Censis Technologies, Power Panel: Quality vs. Productivity, the Sterile Processing Battle Royale

Alright. Come on now. Where about to get ready and get started here this evening. Want to welcome everyone to This power panel discussion, this evening, sponsored by Census Technologies. Before we get too much further, let's give a round of applause for census. We're putting this awesome event on for everyone. So quality versus productivity Obviously, it is one of, if not, the biggest debates in the industry today. Where do you land on that Spectrum? Is it a spectrum at all? Is it a either or? Is it a both end? We're gonna talk through those questions in more on tonight's panel. Discussion. But before we get too far, let me introduce myself. My name's Hank Bulch. I am the founder and president of Beyond Clean. Started sterile processing not too far here from Nashville in Louisville, Kentucky in two thousand nine as a frontline technician. For the years, ended up serving as manager and director at a few health systems in Kentucky and Texas. Twenty seventeen, we founded Beyond Clean, which is clinical education and networking platform that does fun things like podcasts, or virtual events, and Things like these. So let's go down the line here to do some more introductions, Layla. Who are you? And WHAT DO YOU DO HERE IN THE STIRAL PROCESSING SPACE? WELL, MY NAME IS LYLA Pryce. I AM CERTIFIED. A technician SIS two thousand and ten, I absolutely love what I do. So it gives me opportunity to travel. So I've been traveling as a manager intermittently for a while. So I can tell you that you get the most experiences as traveling around. But I tell I always tell my staff that I've had every job this thorough processing as a permanent staff member and as a traveler. So I pretty much have all the angles covered, and I see it all coming. But I learned something new every day, and I think that's what helps me grow in this field. Awesome. Yeah, we're gonna be talking about the implications of this conversation on travelers as well, so we'll get to dig into that insight. Any other travelers in the anybody else has traveled before in the space. Okay. Yeah. I've got a couple here. Great. Alright. Let's go next down the line to Courtney. I am Courtney Mace Davis. I'm a director of steel processing. My background was actually in medical device I was a quality manager for a medical device company and was also in charge of labeling, which is the IFU. So ended up in sterile processing, and been in the field about twelve years. Awesome. Welcome. Welcome. And rounding out our panel on the very end is seamus. Now, you may have come just to see him in the hot seat. We're gonna be doing that. At the end of this final discussion, but for those of you who don't know seamus, can you introduce yourself? Yeah. Absolutely. Howdy. Shame Johnson. I've worked at Census, almost twenty years now. Most of that time, I wrote code, you're probably wondering, what on earth are you doing up there? I lead our innovation team at Census, and we spent the last year and a half, studying the data behind productivity and quality. So that's I'm here to share some of what we learned here tonight. Awesome. Welcome. So off, the debates. Around quality and productivity, as I mentioned, has been raging for many, many years. It starts from the moment that you walk in as a new technician in sterile processing and those early expectations are set. This is gonna touch a patient, and that's the quality. We've got more than one patient on the schedule for tomorrow. That's the productivity. Where do we balance and how do we balance or can we balance in the current state today around staffing and technology, training, etcetera. So, we're gonna be diving in to that debate tonight, and I just wanna welcome you all again this evening. So before we get into some of the other questions, I wanna start first to your panel. On defining our terms, and there may not be one definition to some of these questions. But I wanna throw this out here first around quality. And maybe let's start here accordingly with your background. You mentioned that you've got a background in this space a little bit. How would you define quality in sterile processing? I would define quality as the right item, at the right time, and in the right way. If you get the right tray and it's dirty, it's not in the right way or if it's not in the right order. So those are the three things I think of when I think of quality. And what about you, Lyle? Do you have anything to add to that or Nuance? I agree, totally. But I would also say that it's the perception to also, right? Because quality is based on who the end user's gonna be. So we may perceive quality in different ways. If it's if it's where being rushed, we have to turn something over quickly versus when we have a little bit more time and a little more dedication, they will put produce a better product. So sometime, I think quality is gonna be a variation depending on the user and the end user whenever they get the actual item, the customer, k. Yeah. You're already kinda setting up some future questions there with that answer. So I like that. So Shane is and kinda rounding out this definition of quality. Can you kinda speak to that maybe from that data perspective, how you would define that in those terms? Yeah. Exactly. So I think Lila put it right. Right? It's the right thing at the right time and the right condition. When it comes to the right condition, we see our customers measure all kinds of things, the obvious things like, is it sterile? Right? Is there bio burden in the tray? But little things as well. Like, sometimes it's important how the things go on the train, how they're arranged, stuff like that. It just it just depends. So There's all kinds of things that we see tracked. Some of the weirder things I've seen is occasionally people record something like a bug in the tray, like a little bug. Maybe there's bugs that fly into the sterile processing. Depends on what part of the country you're in. You know? I'm surprised at how often I see like hair found in the tray. Right? Or eyelash found in the tray. And and, you know, I mean, that's they're right. That's not right. There shouldn't be an eyelash in the tray. But it it it it opens my eyes to like the level of detail that people go to, you know, bacteria can hide in anything. And and eyelash in the sterilizer isn't necessarily good for the