All right, good afternoon, everyone. Thank you all for attending today's session on Driving Results for LAOTBT Interventional and Electrophysiology Growth. My name is Callie Kalna, and I will be moderating this session along with our speakers that we have today, Amber Lopez, Courtney Hunt, and Joseph Squire. Please note that the slides are available through the app, and if you have any questions that you'd like to ask for the Q&A at the end of the session, please submit them through the app. All right. Hello, everyone. You'll have a great lunch. We awake. It was good Mexican, right? All right, so let's get this puppy started. All right, so my name is Amber Lopez. I am delighted to be here with you today. So I have to admit, when I was first asked to present on this topic, my initial reaction was, me? Why me? What do I have to say to you? And then the more I thought about it, and I thought about it, and I'm like, why not me? I am an abstractor, and I know what it's like to be in your shoes. So today I want to share our processes, how we engage our staff, and how we report out our quality metrics. I've been very nervous. I've practiced several thousand times. So never think you don't have anything of value to say. So reflect on your journey in abstraction and how you can help someone else. And I mention this because this could be you next year. You know, we all have unique experiences and insights that can help benefit others. So let's dive in and explore this together. So today's topic, Driven Results, LAAO, or as I like to call it, LAO, TVT, Interventional and Electrophysiology Growth. So once again, I'm Amber Lopez from Baylor Scott and White The Heart Hospital. Our hospital opened in January of 2007. We began as a 68-bed facility focused on cardiovascular care. Now over the years, we've expanded to 114-bed hospital with 10 operating rooms and 193 physician partners. We've earned national recognition for our innovative care, treating patients from 46 states and 12 countries. Last year, we handled roughly 7,700 inpatient admissions, reflecting our commitment to excellence in cardiovascular health. At our facility, we choose to manage our data internally instead of outsourcing it. So this approach has enabled us to review data in real time and enhance our collaboration with our physicians. So here's a summary of our physical year 2024 registry data. We participate in 10 cardiovascular data registries from STS to NCDDR. To get with the guidelines, we do ECMO, VQI, RVAD, PERT. It just seems like we kind of hit them all. So we average roughly around 8,200 base procedures that we abstracted last year. Our discharge to abstraction time is 11.9 days. We abstracted 6,500 follow-up records last year with that number steadily increasing. So we're proud to say we are a high volume registry site. Now to break that down even further, our quality department operates in what we call pods. A pod for us is organizing our team into smaller specialized groups, each focusing on specific tasks. By working collaboratively within our pod, we've been able to increase productivity, improve job satisfaction, and build stronger relationships. As we streamline our processes, we improve efficiency and ensure high quality outcomes. So I lead the structural heart pod, which includes the TAVR, the LAL, and EP. So here's a look at last year's volumes for these specific groups. Now for TAVR, we handled 436 base procedures. You know TAVR has two follow-up intervals, so that's 388, 30-day, and 354, one-year. For LAL, we did 627 base procedures, and that has four follow-up intervals. That was 461, 45-day, 431, six-month, 343, one-year, and 215, two-year. For EP, we roughly were just under 800 procedures for last year. So by adopting this pod process, we've significantly improved our workload management and boosted employee morale. We no longer feel isolated in our roles, but part of a cohesive and supportive team. So one of the key drivers of our program's growth has been THH, the Heart Hospital's Enterprise Growth Strategy. So this focuses on expanding our hospitals and its services. So Baylor Scott & White, the Heart Hospital, is located in Plano, Texas. We are a joint venture with Baylor Scott & White owning 50.1%, and its physicians owning 49.9%. Again, it opened its doors in 2007. In May of 2013, we expanded by opening Baylor Scott & White in Denton. Continuing on with our growth strategy, we identified a community need and opened up Baylor Scott & White in McKinney in July of 2019. In October of 2020, we further extended our reach by implementing what we call CSAs, Consulting Service Agreements, at several other locations. We offer these CSAs where we will go in and we're basically establishing their quality metrics and training their staff. Our latest venture, which is scheduled to open in 2026, will be a heart hospital located in Frisco, Texas. So in regards to our structural heart growth strategy, initially, we began our extraction efforts at Baylor Scott & White, and we started out with EP. We then expanded that to include Lowell, and then as we opened up our Denton location, we launched our EP program there. And then at Baylor Scott & White, we initiated the TAVR program. And then that extended into our Denton location and as well as LAAO. And then finally, when we opened Baylor