Solution Showcase | Simplifying Value Ratios for Benchmarking Data

Hi, and welcome. I’m Debbie Leich with Origami Risk, and we’re glad you joined our solution showcase on value ratios and ratio formulas. Before we get started, there are a few housekeeping items to cover. All participants are in listen only mode. However, if you have questions, please feel free to use the Q and A button at the bottom of your screen, and we’ll take as many as we can after the demonstration. And finally, you will receive a recording of this event via email in the next twenty four hours, so be on the lookout for that. Today, we’re going to show you a quick demonstration on how easy it is to use origami risk to simplify the creation and application of ratio formulas for calculating, tracking, and benchmarking key data metrics. I’m joined by Bharat Valarou, one of our technical sales consultants at Origami Risk. Bharat has many years of experience in risk management software and enjoys assisting clients with their technology decisions. So, now over to you, Bharat. Thanks Debbie for passing it over. So today we’re gonna be taking a look at values ratios. So before we dive into the demo and the system, we’re gonna talk about what are value ratios. So typically a graph is going to show us a certain amount of events or incidents or anything that we’re trying to capture over a time period or by location data. So on the screen you’ll see just the medications errors general report that was run-in June of this month. Here, I’m going to see a couple different locations. I’ll see a couple numbers on here, sixty six, forty four, twenty six. I can see some are higher than the rest, but I don’t know if this is within the bounds, out of the bounds or something I really need to address. So values ratios comes into play to take this data and then break it down into easier digest format where I can easily say, you know, here is my goal line. Over in every month, I only want to see two and a half or two point five incidents come through or three. So this will take it normalize that data and then say, you know, am I adhering to it or not? So in this example here, I’m looking at a couple different claim data. I’m looking at patient safety and then breaking it out by the locations. So on the left hand side workers comp claims per one hundred thousand, I can see most of it looks good, but there is one extreme outlier. We’ll look at this one a little bit later once we do a little bit digging into RCAs or root causes of why this is so vastly different compared to others. But other examples on the screen, can calculate DART rates, we can look at, you know, encouraged overpaid, we can coming to patient safety, we could look at incidents over a certain amount of days, so falls per a thousand patient days, we can set our goal lines wherever we think is best so we can make sure we’re adhering to it or not have the system automate or shoot out notifications if we ever cross these thresholds. But other things we can monitor are what are the adverse events over one hundred emissions? We can again break it out similar to what we saw up top by locations. So here we can see what that goal line is, what location specifically has more incidents or adverse events every one hundred admissions. We can also take this to medication errors, break it down by the types, but also overlay this data upon each other. So as I scroll down, I can then break it down. If I’m looking at month by month, quarter by quarter, how am I trending up or down as we start doing more of this triage and analysis. But to get here, we have to build those ratio formulas. So how do we do that? We do that by going into reports. So opening the reports tab to get to the ratio formulas, we’re going to click into the reports, hit this more button, and then I will see this option for ratio formulas. Clicking into the ratio formulas, I will see any of those that have already been created. So you can see here that DART rate from the dashboard before we can see pay ratios and again other areas where I can look into it. So odd number of incidents or claims based on the miles driven of our fleet. But from here, I’ll click into one that’s already built out for sake of time to talk through it. So when we click into a ratio formula, it’s going to ask us a couple of things. So one is just to name it, it’s going to ask us what this ratio form is going to do. What information or object am I going to pull this information from? Is it going be incident claims? I could do RCAs. I could do pretty much anything really. Then the numerator data. So what is that is typically going to be the count but where, what is that number that’s going to be referenced? And do I then want to parse it down a little bit further? So do I want to look at specifically all the incidents or all the claims or do I want to specify into only falls per a thousand days or workers’ comp claims over a hundred thousand hours. From there, we’ll look at the denominator. So this is where we can start benchmarking. For the values, we’re going to pull this from the location table. So from the hierarchy record, we’re going to take a look there at the exposures and pull back any of those values that we have already designated there. So in this case, I’ll pull back patient care days. But again, there’s multitude different options that we can go through. Once that is set, I can set my multiplier. So again, giving us that concrete number so we don’t see a huge decimal point. And then lastly, after everything is built, we’ll see a formula. So this is going to put into layman’s terms of, you know, here’s my count of fall incidents over patient care days and multiplying by a thousand. When I go back to this record, the last thing I’ll have to do is generate the data. So this could be scheduled based on daily or monthly, quarterly, annually, depending on when you want these to run and when it wants to aggregate the data. So your dashboards are up to date. But alternatively, if I do this ad hoc, I can always generate the data myself. So if I put the information in today, I can generate it for today’s information. And then once that’s generated, it’s going to spit out all the values. So some locations might not have those values there, but it’s still going to print them out anyway. And what I could do is just take a look at all the values and then export this into an Excel document. When I do this, it’ll pop open an Excel document, have this filtered, but basically I can filter it down to remove those extraneous zeros, maybe the ones that haven’t been filled out, and I’ll see these values on the reports or the dashboards. So when I come back to the dashboard screen, that is what’s pulling in this kind of data right here. So it’s taking all the actual events in the system, multiplying it by that multiplier and then giving us this spit out. And again, we can always split it by locations by