When Data Becomes a Coach: Rethinking Assessment, Coaching, and Learning Trajectories

Data already shapes how we practice medicine. It helps us see variation, benchmark performance, and improve patient outcomes. Yet in medical education, data has often been used in far narrower ways, as static snapshots, checklists, or retrospective judgments.

In this episode of The Precision Educator Podcast, Dr. Matthew Caldwell joins co-hosts Dr. Viji Kurup and Dr. Larry Chu in a conversation to explore what happens when clinical data is reimagined as a tool for learning rather than surveillance. Drawing on his work with the Multicenter Perioperative Outcomes Group (MPOG), Dr. Caldwell discusses how high-resolution perioperative data can help educators understand not only what residents do, but how their learning unfolds over time.

Together, the discussion examines how experience data can illuminate learning trajectories, support entrustment decisions, and strengthen coaching conversations without undermining trust. The episode also addresses the ethical and cultural risks of educational data use, including learner fear, faculty judgment, and institutional misuse, and why governance and transparency must be built in from the start.

A central theme of the conversation is data-informed coaching. Rather than delivering dashboards in isolation, the episode explores how data can anchor reflective, learner-centered conversations that promote agency, growth, and resilience across training.

Key takeaways from this episode:

• Why experience data matters more than isolated performance snapshots
• How longitudinal patterns reveal learning trajectories and support entrustment
• The role of data-informed coaching in precision education
• Risks of misuse, surveillance, and bias, and how to build ethical guardrails
• Practical ways educators can begin using data, even without advanced analytics infrastructure

Especially useful for:

Clinician-educators, program directors, CCC members, faculty coaches, and education leaders interested in using data to support learning, assessment, and coaching while preserving trust and educational integrity.

Related episodes:

For an introduction to the principles of precision education, start with Episode 1: What Is Precision Education? Rethinking How Physicians Learn.

For a deeper look at coaching as a core educational mechanism, listen to Episode 2 on coaching and personalization in medical training.

Transcript

Viji Kurup, MD: 0:09

Hi, so welcome back to the Precision Educator. I'm Viji Kurup, and I'm joined as always by my colleague and co-chair of the Society for Education and Anesthesia's Precision Education Task Force, Dr. Larry Chu.

Larry Chu, MD: 0:25

Thanks, Viji. It's great to be back. In our first two episodes, we talked about why precision education matters and how coaching helps personalize learning. Today we're going to turn to something that underlies nearly every promise we've made so far about precision education, and that's data.

Viji Kurup, MD: 0:46

Yeah, that's so right. So data already shapes how we practice medicine. It's been driving quality improvement, it's been guiding how institutions adapt. And more and more it's uh influencing how we teach, right, Larry? And also how we learn, uh, even when we don't say this aloud.

Larry Chu, MD: 1:06

Absolutely. You know, but using data in education raises some interesting and some tough questions. For instance, what data actually matters? Who gets to see it? And how do we make sure it supports learning rather than turning education into surveillance or judgment or checklist? Yeah, and actually to help us explore that today, we are joined by the perfect person, someone who works right at that intersection of clinical data, analytics, and education. Dr. Matthew Caldwell is an anesthesiologist and researcher at the University of Michigan who's leading efforts to use perioperative data to individualize anesthesiology training. So welcome.

Viji Kurup, MD: 1:53

Matt recently led a multi-center grant submission on educational innovation using data from MPOG, which stands for the Multi-Center Perioperative Outcomes Group.

Larry Chu, MD: 2:05

Yeah, and for listeners who may not know, MPOG is a national collaborative that pulls electronic health records from anesthesia departments across multiple institutions. And many of us know MPOG through these monthly reports that we get, which compare our practices to those of our national peers.

Viji Kurup, MD: 2:25

And Matt's work asks the next logical question. If we have really good, maybe even you could say high-resolution data like this, can we improve patient outcomes? It also helps us understand and support how clinicians learn.

Larry Chu, MD: 2:43

So, Matt, welcome. And we're thrilled to have you with us.

Matthew Caldwell, MD: 2:46

It is so good to be here with you today. And uh, I'm really excited to talk about this topic. I I know the two of you are just uh engrossed in the education community and forward thinking about what could be and what will be. And so I'm I'm happy to join the conversation this morning. So thank you so much for the invitation.

