The Quality Corner Show explores the rise of AI in healthcare and more specifically its use in pharmacy. Podcast Host Nick Dorich, PharmD, PQS Associate Director of Pharmacy Accounts, interviews co-founders of the AI Collective Whitley Yi, PharmD, BCPS, and Christy Cheung, PharmD about how pharmacists can leverage artificial intelligence as a tool in the workplace currently and what does the future hold for AI.
Whitley Yi, PharmD, BCPS, Delivery Manager and Pharmacy Specialist, Well & Adjunct Lecturer, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences
Christy Cheung, PharmD, Lead, Digital Health, Insights, and Learning at Sanofi & Co-Founder of AI Collective
00:00:01:15 - 00:00:26:15
And in a way I see some I as the ability to also improve what we can measure, which then can improve the way that we provide and think about care as well. So pretty much a lot of the low hanging fruit. Right, are going to be taking off the burden of some of those administrative to documentation task. You know, imagine if you did have the you know, you didn't have to spend time doing routine things.
anything this routine can also really be automated. But it's that part of taking off. And you have the time and the ability to spend more focus on the more nuanced and more complex sides of a patient care and the ability to actually spend time with patients.
00:00:48:07 - 00:01:13:08
Welcome to the Pharmacy Quality Solutions Quality Corner show where quality measurement leads to better patient outcomes. This show will be your go to source for all things related to quality improvement and medication use and health care. We will hit on trending health topics as they relate to performance measurements and find common ground for payers and practitioners. We will discuss how the EQUIPP platform can help you with your performance goals.
We will also make sure to keep you up to date on pharmacy quality news. Please note that the topics discussed are based on the information available at the date and time of reporting. Information or guidelines are updated periodically and we will always recommend that our listeners research and review any guidelines that are newly published. Buckle up and put your thinking cap on.
The Quality Corner show starts now.
00:01:50:16 - 00:02:15:16
Hello Quality Corner Show listeners. Welcome to the PQS podcast, where we focus on medication use, quality improvement and how we can utilize pharmacists to improve patient health outcomes. I'm your host, Nick Dorich. In the introduction of the show, I typically wax poetic about our topic and provide some background exposition before introducing our guests. Or, in the case of today's episode, guests, but not today.
00:02:15:18 - 00:02:33:08
Instead, we're going to dive right into our topic and introduce our two guests. And let's face it, we look to bring you the experts on the Quality Corner show. So we're going to have them describe what it is that we are talking about and why they are the right people for this discussion. So today's guest are Whitley Yi and Christy Cheung.
00:02:33:09 - 00:02:43:09
They are co-founders for the AI Collective and they are both pharmacists. So Whitley, Christie, welcome to the show. And how are you both doing today?
00:02:43:11 - 00:02:50:07
Whitley and Christie
Doing fantastic. Thank you so much for having us. Yes, me too. Thank you so much. We're glad to be here.
00:02:50:09 - 00:03:03:22
Excellent. Well, before we begin today's show, let's get to know about you. So, Whitley, I'll ask you first here to give a quick background. What is your experience in health care and in pharmacy?
00:03:03:24 - 00:03:24:19
Yeah, absolutely. So I'm kind of taking, I think, more nontraditional tasks, if you will. You know, at a pharmacy school. And I knew early on and I was really interested in technology and I'm going to be the key thing that was really going to make the biggest difference in thinking about what the next level entrance transformation looks like in health care.
00:03:24:21 - 00:03:42:21
And so going into residency, a lot of what I focused on was think about informatics. And that's when I really started getting interested in. I think at the time it was kind of despite his word, it was almost like, you know, magic, like nobody could describe it and nobody knew what it was. And so I was like, All right, I really want to feel to understand this.
00:03:42:21 - 00:03:57:04
Like, is this something that is explainable? Is it something that we can use? Like, how can this be leveraged? And I'm kind of into an exploration of it. We have a lot of and we're going to say, you know, we have a lot of AI tools, but not not a lot of things actually being used in health care.
00:03:57:04 - 00:04:24:20
And so I began kind of diving into like, why do we not see more use to and barriers in implementation? And that's kind of led me like different pathways going, you know, kind of leaving the traditional health care setting and going more into the consumer directed health and wellness phase, but also focusing on other projects and initiatives like we'll discuss with A.I. A.I. Collective.
00:04:24:22 - 00:04:36:12
So the idea is really around how do we get pharmacists to be more involved in AI and think about how do we leverage these tools in the pharmacy space specifically?
00:04:36:14 - 00:04:41:20
Excellent. Thank you. WHITLEY. Christy, how about for yourself? What's your background in health care and pharmacy?
