AI in Hospice: What Every Leader Needs to Know | Part One

Artificial intelligence is rapidly entering healthcare—but what does it actually mean for hospice leaders?

In this episode of TCNtalks / Anatomy Of Leadership, host Chris Comeaux sits down with Ernesto Lopez, Founder and CEO of 1520 AI, to explore the real impact of AI in hospice care.  Ernesto brings a rare perspective as a registered nurse, healthcare executive, and AI founder, combining decades of hospice leadership experience with formal training in data analytics from Harvard Business School.  

Together they unpack what artificial intelligence in healthcare really does, why many leaders misunderstand it, and how hospice organizations can adopt AI responsibly without compromising the human-centered mission of end-of-life care.  They also discuss the growing number of AI vendors entering the hospice space, the risks around data security and patient privacy, and why leaders must exercise caution before integrating new technologies.

If you’re a hospice leader, healthcare executive, compliance professional, or nonprofit leader, this conversation will help you understand how to approach AI adoption in hospice, avoid common mistakes, and use technology as an accelerator—not a replacement—for compassionate care.


What You’ll Learn

•  What AI actually does (and what it doesn’t)
•  How large language models like ChatGPT process information
•  The biggest mistakes healthcare leaders make with AI adoption
•  Why data governance and security are critical
•  How hospice leaders can use AI responsibly to support mission-driven care


About the Guest

Ernesto Lopez is the Founder & CEO of 1520 AI, a company developing artificial intelligence tools focused on hospice quality, compliance, and clinical operations.  He previously spent more than two decades leading healthcare organizations across hospice, home health, and hospital settings.  


About the Host 

Chris Comeaux is the President and CEO of Teleios Collaborative Network (TCN) and host of the TCNtalks / Anatomy of Leadership podcast.  A respected leader in healthcare and organizational strategy, Chris has spent decades helping mission-driven organizations strengthen leadership, culture, and operational excellence—particularly within hospice and serious illness care.

Chris is also the author of The Anatomy of Leadership: The 21 Irrefutable Laws of Effective Leadership, where he explores how purpose-driven leadership shapes strong teams and enduring organizations.  Through his podcast and writing, Chris equips leaders with practical tools to navigate complex challenges, lead with integrity, and align strategy with mission.

On TCNtalks / Anatomy of Leadership, Chris brings thoughtful conversations with leaders across healthcare, nonprofit, and business sectors—exploring topics such as leadership, innovation, healthcare transformation, and the future of compassionate care.



TCN Talks explores leadership, healthcare innovation, and mission-driven organizations.

👉 Subscribe for

The Anatomy of Leadership podcast explores the art and science of leadership through candid, insightful conversations with thought leaders, innovators, and change-makers from a variety of industries. Hosted by Chris Comeaux, each episode dives into the mindsets, habits, and strategies that empower leaders to thrive in complex, fast-changing environments. With topics ranging from organizational culture and emotional intelligence to navigating disruption and inspiring teams, the show blends real-world stories with practical takeaways. The goal is simple yet ambitious: to equip leaders at every level with the tools, perspectives, and inspiration they need to lead with vision, empathy, and impact.

https://www.teleioscn.org/anatomy-of-leadership

Transcript

Opening And Guest Intro

Melody King 0:00

Everything rises and falls on leadership. The ability to lead well is fueled by living your cause and purpose. This podcast will equip you with the tools to do just that. Live and lead with cause and purpose. And now, author of the book, The Anatomy of Leadership, and our host, Chris Comeaux.

Chris Comeaux 0:21

Hello and welcome. I'm excited today to have a good friend with us on TCN Talks. Welcome Ernesto Lopez, the founder and CEO of 1520 AI.

Ernesto Lopez 0:30

Hi, Chris. Thanks for having me.

Ernesto’s Background And 1520 AI

Chris Comeaux 0:32

It's great to have you, man. You're a longtime friend, and I'm really excited to what we're going to be talking about right now. Been wanting to do a show this year on AI, and it's interesting, you and I already have started corresponding. I had two people email me and said, you need to get with Ernesto and do a podcast on AI. Look, well, that's awesome because I'm already doing that. So let me introduce you to our audience, Ernesto. A lot of people in the hospice and powdered care space know you, but they may not know about your new role. So Ernesto Lopez is the founder and CEO of 1520 AI, where he leads the development of artificial intelligence solutions focused on hospice quality, compliance, and clinical operations. He's a registered nurse by training, makes him a bit of a unicorn. He has spent more than two decades leading healthcare organizations across hospice, home health, and even hospital settings. He combines deep operational experience with formal training in data analytics from Harvard Business School to build technology that supports caregivers, strengthens defensibility, and improves how serious illness is delivered. Ernesto, anything I left out that you might want to add to that?

