everyone and welcome to day two. Thank you for getting here bright and early and ready to go. Got our coffee going, so that's good. Okay, so let me go ahead and today we're gonna follow the agenda on the screen. So we have some really great talks and I think our first one is very important and something that is a little bit undervalued sometimes by, you know, like we talked about the introverts in the crowd, but it is really, really important for your for your future career. So before we get to that though, let me see, there was Dr. Shaw from the Barbara Davis wanted to make a quick announcement. You can shout, yeah. Sorry you guys, I'm kind of turned around. So I just wanted to let the second-year fellows know or even, you know, first-year fellows or people coming into endocrine, the Barbara Davis actually has a conference that they hold in October that's free for second-year adult endocrine fellows that actually goes over diabetes technology. I went to it last year. I thought it was fantastic. There's an application online. It's on diabetesdialog.org. You guys can fill an application. It's paid for. Everything is paid for if you're accepted. So if anyone has any questions, I'm more than happy to talk to you about it, but I just wanted to let everyone know that that was available if you're a second-year fellow. Awesome, thank you. And then one more announcement. If you did not sign in outside, please make sure to sign in. Okay, I heard a significant number of people had forgotten that. It's okay. You didn't have your coffee yet. Now you have and go sign in. Okay, so I'm very excited to announce our first speaker of the day. Dr. Jane Roosh is a professor of medicine, bioengineering, and integrative physiology and associate director of the Center for Women's Health Research and staff physician and merit investigator. She is an elected member of American Society for Clinical Investigation, American Association of Physicians, and the American Diabetes Association 2018 President for Medicine and Science. So we have a past president in the room. She's currently working with the ADA, AHA, and ACC on global strategies to decrease the cardiovascular burden of diabetes and clinical guideline harmonization with a goal to advocate for goal-directed therapy for all. So I'd like to welcome Dr. Roosh. So fair warning, I'm too short to stand behind this thing. Okay, so you're not going to be able to see me, so we're going to go like that. So I won't be able to see the slides and it's not a problem. Anyhow, as we said, I'm Jane Roosh. All things diabetes, all the time. Father died too early from diabetes, never met my grandfather, have a niece living with type 1, all things diabetes. But we are at the diabetes meetings and we are going to have some fun. All right, and the way that you have fun in a meeting this big, in a building this big, is by having a plan. All right, so I'm going to have to like go like this, figure out what the heck's going on. All right, so our goal today is to confidently approach this meeting. All right, how many of you in this room have ever done the Myers-Briggs? Okay, well for those of you who have not done that, this is a personality trait, kind of a scale, and what it does is it tells you are you an extrovert, are you an introvert, are you an idea person, are you a detail person, do you think too much, or do you get hurt easily, or do all these crazy things about yourself. But the one that's going to stick out in your mind is I'm talking about networking and what if I'm an introvert, right? So what if I'm an introvert, but what if I still came to this meeting, and what if I still had a very big agenda to learn something about, you know, we're just talking about device therapy, or something about what kind of a practice you can set up and take optimal care of people with diabetes, or what is my research path, or how do I do that assay in the lab, or how do I set my clinic up for success. All of that information is here, and all of the experts in the field, and I say all and I kind of mean it, like this is the global meeting for diabetes, and even though there's lots of other ones all around the world, this is the it show. All right, so how do you come to this meeting and have a really great experience? And I've had a lot of women's leadership education. Who even knew that was a thing early in my career? I didn't know that you needed leadership education. I didn't know that you could even learn about leadership, but it turns out that, you know, just like learning how to read a CGM, you need to learn how to lead, how to give a talk, how to communicate, how to negotiate. There's all sorts of fabulous things that you need to learn, and there is a negotiation session, there's a how to say no session, and I'm going to put in a little plug for the Women's Interprofessional Network of the ADA. If you get to be president of the ADA, you get to do stuff that you want to do, and I really wanted to start that group, and so now we have some sessions. There's other career development sessions throughout the meeting. You're here as scholars. That might be relevant, but there are also specific ways that you can take this big, crazy, hairy meeting and make it work for you. So in this women's leadership training course that I did called ELAM, one of the sessions was this session called Conference Commando by Keith Ferrante, and I'm going to shamelessly steal from this because he welcomes that, all right? So this conference gives you the opportunity to talk to people in your field, of course the important field of diabetes, and talk to them about the things that you're passionate about, all right? So there is this opportunity at this meeting for you to connect with people who think like you do, who have the same trials and tribulations that you do, and who can really understand work-wise what gets you up in the morning. So anybody here come to this meeting and hope to like meet somebody whose paper you read? Okay, for goodness sakes, I think you did. Okay, I think you do want to meet these people. So these people are here, all right, but you want to build a relationship with these people, and these people have a significant impact on your life. So embarrassing story number one. All right, I, as a second-year medical student, I was exposed to the most fabulous lecture on carbohydrate metabolism. Okay, how many of you here just love carbohydrate metabolism? Okay, now not having your hands up is okay for that one. Okay, so I am a true of true nerds, but also I had this lecture on carbohydrate metabolism combined with my father struggling with type 2 diabetes, for which he was developing complications, my niece just developing type 1 diabetes, and it had a huge impact on their family, not just my niece, but her whole family, and this was the most fascinating sort of, for my dyslexic, fabulous, dyslexic brain, it was perfect, like nerd integrated physiology, and I thought, I'm gonna be a diabetes researcher, and then I had read at the University of Minnesota, where I was a third-year medical student, I did a rotation working on the DCCT data, looking for genetic variables, and I was going to be an autoimmune Barbara Davis Center researcher, except they didn't think that I could really do it as a pregnant woman with no experience in research, so they didn't choose me to come into their labs as an endocrine fellow who was about to have a baby right when I was starting my research year, and so I still kept reading all about autoimmune diabetes, I was really excited about it, and we were looking for a new director of the Barbara Davis Center, and George Eisenbarth, who is now no longer with us, but came in as the director, and I ran up to him, and I'm like, oh, Dr. Eisenbarth, I've read everything you've ever written, and I'm so excited, and I can't, you know, and the poor guy was sort of walking backwards, please get away from me, so when you want to meet somebody at a meeting, you've got to have a plan, and maybe not a plan to assault them and overwhelm them, all right, so what, so, so that requires you to be mindful as you come into the meeting, all right, so you want to learn a lot in the sessions, but you also want to make some connections, because this is your community, and we're so thrilled to have this scholars program to bring you into this community, and to have this be with whom you identify, all right, so you have to have these tactics, and these tactics are something that we're going to have you work, work on for the last, and I can't, like, I am so short, and I didn't even hit the start button, so I have no idea what time it is, or how much time I'm going, okay, great, all right, so I, we're, we're going to, with your tables in a few minutes, we're going to have you sort of think about why are you at this meeting, what, what do you hope to accomplish, what do you want to learn, and then, and then, and then who might you want to meet, and, and then we will sort of talk about a plan, so how do you, how do you ask that question, why am I attending, well, you're all professionals in this room, and you come from many different backgrounds, and so you're all in the field of diabetes, but I don't know, because I haven't been here for the last two days, all about you yet, how many of you are educators, how many of you are in, in allied health professions, how many of you are physicians, okay, how many of you are PhD scientists, how many of you do research, okay, how many of you do primarily clinical care, all right, so you are a diverse group, and that's what it's going to take for us to conquer diabetes, all right, and for us to decrease the burden of diabetes for those with and at risk for it, so you've got to lay out a plan, and that plan is first, never just attend the conference, that's why you got in trouble initially when I said, who, you know, does X, Y, or Z, I can't remember what my question was, if you don't raise your hand, then you're just attending, not because you have to raise your hand for questions that have nothing to do with you, but I figured that that question probably had something to do with everyone, but maybe I was just wrong, how many of you care about diabetes, all right, all right, okay, so, so, so now we've got, we've got it, you're already participating in the meeting, so even if you are at a meeting, and you didn't bring data to present, or you don't have talk to give, or something like that, you don't want to just attend the meeting, you want to really let yourself engage in the meeting, so how many of you have ever been at a talk where you thought of a question, but you thought it was stupid, mm-hmm, okay, yes, okay, and we've got a double hand up here, and like, oh my god, absolutely, yes, that is, that is true, you might think that that question is not important enough to ask, but if you're a very engaged learner in that room, and you had that question, someone else probably had that question, so it's really important to kind of get out of your own way, and ask a question, don't ask a question just because now it's time for me to ask a question, but ask a question if something is like, you didn't quite get it, and you felt like it was important, and if you've chosen to be at that lecture, you thought it was important, okay, so, so you can stand up and ask the question, hello, I'm Dr. Jane Roosh from Denver, Colorado, okay, so you're actually supposed to say who you are, maybe not your first name, I usually say Roosh Denver, but, but whatever, okay, and I noticed this in, that you said this, could you please expand upon it, or, or I, I'm really wondering where you're going to go next, or you've just made this basic discovery, what happens in real physiology, right, it's very, very interesting to learn about worms, but, you know, I'm gonna wonder where, where you're gonna take this, something along that, it doesn't, it, it, it matters that this is a question that you went to that session, and you want to ask a question, but here's the crazy thing that will happen, is after that session, as people are filtering out from that session, the other people in the room who had that same question, they're gonna come and talk to you, and you're all gonna have a little chat, and maybe even some coffee, which is such a good idea, after, after, after the session, and you're gonna, you're gonna start to connect with other people in that room that may be interested in the same things that you are interested in, and Keith Ferrante says, you know, enjoy the, the temporary celebrity of your status as a question-asker, I, I don't know about that, but, but I will tell you that, that being a thinking person in the room also is something that, that the speakers like, like, if, if I ask you a question, or I try to engage, or I stop, and everybody just like walks out to refill their coffee, I'm gonna feel a little bad, like what the heck, I guess I didn't do anything important here. Okay, so what do I want to achieve? So I'm a scientist, so I like to share data, I'm a obsessed mentor, so I like to share mentor stuff, I am a, I'm an educator and an advocate for people living with diabetes, so I want to figure out who, who it, who it is, that is, how can I change the world for them, for those people living with diabetes? All right, so who are you? And so you either came here to share some data, I have some really cool data on COVID and diabetes, Saturday, you might miss it, but it's okay, it's