patient. Right? So, yeah. So I think we all know where the bugs come from, the vendor trays, but moving on. Good answer there, Shammie's, though. So, you know, productivity is another one of those terms that we throw out there and can have many different meanings. So, Lila, maybe let's start with you. Like, how would you kind of start that definition of productivity? Well, productivity is gonna be what we're trying to achieve what we're trying to achieve in in our responses to What the what we have to produce for the for, especially as a manager, right? Will we have to be able to be accountable for productivity is gonna give me my FTEs. Productivity is gonna let me know. My budget is gonna be productivity is gonna be lower than that. You know, I'm gonna need you here all day, or we even going through the weekend. So productivity is a measure of what we've done all day long. But it's very hard to capture in this new world of doing everything. And so you have to really have a great tracking system But how do you utilize it without overworking the tech? Because that's the challenge of productivity. Right? I find that all the time. We make all these scans, but the real front line user of it doesn't have the time to get and capture all that information. But I need it because I need to report it out because someone wants that data to be able to justify all the things that I'm asking for to help improve the apartment. So Productivity is a I'm sorry, I gave you more than what you Yeah, I know. Y'all just can't. You just can't wait to get into the conversation. I get it. You know, great points. Great points. Yeah. So is there anything else new ones or wise coordinating you and I add to the productivity? I just look at it as As This is my engineering background, but inputs and output. So how many trays are we supposed to do in a day? And then how did the impact the inputs or the labor, out of that equate to what the outputs are. And you might have an ortho hospital whose outputs are different than perhaps your level one trauma center. So it's it's what are the expectations? So, Shane, if someone keep coming back to you for the data perspective. Right? So productivity obviously Even maybe more so than the quality today does that. We seem to have a lot of data points like Lila would mention to productivity, but how would you just kind of low level definition what does it mean in the context of data? It depends on who you ask. Right? So but seriously, like, if you ask, if you have somebody in accounting, what what should productivity be in sterile processing? What they're trying to do is predict how many people do we need working in that department? And if you ask somebody in sterile processing, well, it's more than just trays. We get asked to do all kinds of stuff. Right? So to your point, it's measuring everything and figuring out, you know, what are the things we should be doing, what are the things that maybe we shouldn't be doing We see all kinds of different methods that customers try and use to measure productivity. So some of them use minutes of service in the OR, which from the accounting side, that makes sense. Right? Like, if you do a procedure, the hospital gets paid. So if I can allocate a little bit of everybody's time to that procedure, then I can figure out how many people we need to run the hospital. But it's not that simple, right? Like not every procedure is the same. So And then, on top of that, just because you do a procedure, that doesn't capture all the other stuff around that that has to happen. Right? So it's complicated. It depends on how you ask. At the end of the day, it's all part of getting everything there the patient on time. Right? So with those definitions out of the way, and getting into some of the more complex parts of this conversation. But let's start at the beginning of that and just say, why is there a debate in your opinion. Let's just get like real practical. Right? Why are disagreements inside departments and even across the industry on how we How on these two categories, how they interact with each other? And you can kind of start wherever you like, anybody can jump in. For me, I've worked in many different departments and with different tracking systems. But to me, the debate is you go into a department that hasn't maybe had good quality in the past, and they've done they've gotten by with the labor that they've had. And so you come in and the standards change especially in two thousand seventeen, we had. You know, the Amy standards were updated, and we've said now we need these these amount of people. And so administration comes back and says, why? We got by with ten people and the same number of cases all of these years and now you want eighteen people. We haven't had any surgical site infections that we know of related to instruments. So I think they've gotten by with it. There's no reason to change. Where about all to that that technician level and even speaking maybe from that traveler perspective, obviously, you're coming into these new facilities just like Courtney mentioned. And there's an expectation for productivity perhaps. That may be different from the last facility you're at. Why is there that difference? And you know, why do folks just not see eye to eye on your perspective? I think it depends on the focus of that particular institution, right? Because different corporations, like, hospitals can be very corporate and business driven. The productivity is counted differently. It's assessed differently. The data is is collected in a different way. And what I find is that often, they want you to oh, it's just the it's everything. We want you to do more with less. So you have less people and want you to produce more. Or we want you to be able to figure out, well, I don't if I have seven FTs's. We're just throwing a number out there, but I need two of them to be leaders. So that really doesn't count the numbers of productivity because my leaders are doing other things. And they are concentrating on education and in services and making sure the equipment is running and keeping track of meetings because we have a thousand every manager in here knows. You got meetings all day long, and that's keeping you out of the department as well. So that's not a real but that's not a real number, but that's real money associated to that salary and I'm keeping them there. So there's so many things we have to measure, but they