Ernesto Lopez 1:35

No, that's uh that's that's quite that's quite a long list. Thank you, uh humbled by by the introductions.

Chris Comeaux 1:41

You bet. Well, I always do ask our guest this question what's your superpower? And that's a tough question, but look forward to seeing how you answer.

Ernesto Lopez 1:49

That is a tough question, Chris. I would say, I mean, I'd start with my disclaimer is that my wife would probably think differently than what I'm about to answer, but uh but I would say my my superpower, and I I'm thinking way back, um, you know, I'm I'm really good at connecting the dots with when it comes to people and and um either talent or you know specific um things that they may be able to do, or if it's if it's a product or a company, you know, it's it's always been easy for me to see how you know several components and pieces, you know, work together. You know, kind of like putting together a puzzle. And so um I, you know, from early on, it was something I'm really I was really good at. And it's really helped me even today with with what we're doing with with 1520 AI, you know, putting together the pieces and and components of folks that that need to be able to uh come together with the right talent and the right commitment and focus to develop the things that we're we're doing.

The AI Surge In Hospice

Chris Comeaux 2:43

I've had the privilege of knowing you man quite a while. I don't know if it's been quite 15 years, but it feels like quite a long time ago. But I can see what that's actually been really true to your journey. And I didn't tell you I was gonna ask this, but 1520 AI. We're gonna go a little bit more into the company in a little bit. I want to ask some AI questions in general first, but can you just connect the dots? Because I don't think I was gonna ask that anywhere along the way, but it's a very unique name.

Ernesto Lopez 3:08

Sure. So so it's a it's sort of an insider, you know, kind of uh uh uh name that that was developed with with my colleagues uh that uh are a few of them are are partners of ours and and are uh part of the company. And and I I got to know them over our journey at Harvard Business School. We spent two years working together. And um the the 1520 is is basically uh it was a setting that we we uh we had an opportunity to take part of and and uh it was room number 1520. And so we we we kind of uh spent a lot of time uh talking about some of the things that we wanted to do and and talking about the future and and and really um looking to leverage this this you know special knowledge and and experience and into a real business. And so we um you know, after after going back and thinking about this, we we felt like this was a special way to not only recognize that time period, but also um be able to demonstrate how we as as graduates of that program um have come together and have been able to launch forward what we've learned into into a company that's that we believe will be very impactful to to the hospice industry.

Chris Comeaux 4:19

Well, very cool. Well, let's jump into some AI questions first. And um what have you learned in the first year of running an AI company that maybe you didn't expect?

What AI Is And Isn’t

Ernesto Lopez 4:29

Boy, a lot. And I I would say that you know, when you start um this process, I mean, AI has been, as you know, around for some time and and in the world of business, uh many companies have have, you know, over the last number of years, have um designed strategies to try to develop AI. And and uh the last you know, 18 months or so, we're seeing this really explode. And and you know, when I was going through our our journey at Harvard, we um the one thing that really stood out for me was thinking about you know our industry, the hospice industry, and my own experiences as a as an executive and and and looking at technology and and you know, really experiencing when I started in hospice almost 15 years ago, you know, how technology has evolved in our space. I I spent most of my career on the acute, acute uh care side of healthcare and and was kind of accustomed to you know how we utilize and leverage data, you know, obviously technology and healthcare, you know, whether it's it's it's it's equipment uh or research or you know simple things like uh how we document, I found that transition from acute care uh to uh post-acute care to be a significant, a significant drop in in the way that technology was utilized. And so, you know, thinking about that and then experiencing and learning more about AI and seeing how it's evolved outside of healthcare and how it's starting to grow in healthcare, where hospice is just kind of just starting now, you know, where and and so we asked me what what's the you know the things that were kind of different when I s when we started, there was uh you know a bit of a conversation, you know, companies were talking about AI. I'm now starting to see uh quite a bit of a number of organizations that are actually coming in and and and uh reaching out to the hospices and and promoting products that are AI, uh that have AI functionality or are AI based that um that potentially could fit within their their needs. Uh and so that that number or that you know that that um sort of spike of not only interest internally in the industry, but also a spike of of organizations and companies that are coming in to try to serve the uh the hospice industry, that's increased exponentially in that very short period of time since we started.

