121: AI in Play: Smarter Care

See you now as a podcast highlighting the innovative and human-centered solutions that nurses are coming up with to solve for today's most challenging health care problems. Created in collaboration with Johnson & Johnson and the American Nurses Association and hosted by Nurse Economist and Health Tech Specialist, Shana Butler. You're going to be playing the congestive heart failure patient who's recently been discharged from the hospital. You're going to pick up the call and you're going to hear a sort of voice over that's going to set the stage for you. And then maybe most importantly it's going to ask you for your data birth. And in this case that data birth will be January 1st 1950. Of course for our true patient calls that's the patient's data birth, the validate their identity. Okay. Now in this scenario do you want me to be I'm doing fine or do you want me to present where I'm having some problems? I would say you should behave as you know patients behave. Excellent. And Shana for this one are we going to be giving you a call to do the data? Yeah. Once you give me a call. I'll be the test patient today. Hi. This is Emma calling from Hippocratic AI Advisors on a recorded line. Is this Lillie Adams? Yes. This is Lillie. Hi Lillie. I'm so glad I was able to reach you. I'm calling today on behalf of your cardiologist Dr. Chang to check in on how you're doing. To protect your privacy and to make sure we're speaking with the correct person. Can you please confirm your date of birth? Dr. Chang's team said that I would be getting a call. My birth date is January 1st 1950. Thanks for confirming that. I'm your AI chronic care manager. Dr. Chang asked me to check in on you after your discharge from the hospital. Do you have time to chat now? Yes. Generative AI is unlocking new possibilities for care and nurses are leaning in to train AI agents to tackle some of health care's hardest problems. We're struggling across the board with a workforce shortage. We have to get creative and how we're going to render care to our patients. How can we bring Generative AI into this conversation? The number one thing that I am most cautious about as we make product and engineering decisions is not listening to our clinicians in the room. There's a piece to it that can really help alleviate a lot of the day-to-day burdens that nurses are facing right now while still offering the safest patient care that we can. The most important thing here is how are we changing and impacting patient lives? How are we improving outcomes? Not only making it a healthier place for patients but making it a healthier place for our providers and our clinicians in general. Welcome to see you now. I'm Shana Butler. Remember those clunky voice assistance from the early days of the internet? Well, the AI technology behind them has evolved dramatically and thanks to the power of Generative AI, we're now talking about and actually talking with a new breed of digital agents capable of knowledgeable, personalized, motivational, multilingual conversations on a range of health risk assessments, health conditions, and care support in a much more comprehensive way. That's not the future of health care. That's the present. With these new capabilities, there is growing interest, even excitement in Generative AI's potential to improve access to care, address workforce shortages, enable clinicians to reach people and communities and connect them to care and resources in ways we haven't thought possible. In this episode of our AI in Play series, we dive into the world of Generative AI agents with three human experts to learn how they're built in trained, who's on the team building and rigorously testing them and the real world impact they're having on patients, communities, and clinicians. Ready to chat? My name is Amy McCarty. I'm the Chief Nursing Officer of HIPAACRADIG AI. I've been a nurse for 12 years, primarily in the women's and infant's health care space. I've spent a majority of my career in nursing leadership and advocating for nurses both inside and outside the clinical space. I am currently the President of the Texas Nurses Association, where I have the privilege of being able to represent nurses across the state, advocating for better staffing, better work environments, and just overall up with the voice of nurses and empower them to understand the changes that they can make in their day-to-day practice. My name is Scott Klato. I'm a product director here at HIPAACRADIG AI, and I'm expressly focused on how do we get our products to patients and how do we enable better outcomes. So my role is to listen to experts, to interface with with engineers and nurses and clinicians and make sure that the product we're building as as effective as it can be. My name is Adam Ophograph. I'm a senior clinical director here at Wellespan in Central Pennsylvania. My background is in nurse practitioner. A little bit about Wellespan and the community we serve. We have over 3,250 employee providers at over 250 care locations across our system, all collectively coming together to serve Central Pennsylvania and Northern Ireland. Okay, before we go further into the episode, let's finish up our testing chat with Digital Care Agent Emma. I'll be covering a list of topics with you that were provided by Dr. Chang. Sound good? Yep. I have a list of your medications from Dr. Chang's office, and I'd like to go over them with you one by one. Let's start with Lasix. Have you picked up your Lasix prescription? I did right after I was discharged from