CEimpact Podcast

How AI is Reshaping Patient Care

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Artificial intelligence (AI) is rapidly transforming the healthcare landscape—and pharmacy is no exception. This course explores emerging and potential uses of AI in pharmacy practice, including workflow automation, drug information access, and tools that support patient care and clinical decision-making. You will gain insight into practical considerations, limitations, and ethical responsibilities pharmacists must navigate as AI becomes more integrated into practice.

HOST
Rachel Maynard, PharmD
GameChangers Podcast Host and Clinical Editor, CEimpact
Lead Editor, Pyrls

GUEST
Timothy Aungst, PharmD
Professor of Pharmacy Practice
Massachusetts College of Pharmacy and Health Sciences

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PRACTICE RESOURCE
Receive the exclusive Practice Resource to use as a reference guide for this episode by purchasing the GameChangers Clinical Update Series.

 
CPE REDEMPTION
This course is accredited for continuing pharmacy education! Click the link below that applies to you to take the exam and evaluation to claim credit:


 CPE INFORMATION
Learning Objectives
Upon successful completion of this knowledge-based activity, participants should be able to:
1. Identify current and potential uses of AI that support pharmacist-led patient care services.
2. Describe considerations, limitations, and ethical concerns related to implementing AI tools in pharmacy practice.

Rachel Maynard and Timothy Aungst have no relevant financial relationships with ineligible companies to disclose.

0.05 CEU/0.5 Hr
UAN: 0107-0000-26-060-H04-P
Initial release date: 3/23/2026
Expiration date: 3/23/2027
Additional CPE details can be found here.

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Welcome & CE Credit Reminder

SPEAKER_02

Here on Game Changers, we're all about helping you stay ahead of pharmacy practice. But why stop at listening? You can earn CE credit for this episode and hundreds more by visiting CEIimpact.com and logging into your account or creating a new one. Get credit, get inspired, and make your learning count. Hey CE Impact subscribers, and welcome to the Game Changers Clinical Update podcast. I'm your host, Rachel Maynard, and our topic today is something that seems to be on the top of everyone's minds lately. So we're going to be talking about the role of artificial intelligence or AI and how it's impacting or will impact pharmacy practice. I think we've all had experience with how AI is affecting our personal and professional lives. And we've been hearing a lot of real-world examples of how AI is already being integrated into pharmacy practice and starting to transform it in some ways. We've also been hearing a lot of predictions about how AI could impact practice in the future. So for that reason, we thought it would be a great time to take a pulse on how AI is being explored, developed, and piloted across healthcare settings, especially its evolving role in pharmacy and patient care. And so to do that, I am very thrilled to have a pharmacist and thought leader in this area to share his expertise, Dr. Timothy Ungst. So welcome, Tim.

Guest Intro: Dr. Timothy Ung

SPEAKER_00

Thank you, Rachel. Really appreciated being here. You know, when you guys reached out asking to talk about this topic, I was really excited because, you know, this is an area that I've been focused on for quite some time. My background has always been in digital health. And to make digital health function has always been data and ways of collecting it and analyzing it. AI has always been a component of this. And, you know, seeing it evolve over the past decade, especially with the rise of generative AI and now everyone touching it has really, I think, increased awareness of like what is the capacity and how we can change our approach to a lot of different things. So, you know, for everyone listening to my background, I'm a professor of pharmacy practice. I'm also a clinical pharmacist. And then I work in digital health and primarily have been thinking about, you know, how do we use apps? How do we use AI? How do we use wearable devices to change how healthcare is done, especially with a pharmacy lens. So really happy to be here, Rachel, to add any insight on this conversation.