Chris Comeaux 6:47

Not to chase a rabbit too far, but again, I think it's so unique. I mean, a guy who's run a C a hospice, hospice is, and now running an AI company, I mean, it gives you a unique position. And some of our um peers who maybe are in steel wind states, you get that gut drop, oh, I got a new competitor. You probably wake up every morning and read something like there's a new competitor kennel in this face. Does that feel accurate?

Ernesto Lopez 7:12

It does. And actually, even deeper, you know, some of my closest colleagues, um, I'll get an email or a text message with a link to, have you heard of this company? Did you know that they were doing this? And it's like, it does feel like it's every day. And and um, you know, I I'm certain that you know, hospice leaders are feeling the same exact way when you know they have uh these kind of companies reaching out to them, you know, for the first time and and promoting products that you know that don't exist or potentially could be of value to them. And and you know, and and uh it's it's one of those challenges that that um it's a new service. You know, we're we're used to dealing with uh PBMs and DME companies and and you know all the other types of support services that we're we're accustomed to seeing in in the hospice industry. But you know, this is uh a completely different category of service. And so people are asking a lot of questions, you know, leaders are you know wanting to make sure they're they're making the right decisions and and and some you know leaders that I've spoken to, you know, they want to really plan this out. They don't want to jump in at first, they want to really map out the next you know two, three, five years of of where and how they think they're going to integrate and and work with these new tools and this new technology in our space. And and so it's really neat to see that full spectrum of of where people are and in their thinking and and in their journey around uh embracing these types of tools in the hospice.

Accuracy, Hallucinations, And Trust

Chris Comeaux 8:37

Well, let's back up a little bit more because I really do want this show to be educational and helpful to our many hospice leaders that are navigating this. So when people hear AI, they often think magic. How do you explain simply what AI actually does and maybe what it does not do?

Myths About Autonomy And Control

Ernesto Lopez 8:54

Yeah, absolutely. Magic is probably uh a tame word. I think a lot of people that I speak to, um they they are completely scared of AI. They, you know, they think they they read the headlines, they watch a lot of movies, they, you know, they um um, you know, they they think this is the first step of you know of of a transition of life for people. Um, but but ultimately, you know, AI, you know, really has been around since the 60s. I mean, the term artificial intelligence was was first coined in in the early 60s, and and um, you know, the the journey of of developing artificial intelligence over that period of time uh has been steady uh but slow. And the last you know, three years is when we really started seeing this explosion, when these large language models that were doing some really incredible things. And you know, for for folks that you know for the first time uh when you when you experience um any interaction with with you know a large language model such as Chat GPT or or Gemini or Claude, it it's it's it's really it's it's fascinating because you know you see the the ability to gather information and and to you know answer questions that you know uh historically maybe we did a Google search and then we got a list of websites and then we start you know trying to dig in and find out what's you know what's really accurate or what or is the information that I've you know that I've uh searched, is it exactly what I need. And so, I mean, my own my own experience in the beginning was that. And and so as I as you start learning, well, how does it actually do that? And how can it so quickly collect all this information, process it, and and give it to you? And and that's that's the beauty of of these um large language models or LLMs, they they're able to go out and gather information from all over the world and utilize um your prompt or your question to provide you in in its own way the best possible answer. And so, you know, that's been probably the the the the best use case for for consumers, you know, that are utilizing it day-to-day for um you know for for whatever reason. And then, you know, I think I think the the the key here is the ability to to really quickly organize and um and and be able to to analyze data um at such a rapid pace is is is the what you would say the magic, right? And and so doing so, um the LLMs have really you know formed a foundation for some of the tools and some of the things that that we're seeing today from software companies. You know, the that power of of and the ability to process data. Um now you create tools that that you can customize to do very specific things. And so, for example, if if you're wanting to utilize that kind of power to be able to go through uh, let's say, you know, a list of uh hostess claims and and be able to organize them and and be able to look for specific patterns of uh specific inputs that you you want to measure, it's it's pretty easy to be able to do that now with with the power of these foundational LOMs. And so um everything that you see out there that's AI-based, you know, has a starting point with one of these big or maybe small LOMs that are out there that that serve as sort of the the batteries or the the engine that that drives the actual um AI model that that's being designed by different companies, if that makes sense.