00:04:41:22 - 00:05:07:15
Sure. So my catalyst, so to speak, for this intersection of health care and technology really stemmed from a 17 piece application that I put in to participate in a hackathon competition in first year. And at the time I was thinking to myself, hackathons are not for health care. I'm not sure why this is here. I think it's for engineers and programmers, what have you.
00:05:07:15 - 00:05:45:07
But I went and it was one of the most eye opening weekends that I had had going from undergrad to pharmacy school, and it was a full weekend bringing together students and clinicians from health care backgrounds. There were engineers, programmers, computer scientists, there were designers or entrepreneurs. And I just realized throughout that weekend how neat it was to bring such fresh ideas for people outside of traditional health care settings to solve health care challenges.
00:05:45:07 - 00:06:16:12
And I was thinking, okay, this brings a lot more out of the box ideas that we as clinicians could maybe not so easily come up with if it's just us with ourselves. And so I went back year after year, really thought about what I could do in the innovation space, particularly bringing together health care and technology. As I graduated, I was considering startups at the time and still to this day there's not a lot of clinical representation in startups and pharmacy representation.
00:06:16:12 - 00:06:47:20
So for me I decided to pursue more of the corporate setting in the biopharma industry because ultimately that's where a lot of the innovation is happening. And I wanted to take on more of that entrepreneurial technical side just to complement my own science and clinical background, not really having much of that business side of me just yet. And so I moved into biopharma and naturally started in the medical and clinical affairs side of things.
00:06:47:22 - 00:06:59:05
And then over a year and a half ago moved into a digital innovation capacity. So starting to look at leveraging technologies for improving ultimately patient care.
00:06:59:07 - 00:07:18:17
Excellent. Now before we get into our questions, our topic, our conversation, the two of you, I understand correctly, are the co-founders of the AI Collective. So do you mind giving us a quick rundown on kind of how the two of you connected and what exactly is the AI Collective?
00:07:18:19 - 00:07:48:13
Absolutely. So we are always a little bit fuzzy on our origin story because it was probably four or five years back where we were both on a pharmacist Slack group. Now this slack group is a community of innovative pharmacists or pharmacists, you know, trying to push boundaries and look at different innovation areas to advance patient care. And so we were part of this.
00:07:48:13 - 00:08:21:18
Then we started talking maybe one on one, and we recognized that there wasn't a whole lot of information out there on topics like artificial intelligence. But back then we talked about it broadly, like there's not a whole lot in other areas of digital and technology that pharmacists, pharmacy students can consume to learn more. And so we were just idea thinking about putting together these infographics or something just to get the word out there.
00:08:21:18 - 00:08:57:16
And we are by no means experts, but we're constantly learning, so why not share that learning with others as well? And so throughout COVID, we put together these like 11 page documents because we thought that that would be very digestible. Clearly not. But we've since transitioned to a website where we do a bite size format materials to hopefully give people a foundational base for starting to learn about topics like artificial intelligence.
00:08:57:18 - 00:09:33:17
So for us, it's still a work in progress. We've built a road map and we can touch on that later on. We're still experimenting with different educational content, but really our goal, like we said earlier, is to have more pharmacists, students, clinicians at the table when someone is developing or deploying an algorithm. Yeah, And ultimately the aim is how do we empower pharmacists to be able to, to the table and be able to collaborate as the, you know, clinical or subject matter expert and end user with different kind of tool development.
00:09:33:19 - 00:09:45:17
So a little bit the roundabout way that we that we built this. But overall the goal is really to help bring education and make it more accessible to the pharmacist.
00:09:45:19 - 00:10:12:16
Excellent. I will call out and Christy, as you put it, I might be paraphrasing who you hear slightly, but that we're not experts, but we do want to teach, educate others. And I think that's really an area where pharmacists in general and health care providers in general, we are, you know, experts, we're experts amongst our peers. And it but it does take a lot of courage and motivation and initiative to really kind of share some of these parts.
00:10:12:16 - 00:10:40:20
We've also recently done an episode about using social media as health care providers, and it's a part where, hey, whether or not we use it, our patients are using and our patients are asking questions about it. Same thing here with A.I., and this is why we wanted to have this as a topic of conversation in the Quality Corner show, because it is something that it may not be visible to every pharmacist, every pharmacy technician here and in right now for where we work and where we practice or how we interact with our patients.
00:10:41:00 - 00:11:01:13
But it is coming and we are doing potentially ourselves, potentially or definitely our patients a disservice if we're not looking for ways to use it and use it appropriately. So I'm really excited to have both of you here on for the show today. I applaud your efforts to push the topic and your your organization not this far.
00:11:01:18 - 00:11:12:12
And with with that will get into our conversation for today's show. But before we do that, we'll hear a quick message from my teammates here at us.
