Jakson Beyond the Algorithm: Technology, Trust & Patient Care, part 1 | Ep 82 täydellinen litterointi

just need to embrace it and I just need to figure out how it's going to best work for me, my program, my care, and how it can advance the services that we provide. 00:04:27.199 --> 00:04:39.920 And and I think it's really uh going to be a great discussion about that because you have such a broad insight into where we could potentially go with this with your knowledge of AI. 00:04:40.000 --> 00:04:46.720 And so uh I that's what I think is gonna be so cool to see how much of what you predict is going to come true and when. 00:04:47.199 --> 00:04:49.120 Well, that's the interesting part, Mike. 00:04:49.279 --> 00:04:52.560 It we really don't know what the next five to ten years look like. 00:04:52.639 --> 00:04:56.639 You know, I was looking at the stuff before the podcast, it was one of the things mentioned. 00:04:56.800 --> 00:05:01.040 It we really don't know because it it really is gonna depend on what we do now. 00:05:01.279 --> 00:05:04.800 Now, I don't consider myself to be the most technical of experts. 00:05:04.959 --> 00:05:11.120 Like, like I'm not in the models looking at the internal stuff, the calculus, the the integral stuff. 00:05:11.279 --> 00:05:12.720 I understand the basics of it. 00:05:12.800 --> 00:05:16.560 I definitely consider myself to be more of a of an AI strategist. 00:05:16.800 --> 00:05:32.639 And and really, more or less, I'm someone who I I want to prepare us for taking on these tools and deciding when a tool is right and when we maybe we should use something else first before we graduate to using the these AI tools. 00:05:32.800 --> 00:05:43.040 I think a lot of leaders and a lot of clinicians alike we like these things, and it's like, well, we want AI, and that's cool, but what are we looking at that comes out? 00:05:43.199 --> 00:05:44.319 It has to come first. 00:05:44.480 --> 00:05:54.639 There are things that we're gonna have to do first, and of course, I know we'll talk about this more, but I I really think that I like how you both exercise a cautious optimism. 00:05:55.040 --> 00:05:57.279 I think that's balanced, and I think that's a good way to put it. 00:05:57.439 --> 00:05:58.319 Good way to put it, yes. 00:05:58.480 --> 00:05:58.959 Yeah. 00:05:59.439 --> 00:06:22.000 Christian, can you for people that haven't listened to this uh podcast before, can you just give uh a little introduction about yourself, Christian, and kind of your background and your flight experience and how that's kind of helped you get into some of these roles where you've used AI for professional, personal, and kind of how that's led you to to kind of how we're having this conversation today? 00:06:22.399 --> 00:06:22.959 Yeah, sure. 00:06:23.120 --> 00:06:28.480 I mean, so I've been an EMS uh for about 17 years this year, unfortunately. 00:06:28.560 --> 00:06:29.680 It makes me feel very old. 00:06:29.839 --> 00:06:32.399 Um, the paramedosaurus. 00:06:32.480 --> 00:06:33.120 I like it. 00:06:33.360 --> 00:06:33.600 Yeah. 00:06:34.000 --> 00:06:36.800 Yeah, I was a paramedic first, and my then my wife. 00:06:36.959 --> 00:06:42.560 Um I've been EMS for a while, but my wife, bless her, she got me into the nursing world. 00:06:42.639 --> 00:06:43.759 I I fell in love with that. 00:06:43.920 --> 00:06:49.920 And of course, you know, I was working in ICU nursing and and of course jumped right back into EMS on the air medical side. 00:06:50.000 --> 00:06:55.360 And um I this is I right at my sixth year of being in the air medical profession. 00:06:55.519 --> 00:06:56.800 I've worked as a flight nurse. 00:06:56.879 --> 00:07:00.319 I've been an educator for a flight program, a new developing flight program. 00:07:00.480 --> 00:07:05.920 I've been a uh data analytics and integration manager for um care flight in Dallas Fort Worth. 00:07:06.000 --> 00:07:06.639 I was just there. 00:07:06.800 --> 00:07:09.360 We just moved back down to Houston to be close to family. 00:07:09.519 --> 00:07:11.360 I work for Memorial Herman Life Flight. 00:07:11.439 --> 00:07:15.120 I'm one of the flight line flight nurses, and let me tell you, best job in the world, right? 00:07:15.360 --> 00:07:16.959 Just flying doing the job. 00:07:17.120 --> 00:07:24.560 Um, but on the side, I've also really have dove into just in my own interest into the data science, into quality. 00:07:24.720 --> 00:07:28.319 I've also taken a dive into AI, which all those things are kind of married. 00:07:28.560 --> 00:07:37.680 I've also owned some small technology companies that we've worked with AI, and I I currently do some work on the side uh for AI, and I also do a little consulting. 00:07:37.759 --> 00:07:42.000 So I do a little bit of that stuff, just kind of all synergistically working together. 00:07:42.160 --> 00:07:51.759 And I really want to help people to understand what these technologies are and what they are not, and then what we need to do to be successful with using them in our care. 00:07:52.160 --> 00:07:54.879 I think you obviously uh we've known you for a long time. 00:07:55.120 --> 00:08:11.120 We know that you have uh quite a bit of experience and uh you're uh uh an amazing educator that we've been blessed to have part of the Echo family for some time now, and really were kind of the catalyst for or the driving force behind creating the Future Flight Leaders program within Echo. 00:08:11.600 --> 00:08:18.800 Uh and I think you've always had kind of a maybe a little bit of a different perspective on the industry. 00:08:18.959 --> 00:08:37.440 And I think that that is something that has been always one of those things that I've loved when I sit down and have a conversation with you because of the perspective that you bring, I think is just one that uh are is oftentimes things I have never really processed in that particular way. 00:08:37.519 --> 00:08:40.080 And I think that's what's so cool about having those conversations. 00:08:40.320 --> 00:09:03.200 And then when you started talking about

AI and all these other podcasts and interactions that I had with you and kind of talking and seeing the excitement that you have about the possibilities that are out there, uh and I obviously, you know, I've I've long said I'm one that's been resisting this movement, uh, and have been very, very cautiously optimistic. 00:09:03.519 --> 00:09:10.639 But I I am getting on board with it, but I have to ask the question are we ready for it? 00:09:10.879 --> 00:09:11.200 Right? 00:09:11.360 --> 00:09:19.759 Like, are we as an industry ready to take that step and to integrate this in and and where are we already seeing it and maybe not even realizing it? 00:09:20.159 --> 00:09:21.200 That's a good question. 00:09:21.440 --> 00:09:23.759 I'm gonna say it like this yes and no. 00:09:24.159 --> 00:09:25.840 Do we have a use case for it? 00:09:26.000 --> 00:09:27.840 Yes, we do, very much so. 00:09:28.240 --> 00:09:29.679 Plenty of use cases. 00:09:29.919 --> 00:09:32.879 Think about what you do when you're a clinician on the flight line. 00:09:32.960 --> 00:09:34.240 You're very busy, right? 00:09:34.480 --> 00:09:47.360 You're busy with paperwork, you're busy with processes, you're busy with patient care, you're busy with scheduling, you're busy with looking at the finances, especially Mike, you being in the position you are at your program, uh, positions I've been, we're very, very task saturated. 00:09:47.519 --> 00:09:53.519 Most organizations are quite lean, with the exception of a few that are much larger organizations that that provide services. 00:09:53.759 --> 00:09:58.480 We also have increased billing uh issues that are coming about that are creating issues. 00:09:58.639 --> 00:10:00.879 So there's a business use case for it. 00:10:01.120 --> 00:10:09.120 Now, where I say kind of no is is the organization ready to take on these products? 00:10:09.279 --> 00:10:14.799 Because think about what at the very basis, the basics of what this is. 00:10:14.960 --> 00:10:18.320 If I think about large language models, there's several different types of AI. 00:10:18.480 --> 00:10:22.559 There's Agenic AI, there's RAGs, there's LLMs, there's all kinds of different things. 00:10:23.039 --> 00:10:26.799 LLM, what we commonly use, it's a large language model. 00:10:26.879 --> 00:10:29.840 And pretty much all it's doing is predicting the next word in the phrase. 00:10:29.919 --> 00:10:33.679 So if I was to say Mary had a little, you guys would say what? 00:10:34.000 --> 00:10:34.320 Lamb. 00:10:34.639 --> 00:10:34.960 Lamb. 00:10:35.360 --> 00:10:35.919 There you go. 00:10:36.159 --> 00:10:38.000 Fidelity GPT, there it is. 00:10:38.080 --> 00:10:41.679 You know, like uh we'll go and get you on the, we'll get Sam Altman to sign you on. 00:10:41.919 --> 00:10:43.120 But that's kind of what it does. 00:10:43.919 --> 00:10:44.720 We started here soon. 00:10:44.799 --> 00:10:45.679 Don't worry. 00:10:46.000 --> 00:10:50.559 But for those things to work, you need good context. 00:10:50.799 --> 00:10:53.120 You need an understanding of yourself. 00:10:53.360 --> 00:10:55.039 You need to understand your program. 00:10:55.200 --> 00:10:56.720 You need to understand your mission. 00:10:56.879 --> 00:10:58.480 You need to understand the medicine. 00:10:58.639 --> 00:11:02.480 You need to understand the literature, you need to understand your data, your finances. 00:11:02.639 --> 00:11:07.200 You have to know those things before you start integrating these tools. 00:11:07.519 --> 00:11:13.440 And once again, it may seem like I'm not excited, because I am, but I'm excited when it's done correctly. 00:11:13.759 --> 00:11:23.279 Programs are going to have to start getting on board with understanding their data environment, making those investments, understanding even their charting forms, how they're being built. 00:11:23.440 --> 00:11:27.519 I've been a part of that process several times with with charting software vendors. 00:11:28.000 --> 00:11:36.559 You have to understand how your forms are built, the psychology of how your clinicians document things, the how things actually get transformed on the back end. 00:11:36.720 --> 00:11:40.000 Like a data dictionary is also something I think many of us are not aware of. 00:11:40.080 --> 00:11:45.039 Like if I was to say, for each and every program, what do we defined as a missed flight? 00:11:45.279 --> 00:11:47.600 What do we defined as a lift time? 00:11:47.759 --> 00:11:50.639 What do we defined as a good airway? 00:11:50.879 --> 00:11:55.519 What do we define as a patient that needs a crike or a chest tube? 00:11:55.679 --> 00:11:58.080 There's always going to be a different definition. 00:11:58.399 --> 00:12:03.440 Where we're not ready is we're not ready because many programs don't have these things down pat. 00:12:03.679 --> 00:12:05.360 You have to start documenting these things. 00:12:05.519 --> 00:12:06.480 You have to define them. 00:12:06.639 --> 00:12:08.799 Everyone has to speak on the same terms. 00:12:09.120 --> 00:12:16.960 Because when you're putting data into these systems and you're not providing it good or even conflicting definitions, the outputs are not going to be good. 00:12:17.120 --> 00:12:19.759 Garbage in as uh garbage out, as they say, right? 00:12:20.000 --> 00:12:23.200 That's where I think and also policies too. 00:12:23.440 --> 00:12:31.759 The the clinicians have to be protected because, for example, uh, I've heard stories where crew members are using Chat GPT to write narratives. 00:12:31.919 --> 00:12:36.240 They're using ChatGPT to with patient information, uh, and that's not okay. 00:12:36.399 --> 00:12:38.480 You need to be aware of what a BAA is. 00:12:38.559 --> 00:12:40.639 You have to be aware of those things. 00:12:40.799 --> 00:12:41.759 You have to have good tools. 00:12:41.840 --> 00:12:48.159 You also have to have a good relationship with those people that annoyingly tell you to turn off your computer and turn it back on

again. 00:12:48.799 --> 00:12:52.559 Your IT team, you have to be on good relationship terms with them. 00:12:52.639 --> 00:12:53.759 You have to communicate with them. 00:12:54.000 --> 00:12:55.200 So, yeah, is it sexy? 00:12:55.279 --> 00:12:55.840 Is it cool? 00:12:56.000 --> 00:12:56.720 Not always. 00:12:56.879 --> 00:13:02.399 Uh, but the very beginning to get us to yes and yes, we have to get that other part from no to yes. 00:13:02.559 --> 00:13:05.840 And some programs are getting closer to that, and others are not ready for that. 00:13:06.080 --> 00:13:08.000 And that's just kind of the blunt answer. 00:13:08.240 --> 00:13:12.559 Now, to answer the second part of your question, like, where are we using it now? 00:13:12.960 --> 00:13:18.720 So I think in air medical, particularly, I think not many places. 00:13:18.960 --> 00:13:25.679 I think there are certain dispatch CAD systems that are starting to use it to look for closest asset. 00:13:25.919 --> 00:13:30.799 I think the models are getting stronger, as I've read, to the few systems that actually do that. 00:13:31.039 --> 00:13:46.639 I think in radiology, in the in the inpatient setting, that is one of the biggest things that I'm observing now, is that in radiology, they are using AI to assist radiologists on certain reads so they don't miss certain things on scans, MRIs, pet scans, you name it. 00:13:46.799 --> 00:13:48.399 The AI is getting involved with that. 00:13:48.799 --> 00:13:51.120 The big thing is though, is it's not perfect, right? 00:13:51.200 --> 00:13:54.399 And and I mentioned here it assists the radiologist. 00:13:54.879 --> 00:14:05.039 Human in the loop is going to be a massive point of discussion that I think we'll have today is AI will not take over what you're doing and it shouldn't. 00:14:05.200 --> 00:14:09.039 It should assist you with what you're doing, it should take over mundane tasks. 00:14:11.919 --> 00:14:12.799 Data science. 00:14:12.879 --> 00:14:19.759 We're using it for data normalization, business insights, uh, in the more advanced side of things, uh, geospatial. 00:14:19.840 --> 00:14:22.879 Uh Kyle Danielson from he was at Airlift Northwest there for a bit. 00:14:22.960 --> 00:14:24.639 He's a good person to talk to about that. 00:14:25.039 --> 00:14:28.639 Geospatial, being able to find like, hey, where's the next base gonna go? 00:14:28.960 --> 00:14:30.399 Where are we gonna put our next base? 00:14:30.639 --> 00:14:32.480 And is this what's best for the community? 00:14:32.559 --> 00:14:34.159 And what kind of asset do we need there? 00:14:34.240 --> 00:14:35.759 And what kind of people do we need there? 00:14:36.080 --> 00:14:36.720 That's important. 00:14:36.960 --> 00:14:37.519 Research. 00:14:37.679 --> 00:14:51.279 I do literature reviews with with certain AIs constantly because instead of having to look up keywords in a library, I can pull a hundred papers in a matter of minutes, and then I can actually read the papers. 00:14:51.360 --> 00:14:54.960 Now, what I'm not allowing it to do is read the paper for me. 00:14:55.279 --> 00:15:02.000 I'm gonna use this, my brain, to read it and make those inferences from that paper and go, yeah, this study's not good. 00:15:02.080 --> 00:15:06.480 And here's the reasons why I'm gonna use this paper over this paper because of this context, right? 00:15:06.720 --> 00:15:13.120 Clinical documentation is also uh, I know you this is also on the list for us to talk about later, but clinical documentation is gonna be huge. 00:15:13.200 --> 00:15:24.639 And of course, administratively, even things like the IDR process for out of networks claims for the air medical stuff that's going on, no surprise, uh like NSB and all those other companies that are out there doing that stuff. 00:15:24.720 --> 00:15:30.000 They're they're doing well with it and they're helping with the claims to improve reimbursement so that we can continue our mission. 00:15:30.080 --> 00:15:32.559 But um, that's just a very small list. 00:15:32.799 --> 00:15:34.000 It's a lot, right? 00:15:34.320 --> 00:15:38.320 But very small, very small list of what's actually uh going on out there. 00:15:38.480 --> 00:15:40.240 So I hope that answers your question, Mike. 00:15:40.559 --> 00:15:43.679 I think it just generates even more questions, to be honest. 00:15:43.840 --> 00:15:48.399 And so because yeah, like how long have we talked about the human factor, right? 00:15:48.480 --> 00:15:55.679 So if we kind of relate to like, is healthcare prepared for how quickly AI is is evolving and growing, right? 00:15:55.840 --> 00:16:02.080 So if AI is dependent on what we feed it, right? 00:16:02.159 --> 00:16:05.600 You said the context, the information, the the things that go into it. 00:16:07.120 --> 00:16:09.440 And then we also define the human factor. 00:16:09.519 --> 00:16:26.639 And I think we we've found in a lot of cases that the human factor was actually a causation of maybe something that happened, you know, it doesn't have to be super bad, it can be super bad, but maybe different aspects, the human factor was maybe something in it. 00:16:26.879 --> 00:16:39.919 So where do we roll that balance where like we know that uh a mechanical ventilator as long as you is dependent on the information that the human gives it? 00:16:40.080 --> 00:16:47.600 But the information you give it, that ventilator is going to be hands down better at ventilating that patient than I would be on my own, right? 00:16:48.399 --> 00:17:10.319 And so when we look at AI, how do we balance that shift of will the machine learning or this learning process will evolve to be almost as good as a human, or will it be better than humans, or will a human still always hold that hierarchy? 00:17:10.559 --> 00:17:12.480 I'll still be one above the machine. 00:17:12.640 --> 00:17:15.440 Like, how is that gonna evolve for us in healthcare? 00:17:15.839 --> 00:17:18.319 I love what you're the direction you're heading in