Adoption Mistakes And Data Safety

Chris Comeaux 12:31

I'm gonna say something you you push back if you disagree, but someone recently explained it to me and said, picture the language model, the LLM, the abbreviation you keep using, these large language models, as the librarian. And then recently I've worked on a very sophisticated project, and I'd take it a step further. Yes, the LLM is like this amazing librarian, much better than Google, because as opposed to getting 10,000 hits and trying to hit get the best hits, synthesize that information, analyze it. Basically, these AI models could do that for you. It could be the librarian, it could actually synthesize it, give you the book report and even their own analysis. But then you have to do something with it. But how quickly you could get from, you know, think in in college, which you know feels like a lifetime ago and feels like a moment ago, you know, you and I would go to the library, we would pull all the articles, we'd have to read them and synthesize them in the book report. Now all of that's done literally within seconds. And then how you could apply that to whatever problem you're trying to solve, it's really amazing. It really is. So does that feel like an accurate description?

Technology As An Accelerator

Ernesto Lopez 13:38

It does. I think that, you know, to add on that, you you have a librarian, right? But that librarian, you know, if you use the same analogy, you you have a library of books and references and and you know that that information is pretty much static, right? It's not not changing unless there's new books that are being brought into the library. But when you think about AI, it's it's like a librarian, yes, but it's it's it's collecting all these books, right? And and those books are being published every day, every second. You know, there's new information that's being put out there, you know, and the source material could be absolutely you know accurate, secure, and and and safe. Or it may be, or it may not be. It might be, you know, it may be satire, it might be something that you know somebody is just uh, you know, um sharing their opinion about something and their views. And and so you start thinking about uh the contributors to that library, and and that's really the big, big challenge with AI is that those those contributions of data that people may be using in order to make you know serious decisions or big decisions, you know, may not be completely accurate. And and I'll you know, I'll give the example. I mean, you may you may um go to let's say ChatGPT and you will you know put in a prompt and ask a question about something and and you'll get a, like you said, a very organized, structured answer. And you know, try going next week and asking the same prompt, and I guarantee you that the answer is going to be different. It may not be dramatically different, but it absolutely will be different because it's constantly monitoring and bringing in new information, whether it's it doesn't know whether it's accurate or not. And and so that there isn't a filtration system in place to be able to, you know, train you know, these LLMs to be able to differentiate between, you know, this is this is, you know, this is uh true information and or this is something new that I've got to integrate into this answer that I'm providing the user. And so that's probably the biggest frustration. You know, I've seen it around um, you know, if if you're doing analysis specifically with numbers and and mathematical formulas, coding. I mean, there's there's a number of different things where LLMs, you know, um can struggle. And so what I always tell people that that utilize you know um AI every day for for you know for work for personal life, it's like always double check. Like it's not you shouldn't always just take the the response from from from the from the LLM or AI and say, okay, this is this is a fact. You know, make sure, especially if you're trying to make a decision based on the information that that's being gathered, you have to be very, uh, very careful to verify. And and I'll tell you that it's it's uh a lot of times you'll go back and the information isn't accurate, and then you go back and you prompt and say and you you tell the L of them, this is not accurate information. Oh, I'm so sorry. You're right. You know, and then you get the right answer. And so I think I think that's the frustrating point. And and I think that people are distrustful of AI because of it. Yeah, uh, they love the magic, they love the way that it's gathering information, but you know, the moment it starts it starts giving them inaccuracies and and uh what we call hallucinations, then that level of trust changes dramatically. And so my recommendation to people who are using it, obviously, check your math, check your work. But um, you know, when it comes to healthcare tools and and dealing with patient data, you know, you're dealing with you know um elements that are that are really serious, that that you know, that we can't afford to be inaccurate, we can't afford to be short or be off by a number. It's it has to be accurate, you know, across the board. And so this is kind of the next step of how do we evolve from where LLMs are today and create tools that you know are are able to provide accuracy and detail and and be able to allow us as you know healthcare leaders or operators to make those decisions accurately and not compromise patient care, not compromise um uh privacy and security. And so so that's that's the big climb that right now that the companies that are are coming into our into the hospice face are are facing.

Chris Comeaux 17:49

Well, maybe this is a segue question. What are one or two common myths that you would like to clear up?