Scott & White McKinney, again, we started with our EP program there. So with all this growth strategy, how do we stay on top of abstraction? Our quality department is a pod design. So our quality team consists of 15 individuals. And again, I'm the pod leader for structural heart. I have TAVR, EP, and LAO. We also have an STS pod, a cath PCI pod, and a vascular pod. So everyone within their pods is cross-trained. This has helped us create a more versatile, resilient, and motivated workforce. Our structural heart pod process includes submitting data to NCDR on a weekly basis. We just happen to submit every Friday. We live by that faithful motto, submit early, submit often. Everybody knows that motto in here. This ensures we stay on top of our submissions. We also verify that submission status is marked green and compliant. So over the weekend, the data is compiled. So when we arrive on Monday, we are reviewing our NCDR dashboard. We check it for OFIs, Opportunities for Improvement. We check for potential keystroke errors. By conducting these weekly reviews, we stay ahead of any issues and address them promptly before that final submission to NCDR. Now, if there are OFIs, Opportunities for Improvement, we are reviewing them within the pod. A team member on our pod is gonna verify that it's an OFI. They're gonna assess it for discrepancies. We will analyze the chart again and confirm the fallout. Our next step would be talking to providers, letting them know of the fallout. By doing this, we're straightening that connection, fostering a strong relationship with our service line leaders. We have provider engagement. We build rapport with our providers. We are seeking insights. Maybe we're getting clarification or additional information from our providers. And then there's data finalization. We engage with our providers regarding the OFI before that final data report is published. We also maintain an Excel spreadsheet for tracking every case. It is updated daily, and we conduct what we call real-time chart reviews. So we're continuously looking for that shared decision-making, KCCQ, five-meter walk, did they go home on the appropriate medications? We are also communicating with our providers of any key data points that are missing. Because we conduct them in real-time, we have the ability to address these issues early. So for example, if we don't see the proper verbiage for shared decision-making, we use Epic. We can Epic message these providers, say, hey, can you review this case for shared decision-making? We report our quality data out monthly. We regularly participate in clinic meetings, and we use standardized report cards to ensure consistent data delivery for all stakeholders. And the data we present is current. We distribute our quality report cards monthly through various meetings, and we've standardized that format to ensure that everyone, regardless of the audience, receives the same information. This is an example of our scorecards. We use a color-coding system. Green will indicate performance above the 90th percentile. Orange represents the 50th percentile. Red is anything lower than that. So these are areas we might want to focus on. We also have our volumes on here, as well as the NCDR registry benchmarks displayed. This just allows for a quick overview of where we stand. So if they need any additional information, they know they can contact the quality department for more specific patient-related information. So now that you've kind of seen how our POD process operates through our weekly activities, our communication methods, and the steps we take to ensure effective data dissemination, another key aspect of our process is holding staff accountable for managing their registries. We achieve this through what we call POD accountability. This will include a weekly productivity report that's sent out to the entire quality team. This report will cover case-finding turnaround time, abstraction turnaround time, abstraction backlog volume, and follow-up volumes. The purpose of this report is not punitive. Rather, it helps us identify areas where team members might need support as everyone is cross-trained within their PODs. This is an example of the registry productivity report shown here. Again, we use a color-coding system, green, yellow, red. Each section of this report includes specific goals that must be met in order to maintain that green status. Now, these benchmarks were created internally by our quality department. So, for instance, case-finding turnaround time should be 10 days or less as timely case-finding enhances the accuracy of all other productivity measures. Our abstraction turnaround time should be 21 days or less. We have current backlog volume as well as other various measures displayed. So, a team member is considered yellow, means they are meeting the goal, but they are at risk. This is a time where you want to ask for help as another member of your POD can jump in to assist. Now, red will indicate the goal has not been met. We need to address the issues causing this, or if it occurs, maybe it's a workload issue. Maybe there's too much on their plate. So, as this report is sent out weekly, we strive as a team to support those team members that might be in the yellow zone to prevent them from falling into red. Remember, again, this