OLAP Cube Demo: Skilled Nursing Facility Financial Data Drill Down Report

Kevin Baker, of Data Services Partners, demonstrates how using an OLAP cube allows Skilled Nursing Facilities to drill down into their financial data in order to gain actionable insight into specific metrics.

This example uses mock nationwide data to demonstrate how a SNF administrator can view Overtime OT percentage for a specific facility in Arizona.

Systems like Point Click Care (PCC) have Data Relays that can act as a data source for data warehouses. These data warehouses can be automated to populate pivot table style reports that let you look at facts from many different dimensions.

Hi, this is Kevin Baker with Data Services Partners. Today I want to talk to you about OLAP reporting technology and how it can help your skilled nursing business.

OLAP is a fast and secure reporting technology that helps your organization not only report but automate. What you’re looking at on the screen here is an example of a data warehouse and essentially, when you look at it, it is just one large pivot table that has all of your data in one place.

The other powerful thing about this platform is being able to drill down to detail. Let’s give that a shot. Right now let’s do an example where we want to look at overtime percent. We want to find out what’s driving this overtime percent.

Here we can see that Arizona has the highest overtime percent. I’m gonna bring that state back into the filters, then on Arizona specifically. Now we have our OT percent here. To make things simpler I’m gonna get rid of everything that’s not OT percent out of this pivot table.

That’s another question too. Is this something that is a one-time issue that happened in July that maybe the administrator can explain? Or, is this thing that is happening month after month? Let’s take a look so maybe we’ll answer that first question about month after month.

I bring in all the data here for Arizona and I’m going to turn it out by month here. I can see that they have really high overtime as a percent of their their total hours and so this is something that seems like a chronic problem. Now if I want to go and drill in by department here I can drag in my labor expense and I can see here are the totals that we’re looking at but now we have it broken out by nursing, maintenance, social services activities, education, administration. Right away we can see the biggest problem here is definitely nursing.

What we’ll do here is bring in employee … find out which employee is driving it. Now again, I’m showing you data in a mock environment, so this isn’t real data. But, you still get the idea that we can come down to nursing now and we can find out who is driving this OT percent.

Here’s someone who seems to be quite high across the board. This person has a high number as well that seems to be a one-time thing. This person is also a high offender.

You can get the idea that we can take a high level number like OT% and in a data model using this OLAP technology we can drill in and find out really what the driver here is for the issue and find out what action we need to take to resolve it, whether it is talking to the employee directly or the department head in this case.