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Which payers are the worst? Who cause the most problems?  Is it Aetna?  CIGNA?  Or BCBS Alabama or someone else entirely?

Indeed, some payers like Anthem are known to be extremely problematic in terms of high levels of denials and other games that they play. Some of the state Medicaids, California Medicaid in particular, known as Medi-Cal, is one of the slowest in the country to adjudicate and makes tons of errors.

What makes them the worst?

What are the criteria for determining what is the worst payor? Is it that they have the most denials? Fro example number 1 on the list in descending order may have 13,416 denials, all coming from that single payor. Does that mean that they are the “worst” payor?

We have certainly seen a lot of practices that have highly concentrated payer mixes, which might complicate this simple calculation. The number one on that list may also not coincidentally be the largest payer for that practice, and we’