Hello, everyone, and welcome to the podcast episode brought to you by Census. I'm your host for today, Gabrielle. And right now, I'm thrilled to be joined by Stuart Pillow, who's senior program coordinator at VCU Health, as well as Shamu Anthony, SPD, education coordinator at VCU Health. And today, we're gonna be talking about just their experiences with data prior, to using Census AI Squared as well as post. So here to enlighten us on the subject as well as provide actionable industry insights are Stuart and Shamu. So welcome. Thank you. Thank you. Of course. Well, to start off, let's kick this podcast in a year by giving our audience just a brief look into your roles at VCU and just what a day in your facility actually encompasses. So let's go ahead and start with you, Stuart. Just name, title, your facility name, and just talk about your role, you know, what you do on a day to day basis, your SPD, etcetera. Yeah. Sure, Gabriel. My name is Stuart Pillow. I'm the senior program coordinator for sterile processing at VCU Health, and we're in Richford, Virginia. My main duty is to support the leadership of both SPD and perioperative business. So I make sure our efforts are aligned between the two worlds, the OR and SPD. That staffing levels flex in both areas. We have to get creative to solve problems. Some of those problems didn't exist when we were fully staffed. So my daily tasks include administrative duties, like timekeeping, HR files, and supply ordering for the SPD team. But the best part of my job is to create a freedom I have for new solutions to old problems, like since it's AI. And I work very closely with Shamu, our SPD educator. Fantastic. And and let's actually go over to you, Shamu. Go ahead and give us what your day to day is like. Sure. So my name is Shamu Anthony. I'm the STD education coordinator here at VCU Health. So I pretty much oversee the education for, sterile processing. I bring up any process improvement issues that we may have using, Census AI, to target those areas and re to, like, provide more education, and to follow-up, with some issues we may see, with the data that we get from that report. But other than that, that's pretty much my day to day. It's pretty much the day to day. Well, thank you for sharing. Let's, shift this conversation actually over to the data you mentioned, quality data. So, Stuart, how were you measuring your quality metrics and your data, prior to using Census AI? Yeah. Prior to Census AI, we would export our quality feedback data from SensiTrack into a big CSV file and we would import it into Tableau. And we would try our best to catalog our trends and create charts that the staff could easily digest. We used to rely on the data analyst to maintain those Tableau dashboards. And although their efforts were valuable, we no longer have that position. So since this AI is starting to fill that gap that we have, and it's reducing the time it takes to get a full picture of what's happening in SPD. Wow. So AI was really able to fill that role. Well, how were, how has this platform helped you better visualize what's going on in terms of quality events and things of that nature? Yeah. Since it's AI, reduced the time and effort that we need to piece together that narrative of what's happening, and the narrative could be from any number of perspectives, whether it's OR or SPD. We can drill down to a single technician and activity like decontamination or assembly or the whole unit to see trends that way. And although we haven't rolled out since this AI to the old team, Shamu and I have been able to use, the tools to support our leaders with detailed information. It's easier to access them before. You don't have to wait till the end of the month to get that dashboard view. We can just drill straight into Census AI. So leadership's been able to use it. Well, moving a little bit into just quality data for education and training purposes, Shamu, how do you work with Stuart to see where quality events are happening, and how do we use these events to build reeducation and training courses, to prevent them in the future? So, we run the reports, pretty daily. And if we see, like, a spike in something, we take that information and drill it down to see if this multiple texts, they're making the same, having the same issues or if it's just a single tech. If it's multiple techs, I usually do weekly in services, and I'll take that information and and put that into my in service, information that I put out. If it's like a single tech that we may drill in or focus in more to the fact of doing, like, single reeducation. Well, this next question is, for both of you or either. Could you just give us a specific example of a time maybe when you saw a quality issue happening, with one of your staff that helped you educate them on the actually correct way to address the issue? Sure. We came in one Monday morning, and we were just doing our normal report to kinda see how the weekend went. And we saw a huge spike, in the numbers. So we kinda, like, drilled it a little bit more to see what was going on, and it seemed to be just one particular tech having some issues on this labeling. So we took the report. We're able to, like I said, focus on a little bit more and, just do reeducation with that specific tech instead of having to, like, bring it to the entire staff. We do mention we did mention the issue that was going on to the entire staff just to make everyone aware of what's going on. But at the same time, we took the information and was able to, like I said, focus on the one tech that was having the issues instead of, like you said, waiting till the end of the month when we used to have our old report. And, Stuart, do you have any examples of this? I like the one example that Shimon just shared. We have that one tech. All the errors may be found on one day, but that can show us that those errors were completed on days in the past. So although a previous date may have had one hundred percent, accuracy, now we can go back with confidence and say, well, it wasn't one hundred percent on that day. So we can train those patterns over time even going back. So we can also forecast going forward, what kind of trainings that Shumu might need to include for future staff so we don't have those same trends. Yeah. Having that data is extremely valuable. Well, Stuart, do you use these metrics, to measure performance and guide staff through improvements in terms of, annual or quarterly reviews? Census AI will come in handy for annual reviews in the fall. I look forward to helping the SPD supervisors use the the different tools inside Census AI to get more detailed information on their teams. Since this AI is still new and it's receiving updates, so we haven't released it to all the users, but we're looking forward to doing that soon. Oh, yeah. Well, you know, Shamu, I mean, using this quality data, could you or do you use this to create programs, maybe that all staff, attend to maybe solidify training requirements as an example? I am sort of kinda using it for that. We're getting ready to start an SBD one zero one program where we're gonna actually bring in, and basically have interns. So be either there can be internal staff that are not techs that are in that assistant role that we're moving up into a tech role or bringing people from the outside and, that we see the potential of becoming a tech. So we're able to, during the class during