just wanna know the numbers. How many trays did I sterilize today? Well, you know, I we produced as much as we could, but we didn't But it's not based off of the same number that you're going off of because you're thinking about how many cases we procedures we had not looking at, you know, this particular doctor doctor Smith or whatever, he's typically gonna bring in about twenty five loners per case that's gonna make the difference from Monday to Wednesday to Thursday next week. Like it's just what it is. And they don't I don't think they see the bigger picture. They just look at the numbers every day. Yesterday, I did forty trays, but the next day I did one hundred and sixty two. And that's the difference of my of my day. And that's why I need so many people here because I don't wanna burn out my staff every day with the same issues because it it becomes a burnout. I'm tired of having to always produce. Is there a debate on the data side of this seamus, like, for developing these different systems that are out there in the market today? Do people agree generally on where the priority if there is a priority in how we're measuring these things and then reporting these things to end users. So I've got good news. Right? Whenever I talk to people that run sterile processing department, and I asked them, I said, look, you kinda have two some of my phone? Sorry. You you kinda have two different objectives here. Right? You've gotta get all the trays ready, sterilized on time. You got to get them sterilized, right, the right way. So, for you, what's number one? Absolutely, everybody says the patient comes first, everybody. So, to me, somebody that doesn't work in sterile processing, I like to hear that because I'm I'm gonna be patient someday. I've been a patient before, I'm gonna be patient again. And I wanna know that wherever I go, they put the patient first. Right? So, so that's the good news. But we all know that it's hard to find people in sterile processing, As long as I've been working at SENSE, I've been here and it's hard to find people in sterile processing. But with the pandemic, things got worse, right? Because people had to get furloughed, A lot of those folks left the industry. Right? And then now the wages for other industries have come up, but I don't get the sense that that has kept pace in sterile processing. So like I always tell people this out of just south of Nashville, Target down there starts at twenty bucks an hour. Right? It used to be that Target was not competing for the same people. Right? So that's that's tough. So you gotta get stuff done. You gotta get it done right. A lot of the people that had that knowledge left for one reason or another, And now you got new people in there and they're like, well, you know, maybe I want to work in sterile processing. Or maybe I'll go work at Target where I get air conditioning. Right? So, I mean, it's a real thing. Yeah. And, you know, we've spoken in the past name is about that turnover of that head count, you know, that we've kind of been alluding to that's worked into the FTEs. Of the productivity metrics and in that brain drain that you spoke of when we're losing these experienced technicians, that are oftentimes the more productive technicians and replacing them with brand new technicians who are often the most unproductive technicians. Your head count is the same. We got twenty people, but now fifteen of those have less than two years experience. As opposed to all of those that are key in your productivity, and we'll get into more of those specifics, but just wanna call that out. Let's talk about So, we've alluded to and some of the answers already here about whether or not there's a trade off between quality and productivity. So I'm just gonna very simply ask that question, do we have to choose one or the other? And if so, How are we making those decisions? We'll go to Lila first. Because I could, I could tell. I want to say quality. That's what I want to say, because I want to believe that qualities and always be the front of everything that we do because we want to make the time But then it's the issue of pressure. Right? Because the pressure is on to complete the task, to take the words of sharing and going on, we can't clean fast. Don't ask, like but if the reality is the pressure's there every day. It's something that doesn't go right we've gotta answer it. We've gotta be able to now we're gonna apply the pressure. And I I always say I was a parent like, I just wanna make sure that we're doing the right thing. But sometimes you don't have the time to get the right thing done because it's someone's life. We're in the room, pay already under. They're we've all been called there at some point in time or another. We're there. And you've gotta do you gotta make quick decisions and you gotta make them fast. And at that point, productivity is not the first thing I'm thinking about. I'm thinking about quality, and I wanna make sure I give this patient, the best experience that they can get by giving that surgeon exactly what they need. But Even though I'm thinking I'm giving the best quality, am I really taking every measure and step into place? Am I being the advocate right now for the patient? Because I'm also focused on productivity too, right? Because that's that's speed. I gotta do this quickly. So now I have to figure out how to produce this item that they need, whatever it is, in a rushed manner. So they're both playing against each other. And sometimes we may not win. And the real the real person that may lose is the patient and that's not fair. But we will be say as things that we put the patient first. And we do. In our minds, we do. We that's the focus of every day. But Sometimes we fall short and I don't think we're sometimes honest about ourselves about when we fall short and when that pressure is applied and who has the answer to the pressure. And that's usually the leader, right? They have to go take that one. So, seamus, from the data perspective, obviously, you get to look into multiple departments around the country. Not just in what we say. Right? Like, I would say, like, we can say patient first, but does that really mean patient first and always, or does time pressure you know, sometimes move that needle. What does the data say? Is there a trade off in our departments that are more productive? Do you see quality going down? You see the inverse in our high