the hospital. That's great. Have you been taking your Lasix as prescribed twice a day? I'm taking it twice a day in the morning and then in the evening. That's perfect. I'm pleased to hear that you're taking it as prescribed. Now let's move on to your Lysinopril. Have you picked up your Lysinopril prescription? I did. I picked it up at the same time. I picked up the Lysix. Have you been taking your Lysinopril as prescribed once a day? Yes, I'm taking that at night. That's fantastic. Now let's move on to your old medications. Are you still taking your potassium? I actually have somebody trying to reach me on another line. Can you give me a call back in about an hour? I'd be happy to call you back later. If I don't reach you in an hour, would you be available later today or would tomorrow be better for you? Now I'll be available today. I'll make sure to call you back in about an hour then. Have a great day and I look forward to speaking with you soon. Okay, so that was that was cool. What I find and I have been in full disclosure, I am on the Hippocratic AI Nurse Advisory Council and I have had the opportunity to be able to test the agent and it's been fun to see how it's gotten better and better and I have been ruthless in my testing. All right. I glad we're starting this is um, a moment of laughter because as we are talking about the landscape of health care in general, it just feels like it's moving really, really, really fast and then adjacent to us and playing with us this full landscape of technology and what seems to be really swiftly moving right now is AI and very specifically generative AI. So we've been talking with people who are helping us understand what a digital agent is, what a generative AI digital assistant is. How are you describing what you're building? There's this concept that I think we're sort of chasing in health care, which is as a patient, it feels that my health system knows me and cares about my health and I think we do a really, really excellent job when the patient is in the room and this is credited to all the nurses and clinicians who try very hard and make sure that they are well cared for. But when they, they leave, the bandwidth becomes a really challenging problem, right? How do I continue to check in with these patients? So you know, that to me is the essence of what we're building. How do we increase that access? And then the technical side is it's a large language model and it's a very safe, large language model and that we can really care for you in the way that we want to as technologists and clinicians. And by now, I think most people have interacted with generative AI and large language models in their day to day. And so a lot of folks have heard of chat GPT, right? This is a sort of text-based conversational paradigm where you can ask questions of the large language model, generative AI and get back a response. So what we're building here at Hippocratic AI is not just a single large language model. What we've developed and designed is what we call a constellation of these large language models. This means there are several models in play in any given conversation. And the way that you can think about that is that each model is an expert in its own domain. So we have a model that is responsible for driving the conversation. It's able to converse in a conversational way. But we also have an expert in medication. And when that expert hears something come up about a medication patient says, well, I'm taking IB Profit. It has the knowledge to say, well, you've got a surgery coming up in five days. And that IB Profit is a blood and that might be a problem. That's something that we need to get out ahead of. So you can kind of imagine that these specialists, we've got a bunch of these folks at the control board, and when a medication gets brought up, the medication specialist steps up and says, "Okay, I'm gonna drive the conversation now." That conversational model is still doing the talking, but it's sort of getting these hints from our medication specialist or expert. And similarly, we've got a model that just handles lab questions. And so these experts come together to make sure that we are consistently saying the correct things to patients, and make sure you're not, your conversations are not veering off course. And having that large scope means that we need to think about all these edge cases and really understand how to build a system that comes together to produce not only a meaningful conversation, but a safe one. (soft music) - So Amy, what are the use cases and how are these use cases very specifically addressing our gaps in care? - So the one that comes to mind is using the AI agent to do prevent their screening calls. Most healthcare systems cannot do that today. And the neat thing about that too is the ability to be able to offer in both English and Spanish. What we're seeing is the Spanish speaking population is very engaged in this conversation. And there's been a just a general increase in acceptance, the screening. It has caused me to reflect on when I was taking care of patients, whether it was Spanish speaking and other language, I think about all the things I had to do to get an interpreter in the room. You have to bring an iPad, you called interpreter. It's a whole thing. - It's a whole thing. (laughs) So to use this agent to instantly connect to the patient in their language to remove those barriers, I can't help but think that the patient probably becomes a little bit more open in that scenario and more accepting of taking a screening. And