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Calculating denials might seem like something relatively simple. For clarification, we don’t mean, “How do you add and subtract and multiply and divide?” but “What counts as a denial? How do you figure out what to use?” In other words, you’re essentially looking at denials going up or going down. For this, you need to filter out the noise. For things that you don’t want to pay attention to that might distract you and give you an incorrect perception of what’s happening. So we saw this recently where a provider was doing this. And you can use something like the transaction ID from the clearinghouse, but there are some challenges.
If you count the number of transaction IDs, that isn’t going to give you a good idea because:
1) It may not include any denials; it might just be payments and allowed amounts and other transaction information;
2) It may be that a single record has a whole bunch of denials, and it’s five denials for five separate patient encounters in one remittance.
What can you use for calculation?
So how do you figure out exactly what to use if you’re going to be doing this? And how do you reverse engineering check and see whether or not you’re getting the right information?If you have something from your clearinghouse or your billing system or something like that identifies these? Most of the time, I think people are looking at this at a transaction level, which is, “Oh, I get a denial! I got to fix that problem” or “I don’t have to worry about that one. Next!”
That’s at a micro-level because you can see the granularity, and you can say, “Okay, if I get this particular code, then I do this thing.” But that doesn’t give you the 30,000-foot level picture of “are we getting better?” or “are we getting worse?” If you’re adding additional resources or there is some effort to improve your denials rate, you need to see if this is having any success, if it’s getting better or worse or just stagnating.
So one provider that we looked at recently used the patient ID as the unique identifier for the number of denials. We mentioned that some might not be denials, but more importantly, you can have multiple denials. In a single month for the same patients. And there’s not always one-one. There may be a one-to-many or many-to-one, so you might see a situation where, in theory, you could have multiple patient encounters that have denials. If you use the date received for the denial. You might get two different rejections on the same date for two different encounters for the same patients. That’s because the doctor saw them twice in a month, and the payer batched those denials effectively, and it might seem like you only got one denial then.
You might see a situation where you received a denial early in the month. Somebody did something to resubmit it or modify something or appeal, whatever it might be. Late in the month, you got another denial back three or four weeks later. And so you would see two different denials for the same claim for the same patient encounter in one month.
So there are some real challenges in figuring out how to do this at a granular level. And then you run into some additional nuances, like what if it’s a single patient encounter? So one claim has two different denials. I mean, not just for other line items because you might. Have three, four, or five different line items with five different CPT codes, and they all get a denial, and those are all the same. Or you might find a situation that we frequently see where you get two different denials.
On the same claim like you might get a CO 18 and a CO 109. And so one of those is not covered by the payer, and one’s a duplicate. Are those two denials or one? Now, you got two denial codes, but effectively, the claim got denied once. It just got denied for two different reasons. So is that two denials or one? And how are you going to count that? So all of the transactions from a clearinghouse can include payments, allowed amounts, and not all of these are denials. So you’ve got to filter through, and transaction type is the most common way to look at this, so RRC codes.
Now, the challenge is that frequent transaction type, if you filter for that, works. However, occasionally, depending upon how the clearinghouse sets up and what the payer sends through. You might see that in the transaction type, it doesn’t show RRC, so you can’t just use those. It shows the ANSI X12 remittance code like a CO 45 or a CO 119 in the transaction type itself, which means you can’t just filter for the RRCs.
So the other thing you can run into is that all remittance codes are denials. So in theory, if you said, “Okay, we’re going to look and assume that all the RRCs are denials,” that may or may not be the case. And how can you figure it out? Count them. Now some of this is, what’s the definition of a denial? Is a CO 45 a denial, where the charge exceeds the allowable? What about a CO 253, the sequestration? I’m sure you will not be able to get out of that one because pretty much everybody gets. A reduction in payment when the government does that, or at least that should be the case. And there are various opinions on this.
Why look for denials?
But I think what we generally counsel people to do is take a step back and say, “What’s the purpose of looking at denials?” Why are you doing denials analysis or denials management? And the goal, presumably for everybody, should be to identify problems so that you can diagnose and solve those problems to collect more money. So if that’s the goal, it isn’t reporting for the sake of reporting or just seeing what’s going up and what’s going down? It’s really – “What can we do differently to make more money?”. What we might suggest is filtering and including denials. Things that you can do something about. It could be in terms of resubmission or fixing some problem with a claim or an appeal, or it could be something further. Up the line related to the prevention of the problem.
Some are reactive, but some could be proactive, where it’s not possible to solve the problem after the fact once you’ve submitted it. Let’s say, for example, you get a timely filing limit denial. You’re probably unlikely to be able to overturn that unless you’ve got some excellent documentation that shows you did submit it before the timely filing limit. But again, if you missed that, then you’re not going to be able to overcome that, but you should be able to prevent that in the future. So you want to track those and see those and put in place some process to avoid it.
What constitutes denials?
So sequestration is not going to fall into that category. Contractual adjustments are not going to fall into that category. But suppose you have a diagnosis that doesn’t support a procedure code like a CO 11.And you go back into the medical records, and you can’t find an additional diagnosis. In that case, whether there is documentation or you can look through the notes and try to come up with a diagnosis code from the documentation? Even if you can’t do all those things.
You should still be able to prevent that problem even if it means that you have to go even further upstream to “How do you get physicians to document better?” so that they include more information that allows you to get more claims paid. Or even if you go even further upstream, that provides counseling physicians on what to order or what type of clinical care they should get to ensure you’re not providing unreimbursable care. Now, that’s far upstream, and you may find resistance on the clinical side to doing some of these kinds of things. And again, this depends on your organization and other things, but that is preventable. So you do want to track those kinds of things.
Anything that falls into the category of “you should be able to solve it and get it paid or prevent. It so that those claims get paid, so you don’t do unreimbursed care” should count as denials. Deciding what that list of codes is for denials as an organization and make that documentation available to everybody. You can modify it over time, but use that to filter through, “Okay, these are ones we’re going to track. We’re going to see whether it gets better or worse. And then we’re going to put some resources towards it to improve it and reduce denials consistently.”
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