Viji Kurup, MD: 3:01

Amazing. Well, we're glad to have you, and uh, we really look forward to hearing about um how you're thinking about these possibilities with data and what you're doing. Um but before we actually go deep into a little bit of the technical side of things, I thought it might be helpful to ground this conversation in something familiar for our listeners. And in anesthesiology, data didn't revolutionize care or our practice overnight. It crept in slowly. Maybe some people could even say a little bit quietly through everyday practice.

Larry Chu, MD: 3:37

Yeah, and it showed up in really like practical ways in how we've changed our care and our teaching.

Viji Kurup, MD: 3:43

And I would say, you know, once we had some registries or large data sets became available, such as MPOG, we started to see variation we didn't even maybe know existed. What we felt might be standard in our department at Stanford, for instance, looked totally different when compared across institutions like your department at Yale, BG.

Larry Chu, MD: 4:07

Yeah, and that's actually when our idea of an average patient uh fell apart, right?

Viji Kurup, MD: 4:14

Right. And we began asking not just like what usually happens, but maybe what's most appropriate for this patient with these risks, and data moved from reacting to problems to anticipating like post-operative nausea and vomiting, for instance, and trying to prevent them.

Larry Chu, MD: 4:32

Yeah, and so this is actually in education, we are seeing a similar shift, right? Uh so we are kind of uh in for a long time we've assessed learners in a uniform way. We've done snapshots of um assessment uh without really considering the trajectory of the learner, right?

Viji Kurup, MD: 4:55

And so I guess, you know, Matt, what we're wondering is what led you to believe that clinical data like MPOGs, um, and you you certainly have maybe a lot of it, uh, could actually help you understand education better.

Matthew Caldwell, MD: 5:12

Yeah, thank you. It's funny listening to you to you both summarize kind of the state of things because it kind of speedruns the process that I experienced over the course of several years. And so there was a couple of different activities that kind of pointed me in this direction. One was the first half of my career was really around clinical care and uh leading teams. You know, so I oversaw uh one of our hospital operating room systems with you know, 30 operating rooms and a bunch of patients and all that kind of stuff, and was just impressed on the business analytic side of stuff, how well you could take data to make really informed, timely decisions about what has happened, what's coming down the pike, and try to adjust resources and plan proactively. And so kind of learning through that process was was really educational for me. So that was that was one foundational thing. Uh and the other foundational thing was just, you know, our department is the uh collaborating or the coordinating center for IMPOG. And so uh I'm fortunate to work with and around, you know, several really generous people that have um showed how well that data can be used to improve patient care and can be used to, as you mentioned, VG, just understand the variability that exists. And until you shine a light on it, it you just kind of make these assumptions. But as soon as you start to dig under the covers a little bit, you're like, oh, wait, there's something interesting going on here. And then experiencing COVID and the disruptions that went with that, you know, on the on the OR management side of stuff, I felt much better equipped to make timely decisions about, okay, here are the uh resources and the uh obstructions that we have right now. What can we do with those with those um limitations? But on the education side of stuff, we just did not have the tools to be able to adapt to that uh very rapidly. And so, you know, trying to lead a group that was heading towards graduation and were having you know less predictable experiences on rotations just just led me to start wondering, hey, you know what, you know, there we've had so much success on the on the business side of things and on the research side of things. You know, is there an opportunity to use this this data uh to help support our educational process as well? And so that's kind of what got me thinking about these areas.

Larry Chu, MD: 7:23

Yeah, that's that's really you know very eye-opening, especially those monthly MPAG reports, right? Like you open your email and there it is. It'll tell you, you know, how have you managed blood pressure? How have you managed your fresh gas loss? And how are you doing compared to, you know, to national benchmarks? And uh earlier we just assumed that what we were doing was the right thing, and this was, you know, the state of the art of practice. And now I'm walking into my workroom and I'm asking everyone else, hey guys, why am I so low on my PO and V uh, you know, metrics? What are you doing differently that I'm not doing, that my patients seem to have a higher, you know, PO and V risk. And I think that that's why where you know you see learning and action, even if we don't always say it uh loud. But uh, Larry, we've also seen this in other areas.

Viji Kurup, MD: 8:18

Well, I think that's you know where the reflection part kicks in, right? Because you, as the person reading that MPOG report, you you pause for a moment. It's personal. And you got to ask, why does my data look different? Uh and maybe you adjust, or maybe you talk about it with a colleague. But you know, that is learning. And that's learning in action.