in a poster, you might want to discuss how to set up a clinic, you might want to catch up on new data, you might say, I don't practice at the Barbara Davis Center, I practice at the VA, and I might not know everything about all the newest, and latest, and greatest in high sensitivity autoantibodies, so I'm going to go to that talk, so I can actually be educated for my patients. I am so sorry that I'm so short, because opening and closing this is distracting, I'm, I may want to know about these new medicines, so how many of you are, are clinicians taking care of people with type 2 diabetes? Okay, the world has changed, okay, in the, so I want, you, you, you may want to learn everything you can about these crazy new medicines that are transforming care for people with diabetes, okay, you may, you may want to learn about how to stress out your cells in culture, okay, because my technician wrote me with an email this morning about that, okay, so I want to know about that, and so, so it's here for you, and so you want to take your navigator, figure out where you're going to be, you don't have to stay all in one line, okay, there's a whole lines of things that you might be very interested in, but there's also all this other stuff, so take the time, and I usually do it before I go to bed, to really sort of say, what are my can't miss, and what are my, oh, that sounds pretty wild, let's, let's go to that, okay, and then, is there anybody you would like to meet, any of you come to this meeting thinking, I'd like to meet so-and-so, I think that, all right, yeah, well, for those of you who didn't think that, there are so many people here who you would love to meet, okay, I can just guarantee it, there are like-minded kindred souls who think about diabetes all the time, and how to do either better research, or better, or better care, or better advocacy, so this is, this is a, so then, what you want to do is, so what I could have done before I met George Eisenbarth, is I could have said, okay, I've read all his papers, but that doesn't mean that's who he is, what's he, what's he like, what's he interested in right now, and instead of asking him about the entirety of his work over his lifetime, I could have talked about what he just worked on, right, and that's what I love about poster sessions, because the person who is doing that work is standing right there, it's very much more prestigious to get a talk, and it's very much less satisfying to get a talk, because you've got that ten minutes, people are running around, they might, your session might be going slow or fast, who knows, and then that, that moment is over, okay, so, so you want to sort of say, what am I trying to learn from this meeting, and how can I, how can I go about it? Now, this is why it's a little silly that I'm giving this talk this morning, all right, because you're at the meeting, so obviously you couldn't have planned ahead, all right, well, you could have planned ahead, I was turning into Grant, so I personally didn't plan ahead, but, but, but you want to sort of say, what are my priorities, and where am I going to go? So, if I want to meet, okay, I'll use an example, Dr. Betsy Sequist, who you met yesterday, if I want to meet Dr. Betsy Sequist, how do I meet her? Is there somebody who would introduce me to her? Might, might I, might I have the chance to talk to her about hypoglycemia? Oh my god, you know, not only is she interested in, from a, from a research perspective, but clinically, that's what drives her work, right, patients suffering with hypoglycemia, so, so how would I do that? Well, I would say, oh, well, Jane Roosh has, like, hung out with Betsy Sequist for both of their careers, so she could introduce me, so you can ask somebody to introduce you, but you also want to know something about her, okay, not where she goes for summer vacation, but maybe something about her research or her clinical care. So you want to set up a time, but if somebody's giving the plenary, so I want to meet the Banting lecturer, if I want to meet the Banting lecturer, I probably don't do it right after his talk, all right? I do it before the talk, or I set up some time, or I just go in and I say, you know, I'm really interested in your work, I was thrilled to hear that, and can I contact you later, all right? So, and then you, like we were talking about drafting off the Big Kahuna. I do not think I'm a Big Kahuna, but I have been around forever, all right? So my first ADA meeting was 1991. I've been here every year since, except for one baby, the only, I had another baby, but that's why I missed the first ADA meeting. And I can introduce you to whoever it is you want, and now that you're a scholar here, you can bother me that way, okay? All right, yeah, and what did I just say? Oh, yeah, I didn't mean to say that, oh, yeah, okay. All right, so, but as a junior person, people want to meet you. You think, you don't want to meet me, what do I have to offer? How many people have ever thought that? Okay, like, all right, but none of us that are getting a little lung in the tooth are going to change the field for diabetes over the next 30 years, okay? We need you, we want you, we want you engaged, and we want to meet you, and we're really busy, and we might forget that we met you. So you have to meet us again, and maybe three times. I think three times is the thing, but it's okay to politely not say, you've met me three times, but to say, we met before, this was really interesting to me. I just had a student yesterday at the Pathway Conference tell me absolutely that they had met me last year, and that we had talked about this, and I wasn't, like, embarrassed. Instead, I was like, I can't believe you remember that. That's so great. All right, what can we do now? All right, so you're not, you're gonna meet people, your conversation with them may change your life, but it might not have changed theirs. So Ron Kahn, has anybody ever heard of Ron Kahn? Okay, okay, well, for those who have not heard of Ron Kahn, and just don't tell him I said this, he is the most visionary, creative scientist in the field of diabetes over our lifetimes, and every time a new award is created, he wins it first, all right? And so I met him, and we had this conversation, and he helped me figure out, since the Barbara Davis Center didn't want me, what lab I should go to, what I should do as a scientist, et cetera, and so he changed my life, but he didn't remember me to save his life, right? And so, that's this tension, all right? But you're gonna meet these people, you're gonna follow up if it's specifically relevant to you, and you're going to take some wisdom from them, and then over the years, then you sort of like become family friends, and you're hanging out with them, and then it's just like, I wonder why I was so scared of you? Because most of us are quite scary, all right? And the most important thing, and I don't know, did Dr. Bernal talk yesterday? Okay, so the most important thing for any networking that you're going to do in a meeting, and then we're gonna get to how to navigate this meeting, and what your agenda is, because you've got to, we're gonna, yeah, we're gonna follow up here, is that when you meet somebody for whom you want to really learn from them or have a connection with them, what you do, and you can give them your card, they will lose it, but you can maybe share electronically your information, but what you really wanna do is have a conversation, say, hi, I'm Jane Roosh, I'm really into mitochondria, and we've been doing this, and I was really intrigued by that, that you did, I wondered if I could follow up with you, or hi, I'm trying to set up my clinic, I see you have this mega clinic where everything gets done perfectly, I wanna follow up with you, all right? Whatever that is that's your agenda, that's how you connect with them. Now, so in an exercise, which we're not gonna do, because I would really tell you that this slide setup has really screwed me up this morning, I want you to take some time to think about why did you come to this meeting, not just because it was free, because we're bringing you here as a scholar, what do you hope to accomplish, and as you look at each day, who do you want to connect with, what do you want to learn, and if you have a question, introvert, extrovert, whatever, stand up and ask your question, proudly, succinctly, and don't say anything mean. Sometimes you'll hear people asking mean questions to students, and I know you would never do that, but besides that, asking your question, if it's your question, it's somebody else's question. So, what I want you to do, and I've just made you terrified by closing the computer, is that I want you to go to this meeting, I want you to think, what do I want out of it, I want you to take some notes during your break, and I want you to talk to peers about how you might overcome any sort of concerns you have about being an introvert, or being, like me, too extroverted, all right? So, I want to really thank you for your time, I meant to have this be workshopped, I need to grow taller, and actually I'm going to have spine surgery next week, so I'm going to be a little bit taller next year, and so maybe that will work out for me, but thank you for your time. And I know I'm, I haven't, okay, so if it were, what is exactly our timeframe right now? We are right on time. Okay, okay, any questions? All right, yes? Hi, my name is Kamaya, I'm a postdoctoral research fellow at the Jocelyn Bedfordy Center, and I was just wondering, so you meet somebody at the conference, and they give you their email, what is the best time to email them? I usually do ask people, hey, what's a good time that you would be able to see your emails, but if somebody does, what do you think is a good time to follow up with an email? Here's a strategy that I've recently developed. What I do is I, that day, I just send an email thanking them for the interaction, and I tell them that I'm going to follow up in a couple weeks. Because they'll say, oh, this is a good time, but they don't know. I mean, who knows? Life is busy. All right, so thank you very much. I hope that you have so much fun at this meeting, and that you learn so much. Thank you. Good morning everyone. Good morning. You guys awake yet? We're going to have some fun. What's that? Yeah, well, you know, like I'm a veteran of this, but you are as well, so thank you, Jane. Jane is awesome. And listen to what she says because she has so much wisdom. So, my name is Bob Gabay. I'm the Chief Scientific and Medical Officer here at the American Diabetes Association, and I guess you've been sitting here getting lots of advice at times from older people, and so I'm going to continue that tradition and cover a few different areas. Oh, good, and is this my timer? It hasn't started yet. All right, even better. Okay, great. Let's go to the next slide. So, just a little bit about me, places that I've worked, places that I've trained. Some of those may be places that you're all from. A little bit about my life, and we can talk about any of that along the way, but just as a quick intro. Oh, yes, the slide changer. This one, all right. I'm going to put this over here. Okay, great. Well, and so one of the thoughts I want to start with is people's career path. I don't know if anyone's heard about this concept of the squiggly path. So, you know, we all are sort of taught to some degree that our careers work very linearly, and for many of you that have been sort of perhaps through school and training continuously, it has been fairly linear. You knew at one point you would enter a program that would last X number of years, and then you would finish that program and start the next thing, but I think in this day and age, we're more seeing, and these are sort of people that think about careers more broadly than just the world we're all in, that we're generally more likely to be in a squiggly kind of path, and so I'll just share just, you know, a moment or two, a little bit of my path just so you have some context, and what I will say is when you talk to senior people, many times they may tell their story as a very linear path, but the reality is most of them were not, but they make a story out of where they were, so here's my story that I made out of what I've done. I started as a, I grew up in New York City, science geek, I had a chemistry set as a kid, you know, I wanted to be a scientist, you know, went and pursued that, got a Ph.D. in biochemistry, and in my Ph.D. I was working, and this is like in the dark ages, around insulin signaling, but I was so myopic, so focused, I didn't even really think of it relating to a disease at all, and so the first paper I published was this amazing thing, I threw a big party, and, you know, we like celebrated, second paper, a little bit less, third paper, a little bit less, and I started to realize that what I really wanted to do was impact people, and I suspect that that's something that unites all of us in this room in one way or the other, and for me, I was too distal to be able to see that. I think it's incredibly important to do discovery science, and ultimately that's where the big changes happen over time, but it's a process, right? You're very sort of distal from seeing that impact humans right away. So I wanted to get closer to that, and that's what got me to go to medical school, and at the time I was young and brash, and I