here. 00:17:18.480 --> 00:17:20.160 So there's a few things to unpack. 00:17:20.240 --> 00:17:20.559 Okay. 00:17:20.799 --> 00:17:28.640 So the first things first, it let's say a very, very, you know, we all know the ventilator that we all commonly use in our transport field. 00:17:28.720 --> 00:17:30.319 It gives us a lot of feedback, right? 00:17:30.559 --> 00:17:32.559 It's gonna tell us a whole lot of important things. 00:17:32.640 --> 00:17:42.960 So if I see like the C steps out of compliance, if I see the RCE XP, I look at the text battory time consum, look at the minute volume ventilation, it can use data to give you some information. 00:17:43.039 --> 00:17:45.680 And I'm sure that they're that company's probably working on some things. 00:17:45.759 --> 00:17:46.559 I'm sure I'm not sure. 00:17:46.720 --> 00:17:48.079 I'm just guessing here, okay. 00:17:48.319 --> 00:17:48.559 Right. 00:17:48.720 --> 00:17:52.960 But one thing it can't do, it can't look at your patient on the stretcher. 00:17:53.599 --> 00:17:55.200 It can't look at the monitor. 00:17:55.440 --> 00:18:03.119 It can't look, I mean, and maybe in the future there'll be interoperability through what's called APIs, which are data connections between different softwares, how they communicate with each other. 00:18:03.279 --> 00:18:13.680 Maybe in the future there will be things that will communicate with your transport zone monitor, what have you, and it will say, like, hey, your SATs are this, you know, this is what I'm getting on the vent. 00:18:13.920 --> 00:18:15.920 You should do something about this, right? 00:18:16.240 --> 00:18:19.920 But your physical assessment, your visual assessment will never go away. 00:18:20.079 --> 00:18:22.160 Your critical thinking will never go away. 00:18:22.400 --> 00:18:24.480 The human touch will never go away. 00:18:24.720 --> 00:18:30.880 And so what these things are gonna do for you in the future is it's gonna feed you more concise information. 00:18:31.119 --> 00:18:45.680 So instead of me getting 50 pieces of information and converging it to one decision, it's gonna be maybe 25 or even 10 pieces of information to converge that decision, which means my decision point shrinks in time, which means I'm more efficient with my time. 00:18:45.839 --> 00:18:49.039 In other words, I'm faster with doing life-saving things. 00:18:49.200 --> 00:18:53.359 We don't want to take away the human factor because as we know, AI hallucinates. 00:18:53.440 --> 00:18:56.319 Uh, and obviously it's getting less and less and less and less, right? 00:18:56.799 --> 00:18:57.680 Oh, it totally does. 00:18:57.759 --> 00:19:04.720 Uh, you think that thing is eating mushrooms or something on the side, but um, with some of the outputs of gun, I'm like, whoa, well, that's that's not true. 00:19:04.960 --> 00:19:06.559 Um, are you my four-year-old son? 00:19:06.640 --> 00:19:09.839 Are you just telling me a lie here that the sky's purple? 00:19:10.319 --> 00:19:12.160

But that that's to one part of it. 00:19:12.319 --> 00:19:20.640 But as for how can we, you know, human decisions that make end quote bad decisions or maybe a clinical decision that maybe wasn't the best, right? 00:19:20.799 --> 00:19:22.799 And that's where good CQI comes in. 00:19:23.039 --> 00:19:24.880 CQI creates data. 00:19:24.960 --> 00:19:27.359 Um, there are some charting systems that do really good job of this. 00:19:27.519 --> 00:19:39.039 In fact, I I work on the back end of one that does a very good job of this, has rules that are not AI that funnel things to people to help look at that chart and go, hey, we need to do something about this. 00:19:39.119 --> 00:19:45.680 Let's do a root cause analysis and let's go ahead and make some good decisions to help improve patient outcomes in the future. 00:19:45.920 --> 00:19:50.240 And so where I think the AI will help with that human factor is not when you're on the call. 00:19:50.319 --> 00:19:51.759 It's gonna be beforehand. 00:19:51.920 --> 00:19:55.839 It's gonna be when you're in in the classroom, when you're at the base. 00:19:56.000 --> 00:20:01.680 Like, imagine this being able to sit on your phone at the base, which most of us do. 00:20:01.920 --> 00:20:03.279 We're all addicted to our phones. 00:20:03.359 --> 00:20:06.799 Unfortunately, we're all dopamine depleted um individuals, right? 00:20:06.960 --> 00:20:07.119 Yep. 00:20:07.279 --> 00:20:12.400 And being able to just have like a chat bot or something that you can go, hey, you know what? 00:20:12.480 --> 00:20:17.680 I was thinking about it hadn't had a bad Impella page, like bad sit crashing Impella in a while. 00:20:17.839 --> 00:20:20.079 Throw me a case, give me, give me this. 00:20:20.240 --> 00:20:21.839 Uh, I want to practice with this. 00:20:22.000 --> 00:20:39.599 And then you and that machine learning model or rag or what have you, or a combination of both, will allow you to have that simulation ongoing so that you can work through those problems, or even having educators go, hey, we've had a rash of these calls or these things have happened. 00:20:39.920 --> 00:20:54.160 Let's work together, let's create some scenarios with our LLM friend here, and then let's create some scenarios in which the team can work through uh so that when they encounter this in real life, that they're gonna be a lot better prepared. 00:20:54.319 --> 00:20:58.799 And and that and and just having custom simulations, you know how hard it is to write a sim. 00:20:59.119 --> 00:21:05.279 I've been there having to write SIMs for uh your quarterly education, what have you, it's a pain in the butt, right? 00:21:05.359 --> 00:21:07.519 And then you're just like, well, how do I make this thing accurate? 00:21:07.759 --> 00:21:16.960 But imagine the future where data from actual calls and CQI can feed into a model or a rag as we call it. 00:21:17.039 --> 00:21:24.319 So a rag, so you guys are aware, is like if I give my protocols to this AI, for lack

of better terms, and say, you only know this. 00:21:24.480 --> 00:21:25.839 This is the context you have. 00:21:26.000 --> 00:21:27.839 Do not be pulling stuff from outside. 00:21:28.000 --> 00:21:29.519 That's kind of what I'm referring to. 00:21:29.680 --> 00:21:31.759 So if you hear me say rag, that's what I'm saying. 00:21:32.000 --> 00:21:41.359 Whereas the LLM can be from lots of different sources, so like book, internet books, internet magazines, archives, you name it, documents, uh, and just Google searches, what have you. 00:21:41.440 --> 00:21:42.400 You can pull that for the LLM. 00:21:42.480 --> 00:21:44.799 So if you hear me say those, that's that's what that means. 00:21:45.039 --> 00:21:52.079 But imagine being able to feed a rag of some kind, like it to an agent AI where it's like, hey, here's all the calls we've had. 00:21:52.160 --> 00:21:53.599 It's all being fed in here. 00:21:53.759 --> 00:21:56.480 Here's our guidelines, here's our CQI. 00:21:56.640 --> 00:21:59.759 Okay, based on the data you have, let's generate 50 cine. 00:22:00.160 --> 00:22:03.359 Scenarios on the calls that we have not been performing the best on. 00:22:03.519 --> 00:22:06.240 So that we can where's our biggest place of improvement? 00:22:06.400 --> 00:22:12.079 And so instead of having to look through hundreds of hours of all these things to find that out, you can find out in minutes. 00:22:12.319 --> 00:22:19.920 And then once you find that out, then you can educate your team so that when those decisions come up, Blake, you're not making a poor decision, right? 00:22:20.079 --> 00:22:24.160 You're going to make as a human a better decision with the data you've been given. 00:22:24.319 --> 00:22:27.200 And so once again, long-winded answer, right? 00:22:27.440 --> 00:22:29.680 But that that's that's the reality of it. 00:22:29.759 --> 00:22:31.200 Um I think that's how that's gonna go. 00:22:31.359 --> 00:22:32.079 That's my opinion, though. 00:22:32.160 --> 00:22:35.039 And maybe something will change, but that's where I that's what I think about it. 00:22:35.359 --> 00:22:41.200 I think you mentioned a couple of things that kind of want to uh touch on and maybe circle back to a little bit. 00:22:41.440 --> 00:23:10.000 I I vaguely recall in prior conversations in prior podcasts with you or discussions when we've kind of talked about, you know, there's the possibility of, okay, well, one day maybe all of us are going to be wearing cameras, and that camera is now going to document that, okay, I'm giving, you know, four milligrams of Zofran or I'm titrating my Norepi or what have you, and then it can automatically import some of those things into whatever charting system you're using and and vice versa, or it's watching the ventilator changes that you're making or those kind of things. 00:23:10.160 --> 00:23:14.400 And and I think that aspect or that concept of it is pretty cool to me. 00:23:14.559 --> 00:23:23.279 Uh, and then you talk about you just

uh a couple minutes ago with regards to you know some crew members that are utilizing AI to generate narratives. 00:23:23.440 --> 00:23:33.759 And I know that there are some charting uh programs that do have, you know, AI generators built into their charting system already, and and others are working towards doing that as well. 00:23:34.000 --> 00:23:42.480 And and I I think obviously within those systems, that's obviously protected health information that is stored only within those particular areas. 00:23:42.720 --> 00:23:48.079 But I have to still get worried about, I mean, anything that's on the internet can still somehow be discovered. 00:23:48.240 --> 00:24:10.160 Uh, and so that that's I think where my biggest like hang up or concern is with a lot of this stuff right now, whether we're doing a recording and it's recording everything I'm doing and the times and the adjustments and the medications or the ventilator changes or what have you, as well as looking at like these narratives that we're writing on all of these particular patients. 00:24:10.319 --> 00:24:20.640 Like, how secure is this information and and how easily accessed is it by whoever wants it? 00:24:21.680 --> 00:24:24.720 So this is really going to become an administrative problem. 00:24:24.880 --> 00:24:34.240 Okay, so this is one of the reasons why So Mike that's just this is this is where I'm a giant man, this is where I'm a giant brake pedal. 00:24:34.559 --> 00:24:38.000 I'm telling you, I'm putting pedal to the floor on this. 00:24:38.640 --> 00:24:48.160 So the question is really, when you're buying this stuff and you're using it, number one, do you understand the terms of your BAA? 00:24:49.039 --> 00:24:51.440 Are those terms accepted by the vendor? 00:24:51.839 --> 00:24:54.960 Are you protecting yourself legally speaking? 00:24:55.039 --> 00:24:59.680 Is your legal team versed in the these technologies? 00:24:59.920 --> 00:25:04.079 Make sure that whoever your league, your legal team is, they are they are versed in this. 00:25:04.319 --> 00:25:04.880 Ask the question. 00:25:05.039 --> 00:25:07.359 Hey, listen, to AI technologies are coming out. 00:25:07.519 --> 00:25:11.839 We're gonna be getting contracts and things like that with BAAs for these things. 00:25:11.920 --> 00:25:13.519 Uh, are you prepared for that? 00:25:13.680 --> 00:25:14.640 I would ask the question. 00:25:14.799 --> 00:25:19.359 I mean, maybe it's because I'm on spectrum and I'm not afraid to ask questions, but that's a question I would ask. 00:25:19.519 --> 00:25:22.480 Um, get with your legal team, be like, hey, are you ready for this? 00:25:22.640 --> 00:25:24.960 Number two, do you have a policy in place? 00:25:25.200 --> 00:25:27.759 Do you have an AI ethical use policy in place? 00:25:27.920 --> 00:25:35.839 You need to have AI policies in place if you plan on using it, because you don't want to buy or advocate for technologies that go against your own policy, right? 00:25:36.000 --> 00:25:36.880 That's that's number two. 00:25:36.960 --> 00:25:38.640 You got to put those safeguards in place. 00:25:38.880 --> 00:25:44.240 Like, for example, like if you have a big issue with chat GPT on your network, have your IT ban it. 00:25:44.400 --> 00:25:51.759 Just go ahead and and and and block those sites because what you don't want to do, well, and and for good reason, right? 00:25:51.839 --> 00:25:57.279 Because a crew member might think, oh, well, I pay the$21 a month for Chat GPT, open AI, or whatever. 00:25:57.359 --> 00:25:58.720 It's secure, it's safe. 00:25:58.960 --> 00:26:03.759 The reality is you're still feeding data into the LLM, right? 00:26:03.839 --> 00:26:08.559 Right now, when you have like a BAA in place, and and I understand you don't always have to have a BAA. 00:26:08.799 --> 00:26:11.119 I I'm one of those people I'm a little more rigid with that. 00:26:11.279 --> 00:26:17.839 Like, especially right now, since this technology is new, you're gonna need that documentation when you purchase uh that software. 00:26:18.000 --> 00:26:26.400 The other thing is you become friends with your IT people, uh, become friends with your database administrators, become friends with your data analytics people like me. 00:26:26.480 --> 00:26:29.359 You know, it's like I know people either love or hate data people. 00:26:29.440 --> 00:26:31.599 Uh, it just depends on what data you're given, right? 00:26:31.759 --> 00:26:35.759 But you need to be friends with those people and go, hey, listen, we're looking at this technology. 00:26:36.079 --> 00:26:37.680 Let's interrogate this together. 00:26:37.839 --> 00:26:39.599 Let's ask the hard questions. 00:26:39.839 --> 00:26:42.319 You need to ask them things like, how is your data stored? 00:26:42.480 --> 00:26:44.319 How is it protected on your end? 00:26:44.640 --> 00:26:46.480 How are you securing your servers? 00:26:46.640 --> 00:26:48.079 Where are you getting your models from? 00:26:48.240 --> 00:26:49.839 How are you training your models? 00:26:50.000 --> 00:26:51.519 Those are questions you have to ask. 00:26:51.680 --> 00:26:54.960 Now, to help you, there are several frameworks out there. 00:26:55.039 --> 00:27:01.920 Now, the one I personally advocate for uh is the National Institute of Standards and Technology or NIST. 00:27:02.079 --> 00:27:07.839 They have a uh a guide for uh AI technologies being incorporated into your business. 00:27:08.079 --> 00:27:13.759 And I highly recommend looking up that or similar frameworks when you purchase these systems. 00:27:14.000 --> 00:27:16.160 That's the only way you're gonna know it's secure. 00:27:16.400 --> 00:27:17.119 The only way. 00:27:17.359 --> 00:27:19.119 And there's never a guarantee, right? 00:27:19.200 --> 00:27:23.519 Because we cybersecurity is a growing field right now. 00:27:25.759 --> 00:27:42.240 I think that there's also the other aspect of it that I think a lot of crew members and and and myself included uh was this