Matthew Caldwell, MD: 8:39

Yeah, and that dissonance is such a strong stimulus for education, right? Where you say, okay, here's who I think I am, but here's the data in front of me. Okay, now I'm confronted with this. Now, how do I want to respond to that? Um, and that's really one of the founding principles of this work that we're going forward with, is just to say, you know what, this is an opportunity for learners to, you know, they're already doing it, but to really embed that in their training and hopefully extend that into their careers the long term to say, okay, here are the challenges that I have. What do I need to equip myself to continuously improve my practice?

Viji Kurup, MD: 9:09

And it's interesting, you use the word improvement because I think we've seen this before in NISQIP. I don't know if I'm pronouncing it correctly, but the National Surgical Quality Improvement Program. And in that initiative, they they showed that when institutions could see their outcomes side by side with other institutions, it was kind of like the MPOG report for surgical outcomes. And that really did lead to real improvements, just sharing that information.

Larry Chu, MD: 9:37

Yeah. And I want to take this a little uh further and ask you to elaborate a little bit more on how you see uh EMPOG playing that kind of role in education.

Matthew Caldwell, MD: 9:49

Yeah. So thanks for that question. You know, I think I think we're just at the foundational level, in my perspective, the foundational level of what could be done. And so, you know, I currently chair our clinical competency committee within our department. And it would be so nice to have really high quality, frequent uh assessments in uh various different settings about uh residents' performance, but it's hard to get to that, right? I mean, it's a it's a big resource, it's a big lift, um, there are constraints around there. Um, and so the data that IMPOG can provide initially is not around the observed performance from residents and faculty, but sort of the experiences that they're having in the operating room and then uh metrics that are associated with the quality of care. So process of care measures or outcome measures and things like that as well. And so we're really taking a view that this is a very long-term project and that we are trying to build a foundational system where people have trust in the data and how it's used and how it can be effective for people, and sort of together learn where the high-quality signals are uh within that that data as well. Right.

Larry Chu, MD: 11:01

And uh, you know, a single data point really doesn't tell you the full story, uh, but what are the patterns that you're seeing over time? That's probably where the learning lives, right?

Matthew Caldwell, MD: 11:15

Yeah, I think so. Yeah, and and one of the gaps that I see is that, you know, our current recommendations for training for residents include things like time on rotations. And so, you know, uh residents have to have a certain number of months of X, Y, and Z, or a certain number of procedures of various types. But, you know, if if we talk about a specific rotation, what is it in that rotation that we're hoping that they will experience, right? Is it different types of patients with different core morbidities? Is it the anesthetic challenges within the in the case? Is it uh you know, management of specialized monitors or equipment or techniques? Is it the specific perioperative risks of those patients? And so there's a lot to unpack there in terms of under the broad category of a rotation, what are the actual experiences that residents have? And I think there's an opportunity to actually quantify those and then understand, you know, how those experiences are different within a residency for different residents and across residents in different programs as well.

Viji Kurup, MD: 12:12

And I think that's a good time to pause a moment and to make a key distinction here. Because you just, I think, uh brought up a good point, which is you know, quality data helps us understand what happened and maybe what happened in a rotation. Education asks how did somebody maybe get there? And I love how you're kind of unpacking that for our listeners and bringing us through your thought process. VG, any comments there?

Larry Chu, MD: 12:39

So, yeah, so you know, uh, so what are your thoughts? You know, we've been using this data usually to just assess our residents, right? Like so we're looking at the outcomes and we're saying, how does how's our residents doing? But how are we going to use that to guide their growth for, you know, uh that data can also be used for coaching the residents. So what are your thoughts on those?