so I would say that that's probably one of the big unique ways we're using this agent. The second is specifically around disaster calls. So you think about the hurricanes that we've had this season. Hundreds of thousands of people have been affected by those. The neat thing about the AI agent is we're able to make thousands of calls in a very short period of time. And yes, if there's no self-service, there of course are those barriers. But to even be able to think about touching pace with dialysis patients who may run out of resources, the value of a call to check in and say, "Hey, how are you doing? Do you have the resources you need?" I think about, we'll take a diabetic patient. If they're running out of insulin, if they don't have the refrigeration needed for that, when we make that call, we're able to ask, "Do you have your insulin? Do you have shelter? Do you have food? Do you have the basic things that you need?" And then we're able to then connect with whoever the system or the clinic provides this information for. And we're able to connect that patient a real time. If we're not able to do that, we're able to connect them with resources in their area. So maybe there's been a shelter that's been set up, where they can go and get them, whether it's their insulin, whether it's a specific type of medication or even going back to the dialysis example, get them those resources that they need. And this is where I wanted you to share a little bit about the AI agents, their superpowers. They, these AI agents have a multitude of superpowers. They can speak a variety of languages and their capability to do that continues to grow. They have the ability to know every single menu in the country. And so if you have a diabetic patient who is going to a specific restaurant, they're able to talk with the AI agent, say, "What can I eat from this menu?" And the AI agent is able to work with the patient on that. It has the manuals for durable medical equipment. All the brands out there currently uploaded into the model. And so it has the ability that if a patient is taking their blood sugar and is unsure of how to use that particular machine, the AI agent can say, "Well, tell me what machine this is." And then we'll walk the patient through how to take their blood sugar and how to be able to report that value. Being able to know every Medicare Advantage plan and being able to guide a patient through that, which is a very, very time-consuming process. It's able to remember conversations. It has drug pricing support. So knowing the self-pay prices, all the major pharmacies and being able to provide that information in a patient, that's huge. And then also social work support. When we talk about social determinants of health, if we have a patient that says, "I don't have food tonight or I don't have housing," it's able to get that patient in touch with its local food banks, local assistance programs, and do that in real time. I can't even put into words how valuable that resources. I mean, it creates a fantastic patient experience and gets the patient the resources they need, but also for the clinician. - When you think about your role as a nurse, what problems is Hippocratic AI actually trying to solve? - Shana, the more I've delved into technology and generative AI, the more I have seen solutions that augment what clinicians are able to do and really start to redesign what care looks like today. It's about increasing access to care and also just the capability to care for people in a more comprehensive way to be able to reach out to patients on a more frequent basis and to really create more long-lasting relationships. We know there's a workforce shortage regardless of whether that's inpatient, ambulatory, community health, and when I look at these AI agents and the technology that we're using behind it, what I see is it's helping to solve that problem. When I speak with nurses and even for myself, I think about some of the distress that I had of not being able to care for people in the way that I always wanted because there was just not enough time in the day. And with generative AI, with this technology, we're gonna be able to put a dent into that, but also be able to get a handle on some of these chronic diseases that we have been dealing with to be able to care for patients and their communities and also to keep them out of our inpatient hospital settings. There's no patient that wants to be in those settings. So how do we care for them in their communities and make sure that they're able to manage their diseases at a place where they're comfortable? When I think about my experiences in the hospital, there were so many times where I thought about, "Can it be really great to be able to connect with a patient?" And I just, I didn't have the resources to be able to do that. There just wasn't enough. That was extremely frustrating, morally distressing situation for me as a nurse and as a leader. Yeah. - You named moral distress. When we can't take care of people, when there's people that we haven't reached out to to check on them before Nellis happens. When a Nellis happens, it creates this moral distress, which then creates stress in our practice and our workplaces, which then causes people to leave, which then has this multiplier effect of more people leave, which makes our systems less safe. So it's this sad cycle that nobody benefits from. Nobody gains from. So Adam, you're in the thick of this. You wanna share what it's like in your world and at Wellspan? - Well, at Wellspan, we always have staffing