The Precision Educator

What is Precision Education? Rethinking How Physicians Learn

• Larry Chu, MD • Season 1 • Episode 1

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Medical education continues to rely on standardized curricula and assessments, even though learners progress at different rates and bring different needs, strengths, and contexts to training. As expectations grow around equity, accountability, and outcomes, many educators are questioning whether one-size-fits-all approaches still serve learners or patients.

In this episode of The Precision Educator Podcast, Dr. Larry Chu and Dr. Viji Kurup introduce precision education as the educational parallel to precision medicine. Drawing on their work with Stanford Medicine and the Society for Education in Anesthesia, they explore what precision education actually means, why it matters now, and how it differs from earlier reform efforts in medical education.

The conversation moves from concept to practice, examining how personalization can be operationalized through coaching, assessment, and data-informed decision-making in real training programs.

Key takeaways from this episode:

  • A clear definition of precision education in medical training
  • Why standardized approaches struggle to meet modern educational demands
  • How precision education connects learning outcomes to patient care
  • What distinguishes precision education from prior competency-based models

Especially useful for:

Program directors, associate program directors, faculty developers, clinical competency committee members, and education researchers seeking a shared language and framework for personalized learning.

Related episode:

If you are interested in how personalization happens in day-to-day training, listen next to Episode 2: Coaching as Precision Education, which explores coaching as a core mechanism for individualized growth.

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Larry Chu, MD:

Welcome to the very first episode of Precision Educator Podcast. I'm Dr. Larry Chu, professor of anesthesiology at university. And I'm here with my colleague Viji Kurup. Together we co-chair the Society for Education and Anesthesiology's Precision Education Task Force.

Viji Kurup, MD:

Thanks, Larry. We're so excited to launch this series. I've Viji Kurup and the Vice Chair for Medical Education at Yale and Professor of Anesthesiology also. We really have been talking about these concepts for some time now, and we really thought that this podcast might be a space for medical educators, faculty, and learners who want to explore how we can move beyond the one-size-fits-all education that we are so used to right now and tailor our learning experiences for each individual trainee.

Larry Chu, MD:

And our aim is to talk to experts, highlight innovations from programs across the country that are burgeoning in this new field, and to bring to our listeners real stories of how coaching, learning analytics, adaptive learning are changing the way that future physicians are being prepared using this new approach.

Viji Kurup, MD:

That's right, Larry. And I also hope that through this we can create a community of educators who are curious and maybe who are all experimenting at their own institutions and who may want to learn together. So whether you're new to this concept or already building precision education projects, we hope this podcast gives you ideas, inspiration, and even some practical tools.

Larry Chu, MD:

Absolutely. And in today's episode, we'll start with really the beginning, what precision education is and why it matters. Now, one of the first articles on this topic was written by Mark Trilla and Jesse Burke Raffel in Academic Medicine a couple years ago. And they define precision education as a systematic way of using data and analytics to deliver the right educational intervention to the right learner at the right time.

Viji Kurup, MD:

That's such a powerful definition, Larry. But I do think that we want to explore what this means in real world terms. So, how do you apply that idea in a residency program? How do you apply it in faculty development? And what challenges and opportunities does it create for people just like us?

Larry Chu, MD:

Well, Vijay, that is absolutely what this first conversation is all about. So let's let's dive in.

Viji Kurup, MD:

Perfect. So we've talked about why one size fits all approaches don't serve today's learners. So let's shift gears a little bit and ask the big question. What do we actually mean when we say precision education, Larry?

Larry Chu, MD:

