>>Yeah, yeah. >>Welcome back to session, too. Thoughts about everywhere. Unlock new revenue streams with embedded search and I Today we're joined by our senior director of Global Oh am Rick Dimel, along with speakers from our thoughts about customer Hayes to discuss how thought spot is open for everyone by unlocking unprecedented value through data search in A I, you'll see how thoughts about compound analytics in your applications and hear how industry leaders are creating new revenue streams with embedded search and a I. You'll also learn how to increase app stickiness on how to create an autonomous this experience for your end users. I'm delighted to introduce our senior director of Global OPM from Phillips Spot, Rick DeMARE on then British Ramesh, chief technology officer, and Leon Roof, director of product management, both from Hayes over to you. Rick, >>Thank you so much. I appreciate it. Hi, everybody. We're here to talk to you about Fox Spot everywhere are branded version of our embedded analytics application. It really our analytics application is all about user experience. And in today's world, user experience could mean a lot of things in ux design methodologies. We want to talk about the things that make our product different from an embedded perspective. If you take a look at what product managers and product design people and engineers are doing in this space, they're looking at a couple of key themes when they design applications for us to consume. One of the key things in the marketplace today is about product led growth, where the product is actually the best marketing tool for the business, not even the sales portion or the marketing department. The product, by the word of mouth, is expanding and getting more people onto the system. Why is that important? It's important because within the first few days of any application, regardless of what it is being used binding users, 70% of those users will lose. Interest will stop coming back. Why do they stop coming back? Because there's no ah ha moment through them. To get engaged within the technology, today's technologies need to create a direct relationship with the user. There can't be a gatekeeper between the user and the products, such as marketing or sales or information. In our case. Week to to make this work, we have toe leverage learning models in leverage learning as it's called Thio. Get the user is engaged, and what that means is we have to give them capabilities they already know how to use and understand. There are too many applications on the marketplace today for for users to figure out. So if we can leverage the best of what other APS have, we can increase the usage of our systems. Because in today's world, what we don't want to do from a product perspective is lead the user to a dead end or from a product methodology. Our perspective. It's called an empty state, and in our world we do that all the time. In the embedded market place. If you look at at the embedded marketplace, it's all visualizations and dashboards, or what I call check engine lights in your application's Well, guess what happens when you hit a check engine life. You've got to call the dealer to get more information about what just took place. The same thing happens in the analytic space where we provide visualizations to users. They get an indicator, but they have to go through your gatekeepers to get access to the real value of that data. What am I looking at? Why is it important the best user experiences out on the marketplace today? They are autonomous. If we wanna leverage the true value of digital transformation, we have to allow our developers to develop, not have them, the gatekeepers to the rial, content to users want. And in today's world, with data growing at much larger and faster levels than we've ever seen. And with that shelf life or value of that data being much shorter and that data itself being much more fragmented, there's no developer or analysts that can create enough visualizations or dashboards in the world to keep the consumption or desire for these users to get access to information up to speed. Clients today require the ability to sift through this information on their own to customize their own content. And if we don't support this methodology, our users are gonna end up feeling powerless and frustrated and coming back to us. The gatekeepers of that information for more information. Loyalty, conversely, can be created when we give the users the ability toe access this information on their own. That is what product like growth is all about in thought spot, as you know we're all about search. It's simple. It's guided as we type. It gives a super fast responses, but it's also smart on the back end handling complexities, and it's really safe from a governance and as well as who gets access to what perspective it's unknown learned environment. Equally important in that learned environment is this expectation that it's not just search on music. It's actually gonna recommend content to me on the fly instantly as I try content I might not even thought of before. Just the way Spotify recommends music to us or Netflix recommends a movie. This is a expected learned behavior, and we don't want to support that so that they can get benefit and get to the ah ha moments much quicker. In the end, which consumption layer do you want to use, the one that leads you to the Dead End Street or the one that gets you to the ah ha moment quickly and easily and does it in an autonomous fashion. Needless to say, the benefits of autonomous user access are well documented today. Natural language search is the wave of the future. It is today. By 2004 75% of organizations are going to be using it. The dashboard is dead. It's no longer going to be utilized through search today, I if we can improve customer satisfaction and customer productivity, we're going to increase pretensions of our retention of our applications. And if we do that just a little bit, it's gonna have a tremendous impact to our bottom line. The way we deploy hotspots. As