teamplay Insights Webinar

Today we're going to discuss insights in three parts. First, we're going to actually explore the data model a little bit. Then we're going to look at some of the dashboards provided by Siemens as part of insights, and explore how we could modify one of those. And then finally, we will build out an analytic from scratch. So to begin, before creating any sort of visualization and insights, it's probably a good thing for you to start off with looking at the team Play Insights documentation that is provided with team play and a few. Navigate to the question mark and then over to here to teamplay insights. You'll open up the documentation that describes the product and in great detail if you Scroll down to. Towards the ends of the end of the document, around Page 70, you'll notice that the data model is documented. Now. From your knowledge, hopefully your knowledge of team play, you'll know that team play has some various modules in it. The dose module, the usage module, and the protocols module. Each one of those modules is fed by data from your environment up to the team play cloud. That data which is secured through DICOM tag information is uploaded through the receiver up into the cloud where it's stored into this data model. And this data model is documented in detail in this manual, so I would encourage you in your learnings of teamplay insights to start here because when you get into conversations with your end users about reports that they would like created, you'll find yourself coming back to this time and time again to figure out exactly what data is in team play so that you can then pull that data out, expose it to your end users and then get into an agreement. As to whether or not you're working with the correct data. The second, the second thing we like today do today is to go into the actual insights environment and depending on your license, hopefully you're in this discussion today because you actually have insights. Although I'm sure some people want to just get an understanding of how insights work, so you may not have this. This icon in your environment, but if you do, you click on the insights environment. The insights icon just like every other module and team play, will be brought into that module. And when I first log into team Play Insights, this is the environment I get logged into. You'll notice along the left there are. Two areas here, 2 pieces in the in the user interface, one is called public, one is called private. This simply is the list of analytics that have been dealt either that have been public, publicly exposed. This list in our demo environment, or the analytics that have been created by Siemens. And if you have an insights license you will have these or whatever is then current in that list of public analytics and then the private space is what you as an end user. Have created or copied or somehow modified in your own environment analytics that you created in your private environment. If you have the proper authorization can then be published into the public environment for other people to see, but if not, they are only there for your editing purposes. If I scroll through this, you'll see. We start off here by default in the dashboard analytic, and if I start sequencing through these one by one, you'll see that Siemens has built out a number of analytics across the various module areas of team play, which again are dose usage and protocols. And there are some fairly sophisticated analytics here which just out of the box if you didn't have any. For election to create your own graphics you would have a fairly sophisticated analytical environment here that goes beyond the analytics that you'll find in each one of the. Environments within the modules themselves. As a note here and you'll see here, I've got some other screens brought up and I'll go to those in a second. This analytic environment that we've built out is based on something called click or spelled QLIQLIK. And at you, as A and insights user. If you're looking if you have never been exposed to the click environment and you're looking to get up to speed on it, there is a wealth of information available out there both through the click website itself for just general purpose information or for help on things like building out analytics, and you could even go to YouTube and there are lots and lots of recordings out there that various folks have done that show how they've built out analytics in the click environment. They're not. The related to Siemens at all, but you will find that the trainings that click has the in the click world out there that have been populated to be extremely helpful in your in your exploration of team play. Uhm? Whenever I bring up an analytic in team play by selecting through this or through this list, you'll see that there is kind of a. A familiar paradigm to the way analytics or build there will be analytics that are like this that are various pie charts or bar charts or charts of various parts and then other parts of the analytic that are what are called filters. So