So at its core, precision education is about using data in thoughtful ways to personalize the learning journey. So just like we've heard about precision medicine as approaches to tailor treatments to biology in the context of the individual person, precision education aims to tailor teaching and assessment to the needs and the goals of each learner. And one paper that described precision education talks about it as a systematic approach that brings together both longitudinal data and analytics to guide precise and timely interventions to create a continuous cycle of learning that supports the learner throughout their journey. And I like that because I think it emphasizes two things. So first, it's that we're not we're not just talking about one-off uh fixes or interventions, uh, but ongoing touch points throughout the journey based on feedback, based on data, based on analytics. And second, that the goal is to meet learners where they are and with what they actually need. So some authors have created a useful way to think about this. There's of course many um, I think different interpretations of precision education, but the the one I think that has gone out there is something called the P4 framework. And it looks at precision education um in the following ways that education is proactive, it's personalized, it's participatory, and it is predictive. So take a look at those four P's as ways to implement, to actualize, to think about making education more precise for the learner. So the proactive part means uh giving feedback before problems become a crisis. Uh personalized means shaping the learning more precisely to fit the individual. And so for us as educators, that might mean really trying to figure out what type of learning a learner might respond best to, right? Participatory really highlights that learners are partners in this process. So it's not just us as educators trying to determine what is best for the learner, but it's an exchange of ideas and it's a process of collaboration. And predictive, of course, means using good analytics, good learning analytics and data to try to anticipate needs and guide and support learners through their journey. So stepping back a little, this isn't just about uh an individual success of a learner. Some authors are arguing that it ties directly to our social contracts as physicians, for example, that the way we assess and support learners should ultimately translate to better patient care and more equitable outcomes. So it becomes part of diversity, equity, and inclusion in healthcare as well. And that's a powerful, I think, reminder for why we need to engage in learning more about precision education, how we might be able to incorporate it into our practice. So with that, when you think about that definition, Vijay, what feels most urgent for you for how we might train doctors today with that framework?

Viji Kurup, MD:

Yeah, so I I really love the way you put it, you know, the uh P4s, which is proactive, personalized, participatory, and predictive. So it really, I think, brought home all of these things that we had been thinking about. You and me, Larry, we've been thinking for a long time about uh how to fix the two sigma problem. You remember that a few years ago, we uh participated in a panel together where we talked about how we might use the technology that we have right now to fix this age-old problem that we had of this, you know, people or learners responding to personalized education. And I think now we are at the crossroads where we can actually implement it. And so the urgency I think comes from the fact that our traditional systems still rely very heavily on episodic exams and summative milestones. And our learners often don't know what they're struggling with until very late in this process. And so I think that having this precision education framework would flip that for in favor of our learners by offering some continuous data-driven feedback, especially if it is in the form of dashboards that our learners could have access to and you know, almost create that adaptive learning uh platform that we had been uh talking about for a long time. So, uh, what do you think about that? About you know, these four elements that you talked about, the proactive, personalized, participatory, and predictive. Where do you see in your experience the biggest gap in our current systems?

Larry Chu, MD:

I thought for many years, and I I've given a few talks about it, and that my frustration has been that why is it that Netflix and Amazon can learn my preferences and predict what I want so well, but we don't have a similar technology to help educators understand the learning preferences and the needs of our learners, and I think that is a big gap that we could close because we have the technology, we just haven't applied it. Uh I do think some of the challenges are we haven't had maybe as much of the data that we maybe do now, but this is an opportunity, and I think we can take a lot from other fields and apply the predictive part of that for Ps, especially with the emerging work in AI, natural language processing, large language models, to really help us advance that predictive element to help learners on their journey in that personalization part to predict the needs of learners before they reach a critical milestone point. So that for me, I think the predictive part is really interesting. I think it's a it's a gap that we could close. I think we need to work together as a community to really look at that thoughtfully.

Viji Kurup, MD:

Yeah, I think I think that you know uh that's actually something a lot of educators, I think, around the world are uh, you know, currently thinking about. And it also ties in, you know, Larry, with this idea of um physicians having a social contract, right? Like we are entrusted by the public as educators to not just create competent physicians, but also compassionate and adaptable clinicians. And so I think you know, that expectation from society is something that we could probably now very realistically fulfill by linking our program evaluation to patient outcomes. Like how are our learners doing in communicating with patients? What are their safety metrics? What are their equity indicators, right? And now when we can take all of that and input it within the training program so that we can, you know, full fill these gaps that we've had for so long that we thought were unfillable, I think that's what makes, you know, this uh so exciting for a lot of us. What do you think?

Larry Chu, MD:

Absolutely, absolutely. And I I think there's there's a lot of these gaps that we're gonna start to realize they're they're not so unsurmountable that we can really approach them. We have the means. We just haven't really thought through carefully how these new technologies, how the data that we have, that we already have, that that's been growing, could be applied to try to solve some of these problems. But let's take this a step further or maybe a step back, I don't know which. Let's really ask a more basic question because there could be people listening to this podcast who are a little skeptical and are wondering why not just keep doing what we have been doing? Because what worked for me, the one size fits all, the let's teach everyone the same way, worked for me. And I'm a great clinician today. So why do we even need precision education? What's broken with the current model?