AI 101: Patterns To Neural Networks

SPEAKER_02

Fantastic. Yes, we're super excited to have you. And thank you for carving time out of your busy schedule to chat with us about this. And you know, it's interesting that you mentioned sort of the healthcare professional side of it, but also the patient side of it too. Patients are using AI and consumers, you know, so I alluded to that sort of personal and professional lives. We're we're seeing it from both angles. But just to be thinking about AI in general and assuming that we have a sort of a broad spectrum of people who might be listening who may know very little or have not used AI in the past, all the way to maybe expert users. Um, I think it's good for us to sort of level set and get on the same page around what AI is and and how it works generally. So if you assume that I know nothing about AI, how would you explain it to me as a newbie?

Generative And Agentic AI Demystified

SPEAKER_00

Oh, no worries. Okay, I can get in that background. So, you know, for me, artificial intelligence, when you come down to it, it comes on down to assemblance of that premise is intelligence. What is intelligence? You know, we as humans pride ourselves on knowing and speaking and just going about our lives and acting a certain way. But you know, how do you know fire is hot? How do you know the words coming out of my mouth? At some point, you learn these things as a child, and you know, how do we recognize the world around us? A lot of this pattern recognition is what would come down to it. I say, you know, this is hot, and at some point in life you were you experienced heat and you knew that you know fire denotes heat and hot. But you know, what is the word five? You know, how do you write five? How do you write it in English? How do you write it in a different language, like maybe Arabic or in Korean and such? You have to go through some training for that. And I think this really came about in this thought process, not too like not very recently. It's been around for quite some time, especially back to World War II. We were trying to make machines basically, you know, apply this kind of thought process we had. A lot of our thought processes, if this then that. You know, if I am hungry, I should eat food and then I will feel full. If a patient's A1C is elevated, they should go on a drug. Which drug? You know, are they obese? Do they have renal problems? Do they have cardiovascular issues? That would then denote maybe a first-line therapy, maybe SGLT2, GLP, formin, you know, you can go around that. So a lot of what we do is around that semblance. It gets a little bit more nitty-gritty. And what happened was I would probably argue in the past 20 years, we knew if this and that one logic was really good, but then how to apply it? How do we actually make things think? Yeah, we can do pattern recognition, we can look at data and say, if this happens, we should do that. And it's from that that we got our initial like machine learning kind of language developed, where we would feed data into it and we would get some basic algorithms that could spot, you know, commonalities. You know, if the blood pressure is high, it increases the risk of developing a cardiovascular event, for instance. If a medication is missing from a floor, maybe that denotes that medication is missing for some reason. Is something bad, for instance? Is it controlled substance? Does this alert us that we should look into this for possible drug diversion? And this comes down to that data. And what happened for us in healthcare in particular is our data really went digital. Uh, the rise of electronic health records led to a large amount of information, the rise of going from basically paper prescriptions to the use of e-prescriptions or pharmacy management systems. So we can see in our workforce a huge explosion around just digital data, and that data could be fed into better processing power, better computers, better hardware that could then take this data and actually assess it. And so we started moving from just simple algorithms and having machines trying to learn from this data and process it towards things like deep learning, looking for more probability, looking for more things that connected to it. And we're mimicking this off of what how humans think, how our neurons work. And that's why you hear things like you know, neural networks. What are we trying to mimic? Again, it's artificial intelligence and how we perceive intelligence developed is our neurons making connections from recognizing patterns from what's around us and such. And from that, we got even better. Then we started having really good changes. I would say, probably about 2018-17, there was a huge revol revolution in terms of how we handle this data. For those that are in the audience, this is the transformer data that we saw from companies that were developed, and that's what led to the explosion now of things like large language models, the use of generative AI tools. So you can send a prompt and be like, make me a picture of a dog riding, I don't know, a surfboard in the waves. And the tools can recognize this. AI can be like it knows what a dog is. You can be specific on what type of breed of dog you want, but it at least knows a dog what it should generally look like. Um, it knows what waves are, it knows what the ocean and has the context to put something like that together. And it's because of how we basically had this better ability to take data, analyze it, utilize it, and also better processing speed to actually do this fast. We had simple things like this. We one of the first AI tools I know of for pharmacy would have been like I think a mimics back in the 1980s. It was the idea of having an AI tool that can actually create an antibiotic recommendation for a patient. The issue with that though is it would give a recommendation like 30 minutes, which do you could you imagine if you went on a generative AI like you know, Claude, Gemini, Chat GPT, and be like, do this, and then give you a response 30 minutes from now. The value proposition is not there, and that's really a big thing. Is so it's you know 30-year difference, 30 minutes to three seconds, if that really changes that. So to me, that's like the outline of how of AI, and now it's more applicable because it works better, it's faster, it's more convenient, it's not cost prohibitive as it once was, and now we're all thinking about it versus just a very small subset of people compared to the past.