for example, examination date, category names, weekdays, these are all filters. If I start selecting on any one of these filters, you'll notice that my dials here are changing, and in fact even my my circular charts here are changing as I'm filtering the data. And you'll also notice that as I as I did, that this little icon appeared here, and this represents the filters that are turned on. We call these breadcrumbs each one of these will start. Hearing if I start now clicking on these category names, you'll see that now that filter has appeared here and simply. If I simply then click on these to make these disappear. Man, those filters go away completely. You also, you'll also notice that while these are graphics that have been built out or that are representing the underlying data that I'm representing that these two can actually act like filters, and if I select on one of these just like above, that will act like a filter. For now, I'll leave those off. Just leave the graphic in its original representation, but you as a as a user of these of a good feel for how you navigate through these. Now. When I've from one of the things we thought we would show you is an as a way of learning about the environment is to take an existing graphic that someone else built. Now there's someone else that built this. In this case is Siemens. And I wanted to show you that right now we're in the view portion of insights. I can take this analytic and through these three... here. I can duplicate this analytic and what happens is then this gets copied to my private space so you can see that it was automatically given a name of copy of multi site benchmarks and now in the view environment it looks exactly like what we originally had from the public environment and I can then go do something with this. In this case the do something with is. I'm going to go into the edit mode. And show you. How can take an existing analytic and edit it? And I think is is a training exercise for you is when you're building things out. It's always just like in any environment like an Excel. If someone can give you an example of something, it's always better to start with that, then to necessarily start with a blank slate, which is what we'll do next. So what I did was I clicked in that I copied it to my private area and then I went to the... and I clicked on edit and now I've got this same analytic in my edit space and you'll see that it looks pretty much like what we were looking at before, but we've got some different things here. This is now the surface from which we will create analytics and you'll see there are various parts that are available and for any one of these parts that are on the surface. You'll see on the right. That every part has a property and this is simply how, just like again, like Excel. This is how we actually go ahead and modify something. Or give it its behavior and this case for a very simple change of this analytic. What I'm going to do is I'm going to take for this chart the cop, the multi site benchmarks which is showing the exam duration for this organization for both CT and mammograms and emars and then of course with analytics and body parts. I'm just going to take this and change it from this. This dial icon to a bar icon. So notice every time I select one of these, that part gets highlighted. In this case, I'm going to highlight this part and I'm going to go to presentation. And you'll see that this is indeed why this is showing up as a radial dial. If I select on bar. That it changes that just to do that. Once again there's the bar or the radial dial. And now there's the bar. And I could go through this entire analytic and modified. It could change the titles. I could change the underlying data. I can change these from radial dials to bar charts, that sort of thing, but you get the idea. I've copied this to my private area I've made. I've made a change. And if I wanted to, I could publish it out to the public area. For now I'm just going to exit edit mode. And you'll see now. We're back where we were before with this. Now in my private area and now of course with the analytic that has been changed from a radial dial to a bar, a bar line for this particular exam duration analytic. Now that was just a simple way to change something that's already been built. That is a as I said, I I think it's a good way to get an understanding of how things are built. But eventually you're going to want to get into conversations with your end. Users are building out your own analytics, and in that case we would build out something using this functionality. Add new sheet. So I click on that. Give this new sheet a name. I'll just call it test for today. And then we'll have the opportunity to edit that analytic test for today. And you'll see as we do so when this. When this comes up, as opposed to what we saw before, when we when we brought it up, we of course had an existing analytic. So this whole area here was already populated with the existing. Parts and pieces of the analytic. For this one, we're starting with a blank slate. So some