quality departments? Do you see productivity going down? Or are you seeing something else completely? So the good news is it's complicated. No. So we we went into last year when we were looking at all data with that as an assumption. Like, if you push people to go faster and faster and do more, they're gonna make mistakes. Right? And when we map out productivity across a whole bunch of hospitals against defect rates, we find that the hospitals with the highest productivity also have the best quality. Which I kind of went, well, that's weird, right, at first. So we can't run-in the numbers thinking we made a mistake. And then when we look within an apartment, we see the same pattern, which I thought was weird. So then what we did is we said, okay. Let's look at all of these different quality events and trace them back to. Okay. The nurse said they found a hair in the tray. Who assembled that tray? What was going on in the department when that tray was assembled? And we used a machine learning algorithm that was designed to predict mistakes. Winter mistakes most likely to happen. And it said By far and away, the number one way to predict when mistakes are going to happen is count the number of trays that are sitting there waiting to be processed. When that number gets high, people make mistakes. And that's when the light went on. It's like, oh, that's when people rush. Because they're rushing to catch up. Right? So, yeah, it's absolutely true. When they rush, they make mistakes. I mean, that's that's kind of a no brainer. But if the hospital if that department has high throughput, they're turning trays quickly, then that number never gets high. Right? And so they don't rush as much. And you see the same thing with individuals. The more senior technicians who know the instrumentation better tend to rush less because they are fast, they they can get it done, right? And they can get it done the right way. Whereas the younger, the newer technicians, They panic, they don't know what they don't know, and they rush, and they make mistakes. So, that's that's what the data says. So, We have a live audience here. Right? And I'm just kinda curious. Did that data point surprise anyone in here that the data shows the more productive you are, actually the higher your quality is. That's surprising to me. Okay. Okay. So let me throw this question, this next question over to you, Courtney. For sterile processing leaders. How do we ensure that our teams maintain both? That productivity throughput that Shane has just alluded to, but also that quality piece. How do we do that simultaneously? And, you know, is it possible practically? Like, what does that look like from a leadership perspective? I think from a leader perspective, you need to understand your data. You need to understand your business. I'm over six different sterile processing departments currently and that the productivity, I expect it to be different from all six. But I need to understand that data because if I'm making decisions and think that it should all be the same, then I'm not setting our team up for success. And I think, you know, we talk about AND INDIVIDUAL LEVEL HOW PREDUCTIVE PEOPLE SHOULD BE AND WE TALK ABOUT AS A DEPARTMENT, BUT I THINK AS LEADERS PART OF OUR RESPONSIBILITY IS TO MAKE SURE THAT WE'RE SCHEDuling PEOPLE If people in assembly aren't getting the trays are, are they answering the phones from the OR. What else are they doing? Besides assembling trays. And if as leaders, we aren't scheduling our teams correctly, then we aren't setting them up for success either. And so I think it's not just reporting the data. I think we have to understand it and manage to the data as well. Let me ask a follow-up here to that court need. Quality in particular kind of taking that measurement out by itself. Is that only a team measurement, or is there a place to bring that quality information and data to individuals on the team and have those conversations How should folks kind of be thinking through that? So, the way we do it is we do take it to an individual level and we would take it at a minimum at a monthly level You assembled three hundred trays this month and you had two escaping quality events to take that down into A PERCENTAGE AND LOOK AT IT COMPARED TO THE REST OF THE TEAM. ARE YOU A HIGH PER FORMER OR A LOWER PERFORMER? DO YOU NEED MORE AGKES is an accountability issue, but yes, we absolutely but we take into account volume and we take into account the escaping quality events. So that's a new phrase to me. Can you explain that escaping quality a little bit? It sounds kind of interesting. So as an escaping quality event is one that makes it to OR. We don't want inequality events, but if Myla is auditing my tray and she binds it before it goes up to the tar the department, that's an internal quality event. We would still track that because it was something that we found internally, but escaping is what we look at from the OR. Alright. You just coined a new phrase out here in the industry. You heard it here first on the panel discussion. Go ahead, Travis. We actually use the same term in software. If a bug gets out, it's a quality skate. Same concept. Right? So let me ask that same category question here to you, Lila. And, again, I wanna draw on your traveler experience because I think this is it's also applicable to that. Productivity. So I asked the question, is quality also an individual metric? What about productivity? Is there a place to bring those productivity conversations down to the individual. Should we just speak more broadly? This is a team affair. And what have you experienced in the past and where do you sit on how to navigate through those conversations? I will say that some of my best quality in productive technicians are are travelers who have more than ten plus years experience. They bring a level of just experience, knowledge, and they end up being, like, trainers. They are out there helping and supporting staff in a way that some of my permanent staff are either burnout, they they're over it don't wanna do it anymore because they're tired of new people coming in and they just are over that piece of it or they're brand new. So they don't know enough to to teach anyone at this point in their career. So I find that I've leaned on those experienced travelers to bring about because they know different things. They've seen it somewhere They know the name of it from fifteen different