actually on an interview said, oh, I don't plan on doing a residency, I'm not interested in doing clinical medicine, I just want to learn physiology and then go back to the lab. So interestingly, that interview, I didn't get accepted at that medical school. Anyhow, so eventually, yes, I went to medical school, and I discovered that I loved seeing patients. Who knew? I'd never done it before at all. So the squiggle starts. I'm like, okay, I'm going to sort of do more of this. So I was able during medical school to do research a fair chunk of time. Residency, similarly, I got six months of research in my residency time. So I was always going back and forth from the lab and patient care, and I kept sort of reenlisting internal medicine. Diabetes seemed to make sense for a variety of reasons, so like endocrinology. I started my endocrine fellowship, for those of you that are in research fellowship, research endocrinology fellowships, like the three-year thing. I did my first year and almost a half in the lab before doing the clinical time, and a couple things happened. One, I wrote a K award, and I was fortunate to get it, and I realized how much I missed clinical care. And then I did my clinical time, and by the time I found out that I got the grant, I sort of realized I didn't want to work in the lab anymore. And so I had this, what to me was this momentous conversation with my mentor. I was so afraid to tell him, like, yeah, I know I got this grant, but I really don't want to do it. And, of course, he was fine. You know, it's your life, and do what pleases you. And, you know, this is an extraordinary man just to fast forward. He became dean of Harvard Medical School eventually. Anyhow, so I don't want to drone on too much, but I then, you know, after I sort of realized, and my mentor sort of said, well, why don't you try clinical research? So I started doing that. I stayed a year as an instructor, and then I started interviewing for jobs, and I didn't really know what kind of job I wanted because the only versions of jobs I knew were academic. And I was curious what things were like, and so here's one thought that may apply to some of you. I did lots of interviews. I think I did, like, 13 different interviews, and each one was, like, in a very different context, and it got me to learn what different sort of roles could look like, and they're free. In fact, they'll often pay for your way, depending. So you get to travel a little bit, and you get to experience. Anyhow, I began to realize through that that I liked leadership roles, and then I was fortunate to get such a role at Penn State, developing a diabetes center and all that. It was in the early days of disease management. The organization was merged with Geisinger Health Plan and doing a lot of innovative things, population health around diabetes. And so I jumped into that. I loved doing it. I realized I could have an impact more than the one patient I saw, but really on populations of patients. Followed that through a chance event. I got involved in a huge statewide initiative funded by 17 payers in Pennsylvania, implementing the patient-centered medical home in primary care all around diabetes. Led by the governor and got to meet all these people, and I was like, wow, this is fantastic. That was going to be my path. You know, wrote NIH grants, AHRQ grants, CMS grants, you know, all of that, studying this intervention. And then I got asked by my mentor way back, who was comfortable with me switching, why don't you come give a talk back in Boston? I said, oh, okay, yeah, sure, that's great. So I came, I gave this talk. There were people in the audience. Afterwards, two different groups said, do you ever think of coming back to Boston? And I was like, oh, yeah. Little did I know this was all orchestrated. For those of you that go, you know, for seminars along the way in your career, often they're really job interviews, even if you don't think they are. So ended up in this great role of chief medical officer at the Johnson Diabetes Center. You know, 25,000 patients, you know, being able to think about that. Less about primary care, which is where most of my work had been. And I would have been content to stay there for my career because, to me, that was the most impactful role one could have until I heard about this job. And I was like, oh, wow, yeah, this is even more impactful. Like, I could do all sorts of cool things. And so that's how I ended up here. So, again, a squiggly path, a squiggly path. So enough of that. Let me get your thoughts now. Oops. Oh, that was me. Oh, okay. Here's where I'm going to ask you to answer a question. Well, you can start thinking about the question. What one word would you use to describe your future in diabetes? What one word? And it will be anonymous, so, you know, if you write hell or something like that. I hope you don't, but, you know, no one will know that you said that. So, yeah, I'm curious to see in this word cloud. Let's see what lots of different answers. Certain letters that are popping up. Yeah, this is sort of interesting. I don't know about you, but it's hard for me to understand. They haven't started, Dr. Bob. Oh, they haven't started. Oh, okay. I'm like, wow, like what is like people want to be Roxanne? Who's Roxanne? Okay, now they're going to start. Okay. All right. Thank you. All right. Countdown on. Now, yes, think about your future. All right. Drum roll, music. Here's a thing for you to think about. If you could pick a walk-on song, you know, like when you walk. I have like a whole mix of music that sort of goes with that. All right. Let's see. We're getting some answers now. Agent of change. Uncertain. Game changer. Impactful. Nice. Hopeful. Oh, excellent. Wow, this is really good. I love that impactful is really the driver. Absolutely. You can tell from my story that was very much the case. Awesome. Let's go to the next question. And this is one or two words to describe what you think will be the most impactful change in diabetes over the next five years. So look over the next five years. And where is diabetes going? Because it will be the world that you will be in. So let's see what kind of things you're thinking about. It's counting down. Good, good, good. Okay. Let's see. A couple of big answers about to show up. Prevention. AI. Technology. Wow. Yes. AID. Prevention. Prevention. Yeah, prevention really moving upstream, closed loop. Yeah, very cool. I have to agree with all of these, I think you're right on, but AI technology and prevention are really, and I would say AI technology, absolutely, and I hope that prevention is the focus, because we've not really, the healthcare system has not been designed to do that, but clearly we need to. Okay, great. We'll go back to the slides. So I put this in before knowing that you were really sort of focused on being impactful, but that sort of warms my heart that that is where you're coming from. Next, oh, I'm sorry, I have the clicker. Get distracted easily. So now I'm interested in a little bit of the audience and your career paths, because that'll springboard a little bit. So if you look 10 years from now, you're in your dream job. It all worked out. You took the squiggly line, and you are in one of these roles. So why don't you pick, and let's see what we got. What kind of, oh, at first I thought that the blue was the other blue, and everyone wanted to be rich in fame. I'm tired. Although, what can we do? People aren't admitting to that. So researcher and clinical, yeah, academic, okay, excellent. So I'm going to go to the next slide, and thank you. Sorry, I'm making you early on your first thing in the morning. Okay. So I want to talk, for the next few minutes, I want to sort of widen your aperture a little bit and think of careers that you may or may not have heard about. And I know that there was really, let's take this thing off for now. I know that there was a really great panel, yes, a career panel. Some of these areas came up. So one is around quality improvement. Many health systems most, and I would say even more in the future, are you need people that can help drive better quality of care, and there's a bunch of ways to do that. How many of you have been involved in some kind of quality improvement effort? Good, because you know you're supposed to, you know, for those that are endocrine fellows. Yeah, yeah, I think that's an exciting area, and that's an area that I had explored quite a lot in the work that I do. Then there's the research side of that, which is implementation science. I'm pretty sure there's going to be a much larger investment in that in terms of research funding in the future, because really we're trying to solve that problem of we know a lot of good things that will help people prevent them from developing complications of diabetes, and yet they're not really implemented, and understanding how to do that at scale. And this is an area that ADA is very involved in. Clinical trialists. So somewhat different from being just, you know, being a clinical researcher, it's many times these are industry-sponsored trials that are often not looked at as wonderfully within an academic environment. But if you look at many of the big trials that will be presented here, these are largely, you know, studies with new medications and technology, and they're often funded by industry. That area can be a very exciting and impactful area. This world of digital health. So many of you said technology and AI. There's a real hunger for people with expertise around diabetes and obesity in that world. Typically, you know, I work with a number of different companies. Come, by the way, plug Innovation Challenge tomorrow at 4.30. It's going to be a lot of fun, I promise you. I'm not supposed to say this, but it's based on Shark Tank. We're not allowed to say the word shark. So we'll call it a dolphin tank. It's gentler and kinder. But come, and it'll be really fun. But they're usually tech people and not people that know a lot about diabetes. But many of the interventions are either diabetes, obesity, prevention. And this probably is the solution for prevention. Being a chief medical officer, being in a leadership role of other clinicians. Could be chief nursing officer. Any kind of role where you're really in a leadership position and learning what it is to be a leader, because we're not typically taught that for those that are, you know, have gone through training. You become a resident one day, and all of a sudden you have interns under you in a sense. But no one teaches you how to be good at doing that. Well, some of those principles that you learn there are applicable here. Being a writer, communicating. There's such a need to communicate information for the general public. And also for clinicians and scientists. And having that skill can be another opportunity. Entrepreneur. Have an idea, find funding, make it happen. You will see examples of that on stage tomorrow at the Innovation Challenge. Government and policy. An opportunity to have incredible impact in that world. You need to have the right mindset to be able to work within structure, because there is a lot of structure around it, but also very, very potentially impactful. And then non-profits, like the American Diabetes Association. We're always looking for good people, so there's another opportunity there. So with all that, oh, there was one more survey, yes, and then I'm going to just briefly take a turnaround. Okay, excellent. So with all that, I doubt I've changed any of you in terms of like what your career path is based on a five-minute conversation. But, yeah, would you consider any of these other things beyond what you originally answered? Let's see. And this is where the squiggly path comes in, because you could be doing one thing, jump to one of these, do it on the side, come back. You know, there's lots of opportunity. Again, the careers of the future will not be linear. Okay. Oh, I see. Okay, the dots. Quality improvement, excellent. Implementation of science, excellent. Wow. This is great. I'm really pleased about that, because I do think that's what we need so desperately. Let me end now with a few other things, and I'm going to move quicker now. Burnout, important topic, because it is sadly pervasive. What do we mean by burnout? It's that depersonalization and emotional exhaustion, and it can lead to lack of empathy. It increases turnover. It impacts patient quality and safety. There's a whole literature on this, and you've probably been exposed to a bunch of it. There are many issues related to it to think about, and we won't have time to get into all that today. You can think about, and I won't ask you to raise your hands or do a poll, but how have you ever felt these kinds of things? Just to normalize this a bit, here's a whole bunch of different medical professionals, and this is not limited to medicine. It's really across healthcare, and nurses have been studied extensively as well. You can see about half of endocrinology are burnt out, and even the wealthy orthopedic people are more than a third burnt out. Briefly around that, I want you to think a second, what do you do to cope with burnout? I don't know if we have time to get people to raise their ... I'm just going to let you think internally, because I almost guarantee everyone in here at some point has had that feeling that you're just running on empty, and it's affecting how you are. Here are some