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Are denials completely avoidable? By “completely avoidable,” I mean materially, in other words, greater than 99%.
Recently, I interacted with a billing company that claims that they achieve a greater than 99% clean claim rate and denial-free rate. This is effectively a small billing company. This is a small billing company, a one-person show, doing a small volume, where it’s not even enough to fill up the time of a single individual, a single biller who owns and runs the company.
I’m sure they’re losing money like crazy. The amount of work involved in proactively avoiding denials, making all the clean claims, scrubbing upfront, coding checking, working with providers to make sure they’re eligible, and all the other stuff. I mean, there’ll be an extraordinary amount of work to make sure that happens.
The question remains, “Can you avoid all the denials like 99%?” With enough work, maybe. It would be interesting to think about something like, “Does it even make sense to do that? Is that cost-effective? Would you be better off avoiding all those denials or even allowing some greater amount of them to come through?”
Understand what’s possible
Now, this is presuming that that is happening. Back to the like, “Is it possible? Does it make sense?” I have no idea, but I doubt it. I indeed doubt that it’s cost-effective to do that, and I certainly doubt it’s cost-effective for a billing company to do that whether they’re making 8% or 6%, or even 4% or less of 100 bucks or whatever the average collection is for a patient encounter.
Then, much later in that conversation, I get a disqualifier: Except when a payer makes a mistake or eligibility or something on the provider’s part. And then, I’m like, “What?” The record scratches and goes off the track. Wait! So is it 99%? I think you said it’s greater than 99%. Do you mean 99%? Or do you mean like 99% of some other percentage?
All about the percentages
It’s not entirely clear to me anymore. From looking at denials data (and we do look at denials data), we know that denials percent averages and the distribution will look nothing like what this person is talking about. The top quartile, even the top decile is nowhere near sub-1%, meaning we’re not even in the same ballpark. We’re virtually off by order of magnitude.
Now, I don’t know. I do not think this is real, but I’d like to see the data on what 99% plus looks like because many people throw out numbers. We even see it in marketing materials, saying 99% plus clean claim rate or a 99+% first-pass rate. More often than not, I want to be like, “Bullshit! Excuse me. I’ve got something caught in my throat. It’s called Bullshit.”
Perception vs. reality
Then, you get back to many people’s perceptions of what is happening in their business, and they frequently are not backed up by reality. I can remember many times when I’ve had particular material conversations with people around something like their payer mix, their average reimbursement, the top procedures, or providers in their group, whatever it might be. And then, we run the data ourselves, and we see that it has nothing to do with what the people thought.
That’s often the case, even with denials. Ask people to rattle off what their top 10 down are. They may be able to list quite a few of them in their top 10, but the likelihood that they get the top 10, the top 5, or maybe even the top 3 in descending order correctly is improbable.
I’m fascinated by outliers.
The question is, “Is this an outlier, and can we learn anything from it? Or is this just someone who has no good handle on what their actual data is?” I wonder because it would be interesting to find out. Maybe it’s not cost-effective, or perhaps it’s not true. However, maybe, it is accurate, and it’s not relatively cost-effective. But perhaps we can learn something from it. Perhaps some things could be adopted. On the other hand, maybe it gives you some target to shoot for, and you can perhaps automate the junk out of that so that it is cost-effective to do all of that work to make that happen. Still, I wonder, and you should wonder, too.