facilities and so that but they know that's a Kelly. But they but they don't but they'll know it from any other tray because they've seen it in every other system any like a thousand different ways. But it's just one of those things where I feel like I its experience. That really just kinda gets the quality to that level because even when they haven't seen a tray maybe set up the exact same way they're familiar with the process. And the longer they're there, they become the one my highest perform, like, the productivity wise. When I run those numbers and I run that productivity report, I'm like, IT'S ALWAYS MY TRAVELERS AT THE TOP AND THAT'S THE TRUTH. I HONESTLY CAN GIVE YOU THAT THAT VALULED FEED BACK. Reporter: Well, THAT'S GOOD THEY SHOULD BE, BECAUSE WE'RE PAYING THEM ENOUGH TO BE OF THOSE TOP OF FORBERS. FRU. BUT THEY ARE EARNING IT. THEY ARE EARING THAT REPID OF IT. I CAN TELL YOU THAT MUCH. GOOD, NO, I LOVE THAT. My next question I guess off of that is to you seamus on what data is out there and available to that individual level around productivity, just like Lila mentioned, you can kind of tell even without looking at a report who's carrying the the productive load in your shift or across your whole department You know, when miss Shirley's not there, it's gonna be a bad night. But outside of that, obviously, there's a lot of data. It's around that tells that story a little more fully, and you can compare individual to individuals. So what are some of those data points out there that are available today? So, we see a lot of what Live is talking about. When we map instrumentation knowledge, against productivity or quality. What we find is that the people who know the instruments the best, produce the most and make the fewest mistakes. So we what one of the things we did is we went through you know, months and months and months of data. We looked at every technician in that month, counted how many different kinds of trays, not trays, but kinds of trays that they actually processed in that month to get a proxy for their level of knowledge. Right? When you have somebody new, you're gonna start them on the simple stuff, and then as they grow, you add to their repertoire, if you will. Right? And so that was a proxy for us. And what we found is that there's a direct correlation between their breadth of knowledge how many trays they process and how many mistakes they make. And it's it's amazing, and there's some really good news in there too. So Your top performers are processing three, four, sometimes five times as many trays as the younger staff are. They're making fewer mistakes. But what that means is, like a couple things. One, and I get so excited about this, people matter. Like, they really matter. Right? Which is awesome. And like you said, setting people up for success is so important. The investment in those tech initiatives pays huge dividends down the road. I mean, if you can take somebody from a one x technician to a five x, that's huge, especially if they're Quality rates get better along the way. Right? So, that's what the data says. And, you know, the fact that the struggle is getting bodies in the door like we talked about before. And so if you can't get more people, then can you get the people that you have to two x, to three x, their productivity and their output while also not not sacrificing quality, which is the big debate here. And so, it goes back again to the data point that you share that says, the data says, be more productive you are, that your quality is going to continue at that high level. Because of all the reasons you said. So that's that's a great insight. I'm kind of blown away by that data. Alright. Let's talk about technology, seamus. I'm gonna throw this one back to you first. I remember, you know, back in two thousand nine when I started There were still tracking systems, obviously, out there. But we were probably fifty percent in the industry between still manual tracking and now the more automated tracking. And way back in the conversation was, you know, does this actually help us go faster? Or is all the clicks and all the modules and everything else is actually slowing down my assembly process, especially going back to what we said, with that deep tray knowledge of many of our more experienced techs that, you know, they say, hey, I don't need a count sheep. I know what's in this major trade. I'm gonna go in I don't need a click, click, click, click. So we're here in twenty twenty three and my question is, his technology now at the point that it's continuing to speed up that ability to produce at a high level. Are we still have ways to go, kind of where are we in that trajectory of speed and not inhibiting our goal to assemble quickly and correctly. Yeah. Good question. Yeah. So so I don't think we're ever at the end. Right? If you there's there's an old saying, there's no such thing as a mature of just mature product managers. Right? Meaning, the world's always changing. There's always new technology, so there's always something new we can do to make the world a better place. That said, it is absolutely true that a tracking system, a good tracking system, like, maybe, cents a track. Can augment that process. Right? To your point, it helps you collect that data. Right? When you understand the data, You understand where you you can make the biggest impact in changing workflows or streamlining processes or having those data driven conversations with your partners in the OR. You know? I've got several stories from customers where they've been able to use that data to go back to the OR and say, Look, here's the thing we're doing because of a problem. But if we work together, we can solve the problem and stop doing this thing, and process more trays. Right? We're we're down people, so help us help you. And with data, that conversation is much easier than I feel like. You know what I mean? So so, yes, I I absolutely believe a tracking system can help. No surprise of that answer. Yeah. Go ahead. A lot of go ahead. I was also gonna say that track a great tracking system can also help be a trainer, an educator in the process too. Because it's gonna provide if you have a great system and I've worked with Sensatrack so I know that you can have pictures. And you can and those are aids to help technicians who may or be new and may need a little bit more guidance. You can put messages in to give you identification