AI 101 for Nurse Leaders: What It Is, What It Isn’t, and How to Actually Use It

Speaker A: Welcome to the Nurse Leader Network podcast with your host, Chris Coyle. Wherever you're going on your nurse Leader journey, we're here to help you get there. Welcome, everyone, to the Nurse Leader Network podcast. This is our first episode in 2026, and I thought that for today, we should really talk about AI. So AI is everywhere, right? Everywhere. We hear it's like the hot topic. If you look at all of the different conferences, everybody's hosting AI, but how do you dig through the muck? Like, how do you. How do you know what to implement? Right? Every five minutes, I feel like somebody's knocking on the door with a new AI technology. And as a nurse leader, like, how do we decide what we want to use? What's going to be the best in terms of helping our patients, in terms of giving outcomes that we're looking for? And so I thought we would kick off this year with an episode on everything AI to help you sort through the fluff. So what is it that we're going to be doing today? We're going to talk about what AI is, first and foremost, what we can use it for as nurse leaders, the different aspects and the different ways we can use it to transform care. We are going to talk about why we should use it, why we shouldn't use it, when we should use it, when we shouldn't use it. We're going to talk about different types of trends with AI, we're going to talk about ethical considerations for AI. And at the end of today's episode, you're going to walk away incredibly informed about what it is, how to use it, when to implement it and where to get started. And so if you feel lost in all of the AI, or you're just intrigued, or maybe you're an expert in AI and you want to hear around a, uh, nurse's perspective, a nurse leader's perspective, how we can really incorporate AI to really make the biggest meaningful difference. You're in the right spot. So that is what we're doing today on today's episode. And so if you are watching this video, I want you to close your eyes and take a listen to what the future of nursing is going to look like. And if you're driving, because you're listening to this on the podcast, obviously do not close your eyes, please. But I do want you to get thoughtful around where nursing is going, right? And so let's take a listen as to where we actually are going to likely be in 2030. And for those of you who are on the video, because this is going to be on YouTube as well. Um, I'm actually going to share my screen so I can show you the video and you can check it out on YouTube if you are online. But let's, let's take a listen to where nurse is going to be at in 2023.

Speaker B: At 7am I begin my shift with an AI powered handoff which summarizes overnight changes highlighting critical patient updates. Assignments are auto prioritized by AI based on patient vitals and acuity, streamlining my workflow from the start. By 8am M rounds are seamless. My augmented reality glasses let me see real time vitals, medication alerts and care reminders overlaid as I move from room to room. No need to pause for documentation. Voice activated charting lets me narrate care details while AI auto populates the electronic health record in real time. My smartwatch buzzes. Patient room 302's fall risk score just increased. The predictive alert based on the patient's biosensors prompts a quick check in helping prevent incidents before they occur. At 2:00pm um, it's time for training. I sharpen my skills through a high stakes emergency simulation rarely encountered on the floor. In the afternoon, a patient needs help out of bed. I am joined by a gentle robot assistant, reducing strain and ensuring safe mobility for my patient and I. As the shift ends, my personalized dashboard displays my wellness score based on my workload and biometric stress indicators. AI suggests breathing exercises and a debriefing routine to support recovery and work life balance. In 2030, technology isn't replacing nurses, it's empowering them to deliver safer, smarter and more compassionate care.