report is not punitive. It's just designed to help us identify where support is needed. And that, ladies and gentlemen, is our process. So, to summarize, we've covered our growth strategy, the opening of new hospitals, and our CSA consulting service agreements. We've reviewed our POD process and how we manage our registries and how we ensure accountability for productivity. So, if you have any questions, I'm happy to engage with you. I provided my LinkedIn and happy just to collaborate and answer any questions. So, thank you. Thank you. Okay. Well, good afternoon, everyone. My name is Courtney Hunt. I am the Director of Cardiovascular Operations at Indiana University Health Adult Academic Health Center. I'm thrilled today to be speaking with you from the leadership perspective of driving growth. As an operational leader, we have many competing priorities. If you're a leader, some of these words up on the screen here might start to look familiar to you. And even if you're not a leader, some of these competing priorities might start to look familiar. So, we have things like innovation, goals, growth, target team, performance, staffing, strategy, communication, data. But even within the subset of data, we have things like Vizient. We have things like Anthem. We have things like U.S. News World and Report. And finally, NCDR. So, how do you decide with all of these things? All these things come across my desk on a daily basis. All of these things are kind of running through your head as competing priorities. So, how do you decide which area you want to focus your attention? Balancing priorities require a leader to have ongoing assessment, strong communication, and a clear understanding of your organization's goals and targets. But I think we can all agree that one target, regardless of the name, the location, or the size of your institution that we all have in common, is that we're all here to deliver quality outcomes and quality patient care. At the end of the day, all of these things can come through, but we're all here to take care, good quality care of patients. I don't know if there's any other young Sheldon fans. My daughter's a huge young Sheldon fan, so I had to give them a little shout out here. But why is data so valuable? Why? And I recognized as I was setting this up that I'm talking to a room full of people who do data for a living. But we've already seen from an operational perspective that there are a lot of different competing priorities. But as a leader, why are databases such as the NCDR so highly valuable to operational leaders and the organizations that they work for? It provides us a foundation of informed decision-making that leads to better outcomes and increased efficiencies. Data can help us as leaders minimize biases and assumptions. It helps us reveal patterns and trends and reveal what is and isn't working well, lending to continuous process improvement. It also provides enhanced accountability. It's not just me as a leader, Courtney as your director, coming in down into your department telling you, hey, this is the way you need to do things. It provides us a benchmark of what good quality care needs to look like. And it also, as we all know, has significant impact on financial and reimbursement. So a little bit about IU Health as a system. We are the largest and most comprehensive healthcare system in the state of Indiana. We are 16, soon to be 17 hospitals under the IU Health brand, and we have over 36,000 employees strong. We also have a unique partnership with the Indiana University School of Medicine for all of our adult academic sites. And so what's IU Health's unique approach to analyzing the data to help drive growth? We've recently implemented a physician dyad approach that sits on all of our statewide clinical effectiveness councils. I'll talk a little bit more about that here in just a second, but the physician dyad approach really partners the strong clinical acumen of physician leaders with the business acumen of administrative leaders. So we recognize that there's a gap. These clinical councils are supposed to be physician-led councils to really drive clinical growth, but we recognized as processes were hitting roadblocks within our large system that really they needed to continue to be moving forward and driving at a good pace. We really needed to partner them with an administrative leader. So we have done that dyad approach, and it's worked well thus far. A little bit about our clinical effectiveness councils. We have eight clinical councils. These are enterprise system-wide. Councils range everywhere from, we have an AI council, an IIS council, coronary ischemic council, rhythm disorder council, but these are comprised of key stakeholders to keep processes, initiatives, quality, data trend, and contracting compliance moving forward. These meet quarterly, but there are many spinoff groups developed from these councils around quality improvement and best practice sharing. Probably nothing new to anyone in the room, but we also have quarterly QI-PI meetings. Participants include our quality department, physicians, service line leadership, and clinical effectiveness. We devote an hour every quarter to focus on trends and data with the hopes that any metric below the 50th percentile get an action plan develop. Key