Speaker 1: Hi, this is Derek Wilson, and in this video, I'm going to do a quick comparison of using Excel to try to do analysis on your data compared to taking that same data and putting it inside of Microsoft Power BI. In this case, I have an Excel sheet that would take too long for me to load in this video. I've got a lot of detailed claim information here. It's actually over a million rows of data. It's claim data from 2008 to 2010. And then I just threw it into a pivot table, which took at least 45 seconds to a minute on my computer to even go through and create a pivot table in all the fields. And you can see, you know, I've got my claim year, I've got count of claims, and then I can just continue to add on other columns, split this out, and do the normal type of Excel reporting that you can do. So if I take that same data and put it into the Power BI backend, I've taken it, I've loaded the data in from Excel into my model, which is Power BI Desktop. This is the designer tool. The user interface looks very similar. And then I came through and just started building out reports. So you can see here, I've got my claim count, average of paid claim amount, number count by year, claim count by year and quarter, payment amount by year. And things are at different granularity. So here I've got 2008, 2009. Well, because this is a date, I can go ahead and drill into it. And I can expand down to quarter. I could even go down to month. If I had the year underneath that, it would go all the way down to the year. That's kind of small to look at. So there's what's called focus mode. I can click on focus mode and expand out just that visualization and now see my claim count and my average has been going up over time. So now I can go back to my report. I've got my other information. Now I can start filtering. This is one of the really powerful things with Power BI. Say I'm interested in Q1 of 2010. If I filter on that, it filters all of my visualizations to Q1 of 2010. You can see here it's giving me highlighting. I can hover over any of the data points and it will tell me here I had 19,339 claims. And it's showing highlighted because that's the total period. But if I come over here on my claim start date, you can see I highlighted $1 million worth of $3 million, which accounted for Q1. Likewise, if I go to say 2009 Q1, now I can see my percentage of my total paid amount for 2009. So the filtering, it's very interactive, very quick. If I want to turn everything back on, I just click on the same field. I'm back to where I was. But I can also filter on specific information. So here I've got ICD codes. And if I want to look at this lymphatic biopsy, I can click on that. And now it tells me how many members I had in my claim count. This is all the information I had before. But it's showing me the percentage of the total that make up each month. So if I highlight here Q2, I had 11,132 total members, of which 2,334 actually had this structured biopsy. And again, this is the exact same data that I had in the Excel file. I can change the date if I only wanted to look at, let's say, 2008 to 2009. I can change that value. You can see how quickly it just cut through all of that data down to there. I have another tab that I built for claim trends. And here again, I've got my long description of my codes. But now I've actually built out trend reports. And I've added in the average line. So this average line is telling me for this time period that I'm looking at, that's the average. So if I drill into details and continue to go down, I can actually get all the way down to the day. It's too granular. If I hover over a data point, it's going to tell me the average of my paid amount. If I was to change this to a different date range, it would automatically recalculate this average. Again, if I click here, biopsy of lymphatic structure, I click on that. Well, now it changed my average to $41. And that's for that particular service versus here is a fracture, 3426. So again, lots of different things you can do. A few other interesting things that you can do in Power BI. This is just all of my members and total count of claims. And I can quickly see for this member what happened. I paid $4,880. And here are the ICD short descriptions that made those up. If I had further information I wanted to drill into, I could continue doing that. I also created a quick provider stats just by the physician NPI number. So this physician had this many members, this many claims. If I click on them, it's going to filter my graph to show me the claim count by month. I can expand into it and actually see what individual procedures each of those providers had. So you can really see here how you can take the data from your claim system into an Excel file, into Power BI, or hook Power BI directly into the data if you have a reporting environment, or into a copy of your production system. Lots of this stuff is going on right now. And we're helping lots of customers do this. So if you have any questions, please let me know. Hopefully, this gives you some idea of what you can do with Power BI on your claims data versus just trying to do analysis in Excel.