00:11:12:14 - 00:11:37:01
Now it's time for the breakdown as quality corner show host little as three main topic questions. Our guests will have a chance to respond and there will be some discussion to summarize the key points. This process worksheet for the second and third questions, which will wrap up the primary content for this, the 14. After that, it's to end on a closing summary, usually containing a bonus question.
00:11:37:03 - 00:11:43:10
Now that we have the start of the process, let's jump into the questions.
00:11:43:12 - 00:12:07:19
All right. We're now back with Whitney and Christy and ready to jump into today's questions for the show. So our first question for the both of you is very simple. It's how are we defining artificial intelligence or AI in the health care sector? And at the time of recording, which is August 20, 23, what is its current prevalence?
00:12:07:21 - 00:12:43:24
Yeah, so I could start here artificial intelligence or AI in the health care sector, I think objectively speaking, should really hold a similar definition as it does in any other industry, and it refers to a field or a collection of technologies and tools that aim to stimulate certain aspects of human intelligence. And really, you can think of it as a set of tools that we can use to more quickly, let's say, identify trends in data sets to make predictions or recommendations.
00:12:44:01 - 00:13:13:11
Now to your second the second part of your question, I think quantifying its prevalence in the health care ecosystem is incredibly challenging historically. Let's say, as we saw maybe a rise in a lot of the use cases across health care, we might have said, look at the diagnostics field. So, you know, radiology, ophthalmology, dermatology, we might have said in the realm of basic science R&D.
00:13:13:11 - 00:13:42:13
So these are really examples that are very specific to certain domains, But given a lot of buzz recently in the consumer world with tools like Chatty Bird, you're really seeing a growth in opportunity to leverage AI across various practice settings. So, you know, you're thinking you could think about automated summaries of published literature, improved efficiencies in patient recruitment for clinical trials.
00:13:42:13 - 00:14:07:16
This is a big challenge in the biopharma industry. And so yeah, so those are some real world examples and we're definitely seeing more and more of those. And I wanted to, you know, going back to the like what is the I, I, I just want to take a moment. It's worth noting that noting that artificial intelligence is not really the, you know, the ideal description, I think, of this set of tools and methodologies.
00:14:07:18 - 00:14:31:02
And, you know, overall, I think that that term itself has been, you know, the the definition of it's kind of evolved over time for sure. So we think back when the term was first coined in the 1950s, A.I. was really used to talk about expert systems, right? Anything that kind of mimics human intelligence. And so expert systems would be something that is rules based.
00:14:31:07 - 00:15:00:07
So if we think about clinical decision support tools like in current H.R. Systems today, you know, getting a drug alert, if certain criteria are met, then that would be considered an a.i. at one point. And i think our definition has definitely evolved. And now I think, you know, machine learning is a lot more specific of a term. And so machine learning is when we're use, you know, machines are able to utilize and learn patterns from data that they can then apply to new datasets to make predictions.
00:15:00:09 - 00:15:36:04
And I think at the simplest, we have our statistical methods, methodologies and so that's, know, logistic regression I think is our most basic form, but then we're moving from that into our deep learning tools. I mean that right now more from a just from common vernacular like A.I. really is synonymous more with deep learning at this time. And so deep learning utilizes neural networks, and these are models that are extremely powerful at picking up patterns, very large datasets that humans may not actually be able to attempt to detect.
00:15:36:06 - 00:15:58:13
They're also very opaque, meaning that you can't intuitively know how they're making their decisions and predictions as that right. There has been one of the key barriers and limitations and how we see these utilized or whether we see these implemented or used directly in patient care, we are starting to see, as Kristi mentioned, we are starting to see more applications being used.
00:15:58:15 - 00:16:30:00
A lot of it is more on the administrative and more on the administrative side versus like direct patient care at this time. And see me specifically, like if we look at the different A.I. models that have been FDA approved, for instance, A.I. powered devices, most of those all in the radiology or image recognition space diagnostics base, right. There's just very little use cases right now specific to medication management because classifying something.
00:16:30:00 - 00:16:49:14
So if you're using the AI to put a label on something like look at an image of a chest x ray, etc. and classify it, that's a very different problem than asking a model to tell you what's the best medication for somebody to be on. Like what's this treatment recommendation? Those are completely two different types of training that you would have to do and you have to have different kind of data to do it.
00:16:49:16 - 00:17:17:20
And we really don't have the kind of data to be able to fully train those kind of medication recommendation systems. So I know that's a lot of what people think of it right now. Like we're just not in a space and the technology is not there to be able to do those kind of that kind of functionality. But I do want to mention, you know, with and this has been a very, I think, exciting time for A.I. as well with all of the new models that have been coming out with our large language models, chatbots.
00:17:17:22 - 00:17:47:00
And we have now sort of two, two different arms of A.I., if you will. We have our predictive models and we have our generative A.I. models and so Chat GPT and Bard and and those tools are going to fall under our generative A.I., which means these tools are really ingesting vast amounts of data. I mean, tragically, literally, as learned, the training data included everything that was ever on the Internet, like since the beginning of time, basically.