Matthew Caldwell, MD: 13:04

Yeah, um, I I think it's a really big, broad topic. So you know, I won't, I won't pretend to cover or be an expert in all aspects of that. But I would say, you know, I think there's a couple of opportunities there. You know, there are existing quality metrics that have already been agreed upon by the community that, you know, certainly our residents are a part of the healthcare team and need to understand their the care that they deliver to the patients. But there's also um, you know, other opportunities around uh experience data as well. And the experience data, obviously, having an experience does not ensure that a resident has learned or is competent, but it is a foundational aspect of clinical training with this apprenticeship model of training that we have, where you know there's there are didactic components, uh, certainly, uh, but a lot of what our anesthesia residents um encounter is apprenticeship mode of training where they have progressive responsibilities. And we have kind of limited insight on a day-to-day basis as a supervising faculty as to what's the proper level of supervision that might be appropriate for this patient. And so, you know, I'm sure both of you and and others have had this experience where, you know, you're you're taking these cues that you get from the resident to understand where they might be in their learning curve, to understand what the appropriate amount of autonomy is for a particular procedure or a particular aspect of care. You know, you could frame that into kind of an entrustable professional activity type of framework. Um, but wouldn't it be great if uh if there was a vetted source of information to at least understand what a resident has experienced before? And so if you knew that a resident had done 20 or 30 central line placements before, your expectations of what that resident might be capable of and what the appropriate supervision might be able to be fine-tuned a little bit better. Um, and the same thing with uh other different types of cases. And so I think it, you know, that just I'm just giving one granular example, but you know, those are one of the types of things that I think could be built upon a foundation of high-quality data that's specific to the educational process. That comes down to how data is used, is that within our program, it stands for PACE, PACE, which is precision analytics for coaching effectiveness. And the key part there is the coaching part. And so I would just like to highlight that what we're envisioning is that we have a program of data-enhanced coaching where a resident just doesn't receive an email or uh receive a dashboard, but it really is engaging in a conversation with a coach that's not telling them what to do, but sort of guiding them through a conversation of, okay, well, here's the data that is in front of you. How does that make you feel? How do you react to it? What goals and opportunities do you see related to that data? So it's really a conversational piece that we're hoping to accomplish with this as well.

Viji Kurup, MD: 16:01

So it's almost like data-informed coaching, where you're you're helping to surface more in the conversation with reflection when they can see more concretely some of their own data. Well, I think that's important, though, because learning doesn't happen in snapshots. It builds over time, right, through repeated uh exposures and experience. And when as a faculty member, when we're thinking of entrustment, unfortunately, right, sometimes it is, but it shouldn't be necessarily based on a working with somebody once, right? Because entrustment is a prediction about how entrustable this person is, right? For tomorrow, the next day. And that longitudinal data, I have to imagine, is something that uh is an important piece of the puzzle that could drive helping us understand that idea of how does someone get there, but also maybe from VG's point, uh helping us as educators with assessment. So how do you or maybe give us some of your thoughts on how clinical data might be used to trace learning over time, this trajectories concept, um, the longitudinal aspect? You mentioned EPAs before. Is there any other thought you've been you've given to this?

Matthew Caldwell, MD: 17:20

Yeah, yeah, thanks. And I would just highlight that uh I think this, the specialty of anesthesiology for residency training, is particularly challenged, right? And so, you know, if you are a medicine or pediatrics or surgical resident, you might be paired with an individual faculty over the course of a week or a month or a rotation or something like that. You know, within anesthesiology, we have these episodic interactions. And so, you know, you might work with a resident one day and not work with them again for several months, depending on the size of your program. And so then how do you string that together in a way that you could the that both the learner, the supervising resident, and the the program as a whole can have a more complete picture of what the trajectory of that resident is. And so uh again, the the foundational part that we're uh trying to build is based on experiences. And so I'm imagining something like a growth curve that you might have for a newborn infant, right? And so uh your baby is born, it's uh a certain weight. The weights can differ depending on all sorts of factors, but there is a trajectory that is uh typical of a growing infant. Uh when something varies uh outside of that normal range, it just makes you wonder, okay, what's going on, or uh, you know, you know, may prompt for uh a deeper inquiry there. And I and I think that we might have an opportunity to do something similar there for experiences and performances of residents over time as well, to kind of create a shared understanding between those three parties as to you know how a resident is progressing in their training goals.

Larry Chu, MD: 18:51

Yeah, that's very interesting. And I also like to uh like the concept you brought of how we record experiences and equate that with learning. And it's and they're two different things because two people could have the same experience and come out with different learnings from each of them. That kind of uh, you know, like structured me. But we can also move from this with like just like can we just zoom out? Because we've talked so far and looked at precision education and looking at data and seeing how we can help our trainees with this. But really, precision education doesn't stop with residency, right? It does, it continues uh into our lives as uh faculty.