Speaker A: So the future isn't waiting and neither are we. Let's go ahead and dig in. So we're going to start off by just talking about some of the key challenges in terms of the future of nursing. So where is it that we are going to see challenges? So we all know, right? Shortages, right? Staffing shortages and burnout. We know that this has been a thing for as long as nursing has been a thing. We are seeing that there is an aging workforce. We're seeing that there really is an increased patient demand. People are being asked to do more with less. Um, we know that there are really high rates of burnout, a lot of turnover. We know in nursing leadership, for example chief nurse executives, their turnover is like every two, three years, up to five years, but it's really high. And so when we have turnover like that, we are going to be decreasing access, we're going to be increasing costs so that's one of the key challenges. Um, another one is administrative inefficiencies. So excessive documentation, looking at manual scheduling, disjointed systems. Right. Especially as we're bringing on AI, A lot of the AI isn't really talking to each other. And so just having these different types, types of systems, um, different regulatory pressures, these are all going to drive burnout. They're going to drive the different errors. Inequitable access to care. So things like socioeconomic disparities, we know that there's geographic limitations, right? So in la, I might be able to get better care versus if I'm in a rural area in, let's say, Oklahoma, we know that there are systemic barriers and we know that all of those things also lead to inequitable access. Really poor outcomes for patients and then rising patient acuities because the population is aging, because there are complex conditions, right? And they're becoming more and more complex. Nurses are being required to manage more critical cases. Right? So remember back in the day we had folks that had, you know, a total knee or a hip replacement and they were in hospital for two weeks. And now we are trying to get folks out same day or next day. And so we have these acuities that are rising. And so nurses are just really getting burned out. And these are some of the things that are driving the future of nursing in all areas of nursing, whether that be practice, academia and you know, just personal lives of nurses. A couple other trends are obviously AI powered predictive care. And so this is things on, like an emphasis on data driven decision making, right? So nurse A, who Maybe only has two months of experience, and Nurse C, who has 30 years of experience, will be, you know, given tools so that the decisions that they're making are um, equivalent, right? So that they are safe. So that we are doing the same thing based on the same data, looking at different virtual and hybrid, um, hybrid models of care. So different growth and like, like telemedicine, virtual care, care at home, ICU care at home, things like that. And then we know that we want to be providers of choice. And so patients have a choice and we want to really provide hyper personalized patient care, right? So we want to really look at what patient centered care models looks like and really be giving the patient what it is that they're looking for in terms of expectations, not just in terms of quality, but uh, all experience as well. And then we're seeing a ton of different advancements in education and simulation. So we want to make sure that as nurses graduate we are enhancing their clinical readiness, and they're able to do this through these immersive, personalized learning skill sets. So when we talk about the role of AI in nursing, what is AI? So artificial intelligence can be defined as many different things, and we're going to talk about the different various ones, um, in today's episode. But some things are machine learning, natural language processing, predictive analytics, and then real time data processing. So we want to look as nurse leaders and as nurses to AI as a partner, where it enhances but does not replace the nurse's role. Why, why do we want to make sure that we are enhancing, not replacing, obviously, critical care, critical judgment, Right? That, that the human aspect of it, none of that can be replaced by technology. And so what is our goal? What's our goal in nursing? What is our goal as a nurse leader? You're going to want to write this part down. So if you are driving, you come back to this episode. But your goal as a nurse leader is the three E's, something I like to call the three E's. What are the three E's? E as in the letter E? Yes, as elephant. The first one is ease. We want to make things easy to do for our patients and for our nurses. That is our goal as a nurse leader. And so we want to simplify workflows, we want to simplify and enhance experiences, and we want to be able to empower the nurses to focus on their care, not clicks. We want to allow our patients to access care on their terms. So ease, that should be one of your main goals as a nurse leader. Um, the second is emotional connection. So we want to foster deeper human connections between nurses between us and other disciplines, between nurses, between patients and nurses. When we have these human connections, it reduces nurse burnout, it builds lasting patient relationships, it drives retention, and it becomes, it helps you become the provider of choice in terms of really just strengthening your workforce and then strengthening your patient care. And then the third E is efficiency. We really want to streamline systems so that we are getting rid of the fluff, we are providing high quality care, and it is faster, smarter, more affordable and more sustainable. And so if you are not focusing on those three things as a