was my thought process of like, you know, if I'm inputting something into us, I can say, be sure to exclude all patient information, like identifiable patient information kind of thing. 00:27:42.400 --> 00:27:47.359 And and I think that that's all well and good, but that's still you're still inputting some of that information. 00:27:47.519 --> 00:27:49.279 So that's still there. 00:27:49.440 --> 00:27:57.440 And and I guess that's what my concern is with all of this is like how secure are we really talking with this and and making sure that things are non-discoverable? 00:27:57.519 --> 00:28:10.960 And and I mean, I I think at the end of the day, like let's be real, any one of our charting systems, if somebody wanted to truly get into those, there probably would be a way that that could potentially happen, you know, and uh there where there's a will, there's a way. 00:28:11.039 --> 00:28:29.039 Uh, but yeah, I don't want to see any of our crew members, and I and I think it's I think it's becoming a great tool, and I'm seeing this more useful in all the things that I'm doing on a daily basis, and I'm seeing some of my crew members and teammates get excited about the possibility of what AI can bring to the table. 00:28:29.440 --> 00:28:36.880 Uh, I'm just still super cautious because of all of that input into the system and and how secure are we really talking at the end of the day? 00:28:37.200 --> 00:28:37.440 Yeah. 00:28:37.519 --> 00:28:43.599 Now, some of the systems that are out there, I will say for like the charting system companies that are once again, I don't work for them. 00:28:43.680 --> 00:28:46.319 I have no financial affiliations with any of these companies. 00:28:46.559 --> 00:28:50.799 But one thing I will speak for on that is number one, they have to keep up with the rules, right? 00:28:50.880 --> 00:28:54.160 They have to have SOC2 encrypted servers, they have to have BAAs. 00:28:54.480 --> 00:28:56.799 They are a healthcare information technology company. 00:28:56.880 --> 00:28:58.400 They're very highly regulated. 00:28:58.720 --> 00:29:01.279 So there are strict rules on these things, right? 00:29:01.440 --> 00:29:11.599 Now, what I will say is I I'm not really worried necessarily as much for a breach on these like chart, the charting software companies specifically. 00:29:11.839 --> 00:29:17.279 What I worry about is the crew member side of it not being a human in the loop. 00:29:17.440 --> 00:29:23.119 For example, like let's say I use OCR technology and I like scan a face sheet or I scan this or that. 00:29:23.279 --> 00:29:25.839 I pull the information in, I just click apply. 00:29:26.160 --> 00:29:28.559 Didn't look at it, just apply. 00:29:28.799 --> 00:29:37.039 And then I look at the, you know, the now, granted, I I'm not a big fan of narrative generate narrate uh narrative generators with AI. 00:29:37.440 --> 00:29:46.160 I am good with the rule-based templates where you can put a template in there, like on Epic, where you can pull over stuff using dot phrases, what have you. 00:29:46.319 --> 00:29:52.319 That's okay because all it's doing is pulling that data and it knows that's a rule-based type application. 00:29:52.400 --> 00:29:53.519 It's not really AI. 00:29:53.759 --> 00:30:04.400 Where the AI is great, though, is where, like, for example, I've used systems where if I click a button, it will find things in my narrative and go, hey, um, you don't have these in your interventions. 00:30:04.559 --> 00:30:06.160 Like, do you want to plug these in there? 00:30:06.400 --> 00:30:11.119 And then sometimes there'll be duplicates, so I'll just go and click in and go, okay, I forgot I forgot to document that IV. 00:30:11.200 --> 00:30:12.480 I forgot to document this intubation. 00:30:12.559 --> 00:30:14.559 Well, I wouldn't forget that, but it happens. 00:30:14.799 --> 00:30:16.559 Click apply and then it moves it over. 00:30:16.640 --> 00:30:17.519 So then guess what? 00:30:17.759 --> 00:30:21.119 My chart is now lined up with my narrative. 00:30:21.359 --> 00:30:22.000 Vice versa. 00:30:22.160 --> 00:30:27.759 That whole double charting thing that we always worried about, it's not nearly as much of a concern because of the consistency piece. 00:30:27.839 --> 00:30:29.759 So it's good for those things. 00:30:29.839 --> 00:30:32.079 Um, and also like the AI suggestion tools. 00:30:32.160 --> 00:30:40.799 I've seen with some of these uh products where it'll tell you, like, hey, you put that you intubated, did you um put them on entitled CO2? 00:30:40.880 --> 00:30:42.880 You probably should document that intervention, right? 00:30:43.039 --> 00:30:43.759 And what have you. 00:30:43.920 --> 00:30:52.880 But where the issue is gonna lie with the accuracy of these things, Mike, is and not to jump down too far down a rabbit hole because I'm pretty good at that, as you know, uh, is Nempsys. 00:30:53.039 --> 00:30:55.119 Okay, so like Nempsys 3.5. 00:30:55.359 --> 00:30:57.839 Um, we're not in 3.5.1 or anything like that yet. 00:30:57.920 --> 00:31:08.880 One of the biggest things that me and and several of the data folks in the industry have observed is that a lot of the data sets that are found for the national data sets or even state reporting data sets is not consistent for critical care. 00:31:08.960 --> 00:31:10.160 They're custom fields. 00:31:10.319 --> 00:31:21.519 Um and I do want to say out's the world that we should work together to work with Nempsys or other organizations to be able to define data sets for our industry and critical care at least. 00:31:21.839 --> 00:31:23.680 First of all, I'm having PTSD. 00:31:23.759 --> 00:31:27.440 Uh just talking about the Nempsys rules. 00:31:27.599 --> 00:31:32.880 And you know, we talk about all these must-haves in our chart, right? 00:31:32.960 --> 00:31:41.279 And I think for anybody listening to this, you you're probably having that sudden heart rate increase, and you're like, oh shoot, gotta get all these

in there, right? 00:31:41.440 --> 00:31:43.759 Because you and and that's where I guess we're moments. 00:31:44.000 --> 00:31:45.599 My boy, they're not driving and getting road raid. 00:31:46.000 --> 00:31:46.160 Absolutely. 00:31:46.720 --> 00:31:47.759 Please pull over, be safe. 00:31:47.839 --> 00:31:48.079 Yeah. 00:31:48.319 --> 00:31:48.480 Yeah. 00:31:48.720 --> 00:31:49.039 Pull over. 00:31:49.200 --> 00:31:51.599 But like I feel like that's my hope with AI. 00:31:51.680 --> 00:32:01.359 And it and you know, since we're talking about documentation, and you know, I I've used it, like what you said, Christian, to to make like a rule-based template, right? 00:32:01.440 --> 00:32:19.839 And I've used that to kind of guide and make sure that I'm including the necessary data, but it's still incumbent on me to be the person that verifies not only what the data is that I'm inputting and that it's also correct and what's in there. 00:32:20.000 --> 00:32:29.440 And so I feel like I've always tried to keep it as that kind of almost rule-based or framework, especially when it comes to documenting. 00:32:29.759 --> 00:32:31.759 Because I mean, I'll be honest, 3 a.m. 00:32:32.000 --> 00:32:37.599 and you've been up for a while, and you know, a lot of our legs back to base can be at about an hour. 00:32:37.839 --> 00:32:49.200 And so then you're stuck with I have four different sheets of all types of signatures and billing and all this stuff that I have to then upload. 00:32:49.359 --> 00:32:55.759 And then once that gets put in, then I have to start this chart, and then now I have to go back in and put in all these procedures. 00:32:56.079 --> 00:33:30.400 So I feel like there is a very good intended use for AI that my hope is that will help us as clinicians, whether you're in the back of an ambulance, uh an airplane, a helicopter, whatever it is, and even on the administrative side, there's got to be my hope is that there's got to be a way that AI will really help us kind of uh trim the fat, I guess, if so to speak, you know, because I I know uh in a previous charting system, I won't name names, but it was just drop-down button click heavy, right? 00:33:30.480 --> 00:33:38.319 And and I could easily shave off 15 minutes of time for things that can be auto-populated, right? 00:33:38.480 --> 00:33:46.559 And and then I think back to years, like how long did physicians and still do dictation, talk to text, right? 00:33:47.119 --> 00:34:09.840 So I mean, there's gotta be a way going forward that we can use AI to help us as clinical providers at patient bedside on that line to really help us cut down on the amount of time that we spend documenting, but also like helping assist us to make sure our documentation is where it needs to be, but like making it a little bit easier for us. 00:34:09.920 --> 00:34:18.639 And I I I don't know if I'm sound lazy saying that, but I kind of I don't really feel like it should take me three hours for certain charts, you know. 00:34:18.960 --> 00:34:20.159 I agree completely. 00:34:20.320 --> 00:34:23.039 And but here's the I'm gonna tell you the secret, right? 00:34:23.199 --> 00:34:29.440 So AI does serve a place, but maybe not as heavy of a place as you might think. 00:34:29.599 --> 00:34:43.440 Um, because I work in the back end of charting systems now, and tell you what, there's actually a lot of tools that are already in place that accomplish so many of those things that you're already mentioning, it's it's almost kind of hilarious to me, right? 00:34:43.519 --> 00:34:47.679 Because, like, for example, the system I use, there are like preset values. 00:34:47.760 --> 00:34:50.639 I can click a button and it will auto-populate certain things for me. 00:34:50.800 --> 00:34:53.360 I gotta go back and I gotta double check them naturally, right? 00:34:53.440 --> 00:34:56.239 Because every patient's it's a big gray area we live in. 00:34:56.320 --> 00:34:56.639 Yeah. 00:34:57.599 --> 00:35:23.519 But getting your people who build the charting form, getting them educated, whatever certifications and stuff exist for them, and having them understand the culture, having them understand the business, the quality, the CQI, being involved in all those processes to understand and really focus in on how you chart as a clinician and understand your program. 00:35:24.400 --> 00:35:28.320 You don't even really need AI to accomplish 80% of that, 90% of that. 00:35:28.559 --> 00:35:30.880 Because if you think about it, a lot of it's rule-based. 00:35:31.039 --> 00:35:36.719 It's like if, for example, if I fly an ECMO patient, I'm not gonna walk them to the stretcher. 00:35:37.039 --> 00:35:39.119 They're gonna be sheet transferred, right? 00:35:39.280 --> 00:35:39.679 Right. 00:35:40.239 --> 00:35:43.679 They're if they're intubated, their GCS is going to be A3. 00:35:43.840 --> 00:35:45.440 They're not gonna be able to sign for themselves, right? 00:35:45.599 --> 00:35:48.719 There, it's implied consent or family has to sign, right? 00:35:48.880 --> 00:35:54.000 So we can use visibility rules, validations, we can use all kinds of things to reduce that. 00:35:54.159 --> 00:35:57.360 But I think a lot of people get into these systems, they're overwhelmed. 00:35:57.599 --> 00:36:01.119 Uh and and and like I said, I'm not gonna speak to any particular charting system. 00:36:01.280 --> 00:36:03.119 Sometimes the support's not always there. 00:36:03.199 --> 00:36:06.880 Um, sometimes you need to buy extra support or you need to help with that support. 00:36:07.280 --> 00:36:08.320 Or it's outsourced. 00:36:08.719 --> 00:36:09.599 Or it's outsourced. 00:36:09.920 --> 00:36:11.199 Or we just call Christian. 00:36:11.440 --> 00:36:11.679 Right? 00:36:12.000 --> 00:36:13.840 Or yeah, no, it's supposed to be. 00:36:13.920 --> 00:36:20.639 There's there's some people out there in our industry um who who I could call by name who who would just who are just experts in this stuff.

00:36:20.880 --> 00:36:27.519 And when you talk to them and you think about it, it really comes down to culture, it comes down to process. 00:36:27.679 --> 00:36:33.599 Um, having a team of people at your organization who are informatics, I think that's critical. 00:36:33.760 --> 00:36:39.199 If you don't have an informatics group of clinicians that look at your form, if you're in charge of that form. 00:36:39.280 --> 00:36:41.760 Now, granted, there are some companies out there, they're huge, right? 00:36:41.840 --> 00:36:43.440 We got some of our national companies. 00:36:43.599 --> 00:36:51.119 Their charting forms are that that's a whole nother animal, but they have people that work on those things because they have billing and everything else involved on a national scale. 00:36:51.280 --> 00:37:06.880 But if you have a smaller program, like the program I fly for, uh the past programs I've flown for, we have a lot more control over that charting form, which means on the charting side of it, you can reduce your clinician's workload, but you have to put in the effort, you have to put in the time, you also have to test things. 00:37:07.039 --> 00:37:08.960 You can't rush things to market either. 00:37:09.199 --> 00:37:11.039 You have to plan to study act, right? 00:37:11.119 --> 00:37:16.719 And I'm sure all the nurses that are listening to this are probably like, dude, this guy, like, what is his problem? 00:37:17.440 --> 00:37:19.119 They're all twitching right now. 00:37:19.679 --> 00:37:22.159 Believe it or not, it actually works if you do it. 00:37:22.400 --> 00:37:36.639 If you actually do it and focus in and you you work on asking the question, if I'm a crew member charting on this particular patient and I'm the biller and the quality person and the billing person, like, how can we make this work to where it's more efficient, right? 00:37:36.719 --> 00:37:44.639 Because if we build, if if we document more efficiently, we're gonna have better quality, which means better data for models that help us with the AI sign a bit. 00:37:44.880 --> 00:37:47.440 So as you can see, we need to crawl, then we walk, then we run. 00:37:47.599 --> 00:37:54.320 And in this case, you crawl by looking at the things you already have in place that maybe you don't use to the fullest capacity. 00:37:54.719 --> 00:37:56.400 Make those things really good. 00:37:56.800 --> 00:38:02.639 Make sure your policies and process are good, and then move on to the stuff that'll board you up for AI. 00:38:02.960 --> 00:38:05.679 Well, and that's what I was just, you know, hearing you talk about all this. 00:38:05.760 --> 00:38:18.320 I think that's one of the things in my new role that I'm most excited about is I I essentially am also functioning as like, you know, the quality manager side of things and and looking at some of the data and how we can improve the program from the quality side of the. 00:38:18.880 --> 00:38:40.880 And I but but having, and I think a lot of people

share this similar uh challenge out there in those smaller programs in that you have multiple roles and there's just not enough hours in the day, the week, the month, the year in order to be able to truly dedicate to those roles what you would like to. 00:38:41.599 --> 00:38:53.119 And so I think the integration of AI into those roles to help streamline some of that process and make us more efficient is something that I'm super excited to see how that all works out. 00:38:53.679 --> 00:39:05.199 But I think on the certainly on that QA side of things or on that quality metric side of things is really going to be at least what I'm hoping for, the forefront of it. 00:39:05.280 --> 00:39:13.360 But you're almost replacing a quality manager position by utilizing some of these AI integrations. 00:39:14.079 --> 00:39:19.039 For me, if I'm in a smaller program and have multiple roles, that's a great thing. 00:39:19.519 --> 00:39:30.320 But you know, that might not be if that's your sole job, maybe you might definitely sitting over here, maybe like a little bit worried about what that's gonna look like. 00:39:30.480 --> 00:39:46.719 Uh and and so I I think there are so many really cool avenues to go down with this, but again, like you still need to have the time to officially analyze or or work on the input so that you can appropriately analyze the output. 00:39:47.039 --> 00:40:04.320 Like and I think that's that's the biggest task of all of this is like finding that time to be able to and and look, I get it, like the entire healthcare system nationwide, whether you're talking about in-hospital, out of hospital, I don't I don't care what role you have within healthcare, you're under some constraints right now. 00:40:04.400 --> 00:40:06.559 There's a lot of changes that are coming down the pike. 00:40:06.719 --> 00:40:16.000 You're constantly trying to figure out a way to be able to make this whole system work and still be able to save a little bit money, more money. 00:40:16.079 --> 00:40:18.400 And I get it, like I totally do. 00:40:19.360 --> 00:40:29.119 But this is the I I don't know that I see another option without the integration of AI to be able to make some of those things happen. 00:40:29.199 --> 00:41:01.920 Uh and so like I'm excited but terrified at the same time to see what this is all gonna end up looking like because I think that there are a lot of possibilities out there uh that can make my job a heck of a lot easier, but I still need to be able to have dedicated time as a human to look at all of those things and to be able to make sure that I can modify what I can modify to improve the front end user experience for my team and to maximize the back end output uh for the analytics. 00:41:02.159 --> 00:41:09.360 Uh and and and that's that's that's the biggest challenge I I find right now uh is really putting those big pieces together. 00:41:11.760 --> 00:41:15.280 Well, and and that's where good project management's gonna come into play, right? 00:41:15.440 --> 00:41:21.360 So you you have limited resources and you want to use these AI tools because they can help you, right? 00:41:21.599 --> 00:41:25.360 So uh for example, like imagine yeah, there are systems that exist. 00:41:25.440 --> 00:41:35.760 Once again, I won't mention any names, but there are systems that will take your charts, run custom prompts through it for quality questions, and then it will answer them. 00:41:35.840 --> 00:41:37.920 And then you can look at your dashboards and whatnot and see that. 00:41:38.000 --> 00:41:39.360 And that's a great tool. 00:41:39.679 --> 00:41:44.880 The biggest thing from that is gonna have to be it's the initial upfront investment to get that working. 00:41:45.039 --> 00:41:51.119 That's the biggest thing is that it's not these systems are never really out of the box, ready to go. 00:41:51.360 --> 00:41:51.599 Right. 00:41:51.760 --> 00:41:53.760 Because once again, it's a contextual problem. 00:41:53.920 --> 00:41:55.679 And I think that's always gonna be the issue. 00:41:56.000 --> 00:41:56.159 Sure. 00:41:56.320 --> 00:42:02.559 And every program's different and they have different, you know, standards of care and different ways that they accomplish those things. 00:42:02.639 --> 00:42:04.559 And so yeah, I totally get that. 00:42:04.639 --> 00:42:05.840 That makes perfect sense to me. 00:42:06.079 --> 00:42:15.119 But yeah, how you learn that or how you help the the AI tool learn that, I think is where a lot of people may end up struggling. 00:42:15.519 --> 00:42:16.559 I would agree with that. 00:42:16.639 --> 00:42:26.480 And I think the way that you start off is is doing basic things, like answering questions like, hey, let's define what something is. 00:42:26.719 --> 00:42:28.239 Like, you know, I I love the question. 00:42:28.320 --> 00:42:32.559 I took a course on quality once and they asked, what is quality? 00:42:32.960 --> 00:42:34.639 What a great question, right? 00:42:34.800 --> 00:42:37.119 It's like, what is like I don't know. 00:42:37.360 --> 00:42:39.119 There's so many like quality in what? 00:42:39.199 --> 00:42:41.920 Like if I'm at the Doritos factory, is it crunchy chips? 00:42:42.000 --> 00:42:43.199 Is it is it more flavorful? 00:42:43.360 --> 00:42:43.920 I don't know. 00:42:44.639 --> 00:42:50.480 But I think that when it you have to define the basic things first, it it takes time. 00:42:50.800 --> 00:42:53.679 And I know we're very much that group of people. 00:42:53.840 --> 00:42:56.480 We want things as quick as we can because we take care of patients. 00:42:57.360 --> 00:42:57.760 Yeah. 00:42:58.159 --> 00:42:59.519 Absolutely. 00:43:00.000 --> 00:43:05.760 But at the end of the day, these systems, you know, you can buy them, they're ready to go out of the box to some extent. 00:43:05.840 --> 00:43:07.119 Um, you're gonna run them. 00:43:07.199 --> 00:43:21.119 But here's the it's a paradox in the sense that yes, it might be easy when you first get