Viji Kurup, MD:

So what do you think?

Larry Chu, MD:

Yeah, what do we tell those people?

Viji Kurup, MD:

Yeah, I know. And uh, this is not, you know, it's not a theoretical question because we do have these questions coming up in our, you know, national societies when we meet each other. At uh my institution, we have a meeting of uh educators and we get together uh frequently and think about what are the problems we are facing in terms of both for the our trainees as well as uh for our faculty. And that's one of the things that you know comes up most often. And I think you know, the one size fits all has a lot of uh defects. Um, just as an example, let's just look at assessment, right? We give all students the same multiple choice exams, and the scores on that determine whether we accept people into fellowships, uh, whether we uh let um medical students come into a particular specialty of um uh and of uh medicine. And yet, you know, those scores really don't tell us much in terms of uh what is happening with these learners in terms of teamwork, what is happening in terms of their critical thinking, their clinical reasoning, right? And so there are studies now which are looking at all of this, they are looking at whether these multiple choice questions are the best way of assessing our learners, do narrative evaluations uh uh have uh better insights, but all of these, you know, narrative assessments get lost in uh, you know, the data noise that we have. So I let me ask you a question. Do you think standardization still has a role uh in today's uh medical education?

Larry Chu, MD:

Well, absolutely. I think that it's not going away. I think uh standardization is something that exists for a reason. Um in many ways, I I think it helps to, if done right, it helps to ensure fairness, to help with comparability, especially you mentioned graduate, uh postgraduate um fellowships, for instance. But precision education, I think, could be used, though, to add a layer of additional understanding, of additional personalization. And it's actually something that we're already doing, maybe not in as scalable ways, but we do high fidelity simulation, for example, to assess teamwork. And there could be additional ways, though, that we could implement newer technologies and more regularly, right? So not just once a year or not just in a high-stakes milestone assessment, but more regularly during our everyday trainings. People are looking at uh simulation-based haptics uh in assessments, so that we can actually look at how touch and palpation are used, for instance. And that that's just one example. But I think overall it's how can we add a layer to what we're already doing, not to replace. Yeah. So but with that, I think would it be useful uh and maybe it wouldn't, but maybe we could think. You know, are there examples where we could think through where one size fits all? Maybe that's not a good word for it because it feels a little limiting. But maybe we're traditional medical education versus where you could take a uh layer on a precision education approach to that traditional education. Yeah. Think of some examples.

Viji Kurup, MD:

That's that's like a you know, perfect question in terms of, you know, yesterday I was just taking my resident through a thoracic epidural. And, you know, I was just thinking about how we teach our trainees, you know, to perform one of these epidurals, right? Like they either learn by a lecture or we have them practice on a phantom, and they're required to attempt so many epidurals in a thoracic OR and document that they've done these uh these many techniques. And the feedback is usually binary. I got the epidural, I didn't get the epidural, the epidural worked, the epidural didn't work, right? And there's really no differentiation for those who need more anatomic mapping versus those who need better loss of resistance control, or somebody who has deaf difficulty threading the catheter, right? And so we have these learners who have these different problems, but we kind of treat all of them the same, and our feedback is not as nuanced as it could be, and the you know, the further planning for what they could do next is not as granular as it could be. And so maybe if when if we were thinking of a redesign, we could have, you know, we could decompose this epidural placement into like subtasks, you know, from uh advance the needle from skin to the ligament angle, then go from the ligament to the epidural space, then once you identify the space, advance your catheter. And in each attempt, you kind of figure out where is the learner struggling? Is the learner struggling in finding out the anatomic areas and advancing the needle? Or is it in you know the skill level where you have to hold your needle and advance the catheter? And then we can actually, you know, uh if the learners don't meet benchmarks in each of these subtasks, uh, we can then focus on that and maybe even have adaptive phantoms, right? You start with a standard anatomy, then gradually introduce challenging features. You could have somebody with scoliosis, you could have somebody with narrow interspaces, you could have somebody with, you know, calcified ligaments. So you could uh actually make these, you know, different, progressively difficult, just different types of scenarios, and then uh doing you know these uh small micro-simulation modules uh after we've identified where the learner is struggling. So just you know, just taking my learner through this epidural, uh, you know, thoracic epidural. Yesterday I was kind of going over all of these in my head and saying how wonderful would it be if we could actually, you know, uh break these this one task into small micro tasks and then have a remediation almost for each learner that was directly related to their uh particular um uh you know skill level. And uh that would then make it actually more relevant for the learner than uh standard feedback uh you know that we give for our uh learners. So that was something that just got me thinking yesterday.

