in for an appointment. That's all you get, three to five minutes. And that's just the traditional system. But I have often wondered over the years, they take scans every time you're there. And I've often wondered, you know, what if you could take those scans over the period of years, look at those and see if there's some anomaly or something to help? Because I laughingly asked my provider last time I was in there, I was like, have you looked at my scans over the past five years? He goes, Nope, don't have time.

SPEAKER_00 12:01

Yeah, they're they're they're busy.

SPEAKER_03 12:03

They do. But I'm amazed and intrigued by what you're talking about, the ret diabetic retinopathy, and also being able, I would think, to be able to take scans over a period of time to a point that a doctor doesn't have time to do and maybe, or maybe not, but you never know, uncovering that anomaly. So I think if we can embrace that, that would really be so awesome and saving time because we know our traditional healthcare system is fragmented and broken in many ways. So we can, if we could use AI to help. So on that note, um, what what are the biggest risks or concerns that you're seeing today that people actually, not risk, maybe hesitancy, I should say. People have great ideas they want to implement, but somehow getting from that idea to uh creating an actual workflow that works. I mean, there's just hesitancy or somewhere along the way, the wheels fall off. What what what are your thoughts on that?

SPEAKER_00 12:53

Yeah, there's a couple things going on there. The first challenge is overcoming that hesitancy of like, can we implement this AI solution? Is it the appropriate thing to do? Am I gonna lose a job because of this? Is a big concern across many industries, particularly healthcare. And we saw that early on. Radiology, I mentioned, was some of those earliest FDA-approved use cases. And there was prognosticators that were like, it's gonna decimate the radiology industry, and we're not gonna, you know, why bother studying this? That's proven to not be true. Uh, we need those radiologists more than ever because they don't just read images, they provide so many other services and values. And that's what we're seeing across multiple industries. You know, there's also the same sort of thing. It's, you know, I'm in software development. You're not gonna need software developers because AI can write the code. Yes, AI can write the code, but you still need somebody to tell it what to do and make sure that it's it's doing it in the right way, in a secure way. And and that's what we're seeing in healthcare. So first you have to over overcome that fear, uncertainty, of doubt of like, if we're gonna implement this AI, do I even have a job anymore? Yes. You're still gonna have a job, which is important. People using hospitals using the AI are going to start replacing the people that aren't. It's it's making a difference. We're seeing it in the productivity metrics, the efficiency metrics. It is making a difference. So you really have to start on this path. With the second thing is is change management. And this is not unique to AI systems and implementing AI systems. You need to think about change management, thinking about that workflow. How do you implement it in part of the workflow? You know, as Stend, we've done a lot of work in remote patient monitoring, uh, in chronic disease management, all of those things. It's like, great, we can create a standalone solution, but it's if it's not part of the workflow, it's never going to be adapted by the healthcare professionals, the providers. It needs to be part of part of that workflow. So you have to think about how you're implementing AI to be in that workflow, have access to all of the data. It's one of those big challenges still today. You know, I talked about 20 years ago, is gaining access to the data. Like getting the data was difficult. Today, we had so much data, it's probably overwhelming between medical records systems, just the level of knowledge in journals, wearable data that, you know, I I wear an aura ring, you can wear an Apple Watch, you can generate all sorts of information about you as an individual, and then go to your doctor and be like, hey, what do you think? Getting access to that data in the workflow is very important. The AI can help you interpret that data and understand it. We're still struggling from an operationalization perspective of how to connect all of these systems. You have legacy uh EMR systems and getting all the wearable data. It's changing, it's rapid. Um, it's not a technology problem, it's that operationalization, it's the people problem. So I I think those are the big challenges. Overcoming the fear, the workflow issues, change management, um, making sure that that's incorporated into the process, um, and then the the integration and interoperability of data is what we're seeing. But continue to make great strides.

SPEAKER_03 16:25

That's the winning question, right? How do you solve that problem? You go into an organization, you see these issues, how do you help solve that problem? I know it's it's a big question, but but how do you get people from that hesitancy to I'm ready, let's go? We know there's some challenges, but we see the benefits. How do you get people from from that big fear to implementation?