things in a past year brought to this group that they had identified. You can look at some of these strategies and see if they might resonate for you. This was a past cohort of individuals like you. Some of you may have been involved in this. What's an important contributor to burnout? Lack of sleep. Everyone running on empty, right? This is only the first day, so you're probably okay, but I'll catch you all on Monday. I know I will be completely spent, but sleep's important, and it's underestimated. There's now a lot more biology and understanding around how this ... It's what your mom and dad said, make sure you get enough sleep. It actually is important. Here are strategies on a national survey of endocrinologists and how they responded with how they cope. This may be something for you to think about. I'm sort of glad that the prescription drugs is relatively low, but people cope in a lot of different ways. This is a serious issue, and so one that I think you will continue to face in your career, and so thinking about what to do, and there are a bunch of resources out there. Just thinking about what's next, a couple of final things. One is, if there's one single thing that I would throw out to you all that I think is so valuable is to have a growth mindset, that always wanting to grow. There's this concept of a fixed mindset. People are comfortable in what they do. They just want to keep doing it, but having a growth mindset is always exploring new things. Over the course of the next few days at Scientific Sessions, I encourage you to go to things outside of your area, because that's where we often learn the most. Certainly you'll go to things that relate to the work that you're doing, but going outside and widening your view is so valuable, and there's a lot of literature out there that can help you develop new skills. One thing that I have found very helpful over the years and use with a lot of the people I mentor is something called Strength Finder. Has anyone done that? It's a cool ... It's like 20 bucks or something. I'm not endorsing any product, but it's a survey that you do, and it's usually pretty accurate for most people, and it tells you what your strengths are. The idea is, as opposed to working on your weaknesses, focus on your strengths and how to leverage them most effectively. You can find it online. I do recommend it. Finally, as you're here and you're thinking about maybe some different career opportunities of that list, for example, that I shared, and you're curious what it might be like, think about somebody in the program. There are hundreds of speakers and presentations that might be doing something that you wish you would be doing someday, and go talk to them. You'd be surprised how willing people are to talk. The strategy that I've found most helpful is when there's a rush to the stage after their talk, don't be in that first crowd. Let it thin out, and then towards the end, come to them and even offer to walk with them to their next place, and then start a chat. If you feel like there's just some commonality there, say, do you mind if I touch base with you in the future, and we can talk a little bit more. I can tell you, for many of us, this is amongst the most rewarding stuff that we do, so don't be afraid. Finally, don't forget to take care of yourself. You're the future of diabetes. We need you, but you can't burn out. You're just at the beginning of the marathon, so definitely take care of yourself, and thank you for everything you do. We have, oh, we've got like a minute and a half for questions, so questions, comments, thoughts. Anyone see themselves taking a Quigley approach to life? Quigley? Already have, maybe? Don't want to admit it in public. Understood. I don't know if I were sitting in your seats if I'd be like, yeah, I want to learn to surf for a year and then go back to, you know. Questions, comments? Oh, great. Yes. Hi. Good morning. Thank you for the Squigley career. My question is, when you are thinking towards the things that are unexpected, how does one develop the skill set or prepare for the unexpected, so these larger positions, whether it be like chief medical officer or leadership positions, they come along unexpectedly, but how do you prepare for them at the same time? Yeah, that's a really good question. So first, as you might imagine, it's not like you just get to say, oh, I'd like to be chief medical officer, and they're like, oh, great. You've been interested in doing that? We need one. It's working your way through, and you're used to that in your careers already. I think to a certain degree, it's saying yes and getting in a little bit over your head. That's certainly been my approach. I do think there is a literature out there around leadership that I think is really helpful. One of the funny things is that for, you know, a lot of my colleagues in industry, one of the first things they discover there is that, actually, we think in academics that we're all about mentoring and, you know, guiding and all that, and they say, actually, they're much more thoughtful about it many times in industry in terms of trainings and programs and things like that. So there's a lot out there. I tend to like reading short articles and capturing key ideas and then looking back at them as I go along. But, you know, there's this notion that, and this is not the common wisdom, but I think it's good, is that earlier in your career, it's all about saying yes and exploring new things and trying things out because you never know. That squiggly path that I described would never have happened if I didn't say no to certain interesting opportunities that came up. Earlier in your career, it's about saying yes, and later in your career, and I've not learned this skill, is to say no. But you don't have to worry about that. So be open to new opportunities. Maybe one last question, if we have. If not, I'm going to turn you to the rest of the group. It looks like I'm getting a wave from the back. It's time to stop. Okay. Thank you. Enjoy the meeting, and good luck with your careers. Am I supposed to introduce anyone? Oh, great, you're next. Oh, you're in for a treat. Yeah, she is a rock star. Let's see what she has to say. All right, so we're gonna carry on with this next session. I will introduce myself first of all. I'm Dr. Maria Van Vinny. I'm a physician scientist at Jocelyn Diabetes Center in Boston. I was in your shoes not long ago, four years ago, five years ago. So time flies, and right now I'm doing both clinical work and research. And I'm gonna introduce the next speaker. So we have Dr. Rosalina McCoy. She's an associate professor of medicine and the associate division chief for clinical research in the University of Maryland School of Medicine's Division of Endocrinology, Diabetes, and Nutrition, where she has her primary appointment. She also serves as a director of precision medicine and population health in the University of Maryland Institute for Health Computing, where she leads interdisciplinary research efforts focused on improving the health and wellbeing of people of Maryland and beyond. So Dr. McCoy's research, funded by the NIH, ADA, and other institutes, leverages real-world data to improve the quality, accessibility, and sustainability of diabetes care on both individual and population levels. Her methodologic expertise spans observational, interventional, and mixed-method study designs, including epidemiologic analysis of real-world data, causal inference methods to emulate trials, and pragmatic point-of-care clinical trials of patient-centered interventions. She received her undergraduate degree at Harvard, her medical degree at Johns Hopkins, and completed residency in endocrinology fellowship training at Mayo Clinic. Without further ado, Dr. McCoy. Thank you so much. I'm probably gonna go over there so we can see. Thank you so much, and really excited to see so many of you here. It's always wonderful to see endocrine fellows and researchers interested in diabetes. So for my disclosures, my research, which is somewhat, I guess, related to AI, is supported by NIDDK, NIA, PCORI, FDA, and the American Diabetes Association. I'm gonna consult on for non-profit entities. Definitely feel free to take photos, use X, tag on ADA. So I think just think about to you, or we ran a theme on this one word, but how do you feel about integrating AI into routine practice? Does anyone wanna just shout it out? Hopeful, optimistic, terrified, hesitant. Yep. So first, what is AI? Right, I think it's important to take a step back and see where, like, what is it, right? So because there's a lot of misconceptions about AI versus machine learning versus data science, and to me, as very much a non-math, non-computer science person, so I think of it like a clinician. Like, I talk to my patients or my parents, right? So for me, it's a big bubble, right? There's computer science, so using machines through code that we, as humans, input in to do things, right? Within computer science, there's data science, again, mostly programming versus robots, and then within that, there are disciplines, whether it's AI or machine learning or data model, partially overlapping, partially not, but ultimately, it's all unified by the fact that there is computer code that is input into the machine telling the computer to do something, and that's all AI really is. It is commands to perform a task. So AI has been around for a long time. It was initially defined, 1955, as the science and engineering of making intelligence machines, right? That was the principle and the theory, not yet realized at the time, behind what you can program a computer to do. The goal of algorithms, which are those prompts, those commands, is to model human decision-making through those sequential prompts, ultimately allowing the computer to learn from available data, not learning in a way that we learned where we form new ideas, but rather by forming new connections between existing concepts and ideas. Still new, so it's learning, but it's really learning from the data rather than the concepts, but the goal is to produce more accurate classifications or predictions over time, whether it's classifying, right, making observations about what you see or predicting what we're gonna expect to see in the future. So machine learning, neural networks, deep learning, these are all different modeling approaches that use AI as a field of data science, and there's many kind of models and methods within each one, and they're all partially overlapped. So in the literature or in the lay press, you will hear about deep learning or machine learning, right, trying to make it sound as though one is more advanced or complex than the other. That's not necessarily true from the computer science perspective. It's all about the machines. Now, AI is growing, right? In the US, the AI market share in billions, these are billions of dollars, has grown increasingly and is predicted to increase over the decade to come, and that sparked changes in daily life and the tech industry and ultimately, admittedly late to the game, is healthcare. So AI itself is really not new as a concept. What is new and why there's this explosion of AI and of interest in AI is the availability of data and the power of computing. So in 1947, Alan Turing, who is kind of the father of artificial intelligence, he said that what we want is a machine that can learn from experience with ultimately the possibility of letting that machine alter its own instructions. And I think the alter its own instructions is where we think about unsupervised learning, right, and where the next generation of large language models goes, where while humans input the code for the model, the future is where, based on the observations, that code can be altered and optimized. Ultimately, what's fueling the rise of AI is the increasing availability of large data sets. There's tons of data everywhere. All of you, many on the phone, are generating that data right now, whether it's web or social media data, sensors, wearables, right, which is machine-to-machine data, transactional data like healthcare claims that I personally work with. There's biometrics data, fingerprints, genetics biomarkers. Your cars collect information about you as you drive and that information that our traffic institute actually uses to predict where crashes are expected to happen because of heightened driver anxiety in high-risk intersections, right? All that data is there. And there's a ton of human-generated data, whether it's emails, electronic health records, even paper documents that can now be read by algorithms. And with improvements in computing, there's increasing ability to store, index, and analyze that data. So how is it changing healthcare? So one of my colleagues at University of Maryland, he uses AlphaFold, which is kind of an AI tool to do chemistry. He calls it chemical intelligence rather than artificial intelligence and uses it to discover or optimize molecules. So he's a chemist that's next door to me with a very large computer screen. There's ADA-powered symptom assessment apps that are accessible to anyone. And for those of you already in clinical practice or getting there soon, you will see patients who come to you with advice that was given to them by these tools, which I think we'll circle back to this when I close about the limitations of AI. And there's been a lot really of powerful innovation in image