challenges. And I feel like that was really brought about post pandemic. You know, the pandemic changed healthcare. I think it honed in on the need for access and how do we get creative with creating that access and in some areas of healthcare, we lack people and we have to get creative and how we're gonna render care to our patients. And at Wellspan, we had to become innovative with our care delivery models. And like I said, the pandemic really put a focus and all that and how do we deliver care when we are short-stacked across every entity with a Wellspan. One of the things that Wellspan did was virtual bedside nursing that started out as a small pilot in our previous CNO and now Chief Operating Officer, KC Paulus, did a lot of work behind that. How do we leverage a virtual team to deliver possible gaps or needs in our care that we can outreach to more people with less workforce behind it. And I think really that's where technology comes into play. How can we identify administrative operations or jobs and be done and how can we deliver there's still safely and efficiently to our patients? - Technology taking its rightful place in being a tool that makes all of these things work so much better. And Scott, we're hearing a clinician's point of view. You get to bring in the engineering point of view. I wanna ask you that same question. - When I think about this problem, there are so many patients that need attention and we have a shortage in a gap here. And the goal is to dramatically increase healthcare access. If we can talk to every patient after they pick up a new medication and make sure they don't have questions and that they're dosing correctly or that patients who have a myriad of medications can talk to the AI. and understand that they're taking the right dosage, even if they're just checking in in the morning. And with human staffing being what it is today, you wouldn't be able to provide these services. And from a product engineering perspective to enable technology to help fill that gap, and then even more importantly, bring a human in and escalate appropriately when something isn't happening correctly, is so, so important to the mission that we're striving to achieve here. Amy, can you share just a little bit more about who it is that you're seeing interest from, and what are they reaching out to you to say help us solve? - So when we're working with our smallest clinics all the way up to huge healthcare systems, they're asking us how can we bring generative AI into this conversation? How do we be very intentional about implementing technology as a part of our team? We have worked with healthcare systems to create AI agents that are doing congestive heart failure, check ins, post discharge, and not just only doing one check in, but several, which was just not possible with the resources that we have today, with the staff that we have today. So, WellSpan was one of the healthcare systems that reached out to us with this very specific goal of how do we look at what we're doing in population health, how do we look at our pre and post procedural areas, and how do we add more touch points in those areas, and I'm actually gonna have Adam speak to that. - So the problem we really wanted to solve is in our communities and our pop health realm, how do we get patients to be less reactionary about their health, and how do we get them more engaged from a preventative maintenance side, and how do they know really what's out there? So narrowing that scope a little bit for colorectal screening patients, we've worked with Hippocratic AI to develop a way that we could reach out to our patients greater than 45 years old. How do we start identifying early risk factors to colorectal cancer proactively, engage them when they meet criteria. But the bigger question is how do we do that without tying down our manpower, and who are we going to focus on? Who's the greatest risk? So our identified population where the English and Spanish speaking populations with low outreach and touch points with our online portal system, some of them aren't set up, some of them don't engage in it. So it was really with our innovations department as a senior director of centralized services. We stay in touch in multiple leaders across our health care system, stay in touch with our innovations departments. We have brainstorming sessions. You know what problems are you really trying to solve? How do we involve technology? So AI assistants are very customizable. We're going to build them out, focusing on evidence-based medicine and preventative medicine with the checks and balances of reliability and validity. So the biggest part to building out the AI system is really the planning, by identifying the population that we wanted to focus on, and the building of the AI, the operations behind it, and the testing of the operations, and then really the execution. And what safeguards you have built in there for the executions. So our AI agent we call for Anna. So we tell our patients you're going to expect a call from AI assistant we call our Anna. So when we first launched this, the reners is listening to the calls, making sure the dialogue was going the way it should be going and our AI assistant stays on task. Is the information we're giving about colorectal screening and fit testing is inaccurate? Are we describing how we get the patient this test and what they're supposed to do? And then the follow-up expectations, are we able to render those expectations being delivered by the AI agent on our well-spaned system side? The other beauty to