nurse leader, it is time to really look at how each area of your work falls into one of those buckets, because those are the primary buckets that we work with and that we should be driving as nurse leaders. So when we talk about different focus areas for AI, um, there's some big buckets. And so some of the areas we can look at are going to be things like our administrative tasks. So that's one bucket. Another bucket is looking at decision support, personalized care, and then another bucket is really around nursing education and training. So let's talk about the administrative tasks first. When we talk about administrative tasks, two of the goals that we want to look at and that we want to hone in are going to be efficiency and ease. Right? We want to reduce the issues. So the problem with our administrative tasks are we have excessive documentation, we have errors either with scheduling or staffing, things like that. We have incorrect data entry. Right. Patient records might not be accurate, billing might not be accurate, inventory might not be accurate. And so the way we can leverage AI as a solution, or once we've leveraged AI as a solution, we're going to look at reducing the burnout. We're going to look at really optimizing scheduling, having data driven staffing levels, so really looking at like previous, um, data, looking at current data and then really being able to project a schedule that will really meet the needs of patient care. And then we're looking at really providing safer care while we're going to decrease the cost. And so some of the ways that we can do that, some of the enhanced, uh, efficiencies we can do with these streamlined activity tasks. One example is predictive scheduling. And so this is a type of AI where it optimizes staffing efficiencies. And so there's some examples what I'm throwing out now are not organizations that I necessarily recommend, but they are examples that you can look at to kind of see how they work. And uh, you know, I, I really encourage you to look at a variety of different companies or a variety of different products. But I'm just giving a couple examples so that people can kind of go in and look. But one that does the staffing efficiency is going to be shift, um, med. And so you can look at, you know, changing the efficiency around staffing. That's going to help improve work life balance for your nurses. It's going to help reduce cost, it helps optimize shifts, it boosts nursing retention, and it's all done through this data driven driven flexible scheduling. Um, the next one is a workforce optimization. And so with workforce optimization we're going to analyze data to predict the demand, we're going to use it to automate scheduling and to match the right skills to the right tests. Right. We want to make sure that if we have an RN at the bedside that they are doing RN work and that if we need other work to be done that's outside of the scope of rn. Then we have a different discipline in there. We also want to make sure that we're increasing efficiency and reducing the cost. Another administrative task that we could look at are AI scribes. And so AI scribes, a couple are Nabla and Suki. These will reduce EHR workload. And so they're going to turn hours of charting into minutes of charting. Right. And so these are just various AI scribes. You may have seen or used these already. I know in some ambulatory care settings, I've seen them used where it's um, just kind of little recorder. The nurse practitioner or the physician or the nurse or whoever talks into it and it kind of creates their notes. They still do have to go back and edit it. So you really want to look at the technology. Right? We, we, we don't want a tool that's going to add to the workload because then the nurse is working for the technology versus the technology working for the nurse. Um, another type of efficiency that we can be using in, uh, terms of administrative tasks is inventory and billing automation. And so leveraging AI, which is going to automate inventory, it'll automate billing. When we do that, we reduce errors, we decrease, we decrease costs. And then we're going to free nurses to focus on the patients, not the paperwork. Right. So every time they're scanning that alcohol pad or scanning whatever it is, um, we want to make sure that AI is automating it because that is time taken away from the nurse. That does not 100% require a nurse to do. We don't need an RN license to scan in something that needs to be billed. Another one is voice to text and natural language processing. So these are voice assistants. And this is again utilized to reduce documentation time. And so, you know, the nurse will talk and as they're talking, it'll just build the text for them. And then lastly is really looking at various EHR integrations. So electronic healthcare integrations, um, that have AI. So that'll be really useful in terms of data retrieval or organization. Right. And so, for example, if we have a patient that is a frequent flyer that comes in, we might want to be able to pull their records, pull their notes, and if there's not a whole lot of changes in notes, be able to go in and modify them versus having to write a whole brand new note or, you know, something to that effect. Right. If we are monitoring labor and delivery, for example, and everything is normal, we want to have maybe a short code or phrase or something where we can kind of put that in there and make documentation a little bit easier. We also maybe want a search engine, right, where we can go ahead and go in and say like, tell me what this patient's last three blood sugars were or whatever it was. Um, so we can data, we can pull the data versus having to go through and click and click, click, click and go into the chart and look for