time, we'll be able to take some of these, issues that we've noticed from the current staff and be able to hone in and target more onto those, so that we won't have those trends in the future. Well, turning over to productivity and quality, I know you're using, the productivity module as well as quality, but have you seen any correlations between productivity and quality? Yeah. Generally, we see a slight dip in quality with increased productivity, or increased demand from the OI rather. We still see a small dip in things like filters missing or dropping, missing container locks, holes in trays, or a mixed up barkery level. But we try to catch those errors in house in SPD before it goes up to the OR. So we consider those, internal errors. Well, since it's AI, we can see those things quicker than if we just wait for the OR to find an issue. So once we, once an issue has reached the OR, we consider it a major error and major quality errors that could cause harm to a patient. We feel they have to be addressed with the text on a case by case to determine what went wrong, how it could be prevented in the future. So that's where Schumoo's reeducation comes in handy. If we need to do a recompetency on something or make sure the tech just realizes that there was a misstep. Well, I kinda wanna elaborate on just how productivity and quality go hand in hand together. So, Stuart, as you increase productivity, I mean, as you said, I mean, you kinda see the quality go down just by nature of there's there's more. More is happening. So the quality may might go down, but has that, just been your experience overall, that you've seen? I I know we have these forecasting measures, but, just have you ever seen cases maybe where the quality has gone up because you were able to catch that, and maybe kind of prep accordingly? I think if we drill down far enough, we could see that our quality has improved, with better tools like senses AI. We're able to get in front of some of those trending errors. We have a lot of new staff, a lot of traveling staff, so there's innocent mistakes, you know, a lock might be missed. The filter might not be put on, but if we catch those internally using these tools, then they never make it to the OR. So it never becomes a patient safety issue. Well, and that's where the training comes in. So, it's helpful all the way across. Well, how are you using, both of these platforms together to make improvements just, department wide, just across all of the departments? Yeah. We're we're starting to use the Census AI, the productivity and the quality module together to determine if we've gotten an appropriate number of texts during the peak hours. Something in the past that's been hard to forecast is how many texts do you need at any hour of the day? So now we're seeing with our productivity where we have increased demand, but we don't have enough text on the schedule. So we can see that. But then we can also see how it translates over to the quality. You know, if we have more containers to assemble less text, we have more quality issues because they're rushing. So we're able to see that the correlation in the data there. Yeah. Well, you know, just speaking more to both of your experiences using this kind of tech, I mean, you're almost kind of on the cutting edge of this technology as as you mentioned before. I mean, it's not available to everyone. So just using these new AI driven platforms, what do you feel like just in the process of using it? You know, what comes next? What do you hope, to see from Census in the future maybe, with these platforms? I'm always keeping my eyes open for new technological solutions. Our team works very hard in a ever changing and expanding environment. And I believe our technology resources are beginning to fill those gaps where we might have once had dedicated employees. In this case, data that used to take days and weeks to compile and manually make charts and graphs and send it out through email can now be accessed with just a click of a button inside Census AI. So we can see, like I said earlier, the multiple perspectives. You can see the OR perspective, transportation perspective, SPD. So we can see that story and that that narrative of sterile processing from multiple perspectives, and that's all with the help of since it's AI right now. So we're closing that gap on understanding the layers of the variability in the process and creating more efficient and sustainable workflow. The efficiency is just off the charts from what you're describing in your day to day work. And Shamu, do you have any thoughts? Yes. So with that means that we're able to look by looking at both, it helps me to be able to focus in on what reeducation we need, if anything, or what to focus on when I'm doing my presentation. Because a lot of times it's like you said, we do have a lot of new staff. We have traveling staff that are used to stuff at different hospitals. So by able to use these charts and the data that we get from the, Census AI, it allows me to know what the staff needs. Like, what what reminders do they need? You know, sometimes it's not necessarily, a lack in judgment. It's more of a why I didn't know we were supposed to do that or I didn't know we do that here type of thing. So it allows us to just kind of give gentle reminders to the staff and to help the productivity moves smoother and more to be more the, what's the word I'm looking for? To be a consensus across the board so that we're getting the same result no matter the shift or no matter the influx in trays that we have to process. No. Absolutely. And and, you know, as we start to close-up the conversation here, what would either of you, say to just hospitals who are looking to visualize but also improve their quality metrics? I think my biggest piece of advice is to monitor and protect your quality of data. Tools like Census AI are gonna be popping up everywhere. Ai is the new thing, but their usefulness is relative to the quality of the data that you're collecting, So I believe since this AI will be invaluable for other hospitals regardless of the state of their data today. But you always wanna work towards understanding the more nuanced trends in your productivity and your process, but you just need to make sure your data that you're collecting is how quality data. So you can get a high quality product. High quality and accurate. Shamu? I would pretty much do it on that same line. Once you can determine in your own facility what your issues are or what you're wanting to develop or grow or what your end goal is, I think CensusAI can be an amazing product to help get you to that. It provides endless amount of information and and can, like, hone in to, like, the specific details if you're putting if you're, like, asking it the right questions, I guess, I wanna say. But yeah. I think it's like Stewart said, knowing the data you wanna get from it will get you far. Oh, absolutely. Well, that closes up the conversation for today. So thank you, Stewart and Shamu, for joining us on today's podcast to discuss the experiences, with data prior to using Census dot ai and, using it now just in real time. It was a pleasure to have you both on the podcast. Yeah. Thanks, Gabrielle. Of course. And as always, if you wanna learn more, please visit census dot com and look for this podcast wherever it is you get your podcast at. I've been your host, Gabrielle. Thanks for tuning in.