Ernesto Lopez 17:56

I would say the the first myth uh that um that's out there is that um LLMs are are constantly if if they're not fine-tuned correctly, you know, the the the word is that they are they're constantly learning and they're evolving and they're gathering information and becoming smarter. And and you know, partially, yes, I think that that's that's true because the more data you put into a system like this, it's seeing so many different variables and um and adapting to those variables, there's still a human in the loop. Like there has to be a human involved in and um not only being able to measure and be able to verify and validate, and and I you know at this point that's still an important component and in and governance and ensuring that you know that these models are working the way they should be. And so people think, well, they're gonna become autonomous and and self-thinking, and they're they're gonna take over things. And and there's been a lot of stuff in the news. I mean, there's a really uh interesting um um art of not an article, but uh um an interview uh with uh the CEO of Anthropic a few months ago. And and you know, and and there's you know, there's this this feedback commentary of oh, this is you know, you know, things are gonna evolve, it's gonna be really dangerous, and these, you know, these models are gonna you know take over. And and and so I think that that's you know that that's definitely falsehood. I mean, I think that you know today there's so much control over what's happening that you know it would be very difficult for that to actually happen, unless you know, we decided we're not gonna have any boundaries or laws or controls where we'll just let these things run and do what they want to do. And I can't imagine that anyone wants that.

Chris Comeaux 19:42

What are some of the biggest mistakes, or maybe we're on the front edge of them, that you see leaders make when adopting AI in healthcare?

Ernesto Lopez 19:50

Biggest mistake is getting excited about the tech. You know, you you hear that someone is, you know, built something and and you immediately you get excited about how you can integrate this into your workflow or to solve a specific problem and and not really doing the due diligence around how these tools were actually built. And you know, there's been this, you know, people say, well, there's a gold rush rush to, you know, for AI, that people are brushing out and and building these tools. And and even today, you know, there's there's um there are methodologies to be able to build AI that have become so simple that just about anybody, you know, can can you know utilize some of these tools to build other AI and and and create a you know uh an AI model that that could actually do something for them. The the problem is that you know, if you don't have the proper structure and governance, especially in healthcare, where you you're able to ensure that you have guardrails around data and privacy protection, that you know, um you're not bringing in a system into your let's say your database or into your network where you have all your patient information, all your employee information, all these things that if they get leaked out, um, could be extremely detrimental. I mean, obviously there's consequences and fines and and you know reputation, and there's uh so many different things that can happen. And so I, you know, what I have seen, some companies that are coming in aren't even thinking about that. They're they're kind of just uh trying to get a product out, sell it to, you know, whoever wants to buy it, and then and then walk away. And and to me, there you're gonna see a lot of these small companies do that. They come in, they try to get someone to buy into their concept, they they'll build the generic AI, and then you know, they're gone. And so uh the hospice is left with maybe a product that is not safe, um, that's not accurate, and now they have no support for it because you know they they're they simply came in, did what they what what you asked, and they left. And so I I would I would say be very careful, do your research, ask a lot of questions around you know uh HIFA and compliance, uh um make sure that you know the companies are uh there's a there's a there's a uh data safety um certification it's called SOC2 type two and and um all technology companies are are familiar with it you have to make sure that they they're they're certified or they're they're tending to be certified because that means they're serious about protecting your data and if they're not they're they're not gonna have any of those types of certifications or take the time to engineer these kind of protections in in order to you know either build a tool for you or to be able to you know provide you um these tools for for your organization so I would say ask those two questions and and ask a lot of questions asked how, why and you know ensure that you know they they're accountable for what happens if something goes wrong and and and that they are also accountable to um how they're handling your data, that they're not reselling your data as well because that's also a big a big issue.

Chris Comeaux 22:51

So as you're going through these uh data use agreements ensure that that's that's a big part of it um that there's limited use in your in that data and if then that they're not storing it themselves for for use uh for other purposes if that makes sense it it feels almost the the risk just went up by an order of magnitude because right the idea is that you wouldn't allow someone to come in your organization and suck specialized knowledge then bring it out into the public domain certainly especially not PHI, patient health information, et cetera but generally the way a large language model works, it could be like a Hoover depending upon like you could have staff right now taking a great strategy or something in your organization and popping into a Chat GPT. And then it is part of that language model, right, Ernesto? And so it it feels like there's risk of been there, but it feels like the risk is weightier now just because the nature of how the these LLMs work.