recognition, whether here we're looking at predicting brain tumors or retinopathy screening, or foot infection analysis, it's everywhere. So what does this mean for diabetes care? So we'll kind of briefly talk about why should we consider AI in diabetes care specifically, how do we apply it, and what are the implications on practice? So there's a lot of, there's a lot of good and benefit that can come from AI, because diabetes management is really complicated. It is different for every single patient and every person and every situation in front of us. And that's why I personally love caring for people with diabetes, it's a puzzle that you can figure out and help people. There's a lot of heterogeneity, physiological, behavioral, social, and structural in the patients that we see. And perhaps AI can help us put it all together and solve that puzzle. Because ultimately, that's what AI does, right? It finds patterns, finds connections. So through all, whether it's in apps, person-centered decision aid, diagnostics, risk prediction, data analysis, tech, right, AID systems, a lot is possible. So let's think about it, use of AI from the perspectives of different stakeholders in diabetes care world. So we have a person living with type 2 diabetes for 25 years, she's currently treated with multiple daily injections, finger sticks to monitor her glucose levels, and her A1c is 8.3. How can AI help her? One, we can switch from a finger sticks to continuous glucose monitors. You can continuously track her glucose levels, identify patterns and trends, figure out when she's going low and high. We know that use of CGMs lower A1c levels, reduce hypoglycemia, reduce time above range, and increase time and range. She can also switch to a hybrid closed loop system where the CGM communicates with an insulin pump. That way, the algorithm's able to optimize insulin delivery, again, increasing time and range. Alternatively, can use smart pens instead of a pump where the pump can calculate insulin doses, track doses, remind patients to take their insulin. It reduces insulin stacking, allows patients to dose their insulin appropriately, and helps us as clinicians manage their doses better. And there's a ton of apps, whether it's to track food intake, give individualized advice based on blood glucose levels, medications and activity. All these, again, can lower A1c and support weight loss. Ultimately, I think AI can enable person-centered care when used appropriately. So for example, AI can help calculate how much insulin to give based on blood glucose currently, trends in blood glucose, and how the individual responded previously to the same exact dose, using an algorithm, an equation that learns from prior response. AI can also ultimately be used to create a digital twin of a person with diabetes, so that using this technology, we, as clinicians or scientists, can test in this end-of-one virtual trial of what will work best for this patient. We can't experiment on our patients, but we can see what will work best in their digital twin before trying it to them. And ultimately, with sensors, glucose levels, with patient-reported outcomes, these digital twins can allow us to really understand how to better care for people with diabetes, and ultimately, potentially support remission as our goal. So that was the patient's perspective. What about the physician? So we'll talk about Dr. Fisher, who's a family physician in practice for two years. A lot, and a big proportion of his patients have diabetes. So Dr. Fisher is made up, but he's very much like many physicians that I work with every day. He has a large patient panel with varied needs, both clinical and non-clinical. Patients have a lot of demands, he has a lot of demands, and ultimately, there's limited time, which is ultimately the confluence of things that lead to burnout. How can AI help him? So one, in this large patient population, it can help identify who will benefit from what kind of intervention. Who needs more help? Who needs more support? Who needs what kind of support? Who needs a pharmacist? Who needs a community health worker? Who needs a ride to the clinic? It can help analyze a large amount of data, detect trends and patterns, ultimately saving time for the clinician, improving efficiency, making sure we target testing treatments to the right people. Can predict who's going to develop a complication. So we can work not only to treat a complication but prevent it from ever happening in the first place. That's going to, so what AI can do, rather than kind of the traditional models, so if you think about the Framingham risk score or even the ACCHA, cardiovascular risk score, those were developed using simple linear logistic regression models in limited patient populations. We don't know how they generalize. Can AI help us predict better? In the office, it can help the clinician know what medication to prescribe and what tests to run, right, by synthesizing a large amount of data, key in our increasingly fragmented healthcare system where patients may be getting different medications from different pharmacies, different labs from different systems. Really synthesizing the data, reminding the clinician it is time for a foot exam, time for a retinopathy screen, time to renew their insulin, which they haven't asked you for yet because they might have forgotten among their list of things to do, but their script ran out, right, helping you just know what to do, reminding you that they have microalbuminuria. Maybe they will do better with an SGLT2 inhibitor. And then really support the whole care continuum, bringing all clinicians together. Again, saving time, optimizing therapy, engaging the person with diabetes in their care and optimizing staff capacity because you have support. Can help with diagnostics, again, especially with imaging. Screens for retinopathy really enabled many offices to screen patients right then and there, making sure that people have access to tests that they may not otherwise get, especially if there's no ophthalmologist nearby. Again, saving time, increasing accuracy, improving health. And then just more practically in clinic, it can help just with engaging patients through triage or communication, right? There's chatbots, virtual assistants that can work with patients. Patients can do self-scheduling of appointments, saving, again, time, supporting staff. And with administrative burden being probably the biggest drainer on our well-being, health and sanity, to put it mildly, AI is a great tool, right? Instead of typing all of our notes, we can dictate. AI can listen in the office to what you're talking about with your patient and generate a note for you. Hopefully, God willing, someday it's gonna fill out my prior authorizations for me with form completion, right? I can do my billing, fingers crossed there. Again, it decreases time, increases the capacity, increases revenue, but ultimately for me, allows me to do what I love, which is talk to my patients, not look at the computer. And then what about broadly, from the system perspective? Think about public health. AI can support population health strategies to improve outcomes, taking it from the individual person in front of you to the whole population, which is essential because often the people who need our help the most are the ones with the greatest barriers to getting in to see us. We don't even see them. How can we help them? And that's where, again, AI and technology can help. With access to the broader landscape of data and healthcare utilization, these tools can optimize planning, generate reports, identify hotspots and opportunities to improve outcomes. By tracking trends for large populations, we can see signals of problems early, can analyze correlations as hypothesis generation for what should be tested later on. Our goal is to predict problems before they happen and then develop, propose, and ideally test in a digital population twin what policies may actually help so that we have a data-driven policy. And to give you one example from our research of when we do it is we're building a geographically coded individual level confluence of structural determinants of health, right? Using only publicly available data to understand the barriers that an individual patient may face depending on where they live and what they have access to. And all of this is only possible because of data and computing and very smart data scientists who are not me, but that you get to work with. So there's many implications for AI in practice, right? It can help health professionals, people with diabetes, the public health system, all coming together, I think for this vision where we all come together to improve health for all people, not only the ones with the capacity to see us in clinic. But there are downsides. The field is still in its infancy and that's why I think they asked me to speak to you guys is really to hopefully inspire and motivate you to help drive AI in the future to make sure it does good for our patients and for clinicians in our society, right? There's concerns about accuracy. We need accurate, reliable and accessible data which in healthcare is lacking. And it's not lacking because it's not there, it's lacking because it's often not usable. It lives in different systems, it is fragmented. It's very hard to link it together and make it usable. And we need more data storage, which is expensive. And again, as kind of the healthcare system overall doesn't have a ton of money just laying around, right? How do we invest limited valuable resources to do most good? And often data and data storage are not where resources are allocated because it's a long-term investment. But I think that's from a clinical perspective, a big gap. The other thing, but there's a lot that we can do for this, like write very good notes to not pull things, bill correctly so I have claims to work with, right? There's a lot of things that we can do to support this kind of research and the kind of activities, but we need data and we need to be stored. For people with diabetes, there's problems with tech overload, there's a lot of privacy concerns and it can increase treatment burden because it's yet another thing that people have to do. From the public health perspective, I think biased AI algorithms can do a lot of bad because they will perpetuate decisions and actions that worsen health outcomes. There's risk of cyber threats. And as we see all with cyber attacks on one part of the healthcare sector, everything shuts down. So how do we create safe, secure places? And ultimately, all of these challenges lead to decreased trust between everyone, between the public health system and people with diabetes and clinicians because we're just not sure. And again, that's where I think the future is not AI, it is accurate, safe, equitable and accessible AI. And I wanna close really with a plea that AI is a tool. I think the future for diabetes care to me is our relationships with our patients because it's not a replacement for care. It's data without context, right? If we look at CGM data and we see those tracings and we're like, yes, I can finally make insulin dose adjustments, but how correct will those dose adjustments be if we can't talk to the individual and ask, what were you doing? Were you eating? Were you running a marathon? Were you late for a bus? Did you run out of food? Are you taking your medications? How much? Where are you injecting it, right? Without data, without context, it can be more dangerous than no data at all. The others, it's something that probably inspired many of you, certainly did for me to go into medicine, right? It's that art, that relationship. AI tells us, what is the medication that is right for a patient like you? What we need is to know what is right for you, right? Not a population of digital clones, but really for you. And that's where I think the relationship is key. We need to be able to talk with our patients about how do we incorporate all of these recommendations into their life? Because if we recommend something and they can't do it, it will not work. But the only way to do that is through that partnership, through that relationship. Patients won't open up to a chatbot, but they will to somebody that they trust, who they see in the office, who they know is looking out for them. It's really that trust and compassion and understanding that I think are essential. And why I love diabetes care, because it's not a disease of the numbers. It's a disease of people. It's everything, right? And you need that relationship to truly make an impact. And never underestimate the power of the healing touch. It's putting your hand on someone, giving them a hug after getting good news or bad news. I think that's what makes medicine healing and not just a delivery of services, and AI can't do that. So rest assured, AI is not coming for our jobs, but I think it is something that can help us do our jobs easier and better. So my email is here. I would love to connect, reach out, and I wanna thank my team for brainstorming and helping with this presentation and putting it all together. So that's all I have, and there's time for questions. Yes. Thank you very much, Dr. McCoy. So any questions from the audience? Yeah, go ahead. Hi, Sookshin from Jocelyn Diabetes Center. So I was wondering with regards to the target trial emulation, for example, what is considered a good sample size to initiate or