word there being hopes. So again, that's where that key physician-administrator dyad really comes into play. We have a structural heart multidisciplinary huddle that was directly related to less than desirable quality mortality outcomes for our structural heart population. This is a Monday morning huddle with CT surgery interventionalist and nurse coordinators to really provide a multidisciplinary approach for careful consideration for our structural heart patient population. We've seen a great improvement in our mortality metrics once we really got the multi-D huddle up and going. And then we also have a heavy foot in participation in ACC accreditation. So we are multiple cycles deep in cath lab and EP lab accreditation. Really allows us to deep dive into best practices and quality care around cath and EP patients. Some of our recent successes directly tied to NCDR metric improvement. Our first medical contact-to-device time. We're now in the 90th percentile for that, sitting around 97%. We partnered with local EMS crews for transmissions of EKGs straight from the ambulances right into a Diagnote Room. It allows for early activations and cancellations of STEMIs. So the processes, they activate an EKG straight from the EMS crew. It goes into a Diagnotes Room, which is our electronic activation for our STEMI teams. And some of the things that we kind of realized from that is it was an EMS satisfier. So not only did we improve our metric, the EMS satisfier, it gave them a lot of autonomy in the work and empowered them to, you know, you know the heart attack symptoms, you know what this is looking like, so we empower you to activate this. And it was also a byproduct, it was a staff satisfier of the STEMI call team. We saw less burnout from this because we were able to cancel, oftentimes before team members even left their house in the middle of the night. So lots of incidental findings from that work while also driving for a quality metric. Cardiac rehab referrals, we utilized our EMR. We started to think how could we have our EMR work for us? And so we used a trigger for a work list for phase one cardiac rehab referrals. Based on diagnosis code, we added cardiac rehab and revamped those, added those pamphlets to STEMI bags and engaged a multi-phased approach for nurse navigator and cardiac rehab team members to teach and reteach the importance of what cardiac rehab can do for outcomes for those coronary event patients. And we were able to increase our referrals from this from 80 to 86%. So great work in that space. STEMI length of stay, as an academic health system, if any of you are working in an academic space, you know the constant revolving door of physician learners. So it always feels like you're chasing tail on a physician education in this space. So we really started to leverage our mid-level providers, our APPs for early morning rounding with multidisciplinary huddles. And we really started to think about, and this played into a larger length of stay project within our system, but we started to think about how could we start working discharge at admission? So we were finding maybe our STEMI patients were ready to go home day one, day two, if they weren't discharged same day. But then we were waiting until day three for physical therapy or day four for HOMO-2 or day five for social work. And so we were starting to think about how can we start to work discharge on admission? So we developed a 24 to 40 hour discharge power plan, and that's helped significantly drive throughput and have better access for bed management even on the inpatient units. And we were able to decrease our length of stay just from that project alone in our space by 5% thus far with hopes of continuing to develop that work in the future. So all of these projects will not necessarily equate to increased lab volume, right? So none of these projects necessarily relate to increased volume in your lab space. There is considered growth for me as a leader. Even if it's improvements from like a downstream effect, it's growth on access for beds, it's growth in your programs, it's growth for quality improvement. But with all successes, there also becomes challenges, right? It's not all roses and rainbows. So the challenges we're looking for in the next year or so in the spirit of continuous process improvement, we really have a lack of engagement in quality reporting in our ablation space. And primarily that's related to lack of structured reporting. Those cases are long, they're complex, they don't really fit well in the box of structured reporting in our space. We also have a lot of variation across documentation practices across our enterprise. So have a little bit of struggle there with some good quality data regarding our ablations. Structural heart programmatic growth. The program was really developed, you know, in the beginning it was ran out of an OR, it was from our CT surgeons. But really looking at, since these have shifted down to the cath lab, how do we take care of these patients across the continuum? We're still really working these patients very siloed between our CV surgery space and our cath lab space. So efforts are underway to approach programmatic structural heart growth really across the continuum. We've heard a lot about AI this week. It was even in our