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I love looking at data. I’ll tell you a story just from yesterday that illustrates why I like looking at data so much.

Manage your business efficiently

I think most people, whether they’re billing managers or owners of a billing company, or owners of a healthcare provider, have some handle on what’s going on in their business. If they’re good, they have a pretty good handle on what’s going on in their business. Yet, unless we live in the data, nobody has complete knowledge of what’s going on. But we frequently have the fallacy that we do.

As a data scientist, it is amusing to have surprises. When we were going through some analysis yesterday with one of our clients and looking at the data and the information that was in our system, we were going through and saying, “Hey, tell us about your business. What are the top denials? What are the top issues you’re running into?” 

Understand the issues 

They started listing some issues: Some related to eligibility, some related to hospice care, and some associated with other things.

We went through a bunch of these things. Then, we went back and looked at the data to see whether or not that was represented in the data and also what we could see in the data that wasn’t obvious to the client, that they didn’t already know, some problem that they weren’t aware of, something that we could find and say, “Hey, let’s see if we can solve a problem, perhaps, that you didn’t know about. Or even if you know about it, let’s see if we can use data to solve that problem.”


They were astonished at the number one denial they received, which is a 97. This is “the benefit for the service is included in the payment allowance for another service procedure that has already been adjudicated” and some other information. Loop 2110. It completely threw this manager. They were not prepared that this was the number one problem over the last year in their practice, which to me was excellent. I was excited. I was like, “Whoa! We found something that you didn’t know. You didn’t know this was your number one problem.”

She then came up with a hypothesis for that and said, “Okay, well, that could be that it’s Medi-Medi. And maybe, it’s this kind of situation where Medicaid is saying, “Hey! No, that’s covered under Medicare. We’re not going to pay for it.” So we said, “Okay, let’s see if we can drill down in the system to see if we can answer that question: Are these Medi-Medi patients? And is this the Medicaid portion of it that’s giving that denial? That might suggest that maybe we don’t have a problem. It’s just something we have to deal with administratively or whatever.”

Identify the source of the problem

We then went through some other information in the system and found, “Ah, nope. Not Medicaid. These are all Medicare. Or at least, the overwhelming majority of them, like 90%, were Medicare.” Close to 90%. 88% or something like that. We said, “No, her hypothesis was not correct.” She didn’t know the answer. So the number one problem in practice was not known. We still don’t know the source of the problem, and we don’t have a hypothesis for solving it yet.

We’ve identified a problem that we now want to investigate, and we know precisely which claims to go in. We have a list and say, “Okay, here are the hundreds that you got to go in and take a look at and do this kind of stuff.” So it was entertaining, and it was exciting.

There’s one more part to this, which is when we said, “Hey, what are the issues that you’re running into? And what are the top denials, eligibility?“ We listed off a bunch of things like CO 27, CO 31, B 11, that kind of thing. I said, “Okay! Well, let’s dive into the data. We can group denials by a responsible team, denial type, and other things. So let’s go in and look at patient eligibility and verification (because that’s the name of the group that they have internally) and see what the top issues of the top denials are related to that subject.”

It’s impossible to know everything

As I mentioned earlier, this manager had listed off the top ones, and it turns out that they did get the number one right. Cool! They also got the number three right. But they had only listed two out of the top five.

What that says to me is, you know there are eligibility problems. However, there are still surprises you might not realize, “Hey, we’ve got these 109s and 24s and 22s and all these other things that are coming in, that are pretty significant, even above some of the ones that you knew about.”

Taking a step back, what do we learn from this? I think the moral of the story is that even good managers who are really in touch with their business cannot know everything. There are always going to be surprises when we go into the data. We’re going to find things that we don’t expect: significant things that we don’t expect, problems we weren’t aware existed, some other nuance, some issue that’s popped up, something that’s changed about the business, or even maybe, “Hey! We knew something, and we thought we had a good handle on this. But that information is now six months old. Something has changed, and we didn’t realize something had changed.”

Final thought

The beauty of data is that it tells us so much. We don’t have to rely upon remembering things we think we know. It’s impossible to know the top five issues. If you’re outstanding, you might know two or three or something like that off the top of your head. But there’s no way anyone’s going to learn all five all the time, at any given time, because it changes. Unless you’re constantly looking at that data, there’s no way to know that. But that’s the beauty of the data. We can see those things, and we can identify the problems, and we can solve them.