SPEAKER_00 16:49

Yeah, i I I think uh first good step is education. So and and I would start at the top. Uh you're your executives, so you know, board of directors, um department leads. Provide that education such that they can message, you know, the the rest of the staff within the hospital to provide that assurance and understanding of what this actually means and why you're why you're doing it. So education, number one, I think alignment is is the second thing across that executive board, including the technology staff of like, what are we doing? What are the risks? How are we we dealing with these and and embracing the idea of change? One of those challenges above and beyond everything that we're talking about is just the rapidness of change in in AI now. Sort of overwhelming. Uh there's this idea of like if I'm developing a solution in the AI, I can't do it today, just wait three months, it'll be able to do it. That's how fast some of these technologies are advancing. So I think education is a key experiment is that alignment then to experiment with these solutions, get them out there to learn those lessons. We are all learning these lessons of how to implement these systems and what these new challenges are. So I think those are the two keys. Uh, and the third I would add in that that change management. It's like how are you messaging this within the organization, making it part of the workflow and making sure that everybody on the team is on board with what you're doing.

SPEAKER_03 18:22

Your comments about the change management take me back to the day back when, you know, 1999, 2000, when SAP, Enterprise Resource Planning System was so big. And I was actually one of the big five, as it were, at that time consulting firms. And that was my job, change management. And there was an actual position and job for that because so many people didn't want to embrace, you know, the implementation of a new system for all the things that you've mentioned, fear of job loss, transition, more workload, learning something new. That hasn't changed. You know, it's just, I think what has changed is the rapid pace in which AI is moving and trying to get people to move a little bit quicker through that change management process, which is no small feat for sure. So with that said, why don't you tell us more about Estenda Solutions, what you guys are doing, more specifically, service offerings, and maybe a when case that you can share with listeners.

SPEAKER_00 19:17

Yeah, absolutely. Estenda is a custom software development data and AI consulting organization. We partner with large corporations, hospitals, health systems, medical device providers, and some startups too along the way, um, to develop new tools and technologies to take advantage of data and AI to improve that patient health and wellness. Our typical projects are an MD and a PhD. So as we're exploring new avenues, striking new grounds, it's like the doctor can have an idea, PhD can have an idea, but it's it's one thing to have an idea, another thing to implement it, and prove that it actually improves patient health and wellness. Uh, you know, we've been part of many RD projects where it's like, okay, that that didn't move the needle in the way we thought it would be. How do we adapt? How do we change and advance that such that it does? One of the recent interesting projects that that we got through the NIH, National Institutes of Health, here, um, we got an SBIR grant, which is a small business innovative research grant. We're very fortunate to get this. And the idea is to implement a domain-specific repository for smell test data. You don't think about the loss of your sense of smell. And it didn't get a lot of attention until COVID happened when everybody started losing their sense of smell. I said, wait a minute. So now there's a renewed um energy around advancing the science of smell loss and smell testing. The loss of sense of smell can be an indicator um of brain issues, Parkinson's, Alzheimer's, uh, which which makes it fascinating. So there are many smell tests out there um available in the market. It's it's not a new industry, but it's how do we bring all this data together and then apply AI on top of that to learn from this information to again improve the health and wellness of our our fellow humans? So it's been a fascinating project as we're working with different PhDs and MDs out there to bring all of this data together. And one of those challenges is like, can you smell chocolate or rose or gasoline and grass kind of thing? And and what does that actually mean? So that's just a quick example of of a major product that we're working on on these days, which is just so much fun to work on.

SPEAKER_03 21:45

So let me just ask you a question. Let's say if you um kind of give me a comparison, the time it would take to make some progress on that manually and versus using AI. I mean, is it a three months difference, a six months difference? Terms of getting some valid data that you can share.