SPEAKER_02

Right. So great summary there. And you threw out a few different terms that I'm glad you mentioned because I think some of that lingo can be confusing too. You mentioned neural networks and deep learning and generative AI. And again, these terms I think can be hard to differentiate, know, know exactly what they mean. Any other clarification for us to get on the same page in terms of some of those terms like generative versus the other term that I've seen is agentic AI, and so like how how how those things are different. Um, and also if you could speak a little bit more about how these models are trained, because as you said, the idea of intelligence, just like humans learn things, how do these models learn things that then allow them to create an answer? So I know there's kind of two questions in there, but maybe you can uh speak to both of them.

SPEAKER_00

No, I think that's good. I think you know, asking how these things are trained is a really important part. There's a number of ways to train AI tools. I don't want to go into all of them because that'd be like a whole lecture in itself, I feel like.

SPEAKER_02

So I'll stick with the two-minute version.

SPEAKER_00

I'll stick with the one that's really popular, supervised and unsupervised learning. Then there's mixed learning, there's reward systems and stuff like that. Like it's it's so interesting. Some of these will matter for what you just asked about different levels of AI. So we'll get into that.

SPEAKER_01

Okay.

Training Models: Supervised Vs Unsupervised

Agents In Practice: IVR And Workflows

SPEAKER_00

Supervised learning is basically like where you and me would basically look at a model. So let's suppose you want to make an algorithm, and the algorithm would say, what is healthy foods? And it was images. So let's make, let's suppose we want to teach a create a program where you could take a picture of a meal and say, Hey, this is a healthy mood food for someone with diabetes, for instance. So let's start with there. And there's probably things that we could identify probably aren't good. Like we could say, like snacks, like, you know, a cake probably off the bat doesn't look good. Donuts probably don't look good, candy bar probably doesn't look good, apple looks good, right? Maybe some fruits, vegetables, high fiber scenes we could come across. So let's suppose we want to train that way. We could take a whole bunch of pictures and all this stuff and dump it into a database and have a model go through it. And we could say, you know, take pictures and recognize is this good or bad? We could tag it that way, healthy, unhealthy. This is very basic. And then as a model went through it, we would check that be our supervision. We would look at it the as a results and say, yes, that's right. No, that's not right. Like you actually think that box of fruitcakes is actually healthy because you see a picture of an apple on it. That's not actually healthy, that's actually bad, even though you see the apple and you think that interprets it. So this is that feedback that's supervised learning. Unsupervised learning is where we would do the same thing, dump the data in, let it give an output, but we wouldn't check it. That has a higher amount of risk associated with it, but it's cheap to do it because you rely on the computational power to actually figure it out. Supervised learning, you know, it takes a lot of humans involved to tag things to look at things, and it's very costly. This is an example of why a lot of the current AI tools out there around vision is really focused on like oncology and radiology. Because think about how many images we have from how many decades uploaded on the EHR that shows like, you know, mammograms, for instance. There's so many breast cancer tools that have been actually validated and are available to help spot check for high risk of breast cancer development. Um, and that's because you know, think about how many women take pictures. How many of those pictures were then looking at radiologists who then tag those things? It wasn't hard to take those images, drop into a database, and then say, you know, look, there's millions of images here from people. Take this data and help us develop a tool around it based on the fact that they've been looked at and been cataloged. So that's that's one side. And you could do that for a lot of different things. Like you start exporting that idea of the supervisor, you're like, you maybe you want to make a risk tool that can identify risks of developing sepsis, tools that identify maybe drug interactions, because we have that information there, we can start doing it. So that's the supervision component, taking that data, having the model look at it, judge it, and sewing