Viji Kurup, MD: 19:38

Well, absolutely. And it matters a lot for practicing anesthesiologists. And certainly, you know, one aspect of that we've mentioned the monthly MPOG reports that that we get, that obviously it's impactful because we we we both mentioned it as something that, you know, it's has an impact on us. So imagining that CME reflects not just your interests, but I'm thinking, and I'm sure Matt, you've probably thought of this. What if MPOG could help education do more than that? If CME could not just reflect uh practicing anesthesiologists' interests, but maybe their actual practice? What would that look like?

Matthew Caldwell, MD: 20:16

Yeah, I mean, I think I mean that that is sort of the holy grail, right, of of trying to understand how educational processes affect real world outcomes of patients and healthcare providers and things like that as well. And so that's not the scope of our current work right now in terms of uh practicing physicians, but I think that there is an opportunity to be able to link what residents have done and experienced and performed during residency program with the actual care that's delivered. And you know what, you know, there are certainly studies that indicate that kind of the structure of the healthcare system that you're in influences very highly the type of practice that you have after you leave that training environment. And so you get imprinted on by the uh the healthcare system that you're in as well. And so I think you know, this concept of a learning healthcare system where you are, as a healthcare system, inputting data on how your patients are doing in your care processes and using that to continuously improve quality of care, that can be done through the individual clinician, but also through structural uh techniques within the healthcare community. And I think it'd be interesting to see how that translates into a practice a year out, two years out, five years out, uh, and that as well. And so um that's certainly something that we have an eye on uh in the future. But for this next few years, we're really focusing on the residency uh period.

Larry Chu, MD: 21:37

Yeah. And that's uh, you know, I like that our focus in the long term can be on how do we support lifelong learning for everyone. Uh, but in the short term, we're also going to, you know, I think we need to talk a little bit about uh the risks associated with collecting this data, right? Because anytime we collect Collect data, this data can be misused. And learners' assessment naturally drives learners to fear, right? They have fear, this fear of being watched, faculty have fear of being judged, institutions fear legal risk. So how do you, you know, how do you uh figure out when it comes down to trust, how can we ensure that data that's being collected is used in a supportive way and not in a punitive way?

Matthew Caldwell, MD: 22:39

Yeah, I think I think you hit a really important topic right there. So I'll first just pause and say that the Impog organization has been around for about 20 years now and has learned a lot over that time under the directions of many people, but including Dr. Trimper and Dr. Keterpal, who were really instrumental in developing this and the safeguarding of data, how it's stored, how it's stewarded, you know, has been pretty well established there. And I think so. I'll kind of focus my comments on the educational aspects and kind of this the sensitive nature of that as well. And I think the approach that we're taking is to basically be as open and as transparent as possible in the development of this. And so, you know, our this is data is a shared resource that the residents and other learners should have an input into how data is used, how is it collected, how it's stewarded, and you know, what sort of downstream implications there are from that. And so, you know, I think there are with any new sort of data, there are going to be concerns around how it's used. And and I think it's it's incumbent upon us to sort of navigate that in a way that's transparent and is malleable over time as as we get feedback from people as well.

Viji Kurup, MD: 23:54

And I think the the governance aspect and the trust aspect of data in education, I feel like it it has so many more nuances than probably we have time for in this podcast. But I, you know, I I relay it to my own observations, just that, you know, in in medical education right now, for instance, you know, we have we have the fresh start, I call it the fresh start, which is you go from one stage of your training to the next, and your past doesn't necessarily follow you. So you might have been the med student who took a little longer in the clinics to grasp things, right? But that past doesn't follow you to residency. You get a fresh start. And and I wonder, and it's not maybe we're gonna solve it on this podcast, but you know, in in this emerging age of of data, and especially if, and it's not now maybe, but as we move to start collecting data through all stages of a learner's trajectory, those conversations about governance, and maybe not, you know, just the fresh start, but bias in the data, amplifying that bias, all sorts of questions come into play. And and I I like what you said, Matt, about involving residents, involving the end users in those conversations, because I think governance is going to be a big part of how to make sure those kind of concerns get addressed.

Matthew Caldwell, MD: 25:16

Yeah. Yeah. And I think I think trust has to be earned over time as well. And, you know, I think hopefully by starting with experience data, that's not, there's not a judgment aspect in there. It's just, hey, did you have this experience or not? I mean, I think that's a pretty good starting point to work out kinks and to generate conversation and get feedback and essentially secure trust from all the parties that are involved. And that's one of the reasons that we're really emphasising the emphasizing the experiential aspect of it as opposed to evaluative aspect of clinical clinical care.