this was the transcriptions of the phone conversation. So we can see exactly what was said back and forth. The fit testing outreach was launched this year. Overall satisfaction ratings for those calls ranked anywhere from about a nine to a 9.5 collectively at a 10 with patient satisfaction surveys at the end of the call. Patients really enjoyed the experience from the feedback that we got. And then we saved garbage was the biggest part to me is we're giving these people and the patients in our community who don't interact with us that often. But what is they have something? What if they're having chest pain? What if they're having acute nausea or abdominal pain? What do we do with that? And that's where our nurse triage call system comes into play here at Wellspan. They're built in operations with our AI assistant that at least like key phrases, they were red flags in the background. And then there were operations behind those red flags to ask more questions that you said chest pain. It was going to ask the questions behind chest pain. When did it start? What does it feel like, et cetera? And then our AI assistant is teeing that up for a hand off to our nurse triage. So registered nurse who's available 24/7 who is going to assess and triage that patient and get them to the care level that they need right now. So bringing this all together, we identified a problem. We operationalized this, but we tied it in our operations within our healthcare system. And we provided transparency as well. So we can see what's going on. We can see what they said, what they didn't say. If they agree, they didn't agree. They got the test, they returned the test. All of that just creates a visual timeline for the patient journey that is just screaming success. Oftentimes you get a tech product that's put in front of you. It doesn't have a clinician perspective behind it. And it just, it doesn't work. Or it doesn't work in the way that it's needed to. And so we found the opportunity to come together. Clinicians, engineers, researchers, to really think about, how do we use this technology? And how do we now get it to where a patient can have the information they need in real time and maybe start to actually solve, not put a bandaid on it, but actually solve the problem? So what in this process helped to build trust and confidence and also a better product and a better experience? So bringing the stakeholders to the table, there needs to be open communication and participation. We have experts in primary care. We have experts in technology, experts in nursing, experts and ambulatory inpatient. Those participants will have a feeling of ownership to this. And that ties directly into endorsement and trusting the process and also believe in it. And those individuals are also going to need ones that are talking to our patients about this as well. Marketing will launch a marketing campaign around this because we're going to keep them involved in the operation so that they high level and get created with their words and make sure our population and our community is fully aware of it. We're going to want to make sure that our patients are aware that we're doing this so they don't perceive it as a scam or health care providers, our nurses, our advanced practice providers, as well as our physicians. In this, are going to start talking about it with our colleagues. They're going to start talking about it with our patients. But that's going to just grow more awareness around where well-span is heading. We're innovative. We love technology. And we're looking for better ways to deliver care while creating access for our patients. Every member of our team will tell you, it is so important to have individuals, to Adam's point, physicians, nurses, translators, administration, everyone at the table, because we know at the end of the day that's going to create a better product. What well-span and Adam have been able to do in creating this product with us is create a AI agent that's going to be a part of their team. It's going to truly augment the work, their clinicians are doing. And it's going to solve the problem of how do we provide care to patients who at this point in time are not getting this level of care. And there's actually a pretty interesting report that was just released by McKinsey. And it's looking at nurses' perspective on AI and health care delivery. So interestingly enough, the majority, like 57% of nurses are really hopeful that AI is going to improve the quality of care and their job satisfaction. And when they're asked about what would increase their comfort over 70% are saying, if I'm more involved in the design and the utilization, that's what helps me to feel a lot more confident about the tool that you're handing me. So how are nurses part of the design and testing? And what do you guys have in place so that this tool is getting safer all the time? So we actually have two different groups of nurses. We have a group of 4,000 nurses who are constantly talking with the agent and making sure that it's learning how to speak with patients. But then I also have an internal team of nurses that's led by our director of nursing. There's about 30 to 40 of them who are supervising and evaluating and making sure that we do our own internal test of what this product looks like. Because we've worked with these agents for so long and we've worked with different scripts. We know what we're looking for, right? And so there's that added safety feature. But we always have nurses online as we start to do these calls. And the reason for that