00:17:47:02 - 00:18:14:15
So it's it's all, you know, seen everything and it's able to replicate all those patterns and its use, how words are typically used together and combinations. And it uses those patterns then generate new text, right? And it does it so well that you can't really distinguish it from human text. And just that capability of being able to understand and have a probabilistic word generator, we've realized, has so many other capabilities.
00:18:14:15 - 00:18:33:12
Like it turns out that these models, you know, appear to have learned a lot of basic information. Right. You can ask them a lot of medical questions or even, you know, try to ask them drug questions. And, you know, it seems like they've learned a lot. And it is a little bit deceiving because these models don't actually learn facts, saying it's really based on what's represented in the training data.
00:18:33:17 - 00:18:49:20
And so as we talked, Nick, I like how you mentioned, you know, that's it's critical for us to understand these things even even if we're not certainly using them right now because like patients will be right. So if you have patients come up and say, Listen, I asked about what dose of medication I should take in this one, it said like, why?
00:18:49:23 - 00:19:19:15
Why is that not correct? You know, like, this is what I should be doing. So we will have to be interacting and like explaining these kinds of things to patients. And so right now, when we think about like our traditional AI and the problems in the health care space, I mean, we look at some surveys that can 2021, I think most hospital systems were in kind of like 25% of hospital systems were in maybe implementation phase, but most are not very operational.
00:19:19:17 - 00:19:36:12
I think a recent survey now looked and saw it's only 6% of health systems have even really started to put a generative A.I. strategy in place. So we're very much, I think, at the early phase with using that sort of subset of our A.I. tooling.
00:19:36:14 - 00:20:00:18
It's a great description or definitions here so far, and this is an area where I'm a bit of a nerd myself, and I say that lovingly, right? But where a lot of folks that I work with are in the technology space, I think there's been somewhat a misconception from the general public, the general audience, on what A.I. is, and in some cases it's, Oh, this is really just no different than a glorified Google search, right?
00:20:00:20 - 00:20:34:18
Whitley As you said, this is hey, it's pulling in everything that's been on the Internet or potentially that's there, but it's also going to be giving you information based upon, well, what is the prompt? What is the depth of that? And we you both already spoke to that. This is really a more advanced machine learning. And that's how I've kind of looked at at a at AI in this sense in that it's only going to be as good as the prompt that's given the amount of knowledge that's there and the more complicated, the more intricate something can be that it can help us speed up how we learn, how we describe those elements.
00:20:34:20 - 00:20:56:04
But it can have its pitfalls as well as it relates to, well, what is the prompt that's given? How is that defined? How much breadth and depth of information is there? So I think there's already been outside of outside of pharmacy in health care. There's been a number of items, even as it relates to some lawyers or paralegals trying to write cases based on AI prompts.
00:20:56:04 - 00:21:23:22
And hey, they pulled information or cases that don't actually even exist, Right? So there's an area where it is not foolproof and that's where it brings up a lot of question and concern from folks on, hey, how does this get used? Is it used appropriately or do we continue to snowball to, you know, with the use of AI, where we end up in a situation like one of my favorite movies of all time, Terminator two, Judgment Day, where this is, you know, A.I. takes over, right?
00:21:23:22 - 00:21:48:12
I couldn't go through this episode without mentioning that. Obviously, that's the doomsday scenario. We're nowhere near that, and I'll bring it back to us as far as the health care conversation and move it to our next question for today. But as it relates to A.I. now and we already spoke about some of the WITLI Christi, both gave some examples on already that this is actively used, especially in radiology and some other areas.
00:21:48:17 - 00:21:57:04
But how do health care providers, including our pharmacies, how are they utilizing A.I. roadmap and with their practice setting today?
00:21:57:06 - 00:22:27:17
So we think the AI roadmap that we've put together on there collectively, that's really meant to help think lay the foundation for understanding what the strengths and limitations of these these tools are, and put it in a perspective and a framework of what clinicians are already familiar with. You know, a lot of if you're, you know, is a pharmacist, you're already familiar with a lot of evidence based terminology and methodology, and a lot of that can be applied to A.I..
00:22:27:18 - 00:22:54:08
It's just it's a different language, right? A lot of the concepts are the same, but we're the AI field has used different terms for everything. And so part of it is helping pharmacists realize, you know, a lot of these concepts are already there and a number of being able to understand what the true capabilities and limitations are really allow pharmacies, I think, to feel more empowered to know like, what's the what's the right situation where I can utilize these tools?