it back and you know, assuring that you know things are attacked the way they should be. Now, you asked me about some developments of AI around that then. You know, this is kind of how we got to those, like, you know, I give the example like you know, the dog running a surfboard. Like, well, how did we know what what dog looked like? It took years of training for us to know that what the surfboard looked like versus like a you know, a skateboard or a snowboard, like someone had to tag all those things, and we've gone through that process. And the models actually got better, and then they went unsupervised, they could tag it themselves based on that. That's the reason, though, why we can still have hallucinations and the models can still make some things up because not everything's been supervised learning, some has been unsupervised learning and some other things, and things like we'll skirt through that way, unfortunately. Going beyond that, you asked about different types of AI, you know, generative AI, where you can ask a prompt to say, like, make me an image, make me a movie, make me a video, make me a clip of sound, make me lyrics, you know, that's creating something. Agenic AI, and what we're seeing is this rise of agents in AI is really taking multiple things together. One is leveraging tools like generative AI, large language models, vision AI, and such. And I would call it chatbots 2.0. And they're basically tools that are developed to do a certain action. So it's almost like you give a prompt. I want you to handle, let's say, uh doing prior authorizations. Like your whole job is to handle prior authorizations. Um, I'm gonna set rules and limits on what you can do, certain medications you're going to handle, you're gonna work with these payers and handle prior authorizations. How do I make sure you do a good job? First, you know, we're gonna train you to how to do these things, and then we can tell you if you're doing a good or bad job. Like, what is a good job in terms of handling your prior authorization? Is it speed? Is it acceptance? Is it turnaround time, etc.? And this is where you can set rewards up for certain agents. Like, and this is where you can direct them. Suppose it was like a call system that did so they call someone and you want to make sure. Do you always ask for that person's name? Do you always identify what you're doing? You find a you assign a high-level reward for yourself. It's almost like a rubric, I would say. You know, a lot of us were students, so we know like I have to do XYZ so I get the maximum points on this assignment. It's the same idea. You have like a rubric and you assign reward systems for these agents so that they know I have to do XYZ as well. So they have a process, they kind of get reward for what they're doing, the strength system, and they're gonna do some kind of output at the end. They have some kind of scope that they have to get assigned and do. Maybe it's doing medication reconciliation, maybe it's doing uh refill reminders, maybe it's handling prior authorizations, but they're leveraging a whole bunch of different AI tools to do an action at the end of the day. These are still in their infancy, I would argue, but we're seeing a lot of that come in in pharmacy. What I would say is where I see the biggest push is IVR, so interactive voices for calls. So instead of like calling a pharmacy and pressing one, two, three, you know, whatever, you would have like someone talking to you. And you may not realize it's an AI voice, but it's going to like answer questions for you. It's an agent that's serving as a front end for the pharmacy, delineating. Can I answer your simple question because I have access to data, like oh, your refill, you're asking about one is. I can look through that on the pharmacy management system and tell you when, versus like, hey, you actually have a clinical question. I'm going to now route this to an actual pharmacist to answer for you.

SPEAKER_02

Okay. Okay. So great clarification there. And again, going back to the concept of data, the data we have, I really love the example of the mammogram data being able to use that to then generate responses based on that wealth of data that we've gathered over decades. Great example. In terms of and then the example of the IBR as a key way that pharmacies may be starting to see AI being incorporated. Let's talk about some other examples for pharmacies specifically. So that's that's a really good one. Uh, what where else are we already seeing pharmacies using AI, whether or not we even necessarily realize it? And do you have any specific cases, you know, use cases where pharmacists have integrated this into their practice already? What does that landscape look like in pharmacy right now?