advice on starting with this when you're starting with a blank slate, a very handy way to to think about creating analytics is to think in terms of three things, a table. Or tables, filters, and then parts or graphics. Now the reason why? We suggest thinking about tables is because as I was showing you at the very beginning, it's those tables of data that we're using to create our analytics an, so that's that's reason number one reason #2 is a lot of times by having those tables of data. If you're working with your end users towards building an analytic and you're having conversations about the data, if you have a table of data in your analytic and you can show your end user well, this is exactly what we're getting out of the system. This is what the field looks like. These are the other columns of data that we're pulling. You can then quickly get to an agreement as to whether or not you're showing the correct data now, whether you actually keep that in the visualization becomes irrelevant, and actually I'll show you. In the example here, how we'll start with the table then will delete it because it's not. It's not necessary for the visualization, it's just something that I think is handy for you when you're building things. Now, I'm not going to go through every single one of these parts here, but I'm going to start with this icon here charts, which gives me all the basic parts that I have available in the click environment to build out my analytics and you'll see that indeed I've got tables. I've got filters and all these other analytics that are available for today's recording. We're just going to do a very simple example to give you an idea of how this all works, and as I said, will first start. With the table. When I select that icon and slide it over, you'll notice that the surface activates and I can drop that part on on the surface. So I put a table on here and then. For any part that we put on the surface, you'll notice that just like like in a PowerPoint environment or in an Excel environment, that's a selectable entity and I can resize it. In this case, I'm going to resize it to be wide so that I can add a bunch of columns to it. And it is very simple example, what we're going to do is we're going to build out a use case that says, OK, I would like to I as the user RIS, the analyst, and the user, we're putting together a little use case to see if we can get a feel for the R installation base of scanners in our enterprise, because we're not sure where those scanners are, what what models they are, what software versions can we? Build out a quick analytic to take a look at that, so let's see if we can go do that. When I add a, when I add apart a table or a filter or one of these analytic parts, one of the first things it's going to ask me for is tell me about the dimension. So the dimensions in the case of a table are the columns that we're going to use, so I'm going to add a bunch of columns here. To just give you a feel for the sorts of data that you can pull out of the system. The first one I'm going to pick is dose study date. Now again, this is our demonstration system, so some of our our demonstration data will look completely different from what you will see in your environment, but you'll get a feel for it here. Every time I added dimension, I'm adding a column to the to the table and I'm picking on the dose table here. Obviously I could start picking on other tables as well, but I'm picking everything right now, just out of the dose table to build out a very simple example. So the first thing I picked was dose study day. Then I'm going to pick institution, then I'll pick. The. A manufacturer again to build out my theoretical example or my hypothetical example of an environment wanting to get a feel for the the various modalities and the level of software on those systems so you can see as I'm building out this table, I'm getting a feel for my underlying data and this is again this sort of thing I could share with one of my end users to say look, this is what we're seeing. We're seeing in these institutions. These these manufacturers, the scanner IDs is does this look right to you is just the sort of thing you want visualized in the analytics that we want to build out and the final one I'll put in here is DOS software version. So assuming that looks somewhat correct, then the next thing that I'd probably want to do in in any analytic I'm building is then add some sort of filter to it. So just like before, when I picked the table and moved it onto the surface, I'm going to do that now with the filter pane, and I'm going to add this filter and just like before, when I added a dimension to the table, I'm going to add a dimension to the filter. And in this case I'm going to pick. DOS software version. And you can see the same sort of data that is in my table is now exposed as a filter, and then the final thing I'll add is an analytic. In this case a pie chart. And just like before. I need to add a dimension and this pie chart I'm going to show the dose. Software version. And then now I'm being asked