it, but when you start running stuff through it, what'll happen is if you don't have the the foundation built like anything else, it's gonna sink, it's gonna fracture. 00:43:21.280 --> 00:43:28.880 And so what'll happen is you're you'll you'll start making decisions on outputs, and then those output decisions will harm your program uh down the line. 00:43:29.039 --> 00:43:34.639 And it will also allow us to not make good decisions when it comes to you know clinical care, et cetera. 00:43:34.880 --> 00:43:40.960 So where we have to start, if you don't have a lot of resources, the first thing I would tell you is a few things. 00:43:41.199 --> 00:43:45.440 Look at your number one, look at your policies and make sure they're consistent before Kynes comes around, right? 00:43:45.599 --> 00:43:47.119 Everyone's like, oh, Keynes is here. 00:43:47.199 --> 00:43:48.480 Let's look at our policies, right? 00:43:49.039 --> 00:43:50.639 Look at them all the time. 00:43:50.800 --> 00:43:52.079 Do they make sense? 00:43:52.320 --> 00:43:53.360 Do they flow? 00:43:53.599 --> 00:43:55.840 Do they do are there contradictions? 00:43:56.000 --> 00:43:58.000 Clinical guidelines are also very similar. 00:43:58.079 --> 00:44:04.800 I work for multiple places and And you'll always find one or two hanging out in there, making sure that consistency is the key. 00:44:05.039 --> 00:44:07.519 Uh I'll I'll take I take that from Dave Lebesk. 00:44:07.599 --> 00:44:11.440 Um consistency, you know, um, consistency is key here. 00:44:11.599 --> 00:44:30.239 So if you're consistent in your policies, your procedures, you're consistent in your training, you're consistent in your documentation, you're consistent the consistency in things, even without the AI, that's a great place to start because even if you do have limited resources, it doesn't take a lot of resources to be just be consistent, you know, with what you're doing, right? 00:44:30.400 --> 00:44:32.000 If everyone's consistent, cool. 00:44:32.159 --> 00:44:34.880 And once that's done, then it's like, okay, well, we're consistent now. 00:44:34.960 --> 00:44:38.719 Now let's define in that consistency what makes us consistent. 00:44:38.880 --> 00:44:40.960 What is important to us? 00:44:41.280 --> 00:44:43.599 Then you define your own definition of quality. 00:44:43.679 --> 00:44:48.159 And once you have that definition of quality, you're like, okay, these are the five most important things. 00:44:48.480 --> 00:44:57.280 You only need three to five measures to start and go, what are the five most important things right now to keep my operations safe and take care of my patients effectively and make the money I need to keep on going? 00:44:57.519 --> 00:45:04.000 Or to the crew members, you know, give them five, like, hey, what are the five things as crew members that hey, you guys find to be the most important? 00:45:04.159 --> 00:45:13.280 And then get really good at those things and then surround the AI tools you want to

use, which each of one of those five things, and focus on the data points there. 00:45:13.360 --> 00:45:15.440 And that's you that's when you're starting to walk, right? 00:45:15.519 --> 00:45:18.400 Because now you're like, okay, now I define what I want to look at. 00:45:18.480 --> 00:45:21.119 And so because if you look at everything, it's paralysis by analysis. 00:45:21.199 --> 00:45:23.199 You're gonna get, I mean, it's overwhelming. 00:45:23.599 --> 00:45:24.400 Where do you even start? 00:45:24.639 --> 00:45:24.880 Yeah. 00:45:25.119 --> 00:45:25.519 Yeah. 00:45:25.760 --> 00:45:28.480 It's like I think it's funny, like lift times, right? 00:45:28.800 --> 00:45:32.639 I I kind of laugh about that one because it's like, okay, lift times, that's great. 00:45:32.880 --> 00:45:35.599 You know, we do need to move quickly, right? 00:45:35.760 --> 00:45:37.840 But it's like, what are the implications of that? 00:45:38.000 --> 00:45:38.159 Right? 00:45:38.320 --> 00:45:40.480 Some programs, it matters way more than others. 00:45:40.639 --> 00:45:43.360 Like if you're an IFR program, like I fly at an IFR program. 00:45:43.599 --> 00:45:47.039 There's always gonna be a little bit of a time delay because you have to flight plan your flight, right? 00:45:47.199 --> 00:45:48.079 That's just how it goes. 00:45:48.320 --> 00:45:50.719 Move with a sense of purpose, everyone's mission driven. 00:45:50.960 --> 00:45:52.800 That's also your culture, too, right? 00:45:52.960 --> 00:45:56.159 Is your culture that, you know, once again, consistency. 00:45:56.239 --> 00:45:58.400 Is your culture consistently mission driven? 00:45:58.559 --> 00:46:02.320 Are they consistently practicing safety, uh, safety policy? 00:46:02.639 --> 00:46:05.840 Are they consistently documenting things like near misses? 00:46:06.000 --> 00:46:06.639 All those things. 00:46:06.719 --> 00:46:15.840 Because then, like, for example, imagine an AI tool that could look at all your near misses over time and go, hey, Mike, your guys are doing your people are doing this. 00:46:16.000 --> 00:46:17.360 This is this is not safe. 00:46:17.519 --> 00:46:20.400 And it's gonna lead to something down the line that could be catastrophic. 00:46:20.559 --> 00:46:22.480 Like, let's take care of that right now, right? 00:46:22.639 --> 00:46:23.840 That's the prediction. 00:46:24.000 --> 00:46:31.519 And so that's where context meets the models, which meets the outputs, which means consistency with the outputs. 00:46:31.679 --> 00:46:37.039 If you're consistent, it will too be consistent, and thus you're gonna get the things you want. 00:46:37.280 --> 00:46:39.199 But unfortunately, it does take time. 00:46:39.280 --> 00:46:43.440 But that's why we crawled and we walked and we run, especially when you're resource limited like that. 00:46:43.599 --> 00:46:44.639 So I feel you though. 00:46:44.800 --> 00:46:45.119 I do. 00:46:45.199 --> 00:46:47.199 I I know that that's a that's a tough one, right? 00:46:47.360 --> 00:46:49.519 So let's move to the run part. 00:46:50.880 --> 00:46:54.079 We've crawled a little bit, we walked a little bit impatient over here. 00:46:54.159 --> 00:46:55.199 He wants it yesterday. 00:46:55.440 --> 00:46:56.239 He wants it now. 00:46:56.320 --> 00:46:57.760 It's my money and I need it now. 00:46:57.920 --> 00:46:58.159 No. 00:46:59.039 --> 00:47:00.320 So let's talk about this, right? 00:47:00.480 --> 00:47:13.679 Okay, so we've under so we've talked about some data processes, we've talked about how to use AI to really identify some things that maybe we do good, maybe we do bad, and we should improve on. 00:47:13.760 --> 00:47:14.639 So we have that. 00:47:14.880 --> 00:47:18.800 We've talked about using it into building good scenarios, right? 00:47:18.960 --> 00:47:21.679 So let's say you discovered you had a bunch of near misses. 00:47:21.760 --> 00:47:23.760 Okay, how do we train and recorrect that? 00:47:23.920 --> 00:47:30.639 So A, so you used AI identified you had a bunch of near misses, so now you do some training, you write it writes some scenarios, right? 00:47:30.719 --> 00:47:42.079 So you practice as a crew member, and I I've often heard of AI starting to be that digital crew member, right? 00:47:42.239 --> 00:47:46.239 Like that third wheel. 00:47:47.119 --> 00:48:12.480 So what would be the probability that so if it's capable of learning and predictive software, again, it's based off of trends, how much can, could or would AI help us treat our patients like live in the field based on data feedback, right? 00:48:12.559 --> 00:48:16.239 So you were talking before about let's say two data points were able to talk to each other. 00:48:16.320 --> 00:48:27.280 Let's say in the future the ventilator was getting real-time data back on from the monitor, more particularly end titles and spot2s. 00:48:29.519 --> 00:48:39.039 What's that possibility look like of either suggestive or actual real-time management of the patient? 00:48:39.599 --> 00:48:42.159 Autonomous based on data. 00:48:42.719 --> 00:48:43.920 Is that a possibility? 00:48:44.079 --> 00:48:50.559 Like, would that be something that would help us in the future or be part of how we take care of patients in the future? 00:48:50.960 --> 00:48:56.400 I think to your point, you know, that that's a very, very loaded question, but a great question. 00:48:56.719 --> 00:49:00.559 So, yes, I think it very well could assist us, right? 00:49:00.800 --> 00:49:02.079 So a couple things. 00:49:02.239 --> 00:49:04.960 Number one, business can't take that out of this, right? 00:49:05.119 --> 00:49:05.360 Sure. 00:49:05.519 --> 00:49:10.079 You know, you have competing equipment device providers out there. 00:49:10.320 --> 00:49:13.119 The question is number one, are they willing to share data with each other? 00:49:13.360 --> 00:49:18.400 Are they willing to communicate with each other and go, okay, hey, listen, we're gonna do this for the betterment of the

patient. 00:49:18.559 --> 00:49:22.239 You know, are these device companies gonna allow each other to communicate? 00:49:22.480 --> 00:49:24.639 And is the data gonna be consistent on the back end? 00:49:24.719 --> 00:49:26.400 And that's that's kind of the big deal, right? 00:49:26.480 --> 00:49:28.480 Is how we allow that stuff to communicate. 00:49:28.639 --> 00:49:34.320 What I would love is a world where my IV pumps, my monitor, and my vent talk to each other. 00:49:34.480 --> 00:49:42.639 My ECMO pump talks to my monitor, talks to this, talks to that, talks to the other, because that's all data points at the end of the day, right? 00:49:42.800 --> 00:49:43.039 Yeah. 00:49:43.199 --> 00:50:03.440 So, like, for example, you know, if let's say I've got a PEEP of 15 on my ventilator and I'm getting a lot of art line variability, and my blood pressure's dropping, and and my SATS are still okay, the monitor could say, Hey, could we suggest doing this with the ventilator? 00:50:03.599 --> 00:50:04.719 Hey, let's give some volume. 00:50:04.800 --> 00:50:12.079 Hey, we have intrathoracic pressure increases, like alarming or warning or advising in real time. 00:50:12.239 --> 00:50:16.239 I could see that being a future uh in the future, I could certainly see that. 00:50:16.400 --> 00:50:21.599 I think the first barrier, once again, is going to come down to are the device companies willing to communicate with each other? 00:50:21.760 --> 00:50:23.679 Are they willing to allow the devices to do that? 00:50:23.760 --> 00:50:24.480 That's number one. 00:50:24.719 --> 00:50:26.960 Number two, we also need to communicate what's important. 00:50:27.039 --> 00:50:31.039 Like I was telling Mike, uh, we have to define what is important in that moment. 00:50:31.199 --> 00:50:39.360 We have seen in the literature and in my studies, I have found that paralysis because that alarm fatigue is a big deal, right? 00:50:39.519 --> 00:50:41.039 We need to think about what's important. 00:50:41.199 --> 00:50:46.400 Once again, if we can use the AI to take those 50 data points, reduce them to 25, and maybe even 10. 00:50:46.800 --> 00:50:48.400 So that's a lot less for me to look at. 00:50:48.480 --> 00:50:52.559 So when something actually alarms at me, it's not, uh, it's the it's the O2 thing alarming again. 00:50:52.719 --> 00:50:56.239 Yeah, because we're at night, you're at 100% FIO2, it's alarming again. 00:50:56.320 --> 00:50:57.360 Can you change that, dude? 00:50:57.519 --> 00:51:00.480 Whatever the case may be, we eventually start writing it off, right? 00:51:00.559 --> 00:51:05.679 Because as humans, that that's where we we we falter is that alarm fatigue. 00:51:05.840 --> 00:51:09.519 So we would have to find what we actually look at as being important. 00:51:09.599 --> 00:51:10.719 Now, how do we do that? 00:51:10.960 --> 00:51:13.760 The way we do that, it it's not it's not fun. 00:51:13.920 --> 00:51:14.880 It's it's research. 00:51:15.039 --> 00:51:22.880 We we're going to have to conduct research to find out what is going to be the most important and how effective is that going to be. 00:51:22.960 --> 00:51:27.679 And once we have that research, in our field, we we borrow a lot of knowledge. 00:51:27.760 --> 00:51:33.599 You know, I was saying this to someone recently because they're like, you know, why are you doing you're in school, you're doing all these things. 00:51:33.760 --> 00:51:36.079 Like, what's the point of doing like research air medical? 00:51:36.159 --> 00:51:42.239 I'm like, well, the point is that when you borrow knowledge from other professions, it's their context, it's not yours. 00:51:42.559 --> 00:51:45.440 So our industry, we need to start conducting more research. 00:51:45.599 --> 00:51:47.280 I'm very glad that we have the aeromedical journal. 00:51:47.360 --> 00:51:51.119 I think it's a, like I said, no affiliation, but I I will give a shout out to them. 00:51:51.280 --> 00:51:52.719 They are doing amazing work. 00:51:52.960 --> 00:51:57.920 And I think that that journal needs, in my opinion, we need to start giving it as many papers as possible. 00:51:58.000 --> 00:52:02.400 We need to start really finding good research questions, conducting research on behalf of our industry. 00:52:02.559 --> 00:52:12.400 Because if we can find measurements, if we can find things that matter, we can find essentially bundles of alarms or bundles of parameters that are evidence-based in transport that work together. 00:52:12.639 --> 00:52:20.960 Then in the future, once the devices start talking to each other, we can go and the AI is saying, Hey, Blake, you why don't you reduce your title volume, man? 00:52:21.039 --> 00:52:22.320 You should probably come down on that. 00:52:22.559 --> 00:52:26.800 Um in this case, my guy sounds like the big Lebowski, but that's me. 00:52:27.039 --> 00:52:28.719 I'm struggling I'm struggling over here right now. 00:52:28.800 --> 00:52:32.320 I'm I'm I'm really I'm really struggling over here right now. 00:52:32.559 --> 00:52:46.239 And and so I have to say this that maybe this is just because, you know, I I'm I've been in this industry for a little while now, and and every single one of these scenarios that you're talking about, I can think of 10 other patients that I've had. 00:52:46.400 --> 00:52:48.880 Like the patient, this is all well and good. 00:52:48.960 --> 00:52:50.480 This is great, this is fantastic stuff. 00:52:50.639 --> 00:52:52.400 Have them all communicate together. 00:52:52.719 --> 00:52:56.960 But I think the negative effects of this have the potential to be so great. 00:52:57.199 --> 00:53:01.840 I mean, we all know that none of now never once has our patient followed the textbook. 00:53:02.000 --> 00:53:02.960 Never once, right? 00:53:03.039 --> 00:53:03.199 No. 00:53:03.360 --> 00:53:12.960 And especially when we talk about critical care medicine and we talk about transport and in the hems industry, you add in all these other

factors of the sound, the vibration, the movement, all of these other things. 00:53:13.039 --> 00:53:20.159 And that textbook patient that has been sitting in that ICU bed playing by all the rules, suddenly decides, nope, not happening today. 00:53:20.400 --> 00:53:20.719 Right. 00:53:21.199 --> 00:53:24.719 And and it it just becomes very frustrating in that situation. 00:53:24.800 --> 00:53:25.840 We're constantly changing gears. 00:53:25.920 --> 00:53:30.159 And it's like, okay, well, you know, you get to the receiving facility, and they're like, well, why do you have to change all these things? 00:53:30.320 --> 00:53:32.159 Well, see what happened was. 00:53:33.599 --> 00:53:39.440 You know, and so and so I gotta play devil's advocate here because I'm like, I'm sitting here listening, okay, great. 00:53:39.599 --> 00:53:52.159 If if my vent and my monitor can talk to each other and my vent can suggest that I should make changes based off of my entitled CO2 and my pulse oximetry, well, that's not what needs to be changed right now, is because my patient needs to date it. 00:53:52.320 --> 00:54:05.039 Like it's those little things that that I what I worry about, what I truly worry about in this situation, is that while AI is a great tool, is it going to make us as providers dumber? 00:54:05.360 --> 00:54:13.679 Are we only going to be able to look at this input from the AI and say, oh, well, AI is smarter than I am. 00:54:13.920 --> 00:54:15.519 It suggested I do this now. 00:54:15.679 --> 00:54:17.360 Like that, that to me is not okay. 00:54:17.440 --> 00:54:21.039 And and I'm gonna stand on my soapbox. 00:54:21.119 --> 00:54:23.280 I am absolutely gonna stand on my soapbox here. 00:54:23.519 --> 00:54:32.800 Look, it's got a time and place, but I really struggle with how this is gonna integrate into the clinical side of things because there are so many variables. 00:54:32.880 --> 00:54:39.599 And then again, this is this is what I worry for our newer clinicians, who I think that our newer clinicians are very brilliant. 00:54:39.760 --> 00:54:44.559 And I think I love their passion and drive and desire in the industry. 00:54:45.519 --> 00:54:55.519 But they're also more technologically advanced and I think much more apt to tie into what's going on with them technologically. 00:54:55.760 --> 00:55:06.960 And that gut instinct from 27 years of medicine, in my opinion, for me, is never going to replace what a machine is telling me about that patient. 00:55:11.840 --> 00:55:24.960 And I struggle because we have a lot of clinician, you know, we we as as programs, we look at those clinicians that have been in as from nursing side of things working in an ERICU for three to five years. 00:55:26.239 --> 00:55:26.960 That's great. 00:55:27.119 --> 00:55:27.599 That's good. 00:55:27.760 --> 00:55:37.119 I think that for most people who have the passion and and drive and desire to do this job, that they've probably got that solid gut instinct at that point. 00:55:37.519 --> 00:55:39.280 But that's not everyone. 00:55:39.679 --> 00:55:53.360 And I I I I'm sorry, but I struggle to believe that this is going to make us necessarily smarter as clinicians if you don't have that gut to back up what is in front of you. 00:55:53.679 --> 00:55:56.480 Mike, you have no idea how much I love you right now. 00:55:56.960 --> 00:55:59.599 As we say in the South, preach, brother, preach. 00:55:59.840 --> 00:56:01.920 Listen, no, this is good. 00:56:02.079 --> 00:56:02.639 This is good. 00:56:02.800 --> 00:56:04.480 This is exactly what I'm saying. 00:56:04.719 --> 00:56:08.559 What you're saying right there, don't ever lose that. 00:56:08.800 --> 00:56:09.920 Because you're right. 00:56:10.159 --> 00:56:10.880 100%. 00:56:11.599 --> 00:56:14.239 This is why I'm a brake pedal about some of these things. 00:56:14.320 --> 00:56:16.960 And now you see you see exactly what I'm saying, right? 00:56:17.119 --> 00:56:18.719 Like we have a lot of possibilities. 00:56:18.960 --> 00:56:22.079 Yeah, I totally get it on like the admin or the charting or the other things. 00:56:22.159 --> 00:56:26.480 But when it comes to patient care, yes, I I'm I'm okay with with feedback. 00:56:26.559 --> 00:56:31.039 I'm okay with suggestions, I'm okay with, hey, this is a possibility. 00:56:31.280 --> 00:56:38.079 But if if I don't have that gut instinct to know, this isn't because it's a ventilator issue, this is because my patient needs sedation adjusted. 00:56:38.239 --> 00:56:38.559 That's right. 00:56:38.719 --> 00:56:44.800 Like that, it it that's that's where the gut comes in that I feel as though we're gonna we're gonna struggle. 00:56:44.880 --> 00:56:50.480 And I don't want to see us as clinicians lose our ability to think on our own. 00:56:50.880 --> 00:56:51.599 That's right. 00:56:51.840 --> 00:56:55.119 And that's where, Mike, the education is helpful. 00:56:55.599 --> 00:57:07.440 So that is why if I had to pick between education using these systems and clinical, live clinical decision support, clinical decision support is always gonna lose. 00:57:07.760 --> 00:57:17.440 Because for me, quality, CQI, and education are gonna be the first places that we need to really be looking at these tools for. 00:57:17.599 --> 00:57:17.840 Yeah. 00:57:18.000 --> 00:57:18.400 Right? 00:57:18.639 --> 00:57:25.280 Because that's where we can take our experience, we can take our knowledge, we can build scenarios, we can educate better. 00:57:25.360 --> 00:57:31.599 You know, your your your trainee can sit on the couch because they're tech driven anyways, because this generation, and they can ask questions, right? 00:57:32.239 --> 00:57:35.119 I can't even imagine that day for me personally. 00:57:35.280 --> 00:57:37.119 Like that is not how I learn at all. 00:57:37.360 --> 00:57:37.519 Yeah. 00:57:37.679 --> 00:57:39.440 But being a typewriter. 00:57:40.159 --> 00:57:41.039 Typewriter. 00:57:41.280 --> 00:57:42.719 I want to see this