Ernesto Lopez 23:46

Absolutely and I think that you know taking a page from you know Fortune 500 companies that that you know are using AI, I mean they have you know serious levels of security and close systems with these LLMs where you know they're they they have to protect their IP, their intellectual property. And so if an employee you know by mistake uploads you know a spreadsheet that you know that's that's that's confidential or you know the specs on on some product that's being built or designed, it's out there. I mean, you know it it's out there and you know if somebody else is using AI they they could actually you know prompt to to get information about this product they're building and it's and there it is because somebody put it out there. And so it's a serious serious concern and I think that you know in healthcare um obviously it's a different type of of security but because we're talking about you know THI and and and how um how important and how significant it's it's governed we have to have those levels of control. And um you don't want a a hospice you know inadvertently putting that information out putting them at risk uh and and potentially you know there are elements of of IT competition as well you know strategies that you know hospices are doing in a market where there's competition you know you don't want some of those details to get out where your competitors can get that information. But I mean you'd be surprised there's a lot of information out there today. So so there's been uh there's been inadvertent leakage of data and and details and even IP that you know that maybe some people don't even know that it's it's happening or happened.

Chris Comeaux 25:22

Well said well let me um set the table this way I'm hoping later this year we're gonna have Jim Collins on a podcast. I want to call this the lost chapter of good to great. If you remember you know he had all these great principles in good to great people remember the hedgehog concept, the flywheel, the doom loop, get the right people on the bus. The one everybody forgets is technology as an accelerator. And I don't know if you remember Ernesto, you know it was all the rage even our national hospice organization meeting Jim Collins came. And so one of the premises when he wrote the book and if you are listeners, you know, Jim Collins the great thing about him is he's data oriented. He has an army of MBAs they crank data and then the organizations that rise to the top based upon the data they then go and do like basically a postmortem if you will or like a dissection of what are the principles that made that organization rise to the top. Well when they did good to great this was in the dot com bubble. And one of the things he said is our hypothesis is we were going to find like whiz bang technology is what made them go from good to great. So that's not what we found. And this to me is so profound in healthcare Ernesto because we don't do it this way. He said they understood what they did at such a great level they only apply technology as an accelerator. We do the opposite in healthcare we go purchase an EHR and like oh we just purchased the panacea it's going to fix all of our issues and then six months later we're like we hate our EMR had no idea it was going to change my processes and like that is the exact opposite and so I'd love for you to comment on then where I want to go with my question is how did people going forward balance speed innovation with safety trust especially when patient care is involved and really apply technology as an accelerator?

Ernesto Lopez 27:10

I think you you've said it I think I think it's it's taking those principles, those core principles that that you know that we hold very tightly you know in in the industry and and protect um very fiercely and and so I you know I absolutely love and and I love the Collins you know um um way of of thinking of hey technology has its purpose right and and its use. And at the end of the day hospice is touch and feel it's personal it's it's you know it's sacred in many ways and and so you know by by integrating technology into what you know the hospices do are we compromising those those the you know that experience are we are we compromising you know those core values that that you know hospice was built around in order to get something done faster. You know and and so it it's it's it's an interesting perspective and it's also you know when I talk to other other hospice leaders around the country you know they everybody has a different um sort of need you know and and it's it's built around a couple of things. You you mentioned EHR or ARMs um XY medical records and you know the the well I want my staff to to spend less time you know documenting and more time taking care of patients. That's a beautiful concept right but but then you have the you have the the counterbalance of like well you know we're measured by you know by our our our number one payer and and and other payers by what we document in in the medical record in that documentation has got to be you know outstanding. It's gotta it's got to flow it's gotta you know it's got to it's got to cross reference and and connect and and so you know as much as we want to say we want to speed up this process so that we can have more time to to do the things that we think are important, you know, we have to be careful about what we give up by doing so. And so there's there's this balance of you know focus on what the mission of your organization is and what's most important to you and your community and the people you serve and and then understand how you can accelerate certain pieces that add value to to those core to that to that those core principles. And I think that's the approach that I would take versus you know trying to get you know um try to solve a problem that potentially can become you know have a different set of consequences because you'd accelerated that process fight with technology and now you you've created another problem that you didn't want to have and and that problem could be you know it could be patient family satisfaction it could be you know uh you know staff dissatisfier it could be you know a number of different things and so being mindful of how technology impacts your operations based on those principles I think it's it's something that it's the responsibility of every Phosphus leader to do before they jump into any any type of tool that they're integrating into their into their organization.

Chris Comeaux 30:03

That's well said that's a great teach back of what really he meant by technology as an accelerator not as a panacea not as a silver bullet as an accelerator.

Jeff Haffner 30:13

Don't miss part two of this episode coming this Friday.