00:22:54:08 - 00:23:21:17
Or if if we want to look at like implementing something, you know, in our pharmacy, I feel comfortable to be able to partner and be, you know, to help collaborate on like what that should look like and what problem that we need to solve and how it should be implemented.
Yeah, absolutely. Like, I think we really built it with the goal of it acting as a starting point for why oftentimes is seen as a very or still is seen as a very daunting topic.
00:23:21:19 - 00:23:54:14
So we try to tackle it from the angle of building upon what people have already learned. Like Whitley said, from your basic stats courses in college, and then layering concepts that are kind of at the core of how a lot of these AI algorithms are built, how they operate. So leveraging things that people already know and we try we try not to be overly conceptual, like we really want to bring in the health care angle because that was the premise of how we came together to build this in the first place.
00:23:54:16 - 00:24:15:12
It's not that there was a lack of AI literature and research out there. There were there were tons, but in a way that actually makes sense for people who don't have any sort of basic understanding of this topic. I think that's what we were trying to build and are continually trying to work on to share this with our peers and with our colleagues.
00:24:15:14 - 00:24:42:13
And almost to like like Whitney said, you know, if we're able to empower pharmacists or students or other clinicians to have a seat at the table, we can also give them the building blocks to ask the right questions. You know, as it relates to medication management, we mentioned earlier a lot of what we've seen in terms of health care use cases, you know, fall on to the diagnostic side, not not so much the therapy medication side.
00:24:42:13 - 00:24:59:20
And so having pharmacists be comfortable and confident with this knowledge such that they can ask the questions of, okay, where does the pharmacy workflow fall into this? Where does Deprescribing fall into this? So that's really our mission for this roadmap.
00:24:59:22 - 00:25:36:18
Excellent. So even as I think about Whitney and Christy, your your responses as it relates to, you know, your collective and the road map, it's really about understanding that AI is a tool just like any other practice or technology that we have. And just like any tool, it's only as useful as its application, right? If you're building an IKEA shelf and you need a hammer and you're trying to use a screwdriver, then that tool is not going to be effective and it may break or it may not work effectively.
00:25:36:18 - 00:26:01:16
So really the thought process here is and as you're thinking about it, pharmacist, other clinicians, they are educated, they are smart. But if there are ways that we can reduce the time needed to provide a follow up service to identify a diagnosis, identify a medication pathway or alternative, really the application of AI, there is going to be how do we help the pharmacist?
00:26:01:16 - 00:26:18:15
How do we help the clinician make that decision tree And but once they do come up or use the tool for that decision tree, they still essentially need to verify, they need to check the work of the system. Is that is that am I on the right path here myself?
00:26:18:17 - 00:26:42:13
Yes. I think you always need to be aware of know like when when these tools can potentially be wrong. You know, like if you're if you are the one utilizing it, utilizing a tool. So for instance, one type of rehab program that's available as a clinical decision, support is a program that me that recognizes potential errors in medication orders and it does that through anomaly detection.
00:26:42:18 - 00:27:02:05
So in essence, it takes a look at all of the historical orders, in particular health system over the last five years, and then it's able to pick out when something falls outside of that pattern. So if you get an app like pharmacy, work with this and you get an alert saying, oh, this is a potential error understanding, like is this valid?
00:27:02:05 - 00:27:30:15
I mean, if you're prescribing something that is very niche, like it's not very common, you know, the dose you have to go, you know, you're doing something that maybe outside of the norm, then you understand like, well, of course it makes sense that I get off this alert because this doesn't happen very often. It's not very common and I don't, you know, don't need to worry about That's just how this this tool operates as well, being able to understand, you know, what's what's driving those kind of decisions and how it plays into also the human decisions behind it.
00:27:30:15 - 00:27:45:07
Because ultimately all these tools are they operate within the overarching system that involves both human interpretation of it and then the action that we take because of of what we what we see and what the output of those tools are.
00:27:45:09 - 00:28:07:12
All right, Witley and Christy, thank you for defining those parts of it. And we're going to move to our next question. And I think for a lot of our pharmacists, this is the question that might be the most important one or the one that gives them the most question concern, heartburn, use of I does this. Do we as pharmacists, do we as health care providers?
00:28:07:12 - 00:28:23:08
Is this a collaborative tool that we use or is this something that replaces us as health care providers? So, you know, as we think about appropriate use of and we've talked about this, right, is a tool in the toolbox, what does the future use of AI in pharmacy look like?
00:28:23:14 - 00:28:49:24
Yeah, this is definitely a common question. How well maybe start is that if we think about it realistically as clinicians, as pharmacists, we don't dedicate 100% of our responsibilities to patients where, you know, let's say we work in a hospital or a primary care setting. Yes, we see patients, we provide counseling and medications complete for a medication reviews.