to integrate AI into a prediction model? That is my first question. And my second question is, obviously AI has a lot of potential in the context of precision medicine, but I was wondering what about the scalability as well as generalizability of this AI-guided models? Yeah. Thank you. Yeah, really good question. So first is like how much data is enough, right? If I understand correctly. So I think that really depends on, and what's the sample size? So it really depends on what you're trying to predict. Just like with any other statistical model, it's the variability in your outcome. As a data scientist will tell you, more data, the better, there's never enough. But I think if you're looking at CGM data, which has potentially thousands and millions of data points, you need fewer people to make a prediction than you do for something that's more, that has fewer data points and outcomes. So there's really, there's not one, so I can't give you like one answer because it depends on the specific data, the model. And there's different models you can use depending on whether you have a big sample size or a smaller sample size. So I think, I mean, you can't do it if you have like 100 or 200 patients, but I've done AI modeling, for example, to predict future glycemic control, and we've only had like 30,000 patients, which is probably the smallest study that I've ever done. Kind of when we do trial emulations, those typically have several hundred thousand patients, up to seven million patients. So you need a lot of data points, but it's not necessarily a lot of people if each person gives a lot of data points. The second is for precision medicine. So I actually kind of live in that, right in that world, because I do what I call precision medicine on population scale. So the goal is to be able to do two things. One is to apply models to the entire population that's going to predict something for the whole population to see where are people going, kind of who are the individuals from the populations that have kind of a high risk phenotype that you're trying to predict. But then also those same models can actually be applied to individual inputs to understand what is the risk for that individual person. And it's nice, right, because the same type of model can be used by different stakeholders. If you're a health system administrator, you use it to kind of do mass outreach for preventive screening. But if you're a clinician, you can see a person in the office and you can know things that are right for them. So precision medicine, it doesn't necessarily mean individualized medicine, at least not to me, because to me, what it means is that we're making inferences based on data that is precise for that group, meaning it has little variability and has a lot of certainty. So I think it has a lot of different meaning, but I think you can apply these tools for both individual and population-based predictions. And I'm done. But I will be sticking around here. My email is probably everywhere. Google Rosalina with a C. Not that many of us exist. Sorry, I have one question. Denise Dalton from Helmsley Charitables. How are you, Ros? What are your thoughts on the data that AI is trained on and the lack of diversity in that data and how that impacts then the outputs of the products? Because not everyone gives their data, but then we end up caring for those people who may not have trained. Exactly. And I think that's one of the biggest limitations, right? Because it's what goes in is what comes out and the potential for worsening bias and worsening disparities, I think, is big. And I think there's a couple of just methodologic ways that there's, the goal is to overcome it. I mean, one is to overcompensate, which I think can also do a lot of harm because the overcompensation is based on assumptions and those assumptions are human-made and can be biased so they can make things worse. The other is if there is a small sample that is representative, to learn from that sample and kind of reweigh your population to make it generalizable. So there's AI models that can make your population what it should be, but if the data is simply not there, you cannot study it. And that's the biggest limitation and why I don't think AI is our future, right? Our future is through relationships where we can engage people and be able to have the data that we need to make decisions for everyone who we see. It's a method, it's a model, it's a tool. I don't think it's, I don't think we'll ever be good enough to be a replacement or at least maybe I'm too much of an idealist to think so. But I'll be sticking around, so. Thank you very much. Thank you. Hello everyone, I'm Dr. Sarah Cromer. I'm an endocrinologist and clinical researcher. I work at Massachusetts General Hospital focusing mainly on disparities in diabetes care. I was very pleased to see the great interest in quality improvement because we're all very privileged today to have Dr. Osagie Ebikosian here with us today. Dr. Ebikosian currently serves as the Chief Medical Officer of the Type 1 Diabetes Exchange. And in this role, he led the expansion of the Quality Improvement Collaborative for the Type 1 Diabetes Exchange from an initial cohort of 10 clinical centers to now more than 60 clinical centers, as well as the establishment of the Type 2 Diabetes Collaborative. He also serves as an adjunct professor of population health at the University of Mississippi Medical Center, where his research has been well-funded with numerous grants and resulted in many publications and focuses on improvement in health equity through quality improvement, implementation science and population engagement service strategies. He is the recipient of many awards, including the 2022 Eli Lilly Leonard Award for Research and the 2021 ISPAD, International Pediatric Diabetes Innovation Award. And he'll be talking to us today about quality improvement. So everyone, welcome Dr. Ebikosian. Thank you. I appreciate that, Dr. Cormann. It's a pleasure to be here. And I look at that guy, I'm like, that guy looks familiar. I think I know him. So I'm happy to be here and happy to be given this talk and great to see a lot of familiar faces as well. To you, Denise, a friend from Hemsley Charitable Trust. All right, so I'm going to be speaking about quality improvement and it was great. So yeah, some of the excitement and engagement and interest in quality improvement. So I'll talk about that for the next 25 minutes or so and hope we'll have about five minutes for questions and conversations afterwards. A few things before I get started, I was looking at the agenda this year and I was speaking last year. So this year I was like, well, okay, let me see what's so speaking. There was some very interesting sessions last year. And I'm like, wow, that's interesting. Really good content, really good things to share. And then a few of the sessions were not here this year. So now my mind is paused. I'm like, huh, I wonder why they weren't invited for this year. Well, maybe they had a scheduling issue. Maybe there was something. But I'm like, okay, well, if it's content, I think my content is fairly good too. So I'm going to try to give you guys good content today. Then I was thinking about what I did differently that might have made the organizers invite me back again for the second time. I think I figured it out. I showed pictures of my children. So here you go again. This is Harvey and Amaris. And Amaris is nine and Harvey is seven. And I figured that you guys like kids and like the pictures of kids. So you probably will rate me very high in your evaluation. And then I'll get another invitation next year. So I'll give you another picture again, just in case if you're still wondering if this is real. Yeah, so that's Harvey and Amaris. And those are my two little ones. But I will talk about the ADA standards of care. And I'll talk about it in the context of quality improvements and context of population. Now it's the main standard that really sort of addresses some of these issues, the first chapter. And Rosalina, our previous speaker, Dr. McCoy, she is actually the chairperson of the standard one or chapter one. But I'll highlight a few of those standards. So the first one you see on the screen, ensure treatments are timely based on evidence-based guidelines. They're made collaboratively with patients based on individual preference prognosis. And it goes on to highlight a few things. And then there's another standard there that touches on aligning diabetes management with chronic care model. And then go to the third one, care system should facilitate in-person virtual team-based care. There's a fourth one, there's a fifth one. And I like these standards because they are very intentional and it's very intentional that this is also in the first chapter of the ADA standards of care. So how we use this as a 2-1-D exchange quality improvement network is every year, once the new standards are released, so December of this year, there'll be the 2025 standards being released. We go through a comprehensive review of those standards with our faculty and discuss, well, what's changed? What's new? How do we bring those standards from what's been recommended from what's been proposed to clinical practice? And I would speak a little bit about that. And that's the whole context of why I'm really excited about quality improvement. I'm really excited about your interest too in quality improvement and hope that if there are a few of you that are still on the fence about how you can be involved in this, or if this is a path to consider, my goal is really to convert more people to quality improvement, which we all love and share. You know, this is one of the reasons. You think about our healthcare system, there's a lot of science in the healthcare system. There's a lot of research. There's a lot of information out there. There's a lot of new knowledge being generated, being produced, but not all of that gets to make it into evidence. And by evidence, now we're thinking about, well, this is the right way to provide care. This is the right way to care for our patients. This is what's recommended. This is what's the standard. And even when it does make it to evidence, a lot of that doesn't make it to routine care. And when it does make it to routine care, you still have some waste. And many of those things in the routine care doesn't impact every single patient. And then you see this wide variation in patient experience. You see this wide variation in care delivery. You see this wide variation in practice to practice, even in the same practice, clinic to clinic. And one of the things we're really trying to solve with quality improvement is really to reduce a lot of this variation. And our intent with implementation science is really to close the gap and reduce some of this waste you see that takes it from evidence all the way to patient experience. So I want you to think about this figure, this image, as I sort of talk through for the rest of this morning. Think about how do you, what role do you play in making sure whatever evidence that is being generated in the space of science or in space of evidence is making it to routine care and that's making it to the patients. And we all have roles to play. We're all contributors towards this. And we all can also work to fix some of those issues. And that's probably what we're trying to do. And the issue we're trying to solve with the quality improvement network, which has been graciously funded by the Hemsley Charitable Trust. And one way to sort of tackle this whole concept is what's called the learning health system. That's what LHS stands for. Now, learning health system, I would define it in a minute in the next slide or two, but it's not a new concept. It's been known for a while. In 2007, it was described by the Institute of Medicine. In 2012, there was a very extensive publication and it's a follow-up publication in 2020. But in 2024 and this, there've been tons and tons of literature in this space. So the figure I'm showing there, just number of publications and articles really describing learning health systems, the effect of them and what they're doing. In fact, there's actually a whole journal focused just on this topic of how do you make systems reduce wastes across the path? How do you make systems, institutions, practices move on from just generating science to evidence to ensuring that everyone's impacted, everyone that has type one diabetes or type two diabetes or prediabetes actually benefit from all of those standards there. So what is it? I was gonna define it and here's the definition. So in learning health systems, and I'll emphasize a few things. Internal data. So any data we generate or retain, like now you're speaking to a patient or you're ordering a lab or you're receiving the results of a medication or an adverse effect of that, any of the wide variation of things you do that creates some new outputs, and you describe that as data, any of that internal data, including our experiences, our experiences as providers, our experience as a member of the care team, all of that gets integrated in with external evidence. So I'm looking at what's happening here within my own clinic, within my own practice, within my own network. And I'm thinking about what others are saying, what's happening in Nebraska, what's happening in Maryland, what's happening in New York, and what's happening in my own