thing, by the way. 00:57:43.039 --> 00:57:43.599 Maybe offline. 00:57:43.760 --> 00:57:44.960 You just pull it out, man. 00:57:45.119 --> 00:57:45.760 Pull out the typewriter. 00:57:46.000 --> 00:57:49.599 Well, instead of a soapbox that he stands on, it's the typewriter box that he stands on. 00:57:49.760 --> 00:57:49.920 Exactly. 00:57:50.480 --> 00:57:51.440 But that's good though. 00:57:51.679 --> 00:57:59.360 That's that's no, I love that because as someone who is an advocate for these tools, I'm also an advocate for using them correctly. 00:57:59.840 --> 00:58:02.000 And for example, right now I'm on orientation, right? 00:58:02.159 --> 00:58:03.840 I've got an amazing FTO. 00:58:04.079 --> 00:58:06.559 Guy has thousands of flights under his belt. 00:58:06.800 --> 00:58:07.519 You know what? 00:58:07.760 --> 00:58:11.280 And I'll I'll say this on a podcast, a national podcast. 00:58:11.360 --> 00:58:12.000 I'll say this. 00:58:12.159 --> 00:58:16.880 You know, the other day, he was like, hey man, get your head out of the paperwork and look at your patient. 00:58:17.599 --> 00:58:18.480 He's right. 00:58:18.719 --> 00:58:19.840 Absolutely right. 00:58:20.079 --> 00:58:23.199 And I had, I had, I had to get my head out of the paperwork. 00:58:23.280 --> 00:58:29.360 And you know, I work tomorrow night, and that's all I'm thinking about is I'm gonna do a better job at focusing on my patient. 00:58:29.519 --> 00:58:32.719 I'm not gonna be focusing on paperwork, I'm not gonna be focusing on charting. 00:58:32.880 --> 00:58:36.320 You know, the way my brain's wired, you know, neurodivergently wired. 00:58:36.400 --> 00:58:38.639 Like I want to look at the paper because I want all the answers. 00:58:38.719 --> 00:58:45.199 But at the end of the day, you're right, Mike, that's where the human stays in the loop in the in these technologies. 00:58:45.360 --> 00:58:47.920 That's why humans should always stay in the loop. 00:58:48.079 --> 00:58:52.000 Do not let these technologies replace you because it can't. 00:58:52.079 --> 00:58:52.400 Yeah. 00:58:52.639 --> 00:58:57.599 At least not in the foreseeable future, um, it from what I've observed. 00:58:57.679 --> 00:59:08.719 But I think that is if I if anyone could take anything home from this podcast today, it would be if you feel free to let these tools help you, but not replace you. 00:59:09.039 --> 00:59:14.239 Because at the end of the day, you, you know, the patients need that that touch, right? 00:59:14.400 --> 00:59:17.840 Uh, there's a lot of evidence that actually shows that you know the human touch is helpful. 00:59:18.000 --> 00:59:23.679 Yeah, that's why someone was uh someone who's not medically related was telling me it's like, oh man, robots could replace nurses one day, no, they're not. 00:59:23.760 --> 00:59:24.719 No, no, they're not. 00:59:24.800 --> 00:59:31.519 No, no, no, I wasn't, I'm not sitting here saying that like you were saying, oh, we should we should absolutely go down this route. 00:59:31.599 --> 00:59:34.239 I know that you're talking about it as these are possibilities, right? 00:59:34.320 --> 00:59:35.760 Like these are things for the future. 00:59:36.000 --> 00:59:58.079 But I could just feel like my blood starting to boil over here because I I feel like we as clinicians have worked so hard to get to the point where we can independently care for these critically complex patients and make all of these physician-level decisions on how we are managing that particular patient or population or what have you. 00:59:58.239 --> 01:00:01.760 And and that is that is the part I don't want to see us lose. 01:00:01.920 --> 01:00:05.519 We have worked so hard as an industry to get to where we are. 01:00:05.599 --> 01:00:20.800 And I look at, I look at when I when I started in in Hems, when I started in healthcare, period, uh, you know, and again, uh back in that day, we were throwing everybody on 15 liters down our breather because that was that was what the book said we're supposed to be doing, right? 01:00:20.880 --> 01:00:35.280 Like, and and look where we've come in that time period and all the things that we have advanced as an industry, and I don't want to see us lose that because we have a suggestion from AI that that's what we should, the route that we should be going. 01:00:35.679 --> 01:00:37.119 That's where I struggle with it. 01:00:37.360 --> 01:00:39.199 Now, Mike, if I may add this. 01:00:39.360 --> 01:00:44.800 So, you know, I was watching um it was it was like a TV show or something, right? 01:00:44.960 --> 01:00:47.280 You know how we all sit and watch reels and whatever. 01:00:47.519 --> 01:00:50.400 And there was a really, really good point that was made. 01:00:50.480 --> 01:00:53.039 It was like, oh, it was one, it was actually an influencer, Dr. 01:00:53.119 --> 01:00:53.840 Glauck and Black. 01:00:53.920 --> 01:00:54.800 I mean, he's hilarious. 01:00:54.960 --> 01:01:04.880 But he was saying to the senior physician, he's like, Well, back in my day, you know, we we would work 20-hour call days, we didn't have work, you know, work hour limitations for residency. 01:01:05.280 --> 01:01:08.400 And these two, you know, medical students go, oh, let me get this straight. 01:01:08.639 --> 01:01:20.000 So, you know, you're expecting us who have to learn three times as much information as you, you know, medicine is advancing fast because of these technologies, right? 01:01:20.079 --> 01:01:24.159 Like we're doing protein mapping, you know, cancer treatments are getting more customized. 01:01:24.239 --> 01:01:25.119 It's incredible, right? 01:01:25.360 --> 01:01:25.599 Sure. 01:01:25.760 --> 01:01:26.079 Yeah. 01:01:26.400 --> 01:01:29.119 And the knowledge base is getting huge. 01:01:29.519 --> 01:01:32.400 Like how we treat things, or there's so many pathways, right? 01:01:32.480 --> 01:01:37.199 Like I always go down my my methylene blue, you know, presentation pipeline, right? 01:01:39.280 -->

01:01:42.159 Presentations you could probably ever give, just saying. 01:01:43.199 --> 01:01:47.760 I thought it was good, but you know, it's just it is a little bit rabbit hole, right? 01:01:47.840 --> 01:01:50.000 But it I don't give those to you, though. 01:01:50.400 --> 01:01:52.559 It incites the mind to think. 01:01:52.719 --> 01:01:53.760 That's that's what it's there for. 01:01:54.000 --> 01:01:57.519 But but the point I'm that that proves the point, like right here. 01:01:57.760 --> 01:02:00.960 Like, Mike, that like that proves that point, right? 01:02:01.039 --> 01:02:12.320 And and so of why the human factor shall and always must be part of the healthcare and how we treat patients, right? 01:02:12.400 --> 01:02:17.440 I I'm thinking back to what you were saying earlier about let's say using AI as an assist, right? 01:02:17.760 --> 01:02:19.039 Helping radiology. 01:02:19.519 --> 01:02:19.840 Okay. 01:02:20.400 --> 01:02:25.360 So as the radiologist, if you miss something and AI catches it, right? 01:02:26.480 --> 01:02:28.719 Who's responsible for that, right? 01:02:29.119 --> 01:02:38.079 And you know, to to speak to Mike's point, like the the human element factor, it it can't go away, right? 01:02:38.159 --> 01:02:48.880 We train, we've advocated so hard for not only the field, just all of us in healthcare, like whether you're at a basic level, all the way on up to critical care, even physician level, right? 01:02:49.199 --> 01:02:59.119 But the human part of us, like we must always know that that we're all we're gonna make mistakes because we truly are that human, right? 01:02:59.679 --> 01:03:07.119 And and I think in a society where now there's almost this, like I don't know how to describe it. 01:03:07.280 --> 01:03:16.159 Maybe there's like this dependence that technology and tools and all these things will like essentially make us perfect. 01:03:16.320 --> 01:03:24.559 I I think that's the the wrong pathway or or or way to think that this will make us, you know, it to that level. 01:03:24.719 --> 01:03:31.199 But the the human element will always be there, and I think we have to trust that and and never let that go. 01:03:31.360 --> 01:03:34.159 Like, yeah, we're gonna make mistakes, like that, but guess what? 01:03:34.480 --> 01:03:38.159 Artificial learning, and like it's literally in the name artificial. 01:03:38.480 --> 01:03:58.880 So like it's going to make mistakes, and we know that that's that's a possibility, and and I wholeheartedly agree that that human element, because if it was that easy, like if it truly was that easy to take everything that we've learned and all this data and then create something that is so perfect, then why isn't it happening? 01:03:59.119 --> 01:03:59.760 You know, right? 01:03:59.920 --> 01:04:23.760 Which uh it isn't, and I think that really kind of rides this point where a lot of the reasons why I felt this discussion was going to be really beneficial is using it to help us and allow us to

assist us as providers to get us to that next level, but we can't replace that human element to what we do and how we take care of our patients. 01:04:24.480 --> 01:04:27.039 Yeah, so Mike, don't lose your fire, my friend. 01:04:27.280 --> 01:04:27.760 That's great. 01:04:28.000 --> 01:04:31.840 Because but seriously, because you're you're protecting your people and your patients, and that's critical. 01:04:32.000 --> 01:04:32.559 And absolutely. 01:04:32.800 --> 01:04:34.559 And I and there's five points. 01:04:34.960 --> 01:04:45.119 If there's any leaders listening who have any influence on buying these things, there are five things that you have to think about whenever you buy an AI product for your team. 01:04:45.280 --> 01:04:48.400 Number one, is it safe for your teammates? 01:04:48.559 --> 01:04:49.599 Is it safe for you? 01:04:49.760 --> 01:04:51.119 Is it safe for your patients? 01:04:51.199 --> 01:04:51.920 That's number one. 01:04:52.400 --> 01:04:54.719 Number two, is it secure? 01:04:55.199 --> 01:04:57.599 Is it secure for your patients, your people? 01:04:58.239 --> 01:04:59.519 Is it secure for you? 01:05:00.320 --> 01:05:01.280 Data quality. 01:05:01.760 --> 01:05:04.480 Is your data quality good enough to start even using it? 01:05:04.880 --> 01:05:08.000 Is the data going into it even going to help you with good outputs? 01:05:08.320 --> 01:05:09.920 The fourth thing is your culture. 01:05:10.159 --> 01:05:17.679 Is your culture in a place where, to Mike's point, are you leaning too hard on that technology to make decisions for you? 01:05:17.840 --> 01:05:26.639 Or do you have a good group of clinicians who are very, very sharp, who are on the basics, who can take care of patients, who can care for them? 01:05:26.880 --> 01:05:31.280 And this will help offload mundane tasks so they can focus on that. 01:05:31.519 --> 01:05:37.199 And then the fifth thing, are your policies aligned to safely take on these technologies? 01:05:37.679 --> 01:05:39.360 So that those are the five points. 01:05:39.519 --> 01:05:49.280 Like you take those five points with you when you're buying these technologies, or when a salesman comes to you, if you can answer them correct in a way that's like, Yes, this would help me, then okay, go for it. 01:05:49.519 --> 01:05:58.159 But if you can't answer those questions correctly or properly, I I would highly recommend getting yourself in a good state before you take those on. 01:05:59.119 --> 01:05:59.280 Yeah. 01:06:00.079 --> 01:06:02.559 And I and I I I I love that you said that. 01:06:02.639 --> 01:06:06.400 And I'm I'm in agreement with you for the most part on all this stuff. 01:06:06.719 --> 01:06:19.679 Uh but what I keep going, you know, I I think where my fear is is stemming from in all of this is the the power of suggestion can be very compelling at times. 01:06:20.239 --> 01:06:31.840 And there may be that patient situation in front of you where you may not necessarily be 100% sure of what's going on, but you got a strong gut instinct of where you should potentially go. 01:06:33.679 --> 01:06:46.559 I don't want to see us rely on AI in those circumstances to suggest something that may not be in the best interest of our patient because not all of the contextual information is there. 01:06:46.880 --> 01:06:55.760 And that's what I fear for us as clinicians to be able to maintain that gut, to maintain the knowledge base that we have, to continually build upon that. 01:06:55.920 --> 01:06:57.760 And I think that AI is a great tool for that. 01:06:57.840 --> 01:06:58.800 I really do. 01:07:00.960 --> 01:07:22.559 I just I'm very, very cautious about how it's going to be able to have the insight into everything that's going on with our patients as well as we are with the years of experience and the gut that we have and the tools at our disposal. 01:07:22.800 --> 01:07:34.079 I mean, that that doorway assessment that we do is like one of the most important things that that we can ever do for our patients, I think, and gathering all of that information. 01:07:34.159 --> 01:07:48.239 And I can't tell you how many times I have walked into this extremely critical situation, transferring an ECMO or an Impella or what have you, and just stop for 30 seconds or a minute and just look around the room and take it all in. 01:07:49.519 --> 01:07:56.639 And then I'm like, okay, this is how this is what helps my helps me prioritize and organize and gives me my game plan of everything that's gonna happen. 01:07:57.039 --> 01:08:06.880 And and I I I just I don't want to see us relying on AI to take us down a path that maybe isn't where we should be going. 01:08:09.440 --> 01:08:10.800 I agree completely, Mike. 01:08:10.880 --> 01:08:16.000 And this is where now everything you said, we and we had talked about this on other podcasts. 01:08:16.159 --> 01:08:19.600 To me, this is where the future flight leaders program comes into play. 01:08:20.000 --> 01:08:22.399 And this is where good FTOs come into play, right? 01:08:22.479 --> 01:08:23.840 Think about the FTO I was just telling you about. 01:08:23.920 --> 01:08:27.439 My FTO right now, he told me, Christian, get your head out of the paperwork, man. 01:08:27.680 --> 01:08:29.920 Look at your patient, take care of your patient. 01:08:30.159 --> 01:08:32.319 You know, that's the key. 01:08:32.560 --> 01:08:37.840 Having good FTOs, having your your experienced people who are like, they know better. 01:08:38.000 --> 01:08:43.039 They like to if you were my FTO, Mike, I'd love it because you do think about that stuff, right? 01:08:43.279 --> 01:08:44.560 We have to keep that alive. 01:08:44.640 --> 01:09:00.079 And that's gonna come through training, that's gonna come through um having those good CQI discussions, having good FTOs that are trained on how to train people and understand how people learn and those concepts that you can't just quantify. 01:09:00.239 --> 01:09:04.239 Because in my world in data, I