Real Uses Today: DI, Operations, Burnout

Will AI Replace Pharmacists

SPEAKER_00

So this is actually really, really interesting. You asked that question. And I and this is where I might throw a damper on some things. To me, AI never went away, it was always here. But what happened a long like 20, 30 years ago, the whole like AI topic kind of dissipated because it wasn't hot. It's called AI winter, AI spring. So in the 80s and late 70s, that's where you get to those movies like you know, like you know, Odyssey 2001. Like, I can't allow you to do that, Dave. The Terminator movies, all these things start exploding, and then we kind of like got away from AI as much. But it wasn't like we got rid of AI, the technology was still there, we just renamed it. So, like in pharmacy, you know, we turned to like pharmacy informatics, we didn't call it pharmacy AI, it was still data, it's still data science. Yep, you call it business intelligence that's still based off AI technology. So, a lot of stuff that's been around us, you know, even pharmacy automation is based off yeah, AI technology. So we've been using this for a while. It's just the terminology is just got all hyped up again. So, even myself personally, I struggle when people say, Oh, we're now offering AI tool and accents. It's like, are you doing the same thing you've been doing for years, but you're just rebranding because you know everyone wants to hear it? Versus, is this actually brand new stuff, or is it a mixture where you're now doing what you did, but you're actually slapping on maybe a generative AI component for yourself? Um, now I'll give you some examples of what we're seeing within that. Like, for one, that like is just drug information, like that whole experience with it, I think is going to change. Like, so many of my students and I think pharmacists have been trained to like pull up a drug information tool and then find it which question you have. Like, I need to dose this many milligrams of antibiotic for this community acquired pneumonia. Okay, so I know my patient, their demographics, their weight, their synocratin. I have to calculate that out, pull the drug that I want, and then based on that, then make the calculations and dosing, and then that's it. So I'm used to maybe going through a monograph and finding that information. What stops us from having a tool that we could just put the prompt in? You know, for a five-year-old with otitis media that has an allergy to penicillin, which of the following medications is appropriate and at what dose should they give, assuming all labs are with a normal limit? I mean, that to be honest is probably where we're gonna go. And things like that. That's and to me, that's where you already have all the data in there. The drug information is already there, the data's there. You just slapped on a means of facilitating how to access and use that data. I give that example because that's how we'll probably manage a lot of other things and what we're seeing coming down. Our pharmacy management systems might go the same way. We might think about how do we do inventory management, how do we do drug acquisition, how do we do payments? This is the kind of stuff. The IVR, another example. I would tell people the biggest thing that we're seeing is a push is from the logistics of running business, is where we're gonna slap AI on right now. Um is low risk to patience, so the reversion risk is much lower. It's things that we were talking about, you know, we're already burn out from doing a lot of the work it is there right now. So, can AI help facilitate and reduce that burnout to reduce that cognitive load on us from a day-to-day basis and allow us to, you know, spend our time doing other things? Um, that's the theory of what is going on. So that's why I give that IVR. That's why I give like this drug information, and that's why I'm gonna say, like, you know, even pharmacy management systems will quickly go through this because the goal is how to reduce overall workload on the staff and the functions of running the business. Once we got that down, then we can start asking questions like, you know, how can we apply this clinically? How can we do more things? Like, can we then leverage AI tools and actually provide better clinical services, for instance? You know, do we use this like even the whole conversation, you know, medication therapy management and you know, CMM and all that stuff? Does this change dramatically in terms of our approach once we slap AI on top of that too? Do we can we automate that process? Can we just have that done? And it's pharmacists in the back end that's my managing this and making sure it's doing what's supposed to, and speed up that process, for instance, or do we start changing dramatically just the whole denotion of like what we can or cannot do because we have these tools that can help us? Um, so that's some things I want to like highlight there right now, because that's kind of where I see things like hovering, definitely focus on improving the business and operations side with this kind of outlook on like how do we think about future clinical operations that feed back into this once we get our house in order and better constructed, now we can think beyond that box.