you know, from today's conversations in the cloud, it could be a manage class, not offering or could be software that runs in your own VPC. We've talked about that at length at this conference. We've also talked about the transformation of application delivery from a Cloud Analytics perspective at length here it beyond. But we apply those same principles to your product development. The benefits are astronomical because not only do you get architectural flexibility to scale up and scale down and right size, but your engineers will increase their productivity because their offerings, because their time and effort is not going to be spent on delivering analytics but delivering their offerings. The speed of innovation isn't gonna be released twice a year or four times a year. It's gonna It can happen on a weekly basis, so your time to market in your margins should increase significantly. At this point, I want a hand. The microphone over to Revert. Tesche was going to tell you a little bit about what they're doing. It hes for cash. >>Thanks, Rick. I just want to introduce myself to the audience. My name is Rotational. Mention the CTO Europe ace. I'm joined my today by my colleague Gillian Ruffles or doctor of product management will be demoing what we have built with thoughts about, >>um but >>just to my introduction, I'm going to talk about five key things. Talk about what we do. What hes, uh we have Really, um what we went through the select that spot with other competitors What we have built with that spot very quickly and last but not least, some lessons learned during the implementation. So just to start with what we do, uh, we're age. We are health care compliance and revenue integrity platform were a saas platform voter on AWS were very short of l A. That's it. Use it on these around 1 50 customers across the U. S. On these include large academic Medical Insight on. We have been in the compliant space for the last 30 plus years, and we were traditionally consulting company. But very recently we have people did more towards software platform model, uh, in terms off why we chose that spot. There were three business problems that I faced when I took this job last year. At age number one is, uh, should be really rapidly deliver new functionality, nor platform, and he agile because some of our product development cycles are in weeks and not months. Hey had a lot of data, which we collected traditionally from the SAS platform, and all should be really create inside stretch experience for our customers. And then the third Big one is what we saw Waas large for customers but really demanding self service capabilities. But they were really not going for the static dash boats and and curated content, but instead they wanted to really use the cell service capabilities. Thio mind the data and get some interesting answers during their questions. So they elevated around three products around these problems statements, and there were 14 reasons why we just start spot number one wars off course. The performance and speed to insights. Uh, we had around 800 to a billion robot of data and we wanted to really kind of mind the data and set up the data in seconds on not minutes and hours. We had a lot of out of the box capabilities with that spot, be it natural language search, predictive algorithms. And also the interactive visualization, which, which was which, Which gave us the agility Thio deliver these products very quickly. And then, uh, the end user experience. We just wanted to make sure that I would users can use this interface s so that they can very quickly, um, do some discovery of data and get some insights very quickly. On last but not least, talksport add a lot of robust AP ice around the platform which helped us embed tot spot into are offering. But those are the four key reasons which we went for thoughts part which we thought was, uh, missing in in the other products we evaluated performance and search, uh, the interactive visualization, the end user experience, and last but not least flexible AP ice, which we could customize into our platform in terms of what we built. We were trying to solve to $50 billion problem in health care, which is around denials. Um so every year, around 2, 50 to $300 billion are denied by players thes air claims which are submitted by providers. And we built offering, which we called it US revenue optimizer. But in plain English, what revenue optimizer does is it gives the capability tow our customers to mind that denials data s so that they can really understand why the claims were being denied. And under what category? Recent reasons. We're all the providers and quarters who are responsible for these claims, Um, that were dryland denials, how they could really do some, uh, prediction off. It is trending based on their historical denial reasons. And then last but not least, we also build some functionality in the platform where we could close the loop between insights, action and outcome that Leon will be showing where we could detect some compliance and revenue risks in the platform. On more importantly, we could, uh, take those risks, put it in a I would say, shopping card and and push it to the stakeholders to take corrective action so the revenue optimizer is something which we built in three months from concept to lunch and and that that pretty much prove the value proposition of thoughts. But while we could kind of take it the market within a short period of time Next leopard >>in terms >>off lessons learned during the implementation thes air, some of the things that came to my mind asses, we're going through this journey. The first one is, uh, focus on the use case formulation, outcomes and wishful story boarding. And that is something that hot spot that's really balance. Now you can you can focus on your business problem formulation and not really focus on your custom dash boarding and technology track, etcetera. So I think it really helped our team to focus on the versus problem, to focus on the outcomes from the problem and more importantly, really