SPEAKER_00 22:04

It's interesting. This project we're using AI in so many ways from a software development perspective. From a writing requirements, from writing test cases, using it to, you know, so you have all these different smell tests in different research projects and clinical protocols take that data and store it in different methods. You know, if you look at um there's one of the tests, it's called Snippin's Sticks, another is the Sentinel test. And it's even though it's the same test across different institutions and research protocols, you're gonna store that data in different fashions. So when you want to bring this together, create, put it into a domain-specific repository, such that you can compare across these different research protocols, you have to clean up that data. And it's a long science around cleaning up data, but now you can take AI and say, here's one data set, here's the other data set, I want to match this, and it does a lot of the work for you. It just m accelerates the process so much. You know, so we enjoy using it from you know that software development perspective. It really accelerates our ability to experiment um in, you know, in early stages historically, when we were in the design phases of a project, we would use you know, Canva, Figma, different things where you're drawing pictures and then presenting that and working with the client. Now we can create usable interfaces so quickly with AI, we're doing that. It's like, hey, here's the idea, here's an interface, here's something to talk about. Now that gets you to the early stages to be able to talk about it. Then you have to implement, you know, what we we know is is good software development practices to make something that is implementable in production that does apply to you know the HIPAA privacy guidelines and cybersecurity guidelines. AI is capable of doing those things, but you have to guide it in the proper ways to make sure that actually happens. You can get a working prototype pretty quickly, but it takes a little bit longer to get something that is HIPAA compliant, cyber secure compliant. And then, as I said, on the back end of that data management aspects, um, it helps accelerate that. And then the you one of the things what we're doing with this is once we have that repository, now you as a scientist or an end user don't have to know the technical programming languages to look at that data. You can just talk to it. It's like you talk to ChatGPT, you can talk to the data and start exploring it and picking picking it apart and seeing what new patterns and trends you can find in that data, just like you do with Chat GPT.

SPEAKER_03 24:53

It's just fascinating. Yeah, I love the word accelerate. It's hard always pinpoint a time frame on it, but just amazing what you can do. Now, let me ask, as you're going through this process, well, you know, in the world of healthcare, there's a lot of regulatory requirements and HIPAA and things like that that you mentioned. But to companies that are listening and maybe have that concern about data and preserving what, you know, and the the data that you get out, we talked about hallucinations. What uh sort of protocols do you have in place or did that human in the loop to check that data to make sure that everything is accurate? Do you you guys have a process for that to kind of put those fears or concerns that listeners have at ease?

SPEAKER_00 25:32

Yeah, as as we work with healthcare professionals and and they may want to use these AI models, uh, say ChatGPT, I'll just generically say you know, open AI ChatGPT, but there's many models out there. They have created targeted systems for healthcare medical professions to use. You want to, they will sign business associate agreements. So, you know, if you put your data as an individual, like say me, put my data in ChatGPT, that's not protected by HIPAA. Um it's when the healthcare provider comes into the equation that HIPAA comes to play. So you do want to be cautious about putting your identifiable information in into these systems to protect your privacy. The healthcare practitioners, this is definitely something they do at an institutional level, um, but make sure that they sign those business associate agreements, which pulls in those same protections through to the open AI organizations. That's sort of step one. The next two things one, you have to guard against hallucinations, biases uh is very important in the world of AI because it's training on data that we've created, processes that we've created. We as humans have biases. That's then in turn reflected in the AI systems out there. So you have to be cautious about those and think about those challenges. And then with AIs, there's uh it's called drift. You know, these systems learn and change over time. So you want to make sure that it's staying in alignment with your goals, what you're trying to accomplish, and it's not drifting away from where you think it's supposed to be operating and that it's stay on target. But even from that human perspective, I think most importantly, I think the all of these are risks, all of these are important. I think the biggest risk in the use of AI systems as we move forward as a society is critical thinking. How is it impacting our ability to think and learn and understand what's happening? These AI systems are designed to please you. They're going to tell you what you want to hear. So you have to guard against that. And they sound very confident. So when you see something, when it tells you something, and you see it in the research, it's like we humans have the tendency to be like, oh, yep, that's right. Well, you need to think critically. And is it really right? Is it really accurate? Oh, you'd have to guard against that.