because this might not be a tray I handle often I might read the remember that I need to it could be the simplest message, but take apart that depth gauge. Like, that's something that we can add. And those were messages that can't I can't have someone standing next to a new technician all day every day, but I can put those messages in a tracking system and It's up to you to read, but it's but it's up but it's up to us to make sure that we provide as much guidance as we can that they don't have to feel intimidated by going to go find my preceptor or go find my supervisor or go find our goal struggle to try to figure out how to get into one source. Right? Whatever or go find the I f u in that big wonderful book that's sitting in the file cabinet that no one's ever touched. Those are the things tracking systems to help us get to, and I think that that's something that we can use to help us get a more advanced tracking I'm saying, it's also an additional resource, and we gotta utilize it that way. That's fantastic. Especially, in the context of the staffing challenges that we have out there, but we're seeing, you know, even fewer educators than we have in the past. Those are the first ones often get cut out of these budgets and to have a resource in a system that can help to supplement that, you're not gonna replace them. But if you don't have them, what's the next best thing is to have all that information at their fingertips as they need them. So that's a fantastic call out. I want to go to Courtney now, but I want to come off of something that Shaymy said, which is people matter. And we all agree with that, obviously. You know, you may have mentioned about when there's quality events, you know, bringing that back on a monthly basis to the staff. And I wanna talk about the morale implications of our quality data and our productivity data to that individual level. And I remember as a young technician, I was very slow in decon, and I remember being called into my supervisor's office and he was like, hey, you gotta find your second gear. We just you just cannot be the bottleneck in DECon. And I, you know, still remember that conversation this day. So obviously, it stuck. So in these conversations around, personal, individual, quality, and productivity challenges, or maybe successes. What would you say best practice for leaders to be thinking as you're calling that person in the office to go over this information. I think it makes the data makes the conversations easier. Because it's not subjective it's objective. This is what happened. Now you you talked to the employee to find out, you know, what what the problem was. It wasn't blame oriented. But hopefully, those conversations are you can turn it around to the positive as well. You're not just calling in people that made a lot of mistakes. It's You did three thousand trays and you had one quality event to the o r. We want zero. Absolutely, we'll say that every day. But we also realize there's a lot of opportunities to get every single tray right or wrong, and let's celebrate what you did do well. And I think it's just in that messaging and developing your team to have that trust in you that you're trying to make them better. And better may be that sterile processing isn't for them, and that ends up being those conversations, but the data really helps to have those conversations. Right? What about you, Lila? Do you have anything else to add? Because I know, again, you've been on the receiving end as a traveler to being told, hey. You're doing a great job. Or a, maybe there's some challenges here with speed or quality. How have you kind of experienced that? Well first, I wanna just say that Courtney is gonna be She's a great director because she recognizes that everything is different. You said two things that I really liked. One was this that you know that you manage each one of your different sites differently. And I think that's so important because we have to understand what's going on in each level of the process in different areas. But the And then the the quality aspect of it is just that, okay, I have something that I can show you that's tangible that you can work on because I'm gonna I always tell people make I'm a I'm a coach you up or I'm a coach you out. But I'm coaching you every other way because I want you to grow in the process and I want you better. And I actually want to be successful. And and it's always refreshing to know that you're gonna be that your leaders are gonna be honest with you. And I think that was a good thing to say because You sometimes people go through and never know. Like, you think that, you know, they never call in, Lila, to the office. They never call in Hankin to tell them about these things But everybody complains about it all day long and it's just nice to be able to say, hey, I I encourage that. Look at your own productivity. Challenge yourself, grow to today. Pick up the trade that's challenging to you that you that you shy away from so you can improve upon it. Take advantage of the other tools that are out here so you can learn and and become better and become a better technician. It's because you've been doing it for ten years, that means that you can improve in some way. And I just really like that because I can tell that that means you have a passion for your people and that means a lot to hear that because sometimes you don't go places where they're just focused on the number and whatever you can what the output is. They could care less about the people. And that's why we lose so many people in the process. And quality goes down, and we're looking at people who may spend six months to a year with us and then they're they're out the fields altogether. And then we put more restrain on those of us who've been here for fifteen, twenty, twenty five, thirty years and retired. Of holding it together for our department. So So, as we kinda transition to wrap up this portion, I got a couple more questions. The first one's a shame, so this kinda goes back to something that you mentioned earlier about. The ability to one x, two x, five x, your team. And that's true both for productivity but also quality. Right? So maybe they're starting that low level quality, got a lot of challenges, but that doesn't mean they they cannot improve. But, you know, the data is what we need not only to have those individual conversations that you mentioned to be fair, honest, transparent, but