spend some time on visualizing What story are we say? Are we trying to say to our customers through revenue optimizer The second lesson learned first When we started this implementation, we did not dualistic data volume and capacity planning exercise and we learned it our way. When we are we loaded a lot of our data sets into that spot. And then Aziz were doing performance optimization. XYZ. We figured out that we had to go back and shot the infrastructure because the data volumes are growing exponentially and we did not account for it. So the biggest lesson learned This is part of your architectural er planning, exercise, always future proof your infrastructure and make sure that you work very closely with the transport engineering team. Um, to make sure that the platform can scale. Uh, the last two points are passport as a robust set of AP Ice and we were able to plug into those AP ice to seamlessly ended the top spot software into a platform. And last but not least, one thing I would like to closest as we start these projects, it's very common that the solution design we run into a lot of surprises. The one thing I should say is, along those 12 weeks, we very closely work with the thoughts, part architecture and accounting, and they were a great partner to work with us to really understand our business problem, and they were along the way to kind of government suggested, recommends and workarounds and more importantly, also, helpers put some other features and functionality which you requested in their engineering roadmap. So it's been a very successful partnership. Um, So I think the biggest take of it is please make sure that you set up your project and operating model value ember thoughts what resources and your team to make sure that they can help you as you. It's some obstacles in the projects so that you can meet your time ones. Uh, those are the key lessons learned from the implementation. And with that, I would pass this to my colleague Leon Rough was going to show you a demo off what we go. >>Thanks for Tesh. So when we were looking Thio provide this to our customer base, we knew that not everyone needed do you access or have available to them the same types of information or at the same particular level of information. And we do have different roles within RMD auto Enterprise platform. So we did, uh, minimize some roles to certain information. We drew upon a persona centric approach because we knew that those different personas had different goals and different reasons for wanting to drive into these insights, and those different personas were on three different levels. So we're looking at the executive level, which is more on the C suite. Chief Compliance Officer. We have a denial trending analyses pin board, which is more for the upper, uh, managers and also exact relatives if they're interested. And then really, um, the targeted denial analysis is more for the day to day analysts, um, the usage so that they could go in and they can really see where the trends are going and how they need to take action and launch into the auditing workflow so within the executive or review, Um, and not to mention that we were integrating and implementing this when everyone was we were focused on co vid. So as you can imagine, just without covert in the picture, our customers are concentrated on denials, and that's why they utilize our platform so they could minimize those risks and then throw in the covert factor. Um, you know, those denial dollars increase substantially over the course of spring and the summer, and we wanted to be able to give them ah, good view of the denials in aggregate as well as's we focus some curated pin boards specific to those areas that were accounting for those high developed denials. So on the Executive Overview Board, we created some banner tiles. The banner tiles are pretty much a blast of information for executives thes air, particular areas where there concentrating and their look looking at those numbers consistently so it provides them away to take a good look at that and have that quick snapshot. Um, more importantly, we did offer as I mentioned some curated pin boards so that it would give customers this turnkey access. They wouldn't necessarily have to wonder, You know, what should I be doing now on Day one, but the day one that we're providing to them these curated insights leads the curiosity and increases that curiosity so that they can go in and start creating their own. But the base curated set is a good overview of their denial dollars and those risks, and we used, um, a subject matter expert within our organization who worked in the field. So it's important to know you know what you're targeting and why you're targeting it and what's important to these personas. Um, not everyone is necessarily interests in all the same information, and you want to really hit on those critical key point to draw them and, um, and allowed them that quick access and answer those questions they may have. So in this particular example, the curated insight that we created was a monthly denial amount by functional area. And as I was mentioning being uber focused on co vid, you know, a lot of scrutiny goes back to those organizations, especially those coding and H i M departments, um, to ensure that their coding correctly, making sure that players aren't sitting on, um, those payments or denying those payments. So if I were in executive and I came in here and this was interesting to me and I want to drill down a little bit, I might say, You know, let me focus more on the functional area than I know probably is our main concern. And that's coating and h i M. And because of it hit in about the early winter. I know that those claims came in and they weren't getting paid until springtime. So that's where I start to see a spike. And what's nice is that the executive can drill down, they may have a hunch, or they can utilize any of the data attributes we made available to them from the Remittance file. So all of these data, um, attributes are related to what's being