to add a measure and a measure is in the case of an analytic, is the sort of thing that says well for a pie chart you want to look at software version. But how big should each one of those slices of the pie chart be? I can make it any I can make it anything from the available data in the system, but in this case I'm going to pick something very simple and pick number of exams so. The higher the number of exams, the bigger the slice of the pie chart, with each one of the slices. Of course, representing one of the software versions. So you can see here now. I built a very rapidly built out this very simple analytic using this. This very simple methodology of thinking of the underlying data via the table through a filter and then through an analytic and I can make this. In fact we will do that in the next step. Make this more complicated, more sophisticated, but this is a good way to start and actually build things out. Just like before, if I get out of exit out of Edit Mode Now, this will take up the entire surface and you'll see now that it's actually an executable analytic, which again I could promote to the public space. If this was exactly what our end users wanted. And just like before, when we were using the Seamons analytics, I can start using my filter here and as I pick through this. Of course my part pie chart changes and as I do that as well my table changes. Something to point out that is available through almost any analytic and certainly or any part or analytical part is when I click on that in the execution mode and I've selected this part. You can see that blue highlighting here. If I then right click on it, I can export the data and just as I was explaining before. By doing this. I this facilitates conversations with end users. It could be something that they want created. You know, on a regular basis anyway, they want you know when we've created an analytic. They say, wow, that that's exactly what we wanted. If you can build that out and will extract that out once a month, that sort of thing. Or it could be just something that's very useful for when you're building something out to be able to say, look, here's the extract from the table that shows exactly what we were talking about. Does this all this? All this data look correct? Yes or no, yes? OK, let's go ahead and proceed. Now. The final, the final step. I wanted to take you through on building out this analytic was to play with this a little bit busy as I was explaining before this table is, it's not necessary for the analytic. It may or may not be something that you want to use in your particular analytic at what I wanted to show you was by going back into edit mode is that I can actually, you know once I've built out this initial analytic and. You know, I felt confident that we're doing the right thing. I don't need this table here to actually exist to run the analytics, so I've I've selected this table and now I'm going to delete it and you'll notice that it doesn't. It hasn't changed anything about my current environment. These will still work, and in fact I will add two more pie charts to this to show you. Kind of give you a feel for you know the way you would might want to evolve an analytic here. I've got software version charted. I want to throw this on an. Let's add the dimension here of. Dose manufacturer which is one of those underlying data elements we had exposed before and then for measure will use the same exact thing of number of exams and then we'll add one more pie chart that says. Dose institution, which again was that underlying data that we were using in our table. And just like before, will make that number of exams. And now if you'll see when I exit edit mode. I've evolved my analytic from that simple one pie chart analytic to now three pie charts with the one filter just like before I've got this breadcrumb here. If I select this. You'll see that now I've I've quickly assembled a an analytic that in our hypothetical example, has given us a a fairly rapid oversight of the software version of our software that's being used across our installation base of modalities in our enterprise, and gives us a feel for where these things are as well. Now, obviously. The underlying data model of team playoffs Insights is an extremely rich one and this is an extremely simple example of what can be done. But again, if you stick to that simple premise of start with the data, think about filtering and think about the analytics you'll be able to build out increasingly sophisticated analytics using this tool. An eventually going back to where we started with these Siemens Analytics, you'll get a very good feel for the. Sophistication that can be used using the click toolkit. That concludes this session for today. We appreciate your time and look forward to working with you in the future and making your team play Insights environment a very. Productive one Many thanks.