also to go up to the c suite because that's what they wanna see. That's what they wanna hear. They don't wanna hear. I feel like I don't have enough people or I feel like we're not as safe as we need to be So what kind of data would you say is gonna be the most impactful to go up the chain? What are those things that, you know, we need to be printing those reports often taking with us when we're saying, it's gonna be bad if we lose these people. If we lose, you know, two more, if we don't replace these, This is the potential implications. Yeah. Great question. I think it's it's different at every facility It depends on the people involved. Right? I can tell you the stories that I've heard. It's super important to have those conversations with data just right off the bat. In terms of understanding who your top performers are, in terms of understanding where you're at with quality. You you know, the the conversations that I hear a lot of times are around Well, some of the OR said, this particular service line is always having problems. And if you don't have data, then that kind of perception has a tendency to linger. Right? And so being able to show, okay, well, we did. We had a problem six months ago. Here's what we did about it, and here's where we are today. You can't really help. Because, you know, when you're in EOR, people tell us all the time, they're they're thinking about the here and the now. Right? And so, if you're having a problem with a tray right now and you remember, oh, I had this problem with this tray last week and the week before. Now, and you're world, that's a pattern, right? And until you can see the data and see the big picture, you may end up chasing the wrong problem, because that's the person who's screaming at you right now, but that may not be the most important problem to be chasing. So super important to have the data. Right? In terms of going up to the C suite, you know, I think their eyes at least in the stories I've heard, always pop open when they really understand everything that SPD is doing. You say, look, we're processing all these trees and it's great, but look at all this other stuff you're asking us to do what we're doing. Right? I don't I don't know that every CEO of a hospital really understands what goes on in SPD. It may be the case. I could be wrong, but but but that's not what I hear. You know what I mean? But they do understand numbers. So being able to tell that story with numbers, you know, facts, that helps a lot. According to your Lila, you have anything else to add because I'm sure both of you all had have these conversations up to see sweet, so what kind of data has been successful for y'all? One thing, when we talk about productivity, I know a lot of times people think of going fast and speed. One thing that's been helpful for me for the data is Amy came out at around two thousand sixteen two thousand seventeen with the complexity, the trade complexities. And we actually have added those into our tracking system BECAUSE IF ROBOTICS ORTHO TRAS, IF I SEE THOSE COMING THROUGH IN fifteen MINUTES, I'M GOING TO That's a problem. Those are too fast. We are skipping steps and we are not following the IFUs. So I think you can use the data in the other way to say wait a minute, this is how many people I should should need to process these complexity of trays. And, you know, were those numbers perfect? No. But it gave us a really good guideline. We have to start the conversation with this is how long it should take to process these trays. So I think it's been really powerful in getting staffing because we're doing it too fast in some cases, and it's not safe. Well, it it burns people up. See, right? Yeah. And and and that just leads to more turnover and shorting shortages of staffing. So, yeah, you absolutely have to manage that as well. I was gonna say that I tell my staff every day says the track will tell me the story, right? I already know what happened every day because I'm just running my reports in the morning and I'm looking at what happened. So I know when we had a failure, And then I'm looking back at Hoover in the load and okay, you didn't push this in. I can just read the story and I know exactly what happened all night long from the time I left to time I came back the next day. So I can't wait to get back on. Because I'm gonna have about seven days worth of stories to read and as I go through the reports. But it's very helpful. Like you said, because you can I can see it without having to be there? And I know who's productive because I know whether I know what they do. I know that after we walk with the clock in, they go to breakfast, and then they walk around. And we say we're gonna do our rounding, but then we end up, you know, at the front desk talking about the weekend and all these wonderful things. So but I can see it because there's there's no productivity. We haven't scanned anything in in two and a half hours. So I think it's great to to be able to capture that information. And then I can use that and say, well, this is why I only can have two people here and from until eleven o'clock because we don't do So if you want to get the credit for having more people here, let's stay productive, let's use the system, scan everything. I'm one of them, a big fan of, scan it all, go all day long and show me what we're doing so that I can therefore present the case of I need this. I need to be able to do this or, you know, the machine down. Right? So I wasn't I don't have any numbers for this autoclave because, you know, we were waiting for them to come in and and give and fix in the service of the machine. Like there's so many things that can happen but I can see the story because I see what reports I have. I know we didn't run a load after eleven o'clock what happened on this machine. Those are the things that that's when I say what I can it tells me the story. I look forward to being able to read the story because now I have a way to try it back. I'm looking at the user ID. I'm trying to figure out what was going on. Who was training? Who's new? Who's old? Who's been here for a while? What has Oh, we were celebrating somebody. It was birthday. Like, I can look and see all of that information just from the report. So I appreciate it. In that sense, but it helps me because it helps me get an understanding especially when I'm new. I don't know this staff. Right? I've only right now, I've