different tabs and really that kind of thing. You know, those type of search features are really helpful with AI. So we've talked about administrative tasks. So remember those were, that's one focus area. Another focus area is around decision support and personalized care. And so when we talk about decision support and personalized care, we are hitting that emotional connection. We're hitting efficiency and we're hitting ease. We're hitting all three of your goals when we're looking at addressing these. So some of the problems with the decision support and personalized care is that there is a reliance on standardized protocols. Yes, we like standardized protocols. And sometimes patients need to have personalized care, right? It needs to be changed, it needs to be tailored or tweaked. We also know that a lot of research, right, is based on certain demographics, certain ages, certain genders, certain races. And so not everything that is quote unquote evidence based will apply to the patient that might be standing right in front of you. We also know that there's clinician information overload, there's uh, overload on data, there's overload on research, there's overload on patient records. And then we know there's time constraints, right? There's huge variability in nursing care. There might be a difference in nursing care versus not novice and experienced in terms of time it takes to do things right. And so some of the solutions for AI are going to focus on really tailoring care for our, uh, diverse populations, on providing predictive analytics and decision making based on the most current evidence that we have. And so this is nice because you don't always have to go into and grab the newest article and you know, when we have predictive analysts and decision making tools that, that those research articles are automatically kind of entered in through that software so that we know that it's the most up to date evidence. Uh, we don't have to sit there and do a lit review and you know, table to compare and see if this is the newest and greatest with, uh, in terms of research. It also helps improve accuracy and safety so that we have better outcomes. And there's Fewer, uh, human errors. Right. So we're taking out some of the human error element part of it, like being tired or having not eaten or being distracted. So in terms of enhancing patient care and clinical decision support, some of the AI tools that can be leveraged here are things like early warning systems. So for example, you could have a predictive, like analytic warning system that is going to alert nurses to a potential emergency. Right? So this patient, let's say, you know, their acid level changes and whatever, something changes and they're now at a higher risk of sepsis. It'll kind of alert you to, hey, we have a patient that's on their way to sepsis or a cardiac event, right. There's some type of trigger that is going to indicate that is a precursor to a cardiac event. It's going to warn and alert the nurse. So we want to do this prior to symptoms becoming critical. Right. We don't want the stroke to happen, we want the heart attack to happen. These are things that are going to alert us like, hey, we need to do this. And this is applicable to inpatient, outpatient everywhere. Right. So we can have things that are showing us on, um, the outpatient, right, where they're living in the home, they're going to tell us, hey, this patient's at high risk of ketoacidosis or whatever. I'm making it up. The next one is personalized care, uh, planning. And so these are care plans that are based on individual parent patient characteristics. So it would take into account things like genetic makeup, medical history, and then, um, your lifestyle. And by mixing all of those together with evidence based practice, we can provide precision medicine and precision nursing care. Another one is automatic, uh, automated patient monitoring. And so this is another type of thing where there's real time data that'll allow nurses to provide timely interventions without the needs for the patient to be physically present. Right. So they can be in their home. It might be great for a rural, but even living in a, you know, very suburban or an urban area, we still want to make sure that we can monitor and say, hey, you know this and this is happening, maybe it's time to up your dose of, um, blood pressure medication. I'm making it up. There's smarter medication management. So that's another way that we can leverage using AI so we can cross reference patient conditions, drug interactions, allergies, dosages that'll help reduce medication error and it's going to help, uh, prevent adverse drug reactions. And so leveraging AI for medication management, for the clinical decision support system. So this is things that are going to aid with either diagnosis or treatment. And so that's going to analyze patient data like their medical history, their symptoms, different tests. And it'll compare it to evidence based clinical databases. And then from there it'll kind of generate like this is what the recommendation is based on abcd. And then another one is going to be predictive analytics for risk. So, uh, looking at patient risk, right? What is the risk for diabetes, their risk for heart disease. And that'll be based on health data, lifestyle patterns. And that'll allow us to provide early intervention and personalized prevention strategies. And so that kind of would be like the first step, right? What is your risk for it? And then if you are at risk for it, we can leverage the early warning signs or the automated patient manage monitoring, monitoring. And so the third bucket we could look at is going to be nursing education and training. And what are we going to be hitting there? The efficiency and