SPEAKER_02

And to that point, I think a lot of pharmacists have concerns around job stability, security. How does how do these services, these AI tools reconcile with our clinical skills and ability? And so the medication therapy management that you brought up, like that's a great example, I think, of where we pride ourselves on our ability to optimize medications and educate patients and do all of that as part of a patient care service. And how how do you reconcile that with? Where an AI offering might come in and what might that look like in two years.

Skills Shift And New Roles Ahead

SPEAKER_00

I mean, that's a really huge issue, I think, across healthcare is that the notion like what is our expertise. You know, at one point we used to say we're the drug information experts. I always looked at that as like we were the drug information experts because we had access to the databases to begin with. Like no one had on their coffee table drug facts and comparisons. Like, no, not every household had access to a drug information compendium. Who did? It was a pharmacy. By law, we had to have that. So if you had a drug question, you contact the pharmacy because you knew they had a database and we were trained to go through it and provide an answer. Google opened up the ability to find information online, and now artificial intelligence because it's access to all this information, publications, monographs, can put it together as well. So we have given the public the ability to find that information in even independent of their education, allow them to ask a simple prompt that may give them the answer that they want for good and for bad. We can't close that Pandora's box, and we never could. So I think the question is like, you know, can we hold on to what we thought we were versus how do we evolve? Even the name pharmacists is an evolution of what we once were. I mean, we don't compound as much as we did. You know, the term apothecary, druggist, chemist is kind of lost on us in the United States. We compare ourselves as pharmacists. I would challenge us to probably look at that. We are facing a big transition period in healthcare as well. Um, and even our roles and responsibilities will shift immensely based on how technology changes. We've seen that multiple times throughout history. You know, people have argued we've gone through three industrial revolutions, AI may be leading to a fourth industrial revolution. I would say anyone that says, you know, I can just sit comfortably and do what I've been doing and nothing will change. Um is a fallacy. You can't do that. The we will see tremendous shift in terms of like just the actions and the capabilities. Um, right now it's focused on the business operations, but then as we start looking beyond the business and we seek to evolve that, and then we ask about what else we can do, that's probably where I would challenge most current pharmacists be like, yeah, what is going on? What is going to be the future rules and responsibility of us beyond just uh stocking medications and selling them? Because is that an opportunity for automation? Probably. I would say yes. So I do see a dramatic shift undergoing the workforce within the coming decade. Just because, from a business sense, from the use of technology sense, I think that's what will be developed. Uh for that reason, we will have to evolve with it. So I think we'll see some retraining going on. I think there'll be new job developments, I think there'll be new career pathways that open up. And for the pharmacists that are apt to adapt to that and look for it, will be able to transition pretty quickly. For those that drag their feet, may find themselves saying, Hey, you know, what happened? But it's kind of like the world happened around you. Um that's always been the case. I mean, I'm an academic and I always want to say, like, you we, you know, what is the purpose of CE? What is the purpose of everything else? That's lifelong learning. The concept of finishing school, going out and work, and never having to learn or retrain.

SPEAKER_01

Right.

SPEAKER_00

I I I can't point to any industry where that was the really the big case where, yeah, I'm done, and I will do the same thing for the next 30, 40 years. Like, no.

Practical On-Ramps To Learn AI

SPEAKER_02

Yeah. Yeah. And the thing, the the phrase I've heard that resonates with me is that AI will not replace pharmacists, but pharmacists who don't know how to use AI may, yeah, they're gonna be at a disadvantage. And so, what practical advice would you give to our listeners to help prepare for embracing AI, thinking about how it might fit into our practices, opportunities, challenges? What how would you summarize what our listeners should be thinking about in terms of next steps? You mentioned education, keeping ourselves informed of changes. What are what are some practical ways to do that?