can't quantify everything, right? 01:09:04.399 --> 01:09:08.399 Intuition is not necessarily quantifiable, despite what some textbooks might say. 01:09:08.479 --> 01:09:13.680 They say, Oh, well, you know, intuition is based on data that you have that you just don't necessarily fully understand. 01:09:13.840 --> 01:09:21.439 I agree with that, but I also disagree with it too, because I think sometimes the gut feeling is going to help you say, you know what, I think this patient needs this right now, and I've had those moments. 01:09:21.680 --> 01:09:24.960 And so to your point, I think it's once again, it's a balance. 01:09:25.279 --> 01:09:27.199 And you've seen it in the news too, right? 01:09:27.359 --> 01:09:34.880 Have you guys seen the news in the last few days about some of that stuff that's been coming out with AI and being concerned with the dangers of it? 01:09:35.039 --> 01:09:35.279 Yep. 01:09:35.520 --> 01:09:36.479 Good, good. 01:09:36.880 --> 01:09:38.079 I'm glad they're doing that. 01:09:38.239 --> 01:09:42.640 Because as much as I am as an advocate for it, I'm also a cautious optimist about it as well. 01:09:42.800 --> 01:09:57.439 That's why I'm learning about it and wanting to become a strategist in it, because and I think every leader should, in their own way, become a strategist in AI because uh you're gonna have your crew members coming up saying, Hey, get this tool, get this, get that, buy this, buy that, let's do this. 01:09:57.760 --> 01:10:03.439 And that might be great, but we also have vibe coded garbage that's out there too that people are gonna bring you. 01:10:03.680 --> 01:10:07.600 It's vibe coded, and they're gonna go, Oh, yeah, this is this is excellent. 01:10:07.680 --> 01:10:09.039 And you're like, Yeah, okay. 01:10:09.199 --> 01:10:10.239 No, yeah, sure. 01:10:10.399 --> 01:10:17.279 Um, let's look at the the safety and securities piece of it, and then you tear it apart and you find it's the most unsecure, dangerous thing you've ever seen in your life. 01:10:17.439 --> 01:10:20.560 So no, I mean, I I I yeah, I think that's great, Mike. 01:10:20.640 --> 01:10:21.760 I love your insight on this, my friend. 01:10:21.840 --> 01:10:22.640 I think that's a good idea. 01:10:22.880 --> 01:10:32.239 Sorry to get so passionate there, but please I don't I don't feel like I don't feel like even and and I'll apologize to the listeners too because I don't feel like you've heard that kind of passion come out of me in a while. 01:10:32.560 --> 01:10:39.199 But that is definitely it's just one of those things that I I I I'm that's where I struggle the biggest with all of this. 01:10:39.520 --> 01:10:43.439 But that's the whole point of like that these are conversations that we should be having, right? 01:10:43.520 --> 01:10:45.359 And and I know that we have these at base level. 01:10:45.439 --> 01:10:46.079 I have them all the time. 01:10:46.239 --> 01:10:49.199 And and Christian and Mike, I'm gonna throw this example out there, right? 01:10:49.279 --> 01:10:58.960 And this kind of leads me into kind of our like that next section when you're talking about how this can really apply into leadership, the organizational responsibility of using AI correctly. 01:10:59.119 --> 01:11:00.239 I I will never forget this. 01:11:00.319 --> 01:11:01.760 This happened to be about a year ago. 01:11:01.920 --> 01:11:07.039 I had one of my dear friends uh ask me about a patient encounter that they had. 01:11:07.359 --> 01:11:21.279 The the parameters that they used, they they they ended up going with like this, this higher peep initially, because of uh what they did as part of a I know this is strange, a hands-on physical assessment, right? 01:11:21.439 --> 01:11:36.079 Like, you know, using a stethoscope, touching the patient, doing a full assessment, listening to lung sounds, then getting them onto non-invasive, then uh going through a drug-assisted airway, and then continuing that on. 01:11:36.239 --> 01:11:53.279 And I'll never forget because the person, the the people that are responsible for reviewing those types of calls completely came back with like this whole uh well, why did you go to to that level of PEEP, you know, to start off with? 01:11:53.840 --> 01:12:06.319 And it was very clear in the documentation based on what the provider encountered and what they ended up with and then transitioned to and kind of kept that going. 01:12:06.479 --> 01:12:10.239 And the patient acted great with that therapy. 01:12:10.640 --> 01:12:15.680 And I just remember they were they were asking me, hey, what do you think of this? 01:12:16.239 --> 01:12:22.640 And before I even read the the the QA note, I was like, Yeah, that's pretty good. 01:12:22.720 --> 01:12:28.079 I feel I feel like you transitioned well, you continued what you you started to a position where it was working. 01:12:28.319 --> 01:12:32.079 The patient improved, but yet they still had a lot of work of breathing. 01:12:32.159 --> 01:12:34.720 So you know you did the things and they did better. 01:12:35.039 --> 01:12:38.399 And I was like, I feel like you're what you documented supported that. 01:12:38.479 --> 01:13:10.319 And I'll just never forget the reply from the the person, and then my bias in knowing the person that did this is someone that is experienced, and their response and everything why you can read something and totally tell when it's AI written completely, and it just floored me only in the sense of like you didn't even take into consideration reading what physically had like what was documented, right? 01:13:10.399 --> 01:13:28.399 Because that's the only info that they had, they weren't on the call, and just reading this um reply, there was no human, like empathetical, like it was just all AI, like, well, based on the uh protocol or based on this, and it was just like, who are you? 01:13:28.560 --> 01:13:29.840 And like, why would you do that? 01:13:29.920 --> 01:13:36.000 And I just remember feeling for

my friend and being like, Man, that sucks. 01:13:36.239 --> 01:13:59.359 Like, that's not even a way to have like a true actual like socratic discussion about what happened, and it was just it was totally reliant on an artificial like reply of well, these might be my points, but say this, and it didn't even speak to what was really going on with the patient. 01:13:59.439 --> 01:14:12.319 And I think for me that was like, wow, as someone as part of like training and like leading people and with experience, completely not something that I would have expected for from someone in that position. 01:14:12.479 --> 01:14:13.840 That's why I'm just wondering. 01:14:14.000 --> 01:14:32.479 So using that type of example, and then and Mike, that that also points to I think not only are we uh reliant on us, that human element in taking care of patients, that also transitions when you become a leader, when you start teaching and using education. 01:14:32.640 --> 01:14:35.840 I think it's the the podcast that we just did with Dr. 01:14:36.000 --> 01:14:43.279 Uh Jens The only way to get good and better at medicine is doing medicine and practicing. 01:14:43.359 --> 01:14:45.439 So that experience means something. 01:14:45.600 --> 01:14:45.840 Yeah. 01:14:46.000 --> 01:15:02.000 And I'm just curious how we can use like and not lose that part of like the AI reliance, but the assistance to help us have a true foundational leadership and organizational responsibility to do it the right way as we go forward. 01:15:02.319 --> 01:15:07.520 Your situation, Blake, it violated two of my rules culture and policy. 01:15:08.000 --> 01:15:08.399 Right? 01:15:08.640 --> 01:15:08.960 Yeah. 01:15:09.119 --> 01:15:15.439 Like number one, you should have policy that says for any sort of CQI interactions, AI is not permitted, right? 01:15:16.000 --> 01:15:17.520 Number two, culture. 01:15:17.680 --> 01:15:23.039 If your culture's not in a place to use the tools correctly, then you don't need the tools, right? 01:15:23.199 --> 01:15:24.239 That's that's number two. 01:15:24.399 --> 01:15:33.039 So for me, that's two out of the five rules we violated in that, and for me at least, you know, I have to make sure my team is in a place where it could use the tool properly. 01:15:33.199 --> 01:15:38.399 Like, are is our culture in a place where we can accept using this, or is this going to become a a a lever? 01:15:38.640 --> 01:15:42.079 For example, I once got an email, cracks me up, man. 01:15:42.399 --> 01:15:46.479 It said, hello, comma, in brackets, teammate. 01:15:47.760 --> 01:15:50.880 I was like, you can't even put my name on the email. 01:15:51.119 --> 01:15:52.079 You can't even write it out. 01:15:52.319 --> 01:15:52.960 Come on, man. 01:15:53.119 --> 01:15:54.720 Like, you remember that sports channel? 01:15:54.800 --> 01:15:55.279 Come on, man. 01:15:55.600 --> 01:15:56.079 Come on, man. 01:15:56.319 --> 01:15:57.119 Yeah, come on, man. 01:15:57.359 --> 01:15:59.039 Yeah, from ESP Sports Center. 01:15:59.279 --> 01:15:59.520 Yeah. 01:15:59.680 --> 01:15:59.920 Yeah. 01:16:00.399 --> 01:16:07.520 And then the other bit is uh the patterns they pick up on are like M dashes, those long dashes, yeah, run on sentences. 01:16:07.760 --> 01:16:20.239 You sound like a Nobel laureate and you're from like Texas, when you say y'all, but you're like, oh, you guys, and then it's like words that you know, I was like, uh is this from a uh like a Nobel paper or something? 01:16:20.319 --> 01:16:25.359 Because it's or my favorite is it's not just a job, it's a calling. 01:16:25.520 --> 01:16:29.279 Like when you say it's not just this, it's that, like those statements too. 01:16:29.439 --> 01:16:31.840 So it's really it's funny when you when you see that. 01:16:31.920 --> 01:16:39.600 But it to your point though, I think it hurts culture because when you're using these tools for those reasons, like for example, it once again, human in the loop. 01:16:39.680 --> 01:16:44.000 If I write an email and being on spectrum, sometimes I don't come across right. 01:16:44.159 --> 01:16:48.079 Sometimes I come across a little Sheldon Cooper like and it frustrates people, right? 01:16:48.159 --> 01:16:50.000 Because we have social rules in society. 01:16:50.239 --> 01:16:55.119 I will put it in Chat GPT and go, hey, listen, this is the tone in which I think it is. 01:16:55.279 --> 01:16:58.880 Help me understand a better way that I could write this in a tone that works for me. 01:16:59.039 --> 01:17:03.680 You are a blank, like you are a writing expert. 01:17:03.760 --> 01:17:05.680 Um, I use a persona prompt, right? 01:17:05.840 --> 01:17:07.600 And so prompt engineering can help with those things. 01:17:07.680 --> 01:17:10.640 And then it would give me an output and I go, okay, I like this. 01:17:10.800 --> 01:17:17.520 Then I go over on my other screen and I write the email myself using my words, not copy and pasting. 01:17:17.600 --> 01:17:22.960 I do not copy and paste outputs typically because I want to make sure that it's me doing it. 01:17:23.199 --> 01:17:23.439 Sure. 01:17:23.680 --> 01:17:26.560 This just suggested ways that I could do it better. 01:17:26.640 --> 01:17:34.079 It's like having that person, like, you know, if I call Mike Fidel and go, hey, listen, man, I got, you know, this, this, I took care of this kiddo, he's trached on a on event. 01:17:34.319 --> 01:17:35.199 Can you help me out with this? 01:17:35.279 --> 01:17:37.199 I know you have a lot of experience with it, right? 01:17:37.359 --> 01:17:39.680 I would look at it as a similar interaction. 01:17:39.840 --> 01:17:46.000 Um, but the one thing that you would have that the GPT or the LLM would not have is that experience, the context. 01:17:46.159 --> 01:17:50.079 Once again, context is everything when it comes to these tools. 01:17:50.239 --> 01:17:57.119 And that's where as data gets more prominent, hence all these massive controversial data centers are popping

up everywhere. 01:17:57.359 --> 01:18:00.239 That data that's being fed in there is going to provide it more context. 01:18:00.319 --> 01:18:01.600 So, yes, it will get better. 01:18:01.760 --> 01:18:04.079 But once again, that's why we're talking about the five to 10 year thing. 01:18:04.159 --> 01:18:05.199 That's why I say, I don't know. 01:18:05.359 --> 01:18:08.640 It just depends on what the context of these, of these, of these models are. 01:18:08.800 --> 01:18:11.920 Like, are they gonna be good enough to where they can create some context and nuance? 01:18:12.000 --> 01:18:16.079 Not really for medicine, but I think overall, I think it has a place. 01:18:16.159 --> 01:18:18.800 But once again, your culture has to be right for it. 01:18:18.960 --> 01:18:24.319 Your policies have to be right for it, you know, and it that's just what it's gonna come down to. 01:18:24.479 --> 01:18:32.159 And at the end of the day, you know, we have to have good precepting, good people are gonna hold people accountable to good clinical care. 01:18:32.319 --> 01:18:34.000 Like there, there's no excuse for not. 01:18:34.079 --> 01:18:38.079 Um, you can't be lazy and just rely on these on these technologies completely. 01:18:38.479 --> 01:18:42.079 But but that's where this this is something I and I get it. 01:18:42.159 --> 01:18:47.520 The technology world is gonna expand at an exponential pace that none of us will ever be able to keep up with. 01:18:48.000 --> 01:18:50.159 Totally understand that aspect of it. 01:18:50.399 --> 01:18:54.960 But this is one of those things where it's like we do need to pump the brakes a little bit. 01:18:55.119 --> 01:19:06.159 And just because this is a cool tool that can do lots of fun stuff doesn't mean that this is supposed to be the end all be-all for every industry that's out there. 01:19:06.399 --> 01:19:21.760 And and and that's where I think when something grows too fast and all of we we don't we don't slow down for a second, think about all the things that yes, it could benefit, but also think about all the things that it could potentially hurt. 01:19:22.000 --> 01:19:26.640 Yes, that is where I fear we get into more trouble. 01:19:26.880 --> 01:19:31.760 And and I I I maybe that's where my resistance was coming from. 01:19:31.840 --> 01:19:43.439 Maybe that's where I was struggling with maybe accepting some of this right now, is I see how how exponentially this is growing, and I don't want us to do this the wrong way. 01:19:43.760 --> 01:19:45.520 Well, I don't I don't think you're resistant, Mike. 01:19:45.600 --> 01:19:49.600 I think it's it's not cautious, and and and rightfully so. 01:19:49.680 --> 01:19:59.439 I I think that's just a natural, good, ethical way to to really look really kind of look at it, at least from my perspective. 01:19:59.520 --> 01:20:01.199 I look at it as being cautious. 01:20:01.600 --> 01:20:01.840 Yeah. 01:20:02.159 --> 01:20:03.279 I agree completely, Mike. 01:20:03.439 --> 01:20:08.640 You uh if every leader was like you in that perspective, I think we would be much safer with using these tools. 01:20:08.800 --> 01:20:10.239 Once again, it's a tool, man. 01:20:10.399 --> 01:20:11.039 You know what I mean? 01:20:11.199 --> 01:20:13.920 You don't use a hammer to hit to do everything, right? 01:20:14.239 --> 01:20:15.840 I've been a leader for three seconds. 01:20:16.079 --> 01:20:18.720 Like I it literally counts. 01:20:18.960 --> 01:20:20.880 I've been a clinician for a heck of a lot longer. 01:20:20.960 --> 01:20:26.720 And so that's where I think my my most of my fear is coming from is from the clinician side of this. 01:20:29.119 --> 01:20:29.520 Right. 01:20:29.760 --> 01:20:35.680 But that's where leaders are gonna have to step up and understand the strategies on these things. 01:20:35.760 --> 01:20:44.159 And that's why I do hope some leaders listen to this because I want them to understand, like, hey, you know, on the business side, okay, you know, that's analytics. 01:20:44.319 --> 01:20:46.000 That's where this stuff came from. 01:20:46.239 --> 01:20:48.720 In fact, a little history, all right. 01:20:48.960 --> 01:20:51.119 So I can put some notes up here on my screen. 01:20:51.279 --> 01:20:54.880 So so this actually started in 1950. 01:20:55.039 --> 01:20:56.960 Alan Turing, are you familiar with the the name? 01:20:57.039 --> 01:20:57.920 The movie Enigma. 01:20:58.319 --> 01:20:58.960 You familiar? 01:20:59.119 --> 01:21:02.640 So Alan Turing wrote a paper called The Imitation Game. 01:21:02.800 --> 01:21:06.800 This is uh he broke the the code for Nazi Germany against Nazi Germany and the British. 01:21:07.039 --> 01:21:07.279 Yes. 01:21:07.439 --> 01:21:07.680 Yes. 01:21:07.840 --> 01:21:09.840 So that's where the Turing test came from. 01:21:10.000 --> 01:21:12.720 Like, is this human or is this a machine, right? 01:21:12.880 --> 01:21:17.680 And then in 1956, John McCarthy actually coined the term artificial intelligence. 01:21:17.840 --> 01:21:20.720 So this was at uh in Dartmouth at the Dartmouth conference. 01:21:20.880 --> 01:21:22.960 So that's when the field was founded. 01:21:23.119 --> 01:21:27.439 And in 1960, we actually had our first chatbot, believe it or not, named Eliza. 01:21:27.840 --> 01:21:30.880 Um, and then in in 2022, that's when we had ChatGPT. 01:21:31.039 --> 01:21:35.279 So this technology has been building for generations, actually. 01:21:35.600 --> 01:21:39.439 It it just goes to show that, like, you're right, Mike. 01:21:39.520 --> 01:21:46.479 I think what happened was is in 2021, 2022, we hit this point and now we're exponential, in which that should be alarming, right? 01:21:46.560 --> 01:21:51.199 It's like when you look at Enron's stock prices and in the early 2000s, they're rising very fast. 01:21:51.439 --> 01:21:51.840 Why? 01:21:52.079 --> 01:21:52.399 Right. 01:21:52.560 --> 01:21:54.800 And it turns out there are some issues. 01:21:54.960 --> 01:21:56.800 But I