the ease. So let's talk about the problem. Like, standardized learning is not reflective of real world challenges, right? Like we know, uh, a perfect patient might have this in a code, stroke or sepsis or whatever it is, but it's standardized. And there's a lot of challenges when it comes to that. And that's why we have clinical rotations, right? Because we can teach to the T in a book, theoretically, but the experience is going to be different. And so that's part of the problem. Um, another is really limited access to clinical practice and preceptors because of a lot of things. Some of it is workload, right? A lot of people just have high workloads and they don't want to take on a student. Sometimes there, you know, is just a limited access because there's so many schools of nursing now. And so I know with like nurse practitioner students, for example, they struggle getting preceptors. And so, you know, just having that lack of access to that. And then if you do have it, what is the quality, right? Not all preceptors are the same. And so I've had students that have preceptors that really get them involved and they're taking things, you know, by charge and they're running meetings and they're coming up with suggestions. And then I have preceptors that only allow them to watch. And so those are totally different clinical experiences. And then variation in educational quality and the high cost of training. We know that training is astronomical. We know that, you know, there's a variety of different tools that are in play, but those are Some of the problems with nursing education and training and by leveraging AI, we can do things like personalized learning paths, we can provide real time feedback and then adaptive content. Right. So just kind of like the nclex think of it, right? It's adaptive. If you do good, you keep that questions get, you know, supposedly get a little harder and then you cap off at your 75 and then if not it'll kind of adapt and it'll provide you more questions. And so think of it in that way in terms of if you're struggling to understand a concept, the AI can provide more learning material or a different venue in that learning material so that the feedback is personalized, the feedback is real time that allows students to practice in really controlled, safe and scalable environments. Right. So I remember when I was a nursing student, I worked in the ed and like some of the students had really interesting and cool and difficult patients that they got to learn a lot of. And some of us had slower days or slower nights where we didn't really get a lot of those same experiences. And so we should still be able to learn those experiences. And leveraging AI is going to allow that to be controlled, safe, scalable. And then another solution, we really want to look at reducing physical infrastructure, lowering the overall cost of um, education. And so AI can help us with those things. So let's talk about revolutionizing nursing education and training. So virtual reality and augmented training. A lot of schools nursing are already using this and so they're immersive simulations, they have clinical scenarios, they help students develop these practical skills and decision making abilities. And it's in a risk free environment for the most part. Right. We want to make sure that's also psychologically safe for the nurse. The patient, you know, dies or they, you know, they shouldn't die, there should be something in there, but it'll kind of give us some experience. Um, there's also a variety of different adaptive platform platforms. And so these are going to adjust the content based on the learner's progress and the performance. And so this is going to ensure that there's a personalized instruction, there's mastery of the material and adaptive platforms is something that Tiki Das, my company also does. Right. So we have learning information that comes out and then based on what it is that you've taken, we'll do different recommendations on, um, different learning materials that you might want to leverage. There's also AI chat bots. These are cool. They answer student questions 24, 7. Right. So outside of class hours, they're going to provide instant support or clarification on complex topics. And so these are kind of like chat gbt but um, they're, you could even leverage chat GPT. But there's a variety of different chat bots that are available and I know with some in some schools they actually are have it built into their shell. So let's say for example you're using Canva. There is a way to leverage creating a chatbot and that that can be leveraged. With Canva. There's um, smart patient similar simulators. So these are really like high fidelity mannequins. They're going to simulate real life patient responses. They're allowing nurses to practice various procedures and then they get immediate feedback on them. And then there's mobile apps. Mobile apps are really cool because they leverage a lot of the times gamification. And so it lets nurses practice clinical skills like med calculations, wound care, patient assessment and they'll get little badges. So there's a little bit of like a dopamine tick to it, right? Like a little leverage in terms of like wanting to kind of learn more and get you motivated into learning more. And then there's a variety of different wearable technologies for nursing education and training. So smart gloves or biometric trackers, those things can monitor like hand hygiene technique, movement during procedures and they're going to give you objective data to refine your skills and then build self awareness. So that's not all the tech, but that is some of the tech to kind of get us started. But just talking about other tech that is shaping the future of nursing, it would be remiss of me