practice. How do I bring all of that together? And like all of that knowledge gets put into practice. And then the second phase of it, as a result, patients get higher quality, safer, more efficient care, and their healthcare delivery organizations become better places to work. So we're touching on a ton of different things there. So I'm emphasizing the piece first around internal data. And that's one of the biggest struggle we have with the waste we see, is we are doing all this work, there's so much happening. Well, how much of that data do we actually use routinely to help someone else? If I'm generating data, how is that helping, Dr. McCoy's patients? How's that helping Dr. Krummer's patients? How's that helping any of my other colleagues as well? How about external data? Now, you are in the room and in the conference for tons and tons of external data. In the next four days, there's gonna be a lot of different scientific presentations with external data on a wide variety of different topics. How do we use all of that information? And it's overwhelming when you think about it. But more importantly, how about our own experiences? Put all of that into practice, and how would that help us improve care today and tomorrow? So we're looking at all of those different components. There's the data piece, there's the evidence generated from that, and then the practice, and we're going through this whole loop. And that's what we're trying to solve. So when people ask me to describe very briefly what we're doing in the collaborative, we are trying to make sure the science and the research and the evidence makes it to the patients. And it's that simple. Because our system is set up now, it's set up that there are lots of different kinds of waste. And if we can bring together stakeholders, and in our collaborative now, there are 62 different diabetes centers with the sole purpose of, I wanna learn what you're learning, I want to learn what you're doing differently and what you're doing similarly to my clinic, so that if you're doing something different, I can take what you're doing differently, I can bring it to my own clinic and ensure that I'm not having to reinvent the wheel, I'm not having to figure this out the same way you have before. I want to learn as fast as I can, because I care for my patients and I care for those I'm serving, and I really wanna drive and improve care there. So that's the goal, that's the vision, that's the style we're shooting for. And it is something that we've been really laser focused on trying to achieve, and I'll show you some slides on where we are towards the end of my presentation. So 16 needs to be involved in any learning health systems, and I want you to think about this, even in the broader context of implementation science and quality improvement, the first is real-time access to knowledge. And I use knowledge there in a very broad sense. So what information will be helpful to me today for this patient in front of me, or what information will be helpful to me tomorrow for someone else that will come into the clinic? So real-time access to knowledge, the first piece there. Then building a culture of trust and transparency, how freely can I share even some of the mistakes I've made? How freely can I share how our healthcare system is doing, how our practice is doing, what those opportunities are? So sort of the culture of transparency and trust. And you think about bringing competitors together, in some sense, that's kind of what we've done with a collaborative. We've run 62 different diabetes centers that traditionally are competing for a spot in the US News and World Report. Oh, I'm the second best, and I'm the third best, and I'm the fifth. And there's nothing wrong in any of those. Those have their purpose. But if we take a step back and think about, well, I get it that you're the second best ranked, and I'm the 25th best ranked, but I want to actually learn from what you're doing differently so that if there's something that can impact my patients today I can bring that into practice tomorrow. And then the third piece of having engaged and empowered patients involved in this work, leadership with sustained investment, commitment to learning and sharing, and then finally optimizing access to quality data. You're going through this whole loop as well. So that's all we're doing. Now, in different phases, these six components have different sort of variations and strengths. But even as you think about your next step, and as you think about wherever you learn in terms of your career, I want you to think about some of those components of what kind of knowledge do you need, or what kind of knowledge is out there that you can ensure that you and your team have access to in real time to make a difference today. I want you to think about how do you really facilitate this culture of trust and sharing and transparency. And there's a lot of vulnerability in that as well too. Not a lot of us wants to reveal where things are not going wrong. But we learn in this world, in the science of what we do, that even learning from mistakes and failures in systems and the vulnerability and the breakpoints in the systems actually help us be better tomorrow. So how do we have that culture where we can share, this is not going too well, this is what we can do differently, and impact that and bring that into practice there today. So even if you're not a part of some comprehensive piece, I think each of us have a role to play in one or more of the six different components, because you have an influence and you have something you can contribute to be a part of this as well in learning our systems. So you think about the whole concept of learning our systems and quality improvement and implementation science. And then you ask the question, well, you know what, that sounds good. Touchy feely, this guy's giving me the emotions. It's making me think about like, ah, I need to be good people. I need to be trustful. I like it. Let's pause for a second. Does that actually change outcomes? Like who has that helped? Does that help anybody? And that's a fair question and that's a question that I want you to leave feeling confident that has been answered and is being answered as well. So you think about one of the gold standards of evidence-based medicine, the Cochrane Reviews, and like it is, you know, you have a very rigorous definition and rigorous way of looking at data and looking at outcome and looking at systemic review and meta-analysis and you bring all of that together and like, okay, well, let's look at that. And you can read this paper, which was published last year, that really sort of touches on this question of this QI thing sounds really good, but does it make a difference even after you adjust and control for a wide variety of things? So I'll walk you through a few and I encourage you to read more this paper because it really sort of, if you're exploring this field, it gives you a lot to focus on. So, you know, a little bit about the meta, there was random, they looked at different randomized trials and I'm going to paraphrase a few of these and studied different interventions, the broader quality improvement interventions from team changes to, you know, looking at information, I talked a little bit about that, to case management, to having a registry and these are all different components of things we're doing with the 2ND Exchange Quality Improvement Network as well. And this is both in type 1 and type 2, they look at both system interventions, provider interventions, and also patient interventions as well and wide array of different things being tested and the concept of real-time information, culture of trust, culture of transparency, access to data, going through the whole loop. And then there were more than 500 randomized controlled trials and cluster randomized trials, so folks that even want to be trialists, this is your world, I'm like, you're speaking my language, talk to me. So RCTs and cluster randomized trials and then all of this included more than 400,000 people living with diabetes, type 1, type 2, pre-diabetes, gestational diabetes. It's the, to date, the most comprehensive, robust evaluation of, to answer the question, does QI work? So what did they find? Big picture is A1C outcomes, which is one of the main endpoints there, one of the primary endpoints, reduced by 0.4 percent and that is huge and that's significant. And you think about even a lot of the trials for diabetes technology and devices, a 0.3 percent improvement most times is what the FDA looks for. So you have a 0.4 percent improvement that is credible and it's rigorous based on all of this well-powered study for more than 400,000 lives, type 1, type 2, that is something that we can all take to town that yes, this thing works. And then, but that's not only that, now if your patient starts with a baseline A1C of greater than 8.3, quality improvement and quality improvement intervention as a standalone intervention and that's the main thing we're doing, that alone can, you know, show that there's 0.6 percent improvement, so even much higher. Now if the patient starts with a much lower A1C of less than 8.3, that's the one for 0.4 and then 0.6 for a much higher piece. And then they describe even improvements, reliable improvements and a ton of other outcomes there and I'll highlight a few. So this is a little hard to read, but the main piece I want to highlight here, which is the red front you sort of see across there, that, you know, in some of the top ones, even in additional studies, there's some as much as 0.14 percent improvement. When it comes to, you know, looking at some of the specific variables being tried, being tested there and really looking at for PIPs that had lower A1C and then for those that had A1C, I won't go into the weeds of this, but my main message with sharing this slide is quality improvement intervention, even in testing them individually, works. And then the next slide, when you will combine strategies together, that these interventions even have a stronger addictive effect. So, you know, sort of highlight those two again at the bottom. Now I'm not going into the weeds of this, but just to think about it for a second there, that you see a 0.7 percent improvement, 0.72 percent improvement when you combine multiple strategies. So my goal with highlighting this paper and highlighting some of those results is as individual test of change in one specific thing or one specific aspect of quality improvement studies, those are all shown to have significant improvement in outcomes. And even better when you combine all of this together and you can even see a more remarkable piece there. So if we know that something works like this and if we know that the evidence is glaring, more than 400,000 people with type 1 and with type 2, over 500 different randomized control studies, and if answered one simple question that, yes, QI works, what's standing in the way of us doing more of this? What's standing in the way of us taking the full plunge? Because yes, we know it works. Yes, we know it's effective. Yes, we know that even when you adjust and control for a wide variety of things, having that attitude of what's the science? How do I bring that into practice? How do I test out a change? If it works, I keep it. If it doesn't work, I modify it. And again, if I modify it and it's still not working and I abandon it, and then I go through that iterative loop of change and I've shared with others in a collaborative manner and I'm missing a repeat and I'm doing all of that, and that gives us this. And this is what we're looking for. This is the kind of outcomes we're looking for. This is the kind of population health improvement we're looking for. So that's all nice, fancy, well-designed studies. So, dude, how about you? What are you guys doing? I'll tell you. And we're taking this same model, this same approach, and some of the things I talked about, I'll highlight what's happening now in the more than 60 plus centers which I described. So look at where we started from. And a few of you might have seen this paper before. It's this paper from 2019 where we looked at outcomes from the priority one, the exchange registry, from 2012 to 2010, 2012, it's sort of the red fund there, and then 2016, 2018, the blue. My wife tells me I'm colorblind, so I think that's blue. It is blue. Blue, right? Blue? Okay. So that, and the blue one above. And now, what you, to orient you to this, outcomes goal was in that 10 to 10-year, 8-year period. And this was something that was a big shock to the system. This was something that a lot of us in the community were like, wait, what's going on? What's happening? Like, all that evidence, all that knowledge, all that insight, all of the things that have been happening, all of those investments, why are we not seeing some of the outcomes? There are many reasons why outcomes got worse. Now, the reason I like to think about is that we were in a very systematic way taking all of that insights and knowledge and evidence into practice. Again, that's one of the multiple reasons. And part of why I give that is, at this same time period, for our colleagues in Europe, so what you're seeing to the right of your screen, that's the two on the exchange, and that's the two on the exchange clinic registry, and then the DPV, it's the German registry, MPDH, the England and Wales registry. They