years quarters, just depending on how you want to see that data. But from here, if I do want to do further analysis, let’s say this one bar right here for Sanford is super high. We can always dive into RCAs or HFMEAs any real analysis piece of the system. In this case, if I’m looking at this just based on one that’s already been started, This is going to be due to new hires and just inadequate training because we’ve just been so slammed and understaffed. So when we’re looking at RCA, we can break it down, figure out what’s causing this. And as I go through, we can create flow diagrams, we can create timelines, contributing factors and fish bones. So basic premise here is we’re trying to get to the root cause, trying to figure out what went wrong and mitigating, making sure we’re under those goal lines going forward. So all of this is more to come. But basically, we can trigger multitude of different processes and improvements in the system to make sure we’re adhering to our goal lines and thus reducing any incident rates that we want to be below of. With that, I’ll pass it over to you, Debbie. Great. Thanks Bharat. We’ve had a couple of questions come in, so I want to go through those with you. The first question is why would I use origami for this instead of using spreadsheets? Sure, and that’s a really, really good question. So, you know, if this is a multi location hospital or you have multiple departments and there’s different individuals in charge of collecting these values, we all know that it could take a good amount of time. It could take a couple days, a couple weeks for them to aggregate the values and then send them over to you and then you to collect and start aggregating as well. So that’s a pretty time consuming process. So what Origami can help facilitate with is allow that collection of data through values collection or renewals. So let those other individuals get reminders and notifications. Let them put it in ahead of time. When it’s ready for that quarterly analysis where we want to start printing these rates out, we have everything at our fingertips and all you have to do is really click a button and then it’s good to go. So basically trying to save you guys a couple of hours to a couple of days, so you can focus on the tasks at hand. That really makes a lot of sense. The next question we had was around, how does the system capture denominator data to utilize in the ratio formulas? Perfect. So, I alluded to this in the previous question, but the system offers other solutions such as values collection and renewals. So, when you guys are pushing some data out to your department managers, your location managers, they can collect that data for you as well. Or if you’re doing it yourself, you can come into the system and on that location level input those values, whether it’s patient care days, number of meds dispensed, patient hours, whatever you want to capture, it’s all stored on the location level. Then alternatively, can also have data interfaces in play so we can pull that data from maybe a hierarchy EDI system where we can say, alright, here are all the values and pull them in whenever you guys need them. So again, multitude of different ways to help alleviate your own burden of inputting manually. That’s great. What about benchmarking? So one of the questions that came in was, can we benchmark our data against the other healthcare or industry clients of origamis? Sure, so this one is a little bit of a trickier question. So one, obviously we do have the data of our clients. We don’t obviously go in there and pull it out. So as long as we have some consent, so if you do have a sister hospital or an affiliation with someone else and you both agree or all of you guys agree that you know we’re okay dispersing our incident or number of incidents or whatever that numerator denominator data is and say it’s okay to disperse, we can then use that as a benchmark system. Obviously it’ll be de identified. There’s nothing going to say it’s coming from this hospital system versus this one. Alternatively, you know, if that is a little bit harder to come by, there are websites out there that do kind of do some of this analysis for us, whether it’s by state or nationally. So we could also tap into that, albeit that’s not always current or might not be related as wholesome as if you were going to an affiliation. But that’s another option that we have to do benchmarking within the system. Very good. A question that’s come in is around how do you, how does the system help disseminate the information to key stakeholders in the organization? So for this one, I’m going to do like a plug back to another solution showcase, but basically one avenue is public dashboards. So we have the ability to take the dashboard that we saw during the demo and basically put it to a QR code or a URL. So giving it a read only version that we can pull up at any point in time, whether it’s off of your employee badge, you put it on the staff room, but the basic premise there is we have it at our fingertips. We can look at those rates at all times. Alternatively, we also do have things like presentation templates. So if you’re catering it more towards like a board report or a committee meeting, can have predefined templates in the system that will just pull together those graphics, couple data points from the different modules, and puts it in together in like a slideshow format or a presentation for you to just easily disperse out to either a singular individual or to distribution lists. That’s great. It sounds like there’s a lot of different options there, which is good. Okay, I think we have time for this last question. How can you improve the quality of the data that’s being collected for rates? Sure, so again, have incident capture, so we have our portals to bring in the data into the system. But on top of that, as we’re going through that analysis, looking at those different events, having those committee meetings, can leverage things like assessments. We can do record reviews, surveys even, and then utilize all that information that we’re gathering from our committee meetings to better the inputs and the outputs of the data being captured. So having that all in one system helps you navigate through all of it, but also digest all the data that you’re collecting and also use that for trends and analysis so you can get a better taste or sense of what’s going on in the system, whether it’s through indicators or just looking at the data that you’re collecting. Awesome. Well, thanks, Bharat. This was a really, helpful demonstration and great information, and thank everyone for attending today. If you want to learn more, we’ve got some options here on the slide, but you can certainly visit our website and click through to either just start a conversation or request a live demo. And we look forward to seeing you on another Solution Showcase soon. Thank you.