think the same thing applies here. 01:21:56.960 --> 01:22:00.000 That, you know, yes, this is a expanding and growing technology. 01:22:00.079 --> 01:22:01.439 It's going very, very fast. 01:22:01.600 --> 01:22:03.439 Leaders are gonna have to understand it quickly. 01:22:03.520 --> 01:22:11.199 Like they need to go and read on this stuff and understand what they are, what they do, what they can do, what they can't do with it, what the strategy is, where it should be safe. 01:22:11.439 --> 01:22:19.279 That's why I say, you know, uh it's gonna be uh if you really want to take on these technologies, you're gonna have to do a heck of a lot of work up front, you know? 01:22:19.600 --> 01:22:24.640 And if you can't get the basics down of your program, then these tools are not for you. 01:22:24.800 --> 01:22:28.399 Um, you need to have the basic stuff done first. 01:22:28.800 --> 01:22:37.359 Like your pol, you need to be a high-performing program with a good culture, and then you have to find safe niches where this will apply. 01:22:37.520 --> 01:22:40.880 For example, I like I said, charting is I think is a very good one, right? 01:22:41.039 --> 01:22:42.960 Because we get tired, we get fatigued. 01:22:43.039 --> 01:22:46.079 That can help with fatigue, risk, it can help with a lot of things there. 01:22:46.239 --> 01:22:49.840 Um, it can help with, you know, even overtime and other little things like that. 01:22:50.000 --> 01:23:01.119 But once again, the clinical decision and support, I think that is, I think it is best used probably for the inpatient settings and in the academic settings, um, where you have more time to express these things. 01:23:01.199 --> 01:23:13.039 But we mean the nature of our business, Mike, as you know, as long as you've been doing Sam Blake, we're a very time-sensitive bunch, um, which means that I'm not always gonna have time to pull out my up to date, you know, when I'm in flight, right? 01:23:13.119 --> 01:23:18.159 I'm gonna have those patients that are sick and I need to put down the freaking paperwork and take care of my patient, right? 01:23:18.479 --> 01:23:20.000 So I love it. 01:23:20.159 --> 01:23:20.479 I do. 01:23:20.960 --> 01:23:24.319 I'm just really wondering then, five to ten years from now. 01:23:24.640 --> 01:23:29.279 I mean, I'll just be honest, five to ten years from now is uh retirement, you know, for me. 01:23:29.920 --> 01:23:38.000 And maybe just from one aspect of the job, not stopping work completely, but moving on to something else. 01:23:38.239 --> 01:23:49.199 And I can't even imagine what younger, newer clinicians are having to to learn and grow through and do so much that are coming into this field. 01:23:51.279 --> 01:23:56.560 What does this process really look like for us in that five to ten years from now? 01:23:56.800 --> 01:23:59.760 What how is this how is AI helping us? 01:24:00.239 --> 01:24:09.119 Like what should we expect for future flight clinicians five to ten years from now as you're looking to to get into this field? 01:24:09.439 --> 01:24:15.760 What should we what should they expect as they start looking and evolving and growing? 01:24:15.840 --> 01:24:26.159 Because I look at people that are like brand new today that are gonna be looking at in that five-year mark to start embarking on getting flight jobs, breaking into the hems industry. 01:24:26.319 --> 01:24:33.680 But what does the role of AI and and helping people adapt, what does that look like in the future? 01:24:34.319 --> 01:24:36.239 So, very good question once again. 01:24:36.319 --> 01:24:38.880 And I think there's two pathways that we can take. 01:24:39.119 --> 01:24:49.840 Pathway number one is the pathway I would hope we take, which is we as a as an industry start looking at ourselves and going, okay, we need to define our terms. 01:24:50.079 --> 01:24:54.079 We need to make sure that we're consistent in all aspects of our program. 01:24:54.399 --> 01:24:58.079 We need to make sure that the business use case for it is real. 01:24:58.640 --> 01:25:07.600 We need to make sure that the tools that are available to us, we're using them to their fullest extent before we start looking at things like AI tools. 01:25:07.760 --> 01:25:11.680 And then when AI tools are being used, there's a process for it. 01:25:11.920 --> 01:25:15.520 It's you, your IT team has vetted it to be safe and you begin to use it. 01:25:15.680 --> 01:25:21.760 Now, for what applications, I think it's gonna be where we see it now, which is uh EPCR products. 01:25:21.920 --> 01:25:26.000 I think it's a great and safe place for it for the most part, depending that your culture is good. 01:25:26.079 --> 01:25:27.039 Remember my five points? 01:25:27.199 --> 01:25:28.319 Culture is one of those. 01:25:28.479 --> 01:25:29.920 Your culture has to be good for it. 01:25:30.079 --> 01:25:37.119 Are your clinicians just gonna hit accept and submit and then be like, oh yeah, I just I just let the AI do it, but I didn't review it. 01:25:37.279 --> 01:25:38.560 That's that's part of it. 01:25:38.720 --> 01:25:42.159 Um, we're also gonna see it, I think, in things like quality tools. 01:25:42.239 --> 01:25:44.560 Uh the big I think that's the big stuff is quality. 01:25:44.720 --> 01:25:56.640 Uh so like running AI prompts against your charting system, be like, hey, you know, like dash one a uh metrics or or or your your CAMES metrics, your your CAMES PIF when you're plugging in that data, right? 01:25:56.720 --> 01:26:00.319 Sometimes you have to go, well, what's the definition of dash 1A metrics? 01:26:00.399 --> 01:26:02.960 What is the definition of a first pass success rate? 01:26:03.119 --> 01:26:09.439 And using AI tools to be able to help run those numbers for you based on the definition that you plug in through proper prompt engineering. 01:26:09.600 --> 01:26:12.319 Um I will see that coming into market, I'm sure. 01:26:12.399 --> 01:26:16.640 Uh, we're gonna begin to seeing uh the

testing of different recording devices. 01:26:16.800 --> 01:26:25.520 So, like, you know, in doctors' offices, there's a lot of like plot and all these other tools that will record encounters so that charting and documentation is more accurate. 01:26:25.680 --> 01:26:35.680 We're also gonna see it in education, clinical uh decision tools to help with uh education, being able to run literature reviews, being able to help develop research, all these things. 01:26:35.920 --> 01:26:46.479 Now, 10 years from now, I could foresee there being more of the push into the clinical environment, uh, such as what Mike's concern is, which once again, rightfully so. 01:26:46.640 --> 01:26:49.039 Like if it was to happen right now, I'd be very concerned. 01:26:49.359 --> 01:26:57.439 Um, but I think that as time moves on, um, we're gonna learn more about these technologies and how they work and how they work for our industry. 01:26:57.520 --> 01:27:05.600 And then we will begin to learn to say, okay, let's pump the brakes, let's be smart about this, let's, you know, answer the five questions. 01:27:05.920 --> 01:27:07.520 Does this work for our team? 01:27:07.760 --> 01:27:09.600 Will this actually work in practice? 01:27:09.840 --> 01:27:10.640 Yes or no. 01:27:10.880 --> 01:27:16.640 The other thing is it's not like just buying a medical device where you just go, okay, we'll do a little training toss on the aircraft. 01:27:16.720 --> 01:27:30.800 This is gonna have to be full work, like your workflow integration, which means you're gonna have to understand your workflow on calls to see how this thing could actually work uh within your program without causing patient harm. 01:27:30.960 --> 01:27:32.880 And then also we need to conduct studies too. 01:27:32.960 --> 01:27:48.159 So I foresee a lot of people like me and others, uh, myself and others, who want to conduct more research in this realm so that we can understand what it's doing, where it's going, and what we need to do to be keep our patients safe and keep our programs uh steady and doing the right thing. 01:27:48.399 --> 01:27:50.560 Um so once again, it's not a clear answer. 01:27:50.640 --> 01:27:52.640 It's definitely a it depends answer. 01:27:52.880 --> 01:27:59.600 Now, I will add the other way I could see it going is a large majority of programs taking these tools. 01:28:00.079 --> 01:28:04.239 Out of the box and trying to replace that clinical decision, right? 01:28:04.399 --> 01:28:14.000 That to Mike your point, that that that assessment across the room, hey, I did this because my gut was telling me that I know this is gonna work because I've seen this movie play out before. 01:28:14.159 --> 01:28:16.560 I need to take care of this patient and it worked, right? 01:28:16.720 --> 01:28:21.520 You can't always explain it with a clinical decision tool because it doesn't see what you see, right? 01:28:21.680 --> 01:28:29.359 So I could see some programs making some very bad decisions by saying, hey, we're just gonna buy this stuff because it's cool and well, it's AI, right? 01:28:29.520 --> 01:28:34.079 But they don't have a foundation that's a proper concrete slab with rebar and everything else. 01:28:34.159 --> 01:28:34.960 It's gonna be sand. 01:28:35.119 --> 01:28:36.720 You build a house on sand, what happens? 01:28:36.800 --> 01:28:38.000 It falls apart, right? 01:28:38.079 --> 01:28:42.079 Um, I hope that's not the case, but it is a real possibility, I think. 01:28:42.239 --> 01:28:55.119 So, not to all be an alarmist, but I think we should be realistic and go, okay, well, since we're humans, we know that we can either take the easy button path, which is the sand, or we understand this is gonna be a lot of work. 01:28:55.520 --> 01:29:24.560 But if we we we dial it in right, it can help reduce the noise that's out here to where we can focus on what we should be focusing on, which is exactly Mike what you outlined, which is using our our brains, our intuition, our experience, our caring hearts, the things that we care about most, which is our patience, our passion, our drive, things you can't put into a computer to be able to make good clinical decisions. 01:29:24.720 --> 01:29:34.960 To me, that's that's the good vision here is how can I offload the stuff that's frustrating so that my clinicians can have a good culture where they can do the thing that we we we pay them for, right? 01:29:35.039 --> 01:29:38.159 We we're paid as clinicians to take care of people, right? 01:29:38.399 --> 01:29:39.520 Not let machines do it. 01:29:39.600 --> 01:29:40.960 Otherwise, why would we be here? 01:29:41.119 --> 01:29:42.960 So, you know, that's that's how I see it. 01:29:43.119 --> 01:29:44.159 That's the future, right? 01:29:44.319 --> 01:29:48.880 We have one of two pathways, and I certainly hope we take that good pathway uh moving forward. 01:29:49.199 --> 01:29:50.399 Yeah, I certainly do too. 01:29:50.479 --> 01:29:55.600 And and I hope that you just come back and tell us when it's okay. 01:29:57.359 --> 01:30:01.279 I don't, you know, I don't know if it's ever gonna be a hundred percent okay. 01:30:01.439 --> 01:30:19.680 I think we're we have to build frameworks and other things to go, you know, that's what things I I want to do with my life's work one day is hopefully help build us some frameworks and work with other experts to understand what this stuff is and we can and do some research on where the need's gonna be and where it's safe and reasonable to do so. 01:30:19.840 --> 01:30:23.039 Um, because right now that there's a big gold rush ongoing. 01:30:23.279 --> 01:30:27.600 I work a little bit in the software development side of things, and I can tell you there's a gold rush. 01:30:27.760 --> 01:30:32.000 And people are in the rush to build the next best hottest thing. 01:30:32.159 --> 01:30:39.600 And um, you you have to look at frameworks like NIST and others and and ask the question, is this gonna be safe? 01:30:39.760 -->

01:30:41.439 And is this really gonna be useful? 01:30:41.600 --> 01:30:46.800 Or is a salesman coming after me and are they just making really good work of my of my psyche, right? 01:30:46.960 --> 01:30:49.279 Are they are they doing a good job as a salesman? 01:30:49.439 --> 01:30:49.920 Which is fine. 01:30:50.000 --> 01:30:52.000 I mean, there's great salesmen out there. 01:30:52.079 --> 01:30:55.680 Um, but personally I like the ones who are a little more blunt than those who are not. 01:30:55.840 --> 01:30:57.520 But just tell me what it can do. 01:30:57.680 --> 01:31:00.399 And so that's my big thing is don't be swayed completely by sales. 01:31:00.479 --> 01:31:03.199 Uh as they always jokingly say, trust but verify comrade. 01:31:03.359 --> 01:31:04.479 Make sure you're doing that. 01:31:04.720 --> 01:31:07.039 Um and make sure you know what you're looking for. 01:31:07.119 --> 01:31:11.039 Um if you do that, then you if you really care, you'll make good decisions. 01:31:11.359 --> 01:31:12.720 I I hope that we go that route. 01:31:12.800 --> 01:31:13.279 I really do. 01:31:13.359 --> 01:31:16.399 Uh and I am excited to see what this is going to look like. 01:31:16.560 --> 01:31:27.439 And I I think that there are many avenues within our industry, specifically in hems and public safety aviation, that AI tools can be a huge help instead of a hindrance. 01:31:27.600 --> 01:31:28.960 I really do feel that way. 01:31:29.119 --> 01:31:31.680 Uh I just have some significant concerns right now. 01:31:31.760 --> 01:31:36.640 You know, like if AI tells me to push the nuclear codes and like am I gonna do it? 01:31:36.800 --> 01:31:38.479 Like that that's right. 01:31:39.039 --> 01:31:41.359 I I I that's where I struggle. 01:31:41.439 --> 01:31:49.199 So I I you know, again, forgive me for my soapbox earlier, but I I I also want to look at ways that like we're we're in the now. 01:31:49.279 --> 01:31:51.199 Like this is happening all around us, right? 01:31:51.359 --> 01:31:59.119 And and I have many of my teammates that are actively looking at ways that this can be more integrated into everyday life. 01:31:59.279 --> 01:32:15.840 And so as clinicians, and I'm because I think that there's so many other, you know, ways outside of being a clinician that AI can integrate and and be a useful tool for us, uh, despite this the fact that we're putting all of our personal information out there for all of this AI to learn about us. 01:32:16.159 --> 01:32:19.439 But I digress, I'll go back off my other soapbox. 01:32:19.760 --> 01:32:26.800 But I think that there are ways that we can do it both responsibly and ways that we should probably steer clear of for right now. 01:32:26.880 --> 01:32:30.159 And and make can you give us some framework for what that should look like? 01:32:30.319 --> 01:32:34.560 So, as clinicians, how can we be responsible with this technology at the moment? 01:32:34.800 --> 01:32:40.720 And and also what should we kind of have more of