L @ x L @ L @ X x @ @ x @ X @ - X + Sense on [j L)' QIik@ Help Help SIEMENS SIEMENS . English Search Siemens ti on Siemens Demo Siemens on Siemens on ot Con Health i nee rs Health ineers Healthineers Documentation Documentation Videos More Resources Archive Onboarding QIikSense Videos More Archive Onboarding Sense Videos More Resources Archive Onboarding Sense More Resources v Archive Onboarding QIik Sense •m Exit public Apps Siemens Demo and - expiration dates and expiration dates support expiration dates Chart Cha" Cha"t Chan Copy 01 Hencnmarks Copy Hencnmarks Copy Copy 01 dencnmarks Copy 01 Copy 01 Henrnmarks test today test Copy 01 denrnmarks Copy Henrnmarks test toaay dencnmarks test test toaay test tor test today test 'or test 'or toaay CT - Average Exam Duration [min] CT Average Exam Duration MR Average Exam Duration MT - Average Exam Duration [min] MG - Average Exam Duration [min] MR - Average Exam Duration [min] CT • Average Exam Duration CT - Exam Duration - Average Exam Duration CT - Average Exam Duration [min] MR Average Exam Duration MT - Average Exam Duration [min] MG - Average Exam Duration [min] MR - Average Exam Duration [min] MR - Average Exam Duration MR Average Exam Duration CT • Average Exam Duration CT - Average Exam Duration [min] MR - Average Exam Duration [min] Hide Line pine MG Multi-site • • • o Click to add title 0 Click to add title Date Dose Page CT • Average Exam Duration CT • Exam Duration MG - Average Exam Duration CT - Average Exam Duration CT • Average Exam Duration CT - Average Exam Duration [min] MG - Average Exam Duration [min] MR • Average Exam Duration MR - Average Exam Duration [min] CT • Average Exam Duration CT - Average Exam Duration MG - Average Exam Duration MT - Average Exam Duration MR - Average Exam Duration CT - Average Exam Duration [min] MT Average Exam Duration MR Average Exam Duration MG • Number of Exams Number of Exam s CR • Number of Exams MR • Number of Exams Number of Exam s CT • Number of Exams On th page MG • Number of Exams CR • Number of Exams CT • Number of Exams MR • Number of Exams On page 1m inl rminl Iminl Pie chart Iminl rminl n to •t to •t Dose Dose SoftwareVersio Oose SoftwareVersio So 'twareVersio SoftwareVersio Bullet ng a pie The pie chart displays the relation between values as well as the relation of a single value Names Combo ot It teampLay to the total. You can use a pie chart when you have a single data series with only positive to the total. You can a pie chart when you have a single data series with only positive SIEMENS D i stribution Distribution values. 111757 68033 88225 24756 Related ot on Con ot on of the Chart, the dimensions Of the A pie Chart have the Chart, the dimensions form Of the A pie Chart have the Chart, the dimensions Of the A pie Chart Can e5:231 21:38 21:35 21:536 e5:27 05:27 21:35 Gauge dimension and up to two measures. The first measure is used to determine the angle each slice in the dimension and up to two measures. The first measure is used to determine the angle of each slice in the es:27 e5:27 Chart. chart. 05:27 Histogram Seq. SeqÄ Number of Exam s Number CT • Number of Exams MR • Number of Exams Number of Num bet of Exams CR • Number of Exams MG • Number of Exams per in pie Healthineers QIik Sense QIik Alerting Hide Line Page Maps Copy Create your first visualization St of of Multi-site 0 Click to add title o Click to add title 0 Click to add 0 Click to add title items from the panel to the sheet items from the library panel to the sheet items the library panel to the sheet items the panel to the items the panel to the sheet items the panel the Sheet items the panel to the Sheet Create your first visualization CT - Body part MR - Body part Body part CT Top 10 Scanners MG - Top 1 0 Scanners CR - Top 1 0 Scanners MR - Top 1 0 Scanners CT - Top 1 0 Scanners CR - Top 1 0 Scanners CT - Top 1 0 Scanners MG - Top 1 0 Scanners MR - Top 1 0 Scanners MG - Top I O Scanners MR - Body Part CT - Body Part on the top right to start on at top right to start on athe top right to start on top tight to start on at the top to start at the top to start 20721 Pie properties 03/18/2019 SYStEMS SYSTEMS of on ot your data, data, name test 20128 SSA 05/18/2019 03118/2019 SYSTEMS SIEMENS plot 0312012019 20128 Table 03" 8/2019 031221/2019 Nging SSO 20128 Date Samsung Sam-sung CT 20 BA SIEMENS . of 031202019 03/202019 Si Nging •ging SIEMENS SIEMENS . image Hide CT 20128 It Il Healthineers 03118/2019 20128 2012B QIik NPrinting 03/20/2015 Add 031221/2019 031202019 SYSTEMS SSA ot on of It CT 20' 2B chart. data Optionally, a second measure can be used to determine the radius Of each pie slice. This Style Of pie Optionally, a Second measure can be used to determine the radius Of each pie slice. This Style Of pie Optionalty, a second measure can be used to determine the radius Of each pie slice. This Style Of pie 20724 Nging is a Chart. 20128 SIEMENS Sales by sub group in o pie chart with the average sales per invoice sales determining slice radius Sales by sub group in o pie with the average sales per invoice sales determining slice radius Sales by sub group in a pie with the average sales per invoice sales determining slice radius Siemens 05102019 •u Sub Group Hide Line Hide assets Pide Show Hide Sue