SPEAKER_00

I definitely would say reading and just listening to what's going on. Right now, nothing's written in stone about what we are going to do. I think where you'll probably see the biggest change right now is probably within your workforce, where you're at, what your company is doing and adopting and utilizing, asking questions like, oh, we're doing this, how does this work? We're doing this, why are we doing this? And how can I understand it? I think being involved in some level will be very important. How does this stuff work? Asking those questions. It's almost like you know, a new medication comes to market and you're going to dispense it. What's the mechanism of action? What's the safety parameters? What do you have to educate people on? It's going to be the same level of tools that come down to us, like, oh, this is a new thing. How does it work? How does it do this? You don't have to be a math specialist with AI background exactly to do this. I mean, healthcare is the same thing. There are radiologists who read reports that can't operate an MRI on their own. They don't know how that thing works, like if the MRI machine breaks down, the radiologist doesn't enter the room and starts banging on it and saying, bam, it works, right? No, but they know how the how the technology functions to give that insight. To me, that's the same thing for us. Like, yeah, we'll have AI tools to do things, but you have to know how to use it responsibly at the end of the day. I think it's that level that's going to be important. We keep saying we need to keep a human in a loop. We need to keep a pharmacist in the loop. Building upon what you said, the pharmacist that's going to be kept in the loop is the one who understands how the stuff works at the end of the day. You can stop it from harming or causing issues at the end. So that I think should be driving. How to do that? You know, the certification programs, there's CEs. There's a lot of stuff that's really freely available online to read. It's that tenacity, I think, for people to want to spend that time to self-re-educate, that's really important right now.

Start Low Risk And Build Judgment

SPEAKER_02

Yeah. Yeah, agree. It it's it's almost filtering out and finding the most practical resources in the limited time you have. And I I didn't realize, but FDA actually has a digital health and AI glossary. And so, you know, if you're just sort of starting out or wanting clarification on what some of those terms you used earlier means, that's a great reference. But it's it, there's a lot out there. And so I think it's just sort of sometimes you just have to take the leap to to just start educating yourself. But yeah, it's it's it's challenging, I think, and can be overwhelming.

SPEAKER_00

I mean, it might be also good practice, like you know, as you're going through it. Maybe can you have generative AI tool your choice to help you create your lesson plan? Like, what should it be to understand next?

SPEAKER_02

That's what I was gonna say, Tim. Is I I feel like a good first step is just using it also for those who may not have even dabbled in in trying it, playing with it yourself and seeing that really is so illustrative in finding gaps, advantages, use cases. Would you recommend that as a way to just start playing with it and seeing what kinds of results you get and what what you might even not realize you can use it for?

Mindset: Fear, Opportunity, And Next Steps

SPEAKER_00

Do it for low risk activities, use it for things you have domain expertise, is what I would say. So you can identify what's right or wrong. Like, for instance, clinically, I will I don't like I won't I won't use an AI tool like in oncology. I haven't practiced oncology in well over a decade. So I'm not even up to speed on half the things I should be just because I'm focused on you know ambulatory care practice. Yeah, so I wouldn't be able to, you know, vouch for anything done. And this is where I look at AI as a tool. AI is a great tool for something you have expertise in because it speeds you up, because you could look at something and say that looks right or that looks correct, versus like, I don't even know if that's right or wrong. I'm going to have to go back and double check it. Um to me, that's the most important thing. It's almost like you know, when you can go from the slide rule to a calculator, it's probably when you understand the basic mathematical principles. Then you can do the and use these tools. Like, why else don't we just give a calculator to a kindergartner? They can learn to practice with the symbols and such, but we think that you should understand the underlying principles to actually expound upon it. To me, it's the same thing. Whatever you want to practice with, practice within something that you have experience with to get used to it. And that could be something as simple as like, you know, help me with my yard work. I've seen people, you know, I take a picture of their home and say, How should I set up my Christmas lights this year to make it look nice? Um, you know, what's a layout of my room? I want to redecorate, what's a good way of doing this? You know, it's things like that that you could look at like, yeah, that makes sense. I like that, versus like, no, it doesn't look good. I want, I need to ask it a better question to do this thing, and you'll learn from that experience.