been here for two weeks. I'm like, okay. I gotta I get a lot of data and I'm looking and I'm paying attention, but I know who my who my high performers are already. And I know who's gonna who's gonna produce. And I know who struggles a little bit. So I've already put things in place. I'm putting plans in place to help those and coach those along who need a little bit more assistance. Because I see what they shy away from. They don't like to go to decontam too often. Okay. Let's work together in there. Let's let's figure out a plan. But that is those. Those are the stories that a tracking system can help you see that someone's not gonna come to the office and say, I'm struggling here. I don't know how to do this or I'll keep making a mistake here. Those are things that I can see. And I always tell them, like, I can fix it when it's fresh, but I can't fix it the next day. So help me help you in the process. And I think they're starting to trust me more because they see I can see it all anyway. So it doesn't really help to hide it because tracking systems help you see everything. I can appreciate that more as especially just the more advanced they've become over the years. Yeah. That's great. Well, when it's my birthday, I'll come to your department, sit up in You could do the birthday scan and get some productivity hours out of that. Alright. So let's close out this section with a little bit of dreaming of what do we need to know or what do we want to know that we don't know yet about quality and productivity in our departments. You know, Lila, like, you just talked about, there's so many things you can see in the data that can tell the story about what's happening when you're not there, what's happened to the equipment. But what would you like to know that maybe the date is there, but hasn't been brought together in and an easy way to visualize. Maybe it's not there. We want to start capturing those things in the next five or ten years. So it's kind of dream big, and I'll start with seamus. Give you two a little time to think about that. This is probably some of the seamus has thought about a lot. So, yeah, you know, what's coming down the pipeline in the next five, ten years that we should be excited about? I like this question. Yeah. No. So for me, because I learned about what happens in sterile processing in two ways, right, talking to customers, and then looking at the data. The data I have is scan points. You know, what I want to know is what happened between the scans? And so that's what we're working on. We're working on ways to automate the capture information between those scans or to eliminate the scans and allow you to just do the job and let the recording happen automatically. Right? If we can improve the data fidelity, there's no telling what we can learn there. So, so that's what gets me excited. I think it's a very real possibility. We're running some experiments now, but who knows? Right? Maybe we can change the world. Love that. Well, what about you, Layla? I would love to be able to help the technicians more to utilize the system, right? Because we want to capture all this wonderful data. But I know from being a technician and having done the job, it's very difficult to go over to the scanner and scan this. Oh, I wasn't in this right field and I wasn't on Deepgram and I have to go back and figure this out, logging in. Oh, the computer's not working today. Guess what? Not gonna get any data that day because no one's gonna let me know that I need to call IT. And trust me, IT is not coming in five minutes to help me figure it out. So we're gonna keep going because we can't stop the day. So I would honestly love for a a system that is just so automated to the sense where, like, maybe it's just, you know, RFID in real time like it's AI at its best. And it's just we put a chip on us and it follows us through the whole process. And I don't have to scan anymore. Right? Because you because you have you have everything that all of that is collected by just what I've done. Even to the point of when I stopped working. Right? Because now we can know Okay. Well, then she's either sitting in the DND contaminated on the floor, not doing anything, or she went to break whatever it is, but that's how real it is. It will be just nice to know because I feel like we disrupt the technician's day by making them do so many things. As much as automation has improved our world, It the scan points I think that's what I hear the most is that those are the challenges. I don't wanna stop to scan. I don't wanna stop to to to push this over there. Or I didn't push the button. So guess what? I lost twenty minutes because I turned my back and I didn't see that the I didn't start to wash So I lost all that time. So really just kinda keeping things moving, I would really like to be able to see that process improve. But that would be my I don't know the dream SPD, I guess that would be what it would be. Love that. What about you, Cornell? I'll give you the last word. I think that there's so much technology out there. I would just like one solution. So I wanna know how many instruments were not used in a case. I don't wanna have to buy I I don't have to go to a different technology for that. I WANT TO MANAGGE MY LONERS WELL. I DON'T WANT TO HAVE A DIFFERENT VENDER FOR THAT. THERE'S JUST I just want one solution, and I want and right now, are they combatable? Can they work together? But but it's a lot to get our IT involved and to get the different systems approved. I can show you some stuff. So I have my round of applause for this terrific channel discussion tonight. So, we have solved the debate between quality and productivity. Now, you know, you can go out You could tell your friends and family that now we know the answers to all the questions around quality and productivity. But hopefully, automore serious note. Hopefully, this inspired you to think a little differently, maybe about your data and about your team Mac home that's scanning now, you know, they're scanning. They're assembling trays, they're making decisions around quality or productivity around time and training. So take this back continue this conversation, not only you with your team, but also with the folks up here in the panel. Please reach out to them. They're all on LinkedIn. And connect with them. And if you need to get names and contact info, we're gonna be around here for a little while as well.