 So we're live. Okay, so our next to last presenter is Corey Fritch from UW Health, and he's gonna be talking about using R to support COVID response at the health system. Hello everyone. Like she said, my name is Corey Fritch. I'm from the other UW, which would be UW Health and we're based in Madison, Wisconsin. Thank you for braving the Saturday afternoon talk this late. So what I'm gonna do is just over the next 10 minutes or so spend just a little bit of time explaining to what my team is as well as how we leveraged our Studio Server Pro for our work with COVID-19. So kind of at a glance, Madison hospitals are where our main functions are out so we have seven hospitals with one academic medical center. That'd be the University Hospital in Madison. We do have two regional hospitals down in Illinois as well as 77 clinics, mainly around the Madison area. The team that I work on is called the Applied Data Science Team. So there's three of us, three data scientists that work in a larger department called Enterprise Analytics. So we are a full-time analytics team. We basically support all business operations as well as clinical work. So the real nice thing about my team as well as my department that might be a little different than some of the other presentations or speakers that we're talking is we have direct access to the EHR. So we use Epic. So we have access to not only go into any of the instances between prod and test, we also have access to any of the databases and data warehouses that are built off of that as well as any of the other, like our employee databases and stuff like that. So our main focus on the Applied Data Science Team is working on predictive models and getting those into production, into operations at UW Health. So not only do we build our own custom models, we also work with researchers and others to translate and implement those predictive models and get those into the workflows. As well, the other big thing that our department does is we are consistently working on kind of engaging and getting our other analysts in enterprise analytics to use more advanced analytics as well as predictive models. So I'm guessing similar to most data analysts, both in healthcare as well as outside of healthcare, our work changed dramatically in early March going into mid-March and late March. So due to COVID. So our team joined the collaboration of other analysts as well as researchers, clinicians and professors from UW Madison. And we were tasked with, there was an incident command team that was stood up at UW Health as well with UW Madison employees or researchers and teachers. And this model team was tasked with creating some predictive analytics around like modeling out what we thought was gonna happen over the next weeks into the next months, both in Dane County and South Central Wisconsin so that we could try to make recommendations as well as provide data and analytics to that incident command around both ICU and non-ICU bed capacity, what we thought was gonna happen in terms of spread of COVID as well as some other things to make sure that we were prepared as a hospital system if needed. So our studio server pro is the main platform that our team uses prior to COVID as well as it became immensely helpful during our work with COVID. We do have some other visualization softwares that like we use as a whole organization. Our studio is not our like organization wide visualization but what it did allow us to do is it allowed us to stand up stuff extremely quickly and because our team normally codes in the R and Python right inside our server pro, we were able to do our coding, some data federation. We had a lot of data that we had to pull together from a lot of different places. We built notebooks, did some code reviewing as well as all of our versioning. We built a lot of different models and a lot of iterations of models. So using our studio server pro was hugely beneficial. So it also allows all of our team members as well as team people that we were working with that normally don't work in our studio server pro to have the same packages and versions instead of everyone using their own on their laptop. So we could easily share code, share notebooks and be able to run those no problem. I will say it's not widely used by everyone in enterprise analytics yet but hopefully soon, like I said, we're mentoring some people. Another thing that we're doing is trying to bring people into coding in both R and Python which will be done through our server. All right, and what we did need it for was we had to provide a lot of answers. So like I said, we had a instant command. We had to prep stuff for them each week. So we started to use our server pro as the main solution for everything that we did from data collection through visualization. So our studio, just kind of the actual using R as a coding language allowed us to bring in stuff from like Wisconsin's Department of Health Services through their APIs. We started looking at data at the state county and census track level. We really tried to drill down into different things to make sure that we were trying to really predict out and kind of see what trends were happening even compared to just statewide numbers that everyone was getting. So we created scripts and did some automation around getting that data in so that we could get it pulled out and pushed out to an instant command dashboard. That way they had one spot that they were looking at data instead of trying to pull in metrics from hundreds of places. We developed three predictive models on that model team. One of them was a SEER model. So susceptible, exposed, infected and resistant. Again, there was a lot of iterations around this and it's continued to be work on, I think our latest version was finished a couple of weeks ago. Doing that coding in R and R studio allowed us to update it weekly, put some iterations around it and get predictions both in and out of that model pretty quickly and easily. We also leveraged our markdown quite a bit. Like I said, we were trying to be very effective and efficient on some of the modeling that we were doing as well as the analytics around it. So our markdown was really great when we wanted to just export something as an HTML that we could get to any person if we needed to email it to them or if it was a quick look at something. It really allowed us to get everything out there. So we also did a lot of the analysis of current and past active cases. Kind of when we were sending stuff out, our Connect was huge for us. A new piece to our Studio Server Pro. It allowed us to use our Active Directory to not only distribute everything, but authenticate to different groups and stakeholders. And then we developed two pieces to R Connect that were really beneficial to our stakeholders. The first one was an API with Plumber that allowed for simulating the SEER events. So if someone wanted to kind of mess around with what they thought might happen in the next couple of weeks, they could do that. As well as we developed a speedometer that allowed us to look at is capacity gonna be over what we could handle in the next couple of weeks, depending on like length of stay as well as how many COVID beds we had for our capacity. And that is kind of weird to drop, six months of work into like five slides, but that is kind of where we were at. Thank you. So I think we have time for one question. Are you looking at how healthcare usage has changed since COVID? In terms of analytics. It wasn't clear, so, but. So that is our main, so I'll say how our healthcare system was being used was one of the things that we were in charge of looking at, so especially at our clinics. We had closed most of our clinics as did most people in Wisconsin. So we were starting to look at when could we safely reopen, and that was one of the driving things that we had to present each week. Great, thank you so much, Corey. No problem, thank you for having us.