SPEAKER_02

I I I think that's a great example. And the idea of having it be used for low-risk, low-risk activities like that, like trip planning. I I know people have used it for that, or planning a menu for the week, those kinds of things, low-risk activities. And then again, you get to sort of see how how you interact with it and what you can take away and what you might be less comfortable accepting. So, yeah, great, great takeaways and and practical points there. We're about out of time. So I just want to sort of tie this all together and think about in terms of looking ahead and and where we are now, what it's our game changers podcast. So we always wrap up with the game changer. And what would you say is the game changer for this topic that you'd want our listeners to walk away with?

SPEAKER_00

Don't be idle. That's the biggest thing. Like, you know, push yourself to read up on this stuff, self-learn. Probably the biggest issues, I think. As you know, you said, Rachel, is there's a lot of information out there. Do you go to the FDA? Do you go to a traditional pharmacy resource? Do you start reading like mass media, like the verge or anything else for this information? I would say whatever you feel comfortable with right now, but you need to challenge yourself, you need to read and really keep updated right now. I think if you're gonna wait for someone to tell you what to do immediately, you'll be waiting for quite some time. I think you'll find the world will keep moving as fast as it wants, and you'll look around and be like, Well, why didn't anyone tell me to do this? And we're also like, Well, we just figured it out as we went along. I think that's what a lot is going on right now because it's really hard to keep up to date on this. So you have to push yourself. And it's hard to say that, but it's that's gonna be the self-motivation I think a lot of us need at this point.

Closing & Claim Your CE

SPEAKER_02

That's a great, a great point because obviously we all have so many things going on in our lives, and for us to focus on one area or another, it takes it's inertia is a is a challenge, and especially when we're all probably to the max with our our work lives. But as you say, this is a tool, a tool that we're all going to need to be able to use more and more. And if we don't know how to use it, we're gonna be left behind. And so figuring out how we can best educate ourselves to learn to use it is going to be critical, I think. And just it's exciting. I mean, it's exciting to think about where pharmacy may be in a couple of years. I think there's a lot of opportunity and a lot of opportunity for us professionally to grow because of of this tool. But needing to think about those challenges and potential obstacles that we might need to overcome as well.

SPEAKER_00

I I think to close on for what you said there, I think it stands out to me is I've seen two sides. I've seen people vastly scared of it. And some people look at it, this is a great opportunity for us. There are a lot of pharmacists and other people adjacent to our profession that are like, hey, we can now do X. We couldn't do X before because we just didn't have the human capital, the number people needed, the scalability to do X because it wasn't feasible, it was cost prohibitive. But with the leveraging AI, hey, now we can do these things. Right. And to me, that's the other thing. It's like you're gonna find people on two sides of listening to this. You're gonna be like, Wow, I'm scared of this. I need to read up more to know what's gonna happen to me. Other people can be like, wow, this is great. Maybe I can learn and I can start actually. I always wanted to make X. Hey, hey, I can code for me. Well, maybe I can make X instead of paying a company to do X for me in the past, and I can be like a vibe coder or whatever else. I'm not saying like, you know, this is gonna be one size fit all, but we will see people take different paths here. And I think that's what we have to keep an eye out for, is many people will take different routes with this. And I think it will be an exciting time for this because I think some people will create new tools we never could have thought of to do in the past that will really help us and our patients.

SPEAKER_02

I'll I'll just tack onto that. If if for listeners who haven't heard of vibe coding, that is a whole other world that I never knew anything about, but it is it it drastically opens up doors to things that uh me, for example, I never would have thought I could do. And so, yes, that's a great, a great ending to wrap up with. So thank you so much, Tim. This is a great discussion. Very interesting to be thinking about all this and and where things might be headed. So thank you so much for your expertise.

SPEAKER_00

No, thank you for having me. Really appreciate it. Take care.

SPEAKER_02

Excellent. So, listeners, be sure to claim your CE credit for this episode of Game Changers by logging in at CEimpact.com. And as always, have a great week and keep learning. I can't wait to dig into another game changing topic with you all next week.