opening plenary. So how do we look at utilizing technology and AI for surveillance and data mining to start some surveillance programming for at-risk patient populations like aortic stenosis through echo data mining, GI bleeds for LAAL procedures so that we can really medically manage and fine tune these patients in a surveillance type space where you have more of a controlled environment around when it's really time to pull the trigger for these patients to be referred per procedure to have the best outcomes possible. And then shared decision-making. While we're technically hitting our quality metric, I think everyone can attest there's always work to do in this space. Our current successes revolve around human redundancy in this space. I always say if your process is set up on a person and not a workflow, eventually that's going to fail. So we really need to grow our shared decision-making outside of a human redundancy space. But your work as an operational leader is never finished. No two days ever look alike. Wherever there's a challenge, though, there's always an opportunity. So I included this. The patients we care for deserve innovative and forward-thinking care teams. I frequently say to my teams, it's kind of one of my catchphrases, that if we do the right things for patients, the metrics are going to follow. And I really do believe that. So I'm going to leave you with one of my favorite quotes. It hangs in my office as a daily reminder because oftentimes we are put up against these huge, big, impossible-feeling things that we're trying to tackle, but everything is figureoutable. I really appreciate your time and your attention today. And I'm going to turn it over to Joe. I've got to figure out how to use this first. So I'm Joe Squire, Director of Analytics and Data Science at UPMC Heart and Vascular Institute. And we heard from Amber on how our registry teams can handle growth of programs through different frameworks and how they can adjust and adapt to that. We've heard from Courtney how operations can look at the things that are going on within the hospital and how we can produce growth with maintaining a focus on quality outcomes. And both Amber and Courtney talked about clinical registry data. And I think when we think about clinical registry data, there's a preconceived notion of what that looks like. And I'm willing to bet that many of you think of something like this, where we have the manual abstraction, it goes to the NCDR platform, and then we have our primary use cases on the other end, QI operations, et cetera. But I think for us to truly leverage data, clinical registry data, we need to have a mindset shift. And that shift needs to be, we need to view clinical registry data as a small but important part of a broader health care data ecosystem. And what this health care data ecosystem is, is it's all the data that's generated from patient care. And we use that data in synergy with each other to impact health care at a broader scale. And so why is this important for program growth? Well number one, when we think about the area that we work in, primarily clinical registry data, it's very easy to get blinders on about what we're using and the data that we're using. And we don't realize that people that are sitting in operations, people that are sitting in finance, our clinicians, they're being hammered with what we've heard as metric chaos. They're using data from other sources. And in order for us to better partner with them, in order for them to get buy-in with what we're trying to do, we need to understand the data that they're using. We need to go out, understand that data so that we can partner with them, bring them in, and have them understand our clinical registry data a little better. And so what this allows us to do by thinking of clinical registry data in a small but important part of a broader health care data ecosystem is it allows us to preempt care by finding patients and bringing patients in. It allows us to enhance care by improving processes, and it allows us to impact health care at a much more holistic and broad sense by advancing our care. And so what does growth through finding patients, what does preempting care look like if we're to make this mindset shift and view clinical registry data as part of a broader ecosystem? Well, a lot of this is really around marketing. So what you can do with this is, like, we have the NCDR benchmarks. We have other data sources where we can do competitive analysis, and we can reach out to physicians. We can reach out to patients by targeting that marketing material, by being able to bring patients in through those efforts. The other side of this is one thing that Courtney alluded to, is where you're able to find patients in your system. So if you have a larger health care system, we can look for patients that have uncontrolled hypertension. We can look for patients that have valvular heart disease. And they have not been referred to a cardiologist, and we can reach out to them and bring them in for a referral, and we can grow our programs through those efforts. Then what is improving process through adapting this new mindset look like? Well, the primary thing that clinical registry data is known for is quality improvement. And so being able to improve processes, being able to make