Larry Chu, MD:

Listening to your explanation, it it resonates because it makes it make it has face validity with me as an educator, because that I'm listening to you, I'm like, that's the way I'm thinking. That's the way as an educator I would walk someone through it. I break down a complicated procedure into different segmented tasks, right? But the difference is, and I think the beauty of your explanation is in how the learner is assessed and the feedback is given, right? So, in all those uh ways that you described it, I would imagine many people, include myself included, would say, you did a good job. You might consider doing this differently next time. Well, we would never go into the five or six or seven different task segments that might need individual improvement. They would get the one thing. We would tell them the one or two things. And so for the learner, they're not getting the maximum learning that they could be getting from the feedback. And what I like from your example, and I think why it makes it a precision education redesign, is the idea that you're collecting data and you're collecting it in a way that is standardized, so that you're if we can all agree on how to break down the placement of a thoracic epidural into different tasks, maybe skin wheel, maybe, as you said, the skin to ligament angle, the depth of the epidural placement, then we can start talking about the same language to give feedback, the same language to create um like a progression, right, of your skill development. And then um help educators to start to talk and share with the same language. It doesn't mean because I think the other thing that when I first heard about when I first started learning about precision education, I think there was maybe a thought that we would all have to start teaching the same. Yeah. And it's a not only is it a standardization of of um terms, but now we'll all have to teach the same and all the modules will be the same, and it's the same, same, same. But I think what this does though is give us the freedom to create our our different different approaches because it's personalized. But we have to have some shared vocabulary. And I like that idea that you just gave because it because it makes has surface validity for me and and it makes sense, and that I think drives home the reason why we we need this approach to to give a voice to what we're already doing. We're just not maybe doing it as precisely as we should. All right, so maybe this is a good time to uh talk about where precision education is already showing up, or maybe examples where we might already see it has potential? So this is a maybe a question and and I we have to recognize that the concept of precision education is very new. So with that in mind, um I'll ask you, VG, do you have an um thing you've seen, any programs, or even any concepts or ideas that you think really could embody precision education? I know you just gave me this idea of a thoracic epideral, but is there anything bigger that you are thinking about?

Viji Kurup, MD:

Yeah, so remember at the at our uh Society for Education and Anespesia task force that um we both co-chair at the last meeting, uh we had so much excitement among the members who showed up for that task force meeting. And a lot of them talked about some of the you know projects that they have going in their own, you know, in their own institutions. And I know that you know some of the programs use the you know information from your electronic health record, and they then correlate what is, you know, and I know that you've done some work on this, where you kind of take what we what the residents, what cases the residents are having the next day, and then uh, you know, get some uh a shot almost like a write-up or point them to uh information related to that case that goes out to the residents. So it is just in time, right? So when we're talking about uh you know precision education, we're talking about the right knowledge going to the right learner at the right time. And I think that that kind of you know hits home for me that, and we do this informally many times, like we ask them questions when we have resident scholars the previous evening, we point them to you know, either guidelines or articles that we think would be relevant to the next day, but doing this systematically and doing this for all learners, right? I think that that um can uh really make uh precision education a real thing for you know for our learners. And it is it is doable, right? The all we have to do is to link our electronic health record to databases that we have that we point our residents to for their learning. And I know that you did that for some time uh for your residents.

Larry Chu, MD:

That is a project that we are piloting at Stanford. We we have access to the Epic case procedure so we know which cases residents are doing uh the next day as well as which cases they have done historically. So we not only know the case gaps or the, but we can create a procedure learning map and see what are their learning needs. And one of the things that we are working on to pilot is to send them the society guidelines or clinical updates for the learning map for the cases they're doing the next day and see the impact that might have on implementing the best practices.

Viji Kurup, MD:

And I think that there's also some data in literature on um uh places like Vanderbilt and Nvan U, who are using uh, you know, some mode or some parts of this framework to make uh it uh better for the learners.