to not talk about telemedicine. Right. So looking at chronic care management, triaging, education, doing virtual assessments by analyzing like patient speech or their facial cues, different vital signs. We talked a little bit about remote modeling but uh, monitoring but really real time analysis of the patient data. Either the patient are using some type of wearable device or there's some type of intervention. There's also continuous monitoring with the smart alerts. There's wearables for patients as well. So I talked about it with nurse training. But there's also wearables for um, patients. And so this is things like smart patches, smart watches, different sensors and these are going to really give us real time data to empower proactive care. There's robotics and so there's a variety of different type of robotics. We've had robotics in surgery for a really long time. But there's also collaborative robots called cobots and these are things that can assist with lifting, they can help with medication delivery, with mobility. And the purpose of those cobots are really to reduce physical strain on the nurses. Right. We want to make sure that they have physical, physically healthy bodies as they're, um, working. And then blockchain. So the blockchain is really secure credentialing, patient record keeping. And these kind of blockchain really is around, like securing data sharing. It decreases the risk of a cyber attack. So we talked about a lot of AI. Wonderful. It's great. And we need to also preserve the human element of things, right? And so preserving the human elephant. It's not just a nice to have, it's critical. You need to have it for safe, compassionate care. And so tech plus compassion we need to think about. AI is going to enhance, but it doesn't replace human empathy. There is nothing that replaces human empathy. We want to use AI to enhance but not replace human judgment. Right. Nurses interpret, we interpret our context. We can navigate complex emotions, we can build trust. Those are all qualities that AI does not have. It will not have it. And so we need to make sure that we are using it to enhance, right. The predict families, that kind of thing, but not completely run the show with human judgment. We need to have ethical considerations in the place, right? So there are bias in algorithms. Right? Every single one of us has a bias and every single person that's writing AI has a bias. So there's a bias in algorithms. So there is a need for human oversight, there's a need for cultural competency, there's a need for protection of privacy, there's a need for fairness in AI systems. The best way to get started is really for us to make sure that nursing leaders, you are at the seat of the table, that you have a seat at the table, and that you're leading these conversations around AI integration in your workplaces and then obviously making sure that AI design includes nurses, you and other nurses. Right? We shouldn't do anything for the nurses, nurses without the nurses. So we need to, uh, involve frontline nurses in developing and testing AI tools. We want to make sure that it's supporting real patient care needs, not just efficiency. Right. Shouldn't just be an efficient thing. So to wrap up today's episode, AI is revolutionizing how we care, how we train, how we work for nurses. We're going to be empowered by tech, not displaced by tech. So what's next for you as a nurse leader? It's time to really embrace innovation through ongoing education and AI fluency. And what does that mean? It means, you know, don't be afraid of it. Right. I have, I work in the nursing education and there's people that are just like, no, we're still not using ChatGPT in our classes. And that's a huge disservice to our students because the expectation is going to be that they know how to use and implement these things ethically. And if we don't give them time to practice utilizing ethically when they do start using it, we're going to be missing a huge component of that. And so really kind of leaning into it instead of being afraid of it, really leaning into, you know, what can be versus not allowing it. Right. There was a time I work with some folks that told me, you know, there was a time when before calculators came that everybody was really afraid and nobody was allowed to use calculators. And now, I mean, it's like they're on our phone, we just use them for everything. And it hasn't done anything in terms of like bringing us back. Right. It's actually helped us get a little further. And that's how we want to look at and think about AI for leaders. You want to make sure you're investing in AI ready, um, infrastructure and make sure that you're supporting upskilling. So don't just make these decisions around, I like this AI, want to test it and then, you know, roll it out and you're not really upskilling your nurses to do that. And then, you know, you're going to end up with a, with a mess. So you really want to think about making sure you're educating yourself on AI. We've gone through a variety of different tools and types of AI that you could leverage, thinking about which one might give you the best bang for the buck. And really starting with like bringing in a group of nurses and other users of the technology patients and having the discussion around, like, how that would actually look to get it implemented. And then for nurses, you know, make sure that you're preparing through digital literacy. Get curious. Join the innovation tables. So the next decade of nursing is not going to be defined by who works the hardest. It's going to be defined by who works the smartest. So which one will you be? That's what we have for today's episode on, um, the Nurse Leader Network. I'm so glad that you joined us. We will see you next week.