SPEAKER_03 28:00

Yeah, wow, that's really interesting. I haven't thought about that, but it's so from that angle. So I appreciate you bringing that forward. Well, since we last talked, you've written a book. Tell us about your book.

SPEAKER_00 28:11

So one of the fascinating things that I I've been doing over the last two years is I've thought about writing a book for a long time. So I I sat down and was like, what speaks to me as an individual and and what do I want to put out into the world? And I I bring together my experiences of being that entrepreneur and and driving the business, but also my passion for racing and tr in triathlon. And the key element of all of this is the idea of energy. And I think the time management industry has has failed us. It's just not working for us. And it it contributes to what I think is the time management death spire. As you get more efficient thinking about time management, what's the next thing you do? It's like, oh, look at that. I just found an hour of time. You try and do something else, you try and take on more work. So, you know, not only do you just you think you're more productive, you're burning yourself out by constantly trying to do that next thing. AI is just increasing this pattern and trend because AI is a boon to our productivity. It does improve our productivity. But then it's like, wait a minute, I can do one more thing. And that just is leading to burnout. So I think first we have to think about how we use our energy in our day-to-day life to get things done. So I wrote a book called Productive Harmony, and it's all about in the 21st century, in this digital age, the information age, where so much of our work is is cognitive based. How do we have to get that done? You only have 24 hours in a day. You cannot create more time. And if you're lucky you're sleeping seven or eight hours, uh, you know, that you have even less time. You can't change that. You can change how you apply your energy. First, improving your ability to have more energy by eating better, moving more, sleeping, and getting that rest. But then guarding your mental capacity. How do you make uh decisions to be easier to make? Eliminate ones that that you don't have to do. In one of those fascinating stories, is I I started this down this journey, I was looking like, where did time management even come from? You know, and it's been around for a long time. You know, in the industrial age, we you know, we started clocks such that we could get all the factory workers to the factory at the same time. Like this is one of the big drivers of of time. There there was a gentleman, uh Frederick Taylor, uh in Taylorism, sort of the science that came out of this. He's a godfather of scientific management. In the early 1900s, uh Bethlehem Steele hired him to make his workers more efficient. And so he'd get that stopwatch and figure out and say, okay, if you take two steps here, three steps here, if we move this, if you lift this particular steel being this way, you are gonna be more efficient. And this really kicked off what I think of as a modern day time management movement. What everybody fur had forgotten is that he was fired because he was burning people out. He wasn't improving productivity, but we've all sort of like pinned this and said, Oh, time management is the greatest thing. The person that really started this movement was fired because he he failed in that in that job, which is just amazing. And so that that just drives this whole idea of energy management. Think about that energy. Yeah. When you have, I particularly have more energy in the morning. So it's like you want to do things that require more creativity, more thinking. You know, do those in the morning. After lunch, after you've had those couple pieces of pizza, that burger, you know, your energy is gonna go down, it's gonna dip. That's when you do that administrative stuff that doesn't require as much, much focus. So it's a it's a fascinating topic. So productive harmony. And the second aspect of that is the harmony. A lot of people talk about balance. Balance implies equal, all things be equal. It just doesn't work in today's society. Go, go, go, go with your kids and and jobs, different opportunities, kind of thing. Finding balance just doesn't work. You're gonna have these ebbs of flows of energy and things that you have to pay attention to. And that's where that harmony comes in. It's like, okay, you can be more productive. We use some of that time to rest. You don't have to use it to take on like the next challenge. So yeah.

SPEAKER_03 32:37

Well, awesome. Well, we'll make sure link to it uh in there as well. It sounds like a fascinating topic on that. Now, as we draw to a close, I always like to leave the final thoughts that you would like to leave listeners with about Estenda solutions andor AI future of or anything that you'd like to profound thoughts, because you have a lot of them. I always appreciate our conversations, but that you like to leave listeners with.