Larry Chu, MD:

Absolutely. I know that um Vanderbilt has some form of aggregation of longitudinal data of their assessments that they provide back to their students for personalized feedback. And NYU has a very robust effort uh in precision education, and they're using AI in some forms to analyze notes and generate feedback. So burgeoning areas, but also all showing some very interesting and promising um efforts. So, you know, I think with that, uh it might be important to also discuss challenges and opportunities for precision education. Um because as much as we've talked about that it has promised, that it's very interesting uh to implement these new technologies, you know, there are people um who express some concerns or there there are challenges around the use of data. Maybe it's time that we kind of discuss a little bit about what are some of these barriers that may stand in the way of implementing precision education.

Viji Kurup, MD:

Yeah.

Larry Chu, MD:

And I think What do you think might be the biggest barrier?

Viji Kurup, MD:

Absolutely. Like I think there are a lot of barriers, and this also came up again at the you know meeting that we uh had, um, the kick of meeting at the SEA. And one of the things that I feel our faculty are really concerned about is. That they they really lack time to process all of this, you know, large amounts of learner data. So one is in terms of time, right? Faculty are being pushed for productivity. Uh, we recently did a survey of the anesthesiologists in Connecticut as part of our Connecticut State Society of Anesthesia survey. And informally, I can tell you that uh when we discuss the results, one of the biggest things that our uh faculty are concerned about is their, you know, the productivity parameters that are coming as a barrier between, you know, their teaching and you know making sure that we are safely taking care of sick patients, right? So the productivity uh pressures are really high for faculty. And so one of the things is in terms of time, in terms of bandwidth, and in terms of expertise, right? Like if you want a faculty to make sense of all of this data, they also need a little bit of training in terms of expertise to figure out what is it that they're seeing and how can they make sense of all of the data and be able to take what they need to make sure that they can best serve their learner for that day. So I think that uh, you know, in all of those are the things that are, you know, I would feel barriers to this wide-scale implementation of uh precision education, because although all of these things uh sound wonderful, you know, implementation of it is, you know, always takes time and effort. And most of that effort is, you know, the initial effort, right? Where you want to create these dashboards, where you want to, you know, federate all of these earning objects that you have. So all of those are initial work, but the uh, you know, sometimes people are just you know stupefied by the enormousness of this data that is out there. And I think breaking it down to simple forms, uh creating collaborations between our colleagues in um biomedical engineering, in computer science engineering, and our you know, faculty, I think would be key in making sure that we can actually execute on all of the ideas that we have. Do you have similar issues with uh what do you think is, you know, about this?

Larry Chu, MD:

Well, for me, I think one uh challenge that we probably don't talk about enough are ethical areas. And I think it is uh an area that will take some scholarship and some collaboration to work out. So I think ethics in terms of how learner data is used to deliver personalized learning, who owns? So if I am a resident, if I am a a learner, who owns my data? Yeah, how is my data used? How long is my data uh kept? Yes. And can it be sold? If I move to a different program or a fellowship program, is that data shared? So that you know, once we get to the level of how long it took me to place a skin wheel for a thoracic epidural last Thursday with Dr. Kurob. Yeah, there are questions about how people will use the data. So I think there are these types of questions that need to be resolved. And and with that, I think also in the same amount uh we're on in the same theme of ethics, bias. Bias is also a concern. And we know, for instance, that artificial intelligence can be biased because if AI is trained on biased data, for instance, it can reinforce inequities. So part of it is we want to have some assurances or some mechanisms for understanding how the data is collected and what biases there are, right? There are all the the times. I mean, who's measure so for instance, I'm you know, I'm just kind of being facetious, but you know, if we were ever in a position where we're measuring how long it takes to put in a skin wheel, who's measuring that? Right. And are there are there any biases in measuring that time? Because could that affect my future career? So the these are the complexities that come into play when we start to collect larger and larger amounts of data. And maybe we're not thinking about that yet, but I think there will be come a time where it's important to start to think about some of these. And and maybe not even objective data, but what about narrative data and biases, written, written comments? And how do those written comments then become part of the workflow to personalization, right? And and how are they how are they actualized, right? So how how do we convert narrative comments using AI into personalized feedback? And does how does someone's written comment about me affect me later? And how does AI use those?

Viji Kurup, MD:

Yeah.

Larry Chu, MD:

Especially if people's narrative comments could be biased.

Viji Kurup, MD:

Absolutely. Absolutely.

Larry Chu, MD:

Yeah, it's a big ethical. Yeah, I know.