00:28:50:01 - 00:29:29:15
But there are other aspects of our day to day, and those may include administrative work, manual entry of certain information, chart reviews, etc.. So, no, I don't believe that I will replace health care providers, but it absolutely can help manage, you know, a lot of those administrative and operational tasks that do really take up our workday. And then if we think about the pharmacy tackle and biotech industry, I can help to automate logistics related to clinical trials, or it could help optimize manufacturing and supply.
00:29:29:17 - 00:29:53:24
It can help us better identify right patients or right treatments, so on. So what I'm trying to say is that our roles as clinicians in care providers will continually evolve no matter what as technology advances, right? Whether it is artificial intelligence, whether it's a new form of diagnostic device or a new modality of treatment, delivery, what have you.
00:29:54:01 - 00:30:26:17
And as we better understand, AI and its related tools, I think it is very important that we consider opportunities, like you said, where it can improve the way and the speed with which we're delivering care to patients. I like to use this quote, and you may have heard it already amidst the buzz, but the saying that I won't replace health care professionals, rather health care professionals who use AI will replace those who do not use data.
00:30:26:19 - 00:30:54:15
Yeah, I think that's you know, that sums a lot of it up really well. And, you know, anytime that we have a new technology coming to the picture, there can always be pushback against it. Right? Or please people, this does change what I do. I mean, even things that that are like core integral to medical practice today, there really was pushback against, you know, when the stethoscope was first invented, the thought that was crazy and that that'll never take off.
00:30:54:15 - 00:31:20:20
And, you know, and even like blood pressure cuff, the ability to monitor blood pressure non-invasively, again, those are tools that have completely empowered and change the way that we provide care. You couldn't even treat or manage blood pressure, you know, if we couldn't measure it before. And in a way, I see some I as the ability to also improve what we can measure, which then can improve the way that we provide and think about care as well.
00:31:20:22 - 00:31:39:16
So pretty much a lot of the low hanging fruit, right, are going to be taking off the burden of some of those administrative documentation task. You know, imagine if you did have the you know, if you didn't have to spend time doing routine things, you know, anything this routine can ultimately be automated. But if that part is taken off, then you have the time.
00:31:39:16 - 00:32:19:17
The ability to spend more focused on the more nuanced and more complex sides of a patient care and the ability to actually spend time with patients and eating from a broader perspective is going to give us the ability to have a different level of data, especially patient generated data that we've never had before. When you think about all of our air power devices and all of the different digital health tools out there, if we're if we suddenly, you know, previously, if you are trying to make our clinical guidelines, may you say that you can make recommendations or here's recommendations based on like a few points of data, right?
00:32:19:17 - 00:32:39:07
You know, based on like three blood pressure readings or, you know, a certain set number of blood glucose readings, etc.. Like traditionally we've just had small amounts of data that we've made decisions on. But I mean, imagine if you had vast amounts of data of every day, like of what a person's status was, every day of their life.
00:32:39:09 - 00:33:21:17
Right. How how does managing a patient, how does medication manage that different in that space? There's huge opportunity to think about highly personalized and individualized care beyond what we're currently doing. And it's it's all going to be contingent upon ultimately how we capture, analyze and interpret this patient generated data. Ultimately, as I think we're going to be moving into a space that I think is really exciting where we're going from our traditional guideline based care to evidence based care, really moving into algorithm and data driven care ultimately as we're headed.
00:33:21:19 - 00:33:42:21
And so I think as AI continues to evolve, it's going to be critical that pharmacists are involved in the entire lifecycle of this and are able to have a seat at the table and provide input on, you know, what are the right problems to solve, what are the right data that's needed to make these kind of decisions and when is it needed and where is it needed is where.
00:33:42:21 - 00:33:51:03
I think we really need to ensure that we are using these tools, not just to use them because, you know, it's the latest and greatest, but that we're using them to solve the right kind of problems.
00:33:51:05 - 00:34:19:06
Yeah, it's very human of us to think or process anything new and immediately as to how can this negatively impact us or what is the detriment to us. However, I'll counter that with if we pull the group of community based pharmacists and ask them, you know, would they approve of the use of AI to reduce disruption of health care due to drug shortages?
00:34:19:08 - 00:34:43:04
I think most pharmacists would welcome that. If I models can help identify when there may be a shortage of product X or product Y or Z, and if you know a shortage is imminent, how can you work with patients and prescribers to get ahead to identify alternative models or alternative medicines excuse me, and get those prescriptions right? That is a benefit to the system, right?
00:34:43:09 - 00:35:07:11
Or if you're asking pharmacist, Hey, what if I was used to reduce the need or the necessity or the time that it's taken to complete a prior authorization, right, for a patient, Those are all parts where I think every pharmacist would be saying, sign me up. So there's definitely good use of application that that can come from this, but it's how those models get used.
