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One of the things that’s clear is that there are a billion different ways to analyze something and look at different metrics. I’m continually surprised at the different lenses that we can use to calculate things.
We’re doing a denials analysis program for a client at the moment, and we’re trying to distill down all of these calculations. When you’re doing a ton of different metrics, there are so many different things you’re looking at. You often don’t get into the hierarchy of which is the most critical metrics.
In this particular situation, we’ve been trying to distill down what we’re doing to the smallest number of KPIs: just one, two, three, some minimal number of metrics that are the most important when it comes to denials. As we were going through that process and trying to figure out, “Okay, this one versus this one,” we ran into something interesting.
First of all, the number one metric that I think people look at is the volume of denials, obviously, in terms of either on an account basis, in other words, “how many denials,” or on a dollar basis. They look at that trend month-by-month over time. That’s the most common metric we see far and away from that everybody looks at, which is “How many denials are you getting month over month?”
I will tell you that that is by far not nowhere near the number one most important metric. Not number one, not number two, not number three. I almost don’t consider it to be an important metric at all. The reason why I say that is because it’s so problematic in so many different ways. So many things can be hidden by it, and it doesn’t tell you much.
I’ll give you a straightforward example. If your volume of encounters is increasing and you’re looking, “Oh, my gosh! My denials are increasing month over month,” they should be increasing month over month. If your volume of patient encounters is increasing, that doesn’t mean that you’re moving backward. It doesn’t mean you have a problem with denials any more than you did last month or the month before. Looking at that isn’t particularly helpful.
We don’t like data or metrics just for the sake of metrics. If you’re looking at numbers, if you’re outputting some number, you should be able to do something with it. It should tell you something, allow you to conclude that you can then act upon that will allow you to make more money. We don’t like denials count, whether it’s by volume of denials or dollar charge volume of denials trend. Not a great metric. Not number one, not number two, not number three, not top.
One of the metrics that we’re looking at and often done for people is denials paid. There are many different iterations of this, but we’re trying to zero in on the percentage of denials paid. If you think about where claims are falling out and not getting paid as a result of denials, you want to look at effectively “What’s your net? How many of them are staying out?” If they get denied, they don’t get paid. Suppose you look at the percentage of claims that get paid after a denial. Forget days and all kinds of other metrics because there are many things around that denial payment cycle. If you look at the percentage of denials that end up getting paid, we thought about putting that in the top two, and then we concluded that there’s a better metric than that.
Challenges of percentage denials
One of the challenges with looking at the percentage of denials paid and whether that’s on units of denials or a dollar charge basis is – first of all, let me say one thing not to use, absolutely not to use, which is do not do percent of denials charges paid where you are calculating. We collected paymentsis.
I’m going to make something up. We collected $100,000 in payments from charges that hadn’t been denied. You look at that on a percentage basis to determine how successful you’ve been. You had a million dollars in denials of charges, and you collected $100,000. From that, that calculates to 10%. That’s extraordinarily misleading because you shouldn’t expect to collect 100% of your charges anyway. You’re effectively blending two different metrics: a percentage of denials collected and a gross collection calculation.
We hate gross collection. I haven’t done that before. I’ll go off on that some other day in the near future again. Gross collections is a garbage metric, really useless, both internally to use over time and for comparison benchmarking against other organizations. Don’t mix that in! You effectively are mixing that in this case.
When you’re essentially trying to quantify the performance of appeals or quantify your revenue cycle management department’s performance or billing company in terms of how successful they are at overturning denials, the real metric you want to use is the percentage of denials allowed. What we mean by that is, you want to look at if there were.
I’m going to make something up. There are 10 claims; how many of those were adjudicated to have a positive allowed amount on a line item basis for those claims? The insurance company came back and said, “Okay, we allow $100 or $1,000,” whatever it might be.
It’s so critical that you use “allowed” rather than “paid” because, otherwise, you’re conflating two different things. You’re conflating the patient revenue cycle management process and the denials management/appeals process. Suppose you want to isolate and look at how successful your denial appeals are or your collection department, whatever the title for that group is in your organization. In that case, you don’t want to mix in the patient revenue cycle. You want to look at “Did you overturn that denial where the allowed amount went from 0 to a positive number?” not “Did it get paid by the insurance company?” Because there are going to be somewhere, you did not get paid by the insurance company. Still, it did get allowed because the patient deductible or something like that consumed the entire claim.
That’s our nugget for today. A key thing: look at percentage denials allowed as the number one metric for how successful your denials program is. Overall, in terms of denials, there are two key metrics. That’s one of them. The second one, we won’t give that to you tonight. We’ll do that another day. You’ll have to come to talk to us. Have a good night!