Viji Kurup, MD:

And really, you know, like I feel precision education can either narrow or widen gaps, right? Like uh, if it's done well, it ensures that each learner gets the support they need when they lead it. But when it's done poorly, it risks reinforcing privilege, right? And uh so, you know, participatory models that involve the learners and communities can help keep you know equity front and center every time we are making these uh models. So how do you make, how do we make sure that precision education promotes equity rather than reinforce the biases that already exist in our society?

Larry Chu, MD:

Well, you've mentioned that a few times during this podcast. I I love that you have, and I think it's something that doesn't really get a lot of discussion when people talk about precision education, equity, and you use the word social contract. Both of those words I think are so important. I think we have to think about those two words and how we consciously and how we consciously incorporate equity and social contract into our efforts around precision education. You know, one thing that I've always done in in my work with Medicine X and and other uh efforts in inclusion is to try to be as broad as possible in inclusion, in involving people in your design, in your planning as early as possible, and not just creating something and then inviting the end user in and saying, Hey, I did this thing, don't you like it? And and so I think for precision education, we are in the early days, and now is a good perfect time to be cross-disciplinary, to invite in learners, patient advocates, educators, technologists. This is the time to bring everybody to the table to talk about what we're doing so we can we can really see what the impact might be. When you bring in members of the community, when you bring in the and end users of the education, the learners, when you bring in the educators themselves, then you can start to see how the tools that you build might shape the efforts. And I think it's important to have the diverse voices at the table. Um I think it's the beginning, it's a start, it's not the end, and it's an ever-evolving process that requires deliberate practice. So let's let's maybe wrap up with a a view of the future. Um, this is all really exciting. We're at the beginning here, and so maybe it's too early to think about where this is all going. But I want I want to challenge you to think about that a little bit. So if you could design one precision education tool for the future, yeah, what would it be in 2025 if you're gonna design one precision education tool for the future? What would it be?

Viji Kurup, MD:

Yeah, and this is something I've been thinking about, you know, for a long time. And uh, you know, we thought that I never really thought it would take so long for us to do this. And why can we not have a dashboard for our learners, right? So we have the data about the types of you know uh patients they see, what cases are they involved in, what are the patient outcomes? Uh, what are their standardized test results? How have they done in their simulations? We have all of this data, but they're all in different areas, and it's really like we haven't put all of them together. So I if if I could design one tool, mine would be a real-time uh coaching dashboard for our learners that integrates everything, their clinical performance data, the feedback from the peers, any assessments that we're doing for them and their patient outcomes. So the learners can see where they are and where they need to go, and faculty can also guide them more effectively and efficiently, taking a look at this data. So, you know, I really do think that precision education isn't just about making better doctors, but it's also fulfilling our profession's responsibility to patients and the society, right? Like we go back to the social contract and making sure that we fulfill the obligation that we have towards society when they have entrusted to us the care of the future, you know, physicians. And so uh we now have all of the um technology we need to make this a reality. It just needs more of us to bring all of that together and to make sure that we can do this, you know, for our future.

Larry Chu, MD:

Yeah, absolutely. I I think that that would be a great I want to see that. Where is that? Yes, exactly. I think that is a very noble and important goal. And I'd love to see the patient voice in there somewhere too, though I think you did mention it, but um and you were one of the things that I've think patients often don't don't see the endpoint or they don't see the process of education. And I I wonder if they could. Right, and they should or be involved in some way, right? Because what would that look like?

Viji Kurup, MD:

Absolutely. How would that look? Yeah, the uh the ultimate you know beneficiary of all of this is a patient. So if we don't get this right, the people who suffer is going to be the patients. And so having their voice while we are developing all of this, I think is absolutely essential and central to what we are doing.

Larry Chu, MD:

Well, I think this is a good time to wrap up. You have been listening to The Precision Educator, where we aim to explore how data, coaching, and innovation can transform the way we teach and learn in medicine. This has been our first episode, and one of many. And our goal is to help you, our listener, move beyond one size fits all education towards approaches that are proactive, personalized, participatory, and predictive. If you have enjoyed today's conversation, please subscribe and share the podcast with your colleagues. And if you'd like to join the movement, connect with us in the Society for Education and Anesthesiology Precision Education Task Force. Until next time, I'm Dr. Larry Chu.

Viji Kurup, MD:

And I'm V.

Larry Chu, MD:

And this has been the Precision Educator. Personalizing learning, transforming medical education together.

Viji Kurup, MD:

Until next time.

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