00:35:07:11 - 00:35:30:19
And, you know, earlier I referenced Terminator two, but I'm going to quote or think about another movie from the 1990s, one of my favorites, and it's Jurassic Park. I'm not really aware of any use of A.I. and Jurassic Park was quite practical. But from Jeff Goldblum's character, Ian Malcolm, to quote, Your scientists were so preoccupied with whether or not they could they didn't have to think if they should.
00:35:30:21 - 00:35:52:13
I think that applies a lot to how we were going to use A.I., right? Just because we have it doesn't mean we should always use it. It's the importance from it and it's appropriate Use is going to be how we discern where to use it, how to use it wisely. I'll call out as well. You spoke around some some of the items on, you know, patient identified, patient reported data and information.
00:35:52:15 - 00:36:11:03
You know how that gets into data privacy, right in the information. Right. As this is based on what's on the Internet, obviously data privacy, that's another aspect, which I mean, we could have a whole nother topic on that as it relates to this. So there is there are moral and ethical questions that go to it and how these models are based.
00:36:11:05 - 00:36:32:20
But the tools that are there are going to be really helpful. So I think, you know, what I've what I've thought about use of AI going into this conversation today. And I think what we've reinforced, I it's it's really this broader, deeper learning tool able to analyze and assess data at a much quicker and deeper level than we as humans are, I think willing.
00:36:32:20 - 00:36:49:07
Chris, you mentioned it before where this is something where it could be much more intuitive than the human brain can be in a short period of time. But like any tool, it's going to be important for us to double check our work, make sure it's an appropriately used, and that it is really serving us in a way that is that is meaningful.
00:36:49:10 - 00:37:13:18
So, Christine Whitley, I'll I'll wrap us up from the hour for the conversation today. This has been really, really helpful. And at the end, we'll definitely give you a chance here to provide any kind of final thoughts or where people can reach out to you as it goes to this topic. I think this is one of the more interesting conversations that we've had on the Quality Corner show, and I will have to shout out my teammate Adam at us who introduced us to the both of you.
00:37:13:20 - 00:37:36:03
So definitely appreciate how it's a good old fashioned human connection and not A.I. that made this conversation happen. Although I'm sure the future of many of these podcast guests could be identified by A.I., we probably could plan out a whole season using A.I. tool to figure out what we're going to do. But I appreciate you, Adam, for reconnecting us with Whitley and Kristi.
00:37:36:05 - 00:37:51:09
But now we're at the part of the show where we get to have a little bit of a fun conversation. It's not necessarily related to health care, not necessarily related to pharmacy, but really for the both of you, Christine Whitley, it's to help us get to know the two of you a little bit more and how your thought process goes.
00:37:51:15 - 00:37:59:14
So this is our Rapidfire session of questions at the very end of the show. Are you both ready for these hard hitting questions?
00:37:59:16 - 00:38:00:19
00:38:00:21 - 00:38:06:16
So first question, are you a morning person or a night owl?
00:38:06:18 - 00:38:09:07
Morning person 100%.
00:38:09:09 - 00:38:19:02
Okay. What? Like what, Kristi, before. Sorry. What the before? Yeah, we've already like, what time is this? Like 4 a.m.. Are you or is it 6 a.m. 90?
00:38:19:06 - 00:38:21:19
Then it's like a 7 a.m..
00:38:21:21 - 00:38:25:06
Okay, but you're up and at it when you get up.
00:38:25:08 - 00:38:39:04
Well, so I like, like let's say I'm starting work at 830 or nine, so no matter what time my meetings start, I want to give myself at least an hour and a half to 2 hours to myself. It's like my morning time.
00:38:39:06 - 00:38:42:08
Got it. All right, Whitley, how about for you?
00:38:42:10 - 00:38:56:03
I'm the same typically a morning person, at least I think workdays. So around five. I like to have a good time. But then on the weekends, it's like that kind of totally goes out the window. It's a little bit good.
00:38:56:05 - 00:39:14:01
Okay. All right. Next question. And again, Christy, we'll have you start with the answer response. Do you prefer to read the book or watch the movie slash television series or read the book? Do you have and do you happen to have any examples that come to immediately come to mind where this is true?
00:39:14:03 - 00:39:24:19
Not as many, because I feel like I haven't watched or read a lot of books that have turned into movies, but I am just more of a bookworm in general.
00:39:24:21 - 00:39:27:06
All right, Whitley, how about for you?
00:39:27:08 - 00:39:47:15
It's the same. I feel like when you read the book, it's just a different experience and you can still enjoy. I feel like you can still enjoy the TV show after you've read the book, but then it's really hard for me to go back if there's already a movie or TV and then try to read the book because and then I feel like, Oh, I know what's happened, or I think it's just easier to go book to movie.
00:39:47:17 - 00:39:54:10
So. All right, next question. What is your recommendation for living a healthy life?