operational processes more efficient and more cost-efficient as well, we can make a more adaptable, a more agile set of, more adaptable, more agile group of processes that allow us to be and function much more efficiently than we would otherwise. And so by doing this, by bringing in these other data sources, by pairing it with our clinical registry data, we're able to make something that is 10 times better than what we would get otherwise if we're putting blinders on. And then being able to continuously monitor and report on that is something that we always see at these conferences. It's something that we've seen multiple times at this conference. And that's really the important part of being able to adapt and make changes on the fly as we're seeking to make these programs more efficient and more cost-effective. And then growth through impact. How do we advance healthcare by adapting this new set of thoughts and patterns around the data that we're using? And a lot of this is around clinical research and innovation. So being able to produce research that advances care at a global level rather than at a local level, being able to bring in new innovative products and being able to make those products work and serve your patients in a more comprehensive manner. And then also strategic planning and expansion is another area that we hit on. And so from the UPMC perspective, these three pillars, some of the successes that we've had is with finding patients, preempting patients. We've been able to look for patients that have valvular heart disease that may benefit from TAVR, may benefit from MitraClip and be able to reach out to them and get them in and have them evaluated. And then from an improvement standpoint, we've been able, we've done a lot of work since 2020 with moving our LOW programs to one of our highest volume centers to a same day discharge process. And what that's been able to do is it's been able to free up a lot of bed capacity, be able to move those patients through quicker and reduce our costs on providing that care while maintaining the same quality outcomes, if not better than we had when we had multiple day stays. And through impacting care, we have a small but dedicated team of analysts and biostatisticians within our Heart Institute. We put out over 50 peer reviewed articles a year and a backbone of that is clinical registry data. But what we're able to do by adapting this, by adopting this broader healthcare data ecosystem view is we append our EHR data to that. We can look at more variables. We can look at longer term outcomes, five, 10 years, and it makes a world of difference in terms of understanding what's going on with our patients. And so the challenges that we face at UPMC are really getting people to understand that there is this broader healthcare data ecosystem and how clinical registry data fits into that. So working with, and a big part of that is understanding that we have a responsibility to ensure that our patients in the clinical registry can be matched up with our EHR data. And that requires figuring out how we have data governance around getting those identifiers in there that we can easily match up those patients to the rest of the ecosystem. And it's also working with other teams to get them to understand the value of this data outside of the quality realm and allow them to see, hey, there is operational, there's room for using this in operations. There's room for using this in finance. And the other aspect is human resources is always a challenge. They're obviously limited. There can be single fail points in there with having one subject matter expert for a registry. And when they leave, it's a huge knowledge loss. And so at UPMC, our future directions with where we're going to address these main challenges is we're following the mantra of money talks. And so what we're going to do with our clinical registry data is we're planning on pairing it up with financial data. And what this will allow us to do is one, allow us to show ROI on the improvements that we make, as well as allow us to strategically target improvements based on the number of patients that will be impacted, as well as the financial return on investment that we'll have and the effort that goes into that. And so this area here is really where if you have a larger healthcare system, you might have the capacity to build it out, but there's going to be a lot of maintenance around that. The flip side of it, there are vendors out there that you can partner with to help out with this. And there's one particular vendor that we're working with right now that would be a solution that we could push forward, and that's Biome Analytics. And if you want to get a sense of what that strategy would be, you can go out to their website and kind of look at how that would be formulated. But at the end of the day, by pairing this financial data with our clinical registry data, the goal is to get people to understand the value of that data, people outside of the clinical registry to understand the value of that data. And then you can better partner with them, and they'll better partner with you. And then artificial intelligence. So here I have Create Cyborgs, and it's taken from one of my favorite authors on AI right now, Ethan Mullick. And what this is, is it's the concept