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Ensuring Accuracy in your Analytics

Part One - How Powerful Analytics Drives Revenue and Collaboration Across the Revenue Cycle

 

Next: Two - Utilizing Analytics to Drive your Revenue


Transcribed Video Content Below

So today, we're going to focus on revenue and how to drive it throughout our organizations, leveraging analytics. To start off with before I ever talk about analytics, I always want to talk about accuracy. Everyone's heard the term junk-in, junk-out, and our analytics systems are only as good as the data that we use. If the data is not accurate, our analytics are meaningless. So we're going to start off by talking about four of our accuracy metrics and what we use at PMMC to maintain the validity of our contract management system, but the basic concepts of each of these metrics can be applied to a variety of systems. So as I go through this, I want everyone to start thinking about their systems and tools that they have in place today and where accuracy metrics may be missing or may be needed.

So to start off with, across PMMC solutions, the most important metric we have is calculation accuracy. What this says is, assuming our contracts are set up correctly and all the data is coming in correctly, how accurate is our calculation engine? Sure, I don't have to tell anyone, but for a contract management solution, if we can't accurately perform our core function, it won't be very useful. So when you're thinking about the various systems you use today, think about does this system accurately perform its core function? And if you don't know or don't have a metric for it, you need to start questioning, is this really performing the job it should?

If you think back to when we were in high school or college taking our statistics courses, for every model you ran, even if you're using a 10% population sample, you're still using confidence intervals to assess the validity of your analysis. That same concept needs to be applied to the systems that we use day in and day out to ensure that we're driving accurate data into our analytics process so we can make good decisions.

Now that we know our system is accurate, assuming it's set up correctly, the next question becomes, is it set up correctly? So we have to have metrics around that. So with PMMC, we really use two key metrics to assess, are we set up correctly? The first of which is contract accuracy, did I set up my contracts correctly or configure my system correctly?

System setup and configuration is one of the most difficult things to manage. It really does require looking at the output and looking at the result, and analyzing the variances or anomalies to make sure they're valid. Sometimes it's tedious to do so, but the only way you know if your system is set up correctly is if you actually look at it and measure it day-in day-out.

The other metric that we use, and now we've assessed configuration, is, am I getting all the data into my system that I expect? So there's two ways to measure this. The main one that we use is error rates or, put another way, how many accounts have not made it into our system due to processing issues like registration errors or payer mismatches? The key with an error metric is just making sure that it's a rolling metric so that you can ensure that all data is eventually getting into your system. For us, this metric is a rolling 365 days, so if this goes unmonitored and unaddressed, the error rate will continue to climb and we'll be alerted so that we can identify the issue and quickly fix it.

Once you've established that your core functions are accurate, you're set up correctly and you're getting all the data in your system, the question then becomes, is it kept up to date? So at PMMC, what that means is, are all of our current contracts loaded? So what our metric is, is what percent of contracts are loaded within 30 days of the effective date? That very quickly tells us how up to date is our system.

If there any inputs in your system that are done on a regular or timely basis, those need to be measured, tracked, and managed to ensure that you're making decisions based on the most up to date information as possible.

So those are really the four key metrics that we use to assess our core function, our setup, our data, as well as whether or not we're up to date to really manage our process. And those are probably good metrics for any system, not just a contract management system for you to be measuring in your internal business.

So now we've talked about accuracy and ensuring that all of our data is accurate, we can now talk and start talking about data and analytics. What makes analytics really useful is visualization and dynamic drill-down functionality. Using real-time data visualization, a manager can view the latest figures and see the current overall activity or volume, and respond more quickly to a potential growing issue. As an example, static denial report may inform the manager that the overall denial rate with the payer is up for a particular month. However, it won't inform them on why they're up. That's why it's important to have the drill-down ability that will allow the user to assess and determine the root cause quickly so that you can adjust staff, or make changes to your process and address the issues in a timely manner.

With the way most health systems are set up today, I'm sure you've all experienced the frustration of running and pulling a report that identifies an issue, then having to go run and pull another two or three reports to determine what the real issue was or what the cause of the issue was. With dynamic reporting, it's much more real-time, allowing you to identify it quickly and solve the issue on the fly.

Just to highlight the importance of visualization. There was an Aberdeen group study or survey that was conducted. And that said that managers in organizations that use visual data discovery tools are more likely to find relevant information compared to those who rely on traditional management dashboards. It also noted that managers at organizations that use visual data analytic tools are three times more likely to comment and share a report with their peers, and that's going to become very important as we talk about revenue strategy and leveraging analytics.