00:39:54:12 - 00:40:00:12
I would say self-compassion, something I'm working on myself.
00:40:00:14 - 00:40:08:18
Christy, How do you define so that's not I, I mean, I know what those two words mean independently, actually get them together. What do you mean by that?
00:40:08:20 - 00:40:45:06
I think so. If I were to share a bit more of like a personal example, and I think Whitley and I talk about this and many of your audience yourselves could potentially relate to this, but pharmacists like and clinicians, you know, we're we're very driven. We're high attention to detail and ambitious about our careers. And I think self-compassion to me just means sometimes giving yourself grace, like not beating yourself up over something that happened at work or not having maximized on an opportunity to do something more, things like that.
00:40:45:06 - 00:40:47:23
So that's how I would define it.
00:40:48:00 - 00:40:55:07
Excellent. Thanks for that description. Definitely one that I'll have to add to my vocabulary and learn a little bit about. But Whitley, how about for yourself?
00:40:55:09 - 00:40:58:15
Well, it's hard to beat that. That answer there.
00:40:58:17 - 00:41:01:10
Sorry. Sorry that we set you up in this one.
00:41:01:12 - 00:41:23:12
I know. I mean, I would. I would say I was thinking something a lot like something that was along the same lines. But I think daily gratitude and how you think and just having that appreciation for every day. I think that's just an important component of overall, you know, mental health that is key to then physical health.
00:41:23:14 - 00:41:52:16
Yeah, I think some in our audience may appreciate the I think this is right use of irony. I could be wrong, but our experts on the show are having perhaps the most human answers when it comes to how you maintain a healthy life. Final question in this section, Christy, again, we'll have you start what one goal that you are currently working towards, and this can either be in your personal life or in your professional life as a pharmacist or with your work at the collective.
00:41:52:16 - 00:41:55:12
But what is one goal that you are currently working towards?
00:41:55:14 - 00:42:25:06
Sure So I guess I kind of shared the personal goal earlier with the self-compassion piece, so I'll think of a professional one. And so for me, how I've observed kind of this health tech, digital health ecosystem, I feel that for us to really advance how we leverage technology to improve patient care in the future really hinges upon better partnerships that we may or may not have seen in years past.
00:42:25:12 - 00:43:01:12
And so we're talking about corporate companies, startups, academia, research, public nonprofit, super affectionally speaking. I think I'd really like to be in a position or at work in a capacity where I can bring all of these people together to connect diverse perspectives, diverse voices, bring them all to the table such that we can really help shape and contribute to this digital health ecosystem moving forward,
I I'll take a more personal of personal perspective on this.
00:43:01:14 - 00:43:23:16
Bigger me. One of the things that I've been focusing on is, you know, think about more systems goals versus overall achievement goals and just simply practicing more awareness and intentionality and even, you know, minimizing multitasking, for instance, that's been a focus of mine recently.
00:43:23:18 - 00:43:43:16
Excellent. Well, appreciate the answers and the quick run through on the questions here, Christine Whitley. Now we get our actual final segment of the show. And this is just very simple. If folks want to find out more about the ad collective, where can they do so and how can they contact the both of you?
00:43:43:18 - 00:43:53:05
Absolutely. So please feel free to reach out to us through the Air Collective Echo Web website or reach out to either of us personally. LinkedIn.
00:43:53:07 - 00:44:10:08
Excellent. All right. Well, Christy, Whitley, thank you both very much for joining with this episode. This was a lot of fun for me to go through a topic that I'm I would say I'm definitely interested and you two are both passionate about it because obviously it's what what you do. This is more an area of questions for me.
00:44:10:08 - 00:44:30:07
So this was a really great episode. I appreciate that your ability and willingness to come jump on the show, have this conversation with us at the USDA. So thank you for that for our audience. We will make sure to include information and where you can find Christine Whitley's work on the Air Collective in the show notes. So be sure to check that out.
00:44:30:09 - 00:44:43:1
But with that, we have now wrapped up today's episode. And we thank you for joining us and we hope you listen to our next episode of The Quality Show. Before we go, we have one final message from the PQS team.
00:44:43:12 - 00:45:05:00
The pharmacy Quality Solutions Quality Corner Show has a request for you. Our goal is to spread the word about how quality measurement can help improve health outcomes. And we need your help in sharing this podcast to friends and colleagues in the healthcare industry. We also want you to provide feedback, ask those questions and suggest health topics you'd like to see covered.
00:45:05:02 - 00:45:27:06
If you are a health expert and you want to contribute to the show or even talk on the show, please contact us. You can email info at pharmacy quality dot com. Let us know what is on your mind, what we can address so that you are fully informed. We want you to be able to provide the best care for your patients and members, and we wish all of you listeners out there well.