Larry Chu, MD: 25:52

Yeah. And that clarity really makes a big difference, right? It tells people this is for your growth. It's not to punish you or to grade you or judge you. And that's it's an ethical commitment, right? To say uh to set the tone of how innovation happens.

Matthew Caldwell, MD: 26:10

Yeah. And just one example, you know, we looked at our the hemorrhage resuscitation of experience of our residents, you know, throughout their residency program. And as you would expect, there's a lot of variability. And so some of our residents left with a lot of experience with severe hemorrhage in the operating room, and a few with very, with very few, relatively few experiences there. And so that has implications as to, you know, how what do we do with that information? I mean, there's there's things that you can do on the programmatic aspect, but also, you know, you know, if a resident's going to have only a handful of experiences, that really ups the ante on what they need to get out of that experience. And so there's a lot of an opportunity for more deliberate reflection and goal setting along those. If I as a resident know that on average, I get a handful of these experiences during my residency, and I had one today, man, it is it is my job to try to figure out how I can get the most value out of that. Because you know, when you're coming already, you just you don't necessarily have the perspective within that program of you know what the downstream experiences are that you're going to have. And so having a little bit of insight to that, I think could could tie into more efficient learning for those types of things.

Larry Chu, MD: 27:21

I just want to follow up on that, Larry, on that particular study. Did you use that just to collect the information or did you go further and actually act on that information?

Matthew Caldwell, MD: 27:31

For the hemorrhage resuscitation.

Larry Chu, MD: 27:32

For the hemorrhage resuscitation.

Matthew Caldwell, MD: 27:34

We published one study, just look at a single center study just looking at our hemorrhage experience of residents. We have a follow-up study in process right now that has a it's a qualitative study where we're doing interviews with faculty and residents to understand what their experience was. And it's it's fascinating. And I'll just highlight Dr. Lara Zizblatt, who's one of our PhD educators, and Dr. Grant also are heavily involved in this work. But you know, when you for myself, I do a lot of vascular anesthesia. It seems like every day you're transfusing people and all that kind of stuff. But as a resident, it's amazing. Like the first time that you do that, like which button do I push on the on the resuscitation machine? Why are you why are you giving calcium? Why is that important? Like all these questions come out. And so we have a follow-up study right now, just trying to understand, you know, what what I think might be most important to the resident may not be actually what is what the resident needs, depending on what their prior experience in learnings were. And so I think we're we'll have some interesting insights to share with that when that concludes.

Viji Kurup, MD: 28:31

So I think, you know, we've had some pretty high-level conversations about data, about governance, about how it would inform precision education. But I would love to take a moment to bring it down to the listener level, people who are, you know, the practicing clinician or the educator working with residents, the CCC committee member or chair. For those listeners in our audience who are interested in maybe working more with data or making more of their decisions in better informed by data, but they they don't have a Sacha and Ketarpal in their department, maybe, or they're not maybe uh MPAG and Matt Caldwell for sure. What advice could you give them about starting and thinking about data or using it in their work as educators?

Matthew Caldwell, MD: 29:28

Yeah, that's that is a great question. I I think they're I guess my recommendations would be to be trying to solve pain points, right? So identify where the needs and the gaps are in terms of education and training. You know, all of us are now on some sort of electronic healthcare record. And so even for institutions that are not part of the IMPOG network, you know, there are there are reporting type tools within the electronic healthcare record suite of applications as well. And so there is there are opportunities for those outside of IMPOG to do this as well. And I'll just highlight the work that we're doing is is meant to be open and our data standards will be published within a sort of a website format so that people can can access that. Because if you haven't worked with data before, it's it's amazing. You're like, okay, I just want to measure this, right? Whatever that I just want to measure what a polar bear is. But then you have to define what a polar bear is. Like, does it have to be this big? What color does it need to be? How big do its claws need to be? Like all those decisions are actually way more challenging than you might think at first blush. And so hopefully some of the work that we're doing is to, is to define some of those things in a multi-center collaborative work. But then the other people and other with other types of resources or can then take those data standards and be like, oh, look, here are the data definitions. Take that to your colleagues within the data informatics team and say, how can I translate this using our resources into something that I can that I can use locally? And so, you know, we're we're building within the IMPOG network, but we're really trying to make this accessible to all residents within the United States.