SPEAKER_00 33:01

Yeah, always, you know, from an Estenda perspective, always looking for that next opportunity. How can we help people and embrace data and AI, you know, love those challenges. Um, and so we are working on that, always looking for the for the next customer, the next person to help, whether it's grant-based or you know, the startup, you know, just love having those conversations. It doesn't have to lead anywhere, but it's like, let's have a conversation about it. So always willing to do that. Um, from the Productive Harmony perspective of getting the the book out there, that's been an interesting journey. And my my one key takeaway for you, if when I learned when I was learned this, it was like so fascinating. I implemented it immediately. Just having your cell phone, even if you silence everything in the room with you, has an impact on your productivity. If you really need to focus and get stuff done, put your cell phone in another room. As I was writing this book on the weekends and I implemented that, it's it was just so eye-opening to me. I was amazed. You know, I was reading the research around this, and they talk about a 20% improvement in your productivity and ability to focus by moving your cell phone out of the room. Because even having it next to you is a distractor. You're you're just like, oh, what am I missing out? Or might have it silenced, but then it buzzes. Oh, what's happening? You know, oh, let me just check in on Facebook or Instagram or TikTok. And then 20 minutes later you're like, wait, what am I doing? Um put it in another room and and you'll see a difference.

SPEAKER_03 34:31

No, so true on that end. Well, lastly, what is what do you think the future of uh of AI is in healthcare?

SPEAKER_00 34:38

I I think it's going to do amazing things. I think there's a lot to embrace and figure out here because it is accelerating so fast. We don't have enough humans to be able to help all of the other humans that need help, whether it's it's mental health or weight loss or uh just uh um what did I see recently? Um maternal care, like for pregnancy care. They're they're talking about implementing various robots, which is a whole nother story, but um robotic ultrasounds, you know, where you just don't have those humans available to be able to expand the care. That's how we're gonna make a a difference in the world because you know, as as the population ages, a lot of those elders are the doctors, the nurses, the healthcare professionals that are gonna be retiring. And there's more and more people that that need health care. The AI technologies are gonna help us expand that capability and improve the health and wellness of our populations as we control it and make sure that it's done well.

SPEAKER_03 35:42

Will it reduce the cost?

SPEAKER_00 35:45

I hope so. Um I I think in the short term it has it it could potentially you know increase those costs, which nobody wants to hear. Um a lot of the AI right now is subsidized by venture capital money cutting. So sort of waiting for for that to transition out. There's a lot of controversy around data centers and energy use. That's gotta wash out over the next couple of years and and see what that that is. But in in the long term, I I think it'll it'll make a difference.

SPEAKER_03 36:13

Um, we hope so. Well, RJ, as always, it's been a pleasure having you on the show today. I just am always fascinated to talk with you and and your wealth of information. We'll make sure, and again, link to Stend Us Solutions and your book, remind me of the name again.

SPEAKER_00 36:28

Productive Harmony.

SPEAKER_03 36:30

Productive harmony, what we all need. So how can I forget? We'll make sure and link to that as well. Good luck on on getting that out there. And and again, thank you for your time and expertise.

SPEAKER_00 36:40

Great. Love the conversation.

SPEAKER_03 36:43

What an eye-opening episode with RJ and talking about how AI is changing healthcare. Make sure and check out Estenda Solutions in the show notes to learn more about what they are doing. Also, there will be a link there for RJ's book, Productive Harmony. Make sure and check it out. In our next episode, I have George Rivera on the show. In a world where AI is another tool that is competing in andor taking away from our time, George is brings such enlightenment to help successful dad founders or any founder for that matter, escape the trap of building a business that depends on them for everything. After scaling multiple companies to eight and nine figures, he realized success was quietly costing him the moments that mattered most at home. Today he helps founders buy back 10 to 20 hours a week, building businesses that run without them. So they don't miss what matters most. Make sure and join us for that episode. Until next time, take action, execute, and let's run the play.