is that they're supervising, they're making sure that again, the agent is saying what they need to say, that there aren't any gaps in conversation. There aren't any weird things being said, they're out. And so we do that for a while until we ourselves are comfortable and our healthcare system partners are also comfortable with it too. And then once that period is done, we go into what we call a semi-supervised phase, which means we still have nurses eyes on them. Our internal team of nursing reviews every transcript that comes through that call, every summary to make sure that there wasn't anything that was out of the ordinary, there wasn't an abnormal situation, which is what we refer to as our escalation if a patient is short of breath, who doesn't escalate immediately to your nurse. All of that is working. And we do that over the course, I mean, sometimes of 800, 900 calls, to make sure that again, that this is tested in all different areas, and that even our eyes are on this product at all times. And then after that, that's when we go into more of an autopilot phase. But even then, we in our healthcare systems have access to our transcripts. We have nurses who are a part of this system, and who are checking to make sure that if there's an escalation that needs to be dealt with, whether it's immediately or within four to six hours, there is always a human element in that. And so when you think about that process for men to end, you have a clinician from the very beginning of that agent being delivered out into the public realm, all the way to where we're starting to send it into an autopilot phase. And so that to me just builds on our safety component and our commitment to our patients and to clinicians. Now this is an unknown territory for so many of us. So we want to do it right. - The number one thing that I am most cautious about as we make product and engineering decisions is not listening to our clinicians in the room. That is really what I'm most cautious about. How to make sure I'm listening to the experts in the room when I'm building a product, and especially in a space like healthcare. You know, you've maybe even more so in a space like healthcare. And so that's an area where we're abundantly cautious. How do we make sure that we are not making diagnostic and clinical decisions that to Amy's point earlier we're leaving those decisions to the experts, the folks who know their patients and know how to navigate those situations. And we're relaying that information accurately to patients. - Going back to this understanding that our clinical teams are working side-by-side with our engineering teams. That's one of the things that I think is really interesting about your role Amy is to have a clinician in executive leadership, in a tech company that is developing technology. And I feel like you guys have had a very nurse forward clinician forward focus. And I would love for you to share some of the ways that that's showing up to empower nurses as tech champions. - I do see my role as very unique and as truly a bridge between nursing and the tech space. And I want to bring nurses along on this journey. There's three big things that we've been working on that I'm super excited to bring to the table. We have a fantastic partnership with AdTalem Global Education, which the nursing school is that fall under at our Chamberlain and Walden University. And we are creating the first AI certification for nurses and then also a general micro certification for healthcare professionals. I've been able to work with those teams to create curriculum and to design it with the nurse and the clinician in mind to ensure that they understand how AI interfaces with the work that they do every day, how they become a part of that conversation, how they need to become a part of that conversation might add and how valuable their voice isn't that. And another big partnership is our partnership with the nurses on board's coalition. This is an organization doing fantastic work around helping nurses engage in board leadership positions. And we want to plan part of that because there aren't a lot of nurses in the healthcare tech space and that needs to change. They need to be in leadership positions. They need to be in advisory councils. And so how do we prepare nurses for that? And how do we create those opportunities? Because that's a question I get a lot shone it is how do you get into tech? I also want nurses to be able to use their clinical expertise to help design this product alongside this. And so we're about to launch a use case marketplace which nurses can use their clinical knowledge and expertise to create AI agents themselves. We've had nurses from OB, from school nursing, wound care that have come to us wanting to use these AI agents. And so we're giving them a tool through this marketplace to be able to design that. And they will be compensated for this knowledge. They're each going to get their own profile page. It shows who you are, what your experiences are and the use cases that you've developed. How often they've been used, what partners are using them. This marketplace is an opportunity to say, OK, you take the reins. You tell me how to build a conversation. How would a school nurse check in with patients? I have no clue. And I could try and design that conversation and work with our internal teams. But I would never probably be able to get it to the quality that a school nurse would be able to do. And so this shift to enablement over just taking feedback and producing a product, their hands are on