Larry Chu, MD: 31:07

That's great because one of the things that Larry and I care deeply about is this equity, right? Technical equity or techity, because a lot of places don't have access to personnel or resources that a lot of other institutions do. And trainees there should receive no less of uh experience than the ones who are in places that have you know high resource settings.

Matthew Caldwell, MD: 31:35

Yeah.

Larry Chu, MD: 31:35

So I'm glad that that will be available to everyone.

Matthew Caldwell, MD: 31:38

Yeah, and it's really gotten me thinking also, uh, I completely resonate with your comment there. And the other thing that I'm thinking about is that you know, we have this kind of federated system of education within the US, where we have residency centers that are across geographic locations, across urban and rural settings and community settings and tertiary care hospitals. And so, you know, what is it that a resident needs? There's not one standard answer, right? It it, you know, our residents are going on to different practice environments, and you know, different programs might have various strengths and weaknesses or or various goals that they are training for. And they're not necessarily better or worse, but they're just different. And so I think one of the gaps that that we need to be mindful of while we're building this out is that you know the community-based um training programs, their needs may be different than you know, a large academic hospital's needs. And so making assumptions, as you as you meant mentioned, you know, as soon as you start to decide what you're gonna measure, you've already made some judgments about what's important and what's not important. And so I think we just need to be mindful of that as well.

Larry Chu, MD: 32:47

Perfect. And uh so again, like you know, we can zoom out a little bit and look at precision media medicine required new tools, new norms, right? And similarly, precision education is gonna, you know, need that too. So uh when it works well, it can make teaching easier, not harder.

Viji Kurup, MD: 33:11

Great. Well, I think this is a great conversation today. And it reminded us that data already shapes, as has been mentioned, how we care for patients. And the opportunity now maybe is to let it shape how we can support learning. I think not only for our residents, but hopefully in the future for continuing medical education for practicing clinicians.

Matthew Caldwell, MD: 33:37

Yeah, thank you so much for the opportunity to join you in conversation today. Uh, this is something that I'm sure is very exciting to me. I could talk about this for a very long time. And I think both of you could as well. So I appreciate the opportunity to kind of share there. I would be hate to miss the opportunity to just thank a few people as well. And so within my mentorship network, Dr. Caterpillar has been instrumental, as have Dr. Naughton as well. I would just like to specifically thank the Foundation for Anesthesia Education Research as well for funding and support in the past, and the American Medical Association for our current grant and the change meta precision education thing. And that's a great community where we hope to learn not just from anesthesiology communities grappling with these issues, but across the spectrum of, Larry, as you mentioned, the cool thing about this grant portfolio that the AMA is putting together is it's got programs geographically deserved diverse. It's got programs that focus on undergraduate medical education, the, the, the transitions from UME to GME, the transitions from GME to practicing faculty across different specialties. So I'm really hopeful to learn a lot in this process as well. And so it's just humbling always to work with fantastic people and just realize how committed and smart people are and to try to contribute whatever we can in that regard as well.

Viji Kurup, MD: 34:54

Well, Matt, thank you for joining us uh for pushing this conversation forward. I know data is uh not necessarily the easiest topic for some, you know, for many people to understand. And I appreciate you sharing what you know with us today. Uh, VG, any final words?

Larry Chu, MD: 35:12

No, I just want to thank uh Matt for being here and for just you know sharing his uh thoughts and helping us and helping our listeners understand a little bit more about how we can think about precision education. And today was all about how data is uh, you know, which underlies everything else, how data can be um used uh well uh ethically and uh with guardrails placed before we even begin instituting all of this. So thank you so much, uh Matt, for being here. Uh and uh we want to thank uh the AMA Changement, I guess, because I'm excited to see what's gonna come out of it.

Viji Kurup, MD: 35:56

Thank you to our listeners for being part of the Precision Educator podcast. In our next episode, we'll explore how these ideas come together across learners, careers, and institutions. Until then, you've been listening to the Precision Educator, where we explore how data, coaching, and innovation are reshaping medical education. Our goal is to move beyond one size fits all approaches and towards learning that is thoughtful, personalized, and grounded in real-world practice. If you found today's episode valuable, please subscribe, share it with your colleagues, and connect with us through the Society for Education in Anesthesiology. Until next time, I'm Dr. Larry Chu with my co-host B. And this has been the Precision Educator.