sent on the 8 35 fear familiar with the anti 8 35 file. So in particular, if I was curious and had a suspicion that these were co vid related or just want to concentrate in that area, um, we have particular flag set up. So the confirmed and suspected cases are pulling in certain diagnosis and procedure codes. And I might say 1.27 million is pretty high. Um, toe look at for that particular month, and then they have the ability to drill down even further. Maybe they want to look at a facility level or where that where that's coming from. Furthermore, on the executive level, we did take advantage of Let me stop here where, um also provided some lagged a so leg. This is important to organizations in this area because they wanna know how long does it take before they re submit a claim that was originally denied before they get paid industry benchmark is about 10 days of 10 days is a fairly good, good, um, basis to look at. And then, obviously anything over that they're going to take a little bit more scrutiny on and want to drill in and understand why that is. And again, they have that capabilities in order to drill down and really get it. Those answers that they're looking for, we also for this particular pin board. And these users thought it would be helpful to utilize the time Siri's forecasting that's made available. So again, thes executives need thio need to keep track and forecast where they're trends were going or what those numbers may look like in the future. And we thought by providing the prediction pins and we have a few prediction pins, um would give them that capability to take a look at that and be able to drill down and use that within, um, certain reporting and such for their organization. Another person, a level that I will go to is, um, Mawr on the analyst side, where those folks are utilizing, um, are auditing workflow and being in our platform, creating audits, completing audits, we have it segregated by two different areas. And this is by claim types so professional or institutional, I'm going to jump in here. And then I am going to go to present mode. So in this particular, um, in this particular view or insight, we're providing that analysts view with something that's really key and critical in their organization is denials related Thio HCC s andi. That's a condition category that kind of forecast, the risk of treatment. And, you know, if that particular patient is probably going to be seen again and have more conditions and higher costs, higher health care spending. So in this example, we're looking at the top 15 attending providers that had those HCC denials. And this is, um, critical because at this point, it really peaks in analyst curiosity. Especially, You know, they'll see providers here and then see the top 15 on the top is generating Ah, hide denial rate. Hi, denial. The dollars for those HCC's and that's a that's a real risk to the organization, because if that behavior continues, um, then those those dollars won't go down. That number won't go down so that analysts then can go in and they can drill down um, I'm going to drill down on diagnosis and then look at the diagnosis name because I have a suspicion, but I'm not exactly sure. And what's great is that they can easily do this. Change the view. Um, you know, it's showing a lot of diagnoses, but what's important is the first one is sepsis and substance is a big one. Substances something that those organizations see a lot of. And if they hover, they can see that 49.57 million, um, is attributed to that. So they may want to look further into that. They'd probably be interested in closing that loop and creating an audit. And so what allowed us to be able to do that for them is we're launching directly into our auditing workflow. So they noticed something in the carried insight. It sparked some investigation, and then they don't have to leave that insight to be able to jump into the auditing workflow and complete that. Answer that question. Okay, so now they're at the point where we've pulled back all the cases that attributed to that dollar amount that we saw on the Insight and the users launching into their auditing workflow. They have the ability Thio select be selective about what cases they wanna pull into the audit or if they were looking, um, as we saw with sepsis, they could pull in their 1600 rose, but they could take a sampling size, which is primarily what they would do. They went audit all 1600 cases, and then from this point in they're into, they're auditing workflow and they'd continue down the path. Looking at those cases they just pulled in and being able Thio finalized the audit and determine, you know, if further, um, education with that provider is needed. So that concludes the demo of how we integrated thought spot into our platform. >>Thank you, LeAnn. And thank you. Re test for taking the time to walk us through. Not only your company, but how Thought spot is helping you Power analytics for your clients. At this point, we want to open this up for a little Q and A, but we want to leave you with the fact that thought spot everywhere. Specifically, it cannot only do this for Hayes, but could do it for any company anywhere they need. Analytical applications providing these applications for their customers, their partners, providers or anybody within their network for more about this, you can see that the website attached below >>Thanks, Rick and thanks for tests and Leon that I find it just fascinating hearing what our customers are doing with our technology. And I certainly have learned 100% more about sepsis than I ever knew before this session. So thank you so much for sharing that it's really is great to see how you're taking our software and putting it into your application. So that's it for this session. But do stay tuned for the next session, which is all about getting the most out of your data and amplifying your insights. With the help of A, I will be joined by two thought spot leaders who will share their first hand experiences. So take a quick breather and come right back