I’m going to let you in on a secret denials analysis tip. Most people don’t realize that often, there are formatting differences between payers on their denial codes. That means it might be a CO-119, a CO 119, a CO119, all in one string, maybe just a 119. It might even be buried in a note text string somewhere in the middle with carrots surrounding or maybe not any carrots and just many letters around it.

This means that parsing out these codes for the systems that are doing it, whether it’s a billing system or whether it’s a clearinghouse or whoever it might be, or sometimes it’s both of those because it comes through a clearinghouse. It gets routed, and then it has some work done by the billing system or a denials management system. That parsing process is very complicated, and often, it does not work correctly.

This can be challenging for something like a COD6 versus a CO6. If it strips off and identifies the 6, those are two completely different denial reasons even though they’re both a 6, and they both start with CO.

Sometimes, you can even run into a situation where there is no code at all. There’s just a written description: “Claim not on file.” It wouldn’t be “Claim not on file.” That would not happen. “Medical records required” or something like that or “A patient termed” or whatever it might be, but there’s no code at all.

What you need to know about secret denials

Why does all this matter, and why do you need to know? It’s because, in a word, duplicates. Suppose you’re doing analysis and looking at the top denial reasons in descending order. In that case, you may not realize that hidden somewhere below or several other lines in combination below are batches of the same denials. You might see a thousand on line 4, and you might see 312 on line 26, and then another one further down below that has some number of denials.

I remember the first time I ran into this. I didn’t even realize the secret that I had seen duplicates. When scanning down the denials list, we had a denial code and a reason, description, and several denials. I will tell you that I saw a code once, a whole bunch of other codes, and then I saw the same code again, and it didn’t even occur to me that this was a duplicate. Let me be clear, it said CO27, and then down below, it said CO27 again. It didn’t even say CO-27 or some other funky thing. It just said the same denial code on a different line item, with another group of them, hundreds of them. I didn’t pick it up.

Vital To Look At The Register

If you’re good with data and you may remember seeing a bunch of codes, you’re not going to remember, “Oh, I’ve seen CO27 before!” because you’ve seen it so many times in your life that it’s not going to register as something novel. So to see it again, it isn’t vital to look at the register. You’re not going to catalog all the ones you’ve seen in that list so far because there are a hundred of them or hundreds.

It just so happened that at some point, I was there secret scanning a list of multipliers and went, “Wait a minute! I thought I saw that before. Did I see some before?” I didn’t remember exactly, but I had to go back and look and then took the data, exported it into Excel, ran a duplicate check, and sure enough. I was like, “Oh, my gosh! There’s a ton of them. Wow!”

Prioritizing What To Work On

If you don’t pick up on these duplicates or your system doesn’t group them correctly, you will miss prioritizing what to work on. If you’ve been in this business a long time, anyone who will ask you to tell you everything that you know, you could never possibly explain everything you always know. That’s an unaided recall. Without being prompted, you may not remember, “Oh, you know, I know these things are under these things.” We can’t possibly remember all that we know until we’re prompted. That’s the reason why this came up.

This came up recently, just the other day, when we ran into this with a client. We had all denial information and stuff like that. Once we regrouped them, where we consolidated all of those duplicates, the number 2 denial reason changed. That’s how big of an impact it was. It wasn’t like number 34 became number 37. Number 2 in descending order changed.

We’ve seen situations where a number 1 changed and several of the top 5 or 10 changed in order and sometimes quite dramatically. Something that was outside the top 10 suddenly shot up into the top 3.

Eliminating duplicates and understanding what’s going on there is vital for prioritization. It’s essential to understand your secret denials management system, the analysis tools you’re using, the clearinghouse, all these things so that you can root out duplicates or make sure there are no duplicates. If there are, root them out; consolidate secret them to get a better idea of the top problems to prioritize.

What should you prioritize?

Prioritization means you’re going to make the most effective use of limited time and resources because none of us has unlimited resources. This is important in terms of maximizing how much money is recaptured for the provider. It also means that you will solve a larger number of problem claims at one time if you group correctly.

Even if you have the prioritization correct, you may solve 500 when you should have solved 800 by making a similar problem if you identify all of the payers and all of the claims that have that particular problem and then go out and solve that problem all in one giant batch.

It also means that you will be able to track improvements and the results of rebills and appeals. You’ll be able to show more success, whether that’s to a superior within the organization or to a colleague or simply because you want to be able to track the efficacy of your team’s work so that you know what your return on investment is for allocation of resources or investment. It’s essential to be able to show the results of your work and then quantify the improvement.

That’s our secret tip on denials analysis. Happy hunting!

Apache Health

Subscribe to Apachehealth.com for latest blog news.

You have successfully subscribed to the newsletter

There was an error while trying to send your request. Please try again.

Apache Health will use the information you provide on this form to be in touch with you and to provide updates and marketing.