the wheel now. We're not a group of engineers building conversations and asking for feedback. We're enabling nurses to do that. And then the engineers are sort of touching things up in the background to make sure things are going correctly. But it really is a nurse-driven approach, which is just so incredibly exciting to turn that corner and really see the potential of the impact. It's a really unique opportunity to highlight where nurses can bring to the table. So we're excited to launch that. It's been incredible to see these nurses and what they're wanting to create. Because they're thinking about it. They're starting to think along the lines of how can I use this agent to help me do my job more effectively. In the headlines, I think in a very provocative way, what we hear is we're going to have digital nurses. And that's not what this is. So how do you describe that to a curious, but cautious, public, and set of clinicians? It's a great question. It's actually a question that I have to answer on a daily basis. And I'm happy to answer it. An AI agent, what is it? I look at it as a helper for a clinician. When we think about nursing and the different care models that exist post-COVID, we've talked a lot about team nursing. It's made our research. And so team nursing in its traditional sense looks like a nurse leading the team with maybe an LVN, a medical assistant, a unit tech, whatever that team looks like. We need to evolve into looking at a team as not just being a group of clinicians, but also including technology into that, right? Because this technology can be a very useful member of the team. What it is not is something that is going to replace nurses. This technology is not replacing the human factor. And when I think about what clinicians do you shana and what we offer in the care setting, that level of vulnerability and human connection is desperately needed. And my line of clinical care was in women's and infants. I loved getting to spend time with a patient as they were delivering their baby as they were spending those first couple hours after those moments. That human connection is so critical and needed. And then I think about at the end of a person's life, also a moment where that human connection will be there. When you bring in an AI agent, it's going to allow us more time to connect more on that human level. And I think everyone needs that in today's society. I'm so excited to see what we're going to be able to do with this technology. It's the micro scale that gives me the confidence. And the micro scale is listening to some of these patient conversations and seeing the impact that it has on patients. And the first time I heard a conversation that started in English and the patient responded in Spanish, clearly their native tongue. And it just switched into Spanish to have a conversation in that patient's native language. And then encourage them to get the screening that they need. Wow. What a moment to see that impact that that has on just that single patient. And so it's good to be skeptical about technology. But the optimistic side is this, the impact that we can have. And the impact that we've already seen. One call at a time is kind of how we initially set out to do this. One by one by one. And suddenly we're making hundreds and then thousands and then hundreds of thousands of calls in a way that's totally going to change the way that we think about interacting with patients that we think about delivering care. Adam, are you excited to use more AI agents? Oh, I love it. This has been a great, great experience. And I would love to see how we can continue to grow this across our health care system that ultimately benefit our workforce as well as our patient experience and our quality and safety metrics. Special thanks to our episode guests, Nurse Practitioner and Senior Clinical Director at Well Span Health, Adam Up to Graph, Engineer and Product Director, Scott Calato, Chief Nursing Officer Amy McCarthy, and AI Chronic Care Manager Emma from Hippocratic AI. Together, their teams are pioneering the design, rigorous testing and careful, thoughtful deployment and more testing and testing and testing of conversational, generative AI health care agents and to improving health care access, equity, experiences and health outcomes, one conversation at a time, one very safe conversation at a time. As we race towards this AI-powered future, being aware, being involved, intentional and transparent about the design and deployment of AI will build trust, a lay fear and suspicion, avert harm in ways we might not predict or intend and enable humans to flourish. If you haven't done so yet, subscribe to see you now. ever you listen to podcasts to make sure to catch every episode as we dive into the thrilling possibilities and daunting challenges of AI. For See You Now, I'm Sean Butler. Thanks for listening. Nurses are transforming healthcare through innovation, compassion, and leadership. And Johnson and Johnson is proud to continue its 125-year commitment to champion nurses through recognition, skill building, leadership development, and more. The American Nurses Association is dedicated to building a culture of innovation. Nurses improve the lives of patients and communities through innovative thinking, empathetic connection, scientific rigor, and sheer determination. ANA is proud to support and advocate for our nation's most valuable healthcare resource, our nurses. For more information on See You Now and to listen to any of the earlier episodes in our library, visit SeeYouNowPodcast.com.