of co-intelligence, or I think it was collaborative intelligence, is what Ami said at the beginning. But co-intelligence is that concept of pairing AI with humans to augment our work. And so what we're actively doing with some of our registry abstraction is we're gonna bring in AI, and instead of having a data manager sit there and do 1,000 clicks over multiple hours to abstract one case, we're gonna have the AI model go out, perform 1,000 clicks in one millisecond, bring the data back, pre-populate, but provide context. And so what this allows the data manager to do is go through, vet those answers, look at the context and say, this is right, this is wrong, this is the actual right answer. And this serves three purposes. One, it increases efficiency, right? Two, it creates, as the data manager works with AI, it creates a model through this reinforcement learning that becomes a knowledge transfer system. So over days, over years that you're working with it, it starts to understand your system better, and it can more accurately bring back those data elements that you need. And so the third aspect of this is whenever that data manager's ready to leave, there's this knowledge transfer that you can then hand off to the next person who comes in. So the training, the learning curve is much less steep for those individuals coming in because you have this model that can help them out, make them more efficient. You can adapt to growth more readily with these more efficient models that we have. And so if we're to adopt this mindset shift of clinical registry data and seeing it as a small but this important part of the broader healthcare data ecosystem, we can use analytics to grow our programs by preempting care and bringing in patients and finding patients, by enhancing care through improving processes, and by advancing care through impacting healthcare and its outcomes. Thank you. Awesome. Thank you all. We have quite a few questions now to get through. The first is for Amber. Are you able to share the template for your quality report card? Yes, we can. And I think it's already out there on one of the NCDR shareable reports. So yeah, we're more than happy to share whatever you guys need. Great. All right. Amber, again, what is the data source, example, Excel, SQL, et cetera, for the productivity report? What data visualization tool do you use for it? It's Power BI. Courtney, does your organization take NCDR quality metrics into account when making strategic decisions or goals for growth? Yes. So we get trickle-down goals from our organization. Most of them are under a larger umbrellas. So as an operational leader, we get to decide how we wanna go about those goals. So I always take NCDR metrics into account when making personal goals for myself, but they're ultimately trickled down from organizational goals. Joe, how do you see the potential of AI in augmenting clinical data abstraction? What areas could benefit most from AI-assisted processes? That's a big question. I think, I mean, what I talked about is probably the biggest upfront and apparent use case for AI with clinical registry data. I do think the surveillance is a big issue as the population surveillance is also a good one. Obviously, if we sat through the opening plenary, there was a lot of conversation on the risks as well. So the idea of hallucinations is one of those biggest concerns whenever you're dealing with generative AI. But the other side of it is there's plenty of work going on outside of the generative AI space, such as people integrating AI into different imaging modalities, which would lower the threshold for those individuals who can use, say, ultrasound. And so by doing that, by allowing maybe nurses to be able to use ultrasound, when you have that 2 a.m. patient who is post-cardiac surgery or they're in the ED and you don't have a tech, you don't have a resident, you don't have somebody there with an ultrasound, but the nurses now has AI-enabled ultrasound, which will guide them in getting a clear picture and will help with the diagnosis of that. There's a lot of potential in that in terms of delivering better care to patients as well. Amber, how many FTEs are on your registry abstraction team? We have a total of 15 that do 10 cardiovascular registries. And Courtney, with competing priorities, what are some of your leadership tactics to ensure you keep quality and growth top of mind? Yeah, it goes back to me that, for me, it goes back to the concept of patient, that we're all there for patients. So quality outcomes, keeping quality top of mind is always easy for me just because it's a priority to deliver safe patient care. I think this is for everyone. How have you been able to lobby your executives to drive further investment into your registry and data analytics strategy? So maybe more for Joe, but everyone can chime in. I think it's about convincing them of the utility in it, and it can be hard to get that, especially whenever there's lots of operational priorities that might be in play. And there's a lot of financial headwinds in healthcare right now. So it's about being strategic in your ask and using your leadership capital sparingly and making sure that what you're asking for is really what you want. And Courtney, you might have some more insights into that. I mean, I