had a little different approach. They had an approach where it was beyond just the research alone to making it a learning health system, making it a quality improvement collaborative, making it a quality improvement network. Those things I've described that helps us actually drive change beyond the evidence to practice in a systemic way, in a way where data is informing and driving that, they were involved in thinking about it in a slightly different way. And, you know, you think about like technology and the impact of that, and that's all helpful. And what I wanted to sort of highlight here is, yes, pump use was even much higher in the two on the exchange. CGM use was somewhere in the middle for two on the exchange, but the outcomes were actually worse. So, if we're just looking at this cross-sectionally, we're like, well, it can't just be the data, it can't be the technology alone, because technology alone should have given us some of the same outcomes or even better outcomes, because we were doing better with technology. So, with all of that insight, you know, a lot of collaborators and with the funding of Hemsley, like, well, we need to think about the different model, we need to think about a different approach, which is where we started the two on the exchange quality improvement collaborative in 2016. Now, there's still a system virtual registry that is still active and still alive, but has a different approach now as opposed to what it was before. But now, more importantly, we've really grown this network from the nine pilot sites to over 60, and it serves more than 160,000 people living with type 1 diabetes. And this map sort of shows you where those 60 plus centers are located and kind of what they're doing, including here in Orlando, we have a center here in Orlando, the Newmost Children's. Now, these centers are going beyond just talking and reviewing the data to actually contributing electronic medical records data. So, this table, it's sort of, it is the combination or aggregate level of all of the data being contributed for the different centers, and this is more than 87,000. Actually, this slide is a little dated, and our most recent data is like 95,000, because this slide was about two, three weeks ago. Now, every week, we actually get data dumped from all of these different centers into this collaborative network. And the goal and the purpose of this is we want to learn not just from the 1,000, 2,000 patients you're caring for, how do we learn for more than 80,000, more than 90,000 people living with type 1 diabetes? What's changing? What's not? What are you doing differently? And there are 120 different variables that we're looking at and tracking and monitoring. And all of that helps us to also benchmark and think about how we use this data in an easily accessible way. So, one of those components of learning our systems, one of those components in the Cochrane Review, it's the piece around benchmarking, around access to data, access to information. And we have a tool called the QI portal where the centers can actually log into and look at what's happening with their data, but more importantly, look at what's happening with another site's data. Because that's the essence of this. It's make the data visible, share it out. The good, the bad, the not so good. And if it's good, I want to learn from you. And if it's not so good, I want to learn from you as well, right? So, really being very transparent in the data in a way that you're bringing in competitors to now be collaborators and to now learn from each other with the goal of improving outcomes for people with diabetes. And we publish extensively on the work we do and how we've used QI approaches to improve device uptake technology. You see the terminates of our screen in there. As of now, 86 different publications in the last four years for my network on all of this concept and topics I've talked about. But two of them I really highlight first is approaches like this is also helping us narrow the equity gaps. And if you attend some of our other 20 exchange sessions, I'll do a shameless plug. Monday, there are two sessions focused where we'll go deep dive into exactly what we're doing to drive some of this reduction in equity gaps there. You look at 2016, 2017, and some of the gaps and the equity piece that we see there with insurance and look at where we are 2022, 2023. And even just visibly, again, this is significant, but even just visibly, we are already seeing huge improvements. Yeah. And then look at where we started. This was the first chart I showed you when we zoomed in on our QI work in the U.S. and what's happening. Outcomes getting worse. And this is where we are now. Now, from when we took a different approach, a different lens to try to solve for this issue and really be very intentional, 2016, 2017, 2022, and I should say 2023, we're actually seeing these outcomes go in the direction we want to go. 0.45% improvement. And our most recent data is even upwards of 0.6% improvement. And again, and you think about this for 80,000 people with type 1, and now we have data for more than 50,000 people with type 2, and you take an approach like that, and you use it to drive impact in the population, and we publish this extensively as well. That gives us some of the changes we want, and this is what we want to see. This is why we're in this space, and this is why we're trying to do this. So, you know, to summarize, think about quality improvement as a tool in your toolbox, the same way you think about technology, the same way you think about all of different components. Our approach to using data, to collaborating, to working together with each other, it's part of what drives this. And if you want to read more, there's actually a special issue in Endocrinics and Metabolism. It's a textbook which we published earlier with 13 chapters, you know, really touching on all of the work we've done in the network in the last four years of really having this shift in strategy, and that's something you can definitely read. If you're more interested in the type 1 space and what we've done to really drive collaboration and improvement there. So that's been the goal of this, and that's really been the purpose of this. I hope that you all see the impact and the value of this work. It's not just about the 2-1-D exchange, not just about type 1, type 2 for us in the U.S., but even globally. Even when you think about the randomized control trials, think about all of the rigorous ways we've defined and described impacts, QI works, learning health systems, bringing together different institutions to collaborate works, and I do hope that we can count on you to be soldiers of this work. There's one last thing. I figured out there was something else I did last year that the other speakers did not do, and I just remembered I took a selfie and I posted it. So maybe that's another reason. So you guys will indulge me for a second. We're going to do a selfie and we're going to post this later. With that, thank you all. I appreciate it. All right, and I do believe we have a few minutes for questions, so if anyone has a question, please raise your hand or come to a mic. So it's really awesome that you have all this data, but then how do you take the data and move it into implementation, particularly when you have so many sites? How do you coordinate that implementation and how do you take best practices and make sure that those are shared between so many different sites? No, that's a really good question. One way we do that is having many lines of communication. So let me back up a little bit. First is sites self-select and prioritize what interventions they want to work on. So we have 21 different quality metrics that you can be focused on as much as two to four at any point in time. So if you're focused on, you know, say depression screening or CGMs, within that there's a work group of other sites interested and focused on just that intervention. So whatever centers are doing in that space, they're meeting monthly to share that in real time. They're reviewing the data on the portal and reviewing the back-end data as well to also look at what's working, what's not working, adjusting for different variables and bringing and discussing that in real time. Now once those centers working on that issue finalize outcomes that work and test it out and scale it, we work to provide what's called a change package, which is sort of a collection or summary of for the centers that worked on this issue, this is what worked, this is what did not, so did not work well, this is if they were going to do it again, this is what they would do differently. So that becomes a guidebook in that area or for that quality metric. So the next time another center now it's interested in that, you're not starting afresh, you actually start fresh from a pick up the change package, which highlights for 18 months to two years a set of centers that worked on this issue, kids and adults, this is what they did. So you want to start there, you want to start from okay, I'm just going to take the ideas that worked for you and start implementing it because you guys have all figured this out, I've learned from that. Now the other component of this is we also have quality improvement coaches, which are trained implementation scientists as well, and they meet monthly with the site. So even if you're not as plugged into this work group or to that work group and then but you're interested in engaging in some of that, coaches have a full breadth of actually what's happening across the network because they're part of the coordinated center, and they're able to like point you in that direction of oh you know what Investor Florida is doing there, or you know Rady is doing this, or that center is doing there. So there are multiple touch points under their monthly calls, and then we also host what's called a learning session every year, and that's usually in November. I'll be in Chicago this year where it's two days of sharing and learning on what you're working on and what you're doing differently. So you know, so it's multiple at venues and hopefully at one of those touch points you would sort of be able to like get that piece, tap into it and become a part of that. And by the way, if any of you are working in any of the 62 centers, all of our resources are available to you, and you can always find the list of 62 centers on our website 2idexchange.org. So if you're already plugged in, if your center is plugged in, you can reach out to us and you can get connected into any of this. I can even connect you with a PI locally of your site so you can be a part of this. I do encourage that we have fellows be very, very engaged in this work as well for many of the sites, so that's something to plug in as well. I hope that answers a bit. Well, Danielle, let me know that we're at time. So I think we're all set. Thank you, Dr. Ivicozzi, and thanks, everyone. Okay. Well, that was just an amazing morning of talks. So, you know, unfortunately, we're going to have to close. I'm so sad, honestly. But we're about to go to the general scientific sessions, so I hope you take all the information that you learned about networking and some of the talks you heard this morning and yesterday. If they really pique your interest, maybe you can use your newfound networking ideas to go talk to some of these speakers. I know I've done that in the past and, you know, have really established some great working relationships with people. So I hope you get a lot out of the future scientific sessions. I have a few closing announcements before I let you guys get to the main session. So please don't forget to complete the required program evaluation. If you look at your table, there's a QR code. You might just want to do that now to get it out of the way. But it is required, but we do have a nice little carrot for you guys to do it. So 30 scholars are going to be randomly selected who did the evaluation to have free registration and travel to attend the ADA's clinical update conference in person in February 2025 in Tampa. So that would be really, you know, great for you guys to continue your learning. So please do fill out those evaluation forms, and it helps us to kind of know how to tweak things for future conferences for people. A couple of more announcements. If you lost an earring, it is at the registration table. So when I got that text, I checked. I have both of mine. So it's not my earring. But if someone lost one, that is where it is. If you did not yet sign in at the registration table, please sign in before you leave. You know, better late than never. And I think those are all of the main announcements. So again, make sure you visit the ADA Scholars Lounge. It's a really great perk for you guys. There will be lunch and, you know, snacks and drinks and just a place to charge your phones and rest a little bit. I know I always get a little bit overenthusiastic and have like back-to-back talks scheduled the entire meeting, and then at some point you're like, oh, my gosh, I just need to like sit for a second. So it's a good place to do that and go talk, you know, network a little bit and meet other people. But, yeah, it's been great to have all of you. Thank you for your engagement. This has been a really great group, and I hope to see you around the sessions. Thank you so much.

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