that pause or that reservation with? 01:32:41.279 --> 01:32:44.479 So I think it starts with the boring stuff, like I said before. 01:32:44.720 --> 01:32:46.399 Start with the basics, right? 01:32:46.479 --> 01:32:54.560 Like, so number one, if you're a clinician who uses Chat GPT or Grok or any of the other tools to write your narratives, please stop. 01:32:54.960 --> 01:32:55.760 Don't do that. 01:32:56.000 --> 01:32:57.760 It's like that old Michael Jordan commercial. 01:32:57.920 --> 01:32:59.119 Please stop, get help. 01:32:59.279 --> 01:33:01.840 Like, don't don't don't do that. 01:33:02.079 --> 01:33:08.640 If you're doing that now, you need to stop immediately because I guarantee you, if you have a robust enough IT department, they know you're doing it. 01:33:08.720 --> 01:33:16.720 Um, because we track IP that I know for a fact most companies have IT that track your IP addresses and where you go and what you do. 01:33:16.880 --> 01:33:18.399 I stop doing that, right? 01:33:18.560 --> 01:33:23.039 Number two, be very careful whenever you're using LLM to ask clinical questions, right? 01:33:23.199 --> 01:33:25.600 There are approved models that are out there. 01:33:25.760 --> 01:33:32.479 Like, like uh, for example, open evidence, if you have an NPI number or if you're a student of someone, like I'm an advanced practice student, so I will have access to open evidence. 01:33:32.640 --> 01:33:35.359 But even then, once again, it's trust but verify. 01:33:35.600 --> 01:33:37.039 Human in the loop. 01:33:37.279 --> 01:33:39.920 You make the right decision, you verify. 01:33:40.159 --> 01:33:45.520 Anytime you look at any sort of AI output, check it before you before you sign your name to it. 01:33:45.680 --> 01:33:50.800 If you're using a charting system that has AI tools in it, once again, trust but verify. 01:33:50.960 --> 01:33:53.119 Like you need to look and make sure the output is good. 01:33:53.199 --> 01:33:55.600 That is the work that you're gonna have to put into it. 01:33:55.680 --> 01:34:00.159 Is and make sure it's correct because I guarantee you, you every now and then you're gonna find something is wrong. 01:34:00.399 --> 01:34:08.079 The other thing is, I would say as a crew member, uh talk to your leadership and say, hey, listen, we have our concerns about AI tools being used. 01:34:08.239 --> 01:34:09.920 Can we have a discussion about this? 01:34:10.399 --> 01:34:17.039 Have that open collaboration because it's going to incite things like, hey, maybe we should have a good ethical use policy in place. 01:34:17.199 --> 01:34:20.079 Maybe we should talk to our IT and ask IT. 01:34:20.239 --> 01:34:20.880 Ask them. 01:34:21.119 --> 01:34:24.159 A lot of times they're nerdy people who really want to help you. 01:34:24.239 --> 01:34:29.119 Um, as much as they'll tell you to restart your computer and turn it back on again and it magically fixes it every time. 01:34:29.439 --> 01:34:31.600 Like, seriously, ask your IT team. 01:34:31.680 --> 01:34:37.439 Like, hey, listen, like, I like these tools, I use them, you know, what are the limitations of them? 01:34:39.920 --> 01:34:42.880 A lot of them are very versed into it because they have to be, right? 01:34:43.039 --> 01:34:49.359 They're just as uh afraid of the dangers of it as I am, as Mike is, and Blake as you are, as all of us are. 01:34:49.520 --> 01:34:52.800 Um if you're not if you're not afraid of it to an extent, you're not doing it right. 01:34:52.960 --> 01:34:53.600 You know what I mean? 01:34:53.840 --> 01:34:57.920 So um that's that's something that as a crew member, those are the starting points. 01:34:58.159 --> 01:35:06.000 Now, the other things you can do are you can advocate for certain tools, bring them to your leadership, right? 01:35:06.319 --> 01:35:11.840 Talk with them about it, but understand that at the end of the day, the IT team also needs to take a look at it. 01:35:12.079 --> 01:35:13.600 You need to make sure that it is safe. 01:35:13.760 --> 01:35:15.680 Remember the five things I was talking about, right? 01:35:15.920 --> 01:35:22.079 Ask the question is our culture good in a place where it could handle this tool and not irresponsibly use it. 01:35:22.239 --> 01:35:25.119 Are our policies in place to where it's consistent? 01:35:25.359 --> 01:35:29.840 The other thing you can do also as a crew member is consistently document. 01:35:30.239 --> 01:35:32.880 Consistently document, follow your documentation standards. 01:35:32.960 --> 01:35:34.479 That's gonna help with quality of data. 01:35:34.560 --> 01:35:37.600 So when your leaders go, you know what, we're meeting those five things. 01:35:37.680 --> 01:35:39.119 Let's go ahead and buy this thing. 01:35:39.279 --> 01:35:41.760 It's gonna have good outputs that actually can help you. 01:35:41.840 --> 01:35:45.600 But once again, you're trusting, but verifying, human in the loop, right? 01:35:45.680 --> 01:35:47.119 We always stay in the loop. 01:35:47.359 --> 01:35:50.720 The other thing you can do as a clinician is just take care of your patient. 01:35:50.880 --> 01:35:55.520 Once again, I learned the other day from a very, very, very smart FTO. 01:35:55.680 --> 01:35:58.880 Hey, sometimes you get your head out of the paperwork and focus on your patient. 01:35:59.039 --> 01:36:19.039 Take good care of your patients, take good care of them because ultimately your documentation will reflect that good care you provided, which means the models will be filled with good, fed with good things and what you're actually doing so that eventually down the line, when that 10 years comes and we're getting more clinical decision support, it's going to be more accurate. 01:36:19.199 --> 01:36:21.039 But once again, human in the loop, guys. 01:36:21.119 --> 01:36:22.960 I said it a thousand times, I'll say it again. 01:36:23.119 --> 01:36:25.279 Don't just trust things that it puts out. 01:36:25.439 --> 01:36:30.800 If, for example, it's really

funny when people look at papers, because I'm in grad school and I'll tell you it's really funny. 01:36:30.960 --> 01:36:40.960 I'll click on a discussion post or something, and I'll click on a DOI link and it doesn't exist, which means that the the the source was fabricated. 01:36:41.119 --> 01:36:45.920 It happens, and that's every university, not just where I'm going, it's everywhere, right? 01:36:46.239 --> 01:36:54.239 So you need you have to trust but verify these things because you're gonna look silly when you copy and paste hello, comma teammate on an email and look silly. 01:36:54.560 --> 01:36:56.880 I'm gonna address you that way every single time. 01:36:57.279 --> 01:36:58.800 Hello, teammate. 01:36:59.199 --> 01:37:00.000 It's so funny. 01:37:00.239 --> 01:37:02.640 You're not just Christian, you're a flight nurse. 01:37:02.800 --> 01:37:05.840 Yeah, it's like it's so funny with an M-dash in the middle. 01:37:06.159 --> 01:37:09.119 But I would say for crew members, that's the thing. 01:37:09.279 --> 01:37:17.039 You have to treat this stuff like you would treat anything, any medications, any therapies or treatments, you have to do it safely. 01:37:17.359 --> 01:37:19.840 This is not something just to be blindly leaned upon. 01:37:19.920 --> 01:37:26.560 As much of an advocate for it as I am, I also understand that like it's a tool that you have to really understand. 01:37:26.720 --> 01:37:34.800 It's a very, very complicated piece of technology that seems simplistic on the front end, but on the back end it's very complicated. 01:37:34.960 --> 01:37:37.600 So but yeah, that's what I'd say for crew members. 01:37:38.239 --> 01:37:45.840 I I think though that this is not gonna be the last conversation that we'll have about this. 01:37:45.920 --> 01:37:51.600 I I think that this this is just going to be part of our everyday future as as clinicians in healthcare. 01:37:51.760 --> 01:37:55.600 And I think not just in healthcare, it's gonna be in all walks of life. 01:37:56.319 --> 01:38:08.880 But what I feel, at least for myself, is I I feel like I'm at that point where I'm looking at where I started to where I'm at now. 01:38:09.439 --> 01:38:13.840 I'm finally that person that I met like 20 years ago. 01:38:13.920 --> 01:38:20.239 When you met that person, I was like, back in my day, you know, I feel like that's the point that I've that I've gotten to. 01:38:20.479 --> 01:38:30.720 And I could only imagine what when I was brand new 20 years ago, what my FTO was and seeing come down the line. 01:38:31.279 --> 01:38:37.600 So I feel like I'm now kind of or am seeing these things come out. 01:38:37.680 --> 01:38:42.000 Like it's like some of this stuff is just like unbelievable, you you know, for me. 01:38:42.239 --> 01:38:51.119 And it and it's not this, you know, resistance is futile, but I think caution caution is mandatory, right? 01:38:51.279 --> 01:39:23.359 Like you we have to uh I I want to embrace it and I want to believe that this can be something that will help, but I also have to remember that if I am going for me as Blake as an individual is going to be inherently a better person, a better clinician, uh, you know, all that, it's up to me to put in that work, do the things, do research, do study, do all these things. 01:39:23.439 --> 01:39:33.680 I can't rely on something fully autonomous to uh flash it in front of my brain and be like, boom, you got it. 01:39:34.239 --> 01:39:45.920 And until the until those futuristic uh movies start really coming more to life, you know, things that we saw, Terminator, Idiocracy, any of the any of these things. 01:39:46.319 --> 01:39:48.560 I still think in some places that could be a documentary. 01:39:48.640 --> 01:40:02.079 I don't know, statistics to me, but but I just my hope is that I as I'm aging and getting older and and moving through this world of healthcare and as a clinician and and just I'm still gaining more experience. 01:40:02.319 --> 01:40:08.720 You know, it just because it's been 20 years, I I I could still have 20 years to go to continue learning and doing more. 01:40:08.880 --> 01:40:23.840 So I I don't want to say that I'm like aging out, but I feel that when I look at the this process of AI, I want to be optimistic and I want to be like in in the right cheerleader corner for it, I guess. 01:40:24.079 --> 01:40:28.640 But I also need to be cognizant of like I want to see you there with your pom-poms. 01:40:28.960 --> 01:40:30.319 Oh, it could happen. 01:40:31.039 --> 01:40:32.640 It would be fully automated though. 01:40:32.720 --> 01:40:34.800 It would it would like tell me to cheer. 01:40:36.000 --> 01:40:46.000 But and and Blake and Mike, if I could offer a bit of sunshine here, imagine a place like let's say you have to do your monthly checks in the supply room, right? 01:40:46.399 --> 01:40:50.800 I will say there will be a time when there will be a system that really helps you with that. 01:40:51.039 --> 01:40:55.199 What usually was three to four hours, could be 20 minutes or 10 minutes. 01:40:55.279 --> 01:40:57.520 And then what can you replace that time with? 01:40:57.840 --> 01:41:14.960 You could sit with your trainee and you can go over scenarios, you can talk about experiences, you can develop that relationship with your trainee, you can work with your partner to plan, pre-plan for a flight, to practice your walk arounds, to talk about safety, to talk about the evidence and literature that's out there. 01:41:15.119 --> 01:41:29.359 For me, that's that's the the bright part of this, is that we can offload certain basic things that normally are very time consuming with the stuff that matters for the critical thinking parts of this, like being able to, you know, talk about, hey, like Mike, I don't fly Neos that often. 01:41:29.520 --> 01:41:32.800 Like, let's talk about like hypoplastic uh syndromes and things like that. 01:41:33.039 --> 01:41:34.000 I want to learn more about. 01:41:34.319 -->

01:41:38.000 Tell me your experiences of that, like those valuable conversations. 01:41:38.479 --> 01:41:41.680 We will have more of those, I think, if it's done correctly. 01:41:41.840 --> 01:41:43.359 And we and we can do that. 01:41:43.920 --> 01:41:44.399 Just wait. 01:41:44.560 --> 01:41:50.159 Next time we talk, we'll be talking about AI creating VR experiences for us. 01:41:51.359 --> 01:41:53.600 And in terms of our training. 01:41:54.880 --> 01:41:58.079 It's not it, I know those things are probably in development. 01:41:58.319 --> 01:42:03.680 Once again, I don't know every technology, but I mean we're in the phase where a lot of things are being developed. 01:42:03.920 --> 01:42:08.159 We're in a development phase and and experimentation's going to have to take place. 01:42:08.399 --> 01:42:09.920 Projects are gonna have to be done. 01:42:10.079 --> 01:42:21.039 But once again, I think the realm of research is going to help us with those things to help us understand, like I said earlier, you know, we should do research to find out where this stuff could be helpful, right? 01:42:21.359 --> 01:42:34.239 And if we know for a fact like where it could be helpful, and we've done, you know, up to a meta-analysis saying, like, hey, we've done looked over 15 papers about this one particular type of AI technology, and we notice that patient care is better when we use it. 01:42:34.479 --> 01:42:35.039 Okay, cool. 01:42:35.279 --> 01:42:38.319 Patient outcomes are better, we know it's better, okay, then we can use it. 01:42:38.479 --> 01:42:43.840 I I do see some bright spots, but once again, I think we have these, we have several camps. 01:42:44.000 --> 01:42:45.119 We have three camps. 01:42:45.279 --> 01:42:50.880 We have I we have the camp where people are way overly optimistic and they're just like, use it all. 01:42:51.039 --> 01:43:01.920 And then we have the middle camp, which is where I think a lot of us lie, which is where those who who are looking into this stuff is like, okay, it's great, it can do a lot of things, but where and how? 01:43:02.159 --> 01:43:03.439 We need to do this safely. 01:43:03.600 --> 01:43:04.800 We don't want to cause harm. 01:43:04.960 --> 01:43:07.680 And then we have another group, you know, that is staunchly against it. 01:43:07.760 --> 01:43:17.600 But once again, I don't knock the staunchly against it because the the the possible unknowns from this that could cause harm are quite real. 01:43:17.840 --> 01:43:20.079 Um and once again, we have two pathways. 01:43:20.239 --> 01:43:22.560 We're at the fork in the road, which which way are we gonna go? 01:43:22.720 --> 01:43:25.760 That's the could that to those people that are staunchly against it. 01:43:25.840 --> 01:43:30.720 Uh, I have no uh I'm not against what they're saying at all because I think that's what they see. 01:43:30.880 --> 01:43:38.960 They see the fork in the road and they really want to go one way, but they're not gonna jump on board with it till they know it's gonna be going to that right direction as compared to the wrong direction. 01:43:39.119 --> 01:43:40.800 So that's that's my assessment. 01:43:41.119 --> 01:43:50.960 I feel like if my boss hears this, they're gonna be like, Man, we just cut down the date checks from four hours to like 20 minutes. 01:43:51.119 --> 01:43:53.840 Man, we could fill that up with more OSHA training. 01:43:54.000 --> 01:43:56.399 We could find more fun stuff for you to do. 01:43:56.960 --> 01:43:57.279 Yeah. 01:43:57.439 --> 01:43:57.840 Oh, good. 01:43:58.079 --> 01:44:01.199 Outside the scope of this talk, but you know, there is that. 01:44:01.279 --> 01:44:02.560 Like that could happen too. 01:44:02.720 --> 01:44:04.720 But once again, we could fill it with good things, right? 01:44:04.880 --> 01:44:05.039 Yeah. 01:44:05.199 --> 01:44:06.319 And that's what I hope that happens. 01:44:06.399 --> 01:44:07.600 I hope we can fill it with good things. 01:44:07.920 --> 01:44:09.439 Well, thank you, Christian, for joining us today. 01:44:09.520 --> 01:44:11.039 I really appreciated the discussion. 01:44:11.199 --> 01:44:21.760 Uh, I certainly still have many more questions than answers, but I love being able to have these conversations and really kind of uh solve the world's problems, this time all about AI. 01:44:21.840 --> 01:44:25.680 So I have no doubt we could talk for hours and hours more about all of this. 01:44:25.760 --> 01:44:35.840 Uh, and I'm so grateful that you were willing to take some time out of your day to discuss with all of us and all of our listeners as well, because uh I I do think that this that we we can't resist. 01:44:36.000 --> 01:44:39.039 We just need to be cautious in how we move forward with it. 01:44:39.119 --> 01:44:45.119 And and I I appreciate you uh and all of your insight that you have been able to provide over the past couple hours for us. 01:44:45.439 --> 01:44:46.880 Yeah, no, it's it's my pleasure. 01:44:46.960 --> 01:44:48.880 And and I think it's good to get that message out there. 01:44:48.960 --> 01:44:53.439 Once again, not everyone's gonna agree with what I'm saying, and that's okay because I think we're all figuring this out. 01:44:53.600 --> 01:44:56.000 I I certainly don't have it all figured out. 01:44:56.239 --> 01:44:56.800 I don't. 01:44:57.039 --> 01:45:07.439 But I think that there is a number of places where we can get started on the back end of things on the basic level to help prepare us to tee us up to get us into a better position with these technologies down the line. 01:45:07.520 --> 01:45:11.920 So no, it was a great conversation, and and I love the I love I love every bit of it. 01:45:12.000 --> 01:45:13.279 So this is wonderful as usual. 01:45:13.439 --> 01:45:14.239 Thank you for having me. 01:45:14.800 --> 01:45:15.520 Always a pleasure. 01:45:15.680 --> 01:45:20.000 I'll just say again, Christian, fourth time uh on the podcast. 01:45:20.079 --> 01:45:21.840 We'll see if we can go for number five. 01:45:22.079 --> 01:45:24.880 We'll see what uh we'll we'll give you a little break. 01:45:24.960 --> 01:45:26.960 I know we're we don't want to overwork you over there. 01:45:28.800 --> 01:45:29.600 No, it's a pleasure. 01:45:29.680 --> 01:45:30.159 You're good. 01:45:30.399 --> 01:45:50.640 I I think a lot of this again, like you just said, that this this all has a great application and it w whatever camp that you're in, I I just appreciate Christian the ability to really kind of take that unbiased look and really allow us to see both sides of the playing field of what can potentially be coming down the road. 01:45:50.800 --> 01:45:52.319 So I'm super grateful. 01:45:52.479 --> 01:46:01.119 Chrisha is always your one of my best friends, one of my favorite people to uh to not only hang around with, um, but to just we could talk for this for hours. 01:46:01.199 --> 01:46:05.279 I know, I know that we could, and I'm just excited to really see what's in the future. 01:46:05.439 --> 01:46:16.399 I'm probably in that middle camp, but I I just uh whether it's AI or not, I'm just really excited to see where we're gonna be in another five to ten years as as clinicians. 01:46:16.640 --> 01:46:20.640 So I'm hoping that it will all obviously be good. 01:46:20.720 --> 01:46:23.760 I I think everything's been good so far, things that I've learned over the years. 01:46:23.840 --> 01:46:26.960 So I'm um I'm pretty stoked to see to see where we end up. 01:46:27.199 --> 01:46:28.000 Oh, for sure. 01:46:28.159 --> 01:46:39.039 I mean the possibilities are potentially endless, and it and once again, there's probably 15, 20 hours more we could probably discuss on the we didn't cover a we covered the surface today, guys. 01:46:39.279 --> 01:46:39.840 We really did. 01:46:39.920 --> 01:46:41.439 There's so much I didn't talk about on here. 01:46:41.600 --> 01:46:43.680 Some folks, well, why didn't they mention this or that? 01:46:43.840 --> 01:46:45.279 Because there's just so much to talk about. 01:46:45.439 --> 01:46:47.199 So, but like I said, great conversation. 01:46:47.279 --> 01:46:48.000 Thank you, Blake. 01:46:48.239 --> 01:46:49.760 Yeah, I have no doubt about that. 01:46:49.840 --> 01:46:52.079 We hope you enjoyed this conversation as much as we did. 01:46:52.239 --> 01:47:00.319 Once you're all caught up on the Valor Flight Crew podcast, make sure you head on over to verticalhelicast.com where you can check out the Hangers E podcast, helicopter podcast, and the vertical MRO podcast. 01:47:00.560 --> 01:47:05.680 Special thank you to all of our listeners out there for your continued support of the Vertical Hellcast platform and the Valor Flight Crew Podcast. 01:47:05.840 --> 01:47:07.119 Be safe and we'll see you next time.