think that I'm lucky to work for an organization that values these metrics and knows the role that they play within our healthcare ecosystem, as Joe likes to call it. So I have never struggled to create buy-in. So I guess I've been lucky in that sense. I work with a fantastic quality team that just partners really well with us as organizational leaders. So I think as long as you have buy-in across the board, it's an easy sell. Courtney, how do you decide on what metrics to track and the top priority? Yeah, so it goes back to what metrics we're doing well in. I think that I always try to guide my eyes to those areas that are yellow or red for opportunities, back to my statement on opportunities. So I think that don't drive for a metric that you don't have any control over. I always joke that I can't be there at discharge to ride an Ace or an Arnie. So that's probably not the one I'm gonna go after, right? So I'm not to say that it's less important, but I try to drive towards goals that I feel like I could have significant impact over or that have the most impact for our patients. Courtney, again, how engaged are your physicians with NCDR registry data and QI initiatives? Have you experienced any growing pains as an operations leader? If so, how do you collaborate to overcome them? Yeah, I would say there's being transparent. There's a variation of buy-in from physicians. We have some physicians that are super engaged. We have some physicians that I kind of have to go out and wrangle, but I think I leverage my personal relationships. I personally worked for my organization in my hospital for close to 15 years, but always been in the cardiovascular space. So I've always had great successes of leveraging personal relationships and also just explaining the why. I think that there's so many competing priorities. Physicians are always so focused on patient care that oftentimes it feels like a burden or paper medicine, as some of my providers like to call them. You know, it's paper medicine. You know, why does that matter? And I always try to center them and bring them back to the why. Why behind these are important. And I think that it takes someone who's kind of straddling the middle of understanding the importance of registry data, but also having a foot in the clinical world to help bridge that gap for them. And I see someone kind of in my position to be leveraged to be able to do that successfully. Amber, what's the difference between QI coordinator one and two job descriptions or roles? So for QI two, you'll be like the registry site manager. For QI one, you're just learning. You're in the learning stage. And what we've done at our facility is we've created career ladders. So you're not just going to always be an abstractor. You're going to have career growth within the ladders. And that's what we want to design our program for. So we retain that staff. Joe, which methods are you considering to create Cyborgs? I mean, right now it's just bringing in a vendor that would be the AI provider and that they have specific models trained to go through the EHR and bring the data back for a specific registry. We're actually, the plan would be to actually start with our pediatric registries and specifically our STS congenital, and then move on from there. The benefit is that we have people with extensive experience on the pediatric side that are able to train and adjust that model with giving it the proper feedback. And so I think that's really the initial step. I don't think right now that there's anything, I don't have anything else on the roadmap other than getting that kicked off. Joe, do you use a third party system, for example, Biome to combine your clinical data with your finance data? I mean, that's going to be the plan. So I guess I should fill that out a little more. So you can, theoretically, if you have the resources, you can build this out, right? You can take your clinical registry data, you go to your financial registry, you go to your financial registry, you can take your clinical registry data, you go to your finance people, and you say, hey, let's work on this. The issue that you run into, and this comes down to what's called a build versus buy discussion. The issue that you run into with that is that you get what's called tech debt. So you can set it up, it might work really well out of the gate, but as soon as there's a registry change, or there's an upgrade, I should say, if there's a registry upgrade, or if you want to look at something different, it's going to be difficult to re-engage those teams and get what you built updated. And so that's one of those trade-offs if you're considering building versus buying. And so if you buy, then you let the other people maintain that logic and everything behind it. And so the setup and the maintenance aren't on you, it's on the vendor that you're paying. And so that's really what it comes down to. And like I said, you can certainly build it, it's just there's, depending on your resources and how you want to handle it, and thinking long-term is important. We have answered all the questions, so thank you all very much for your time today. Thank you.

interventional care

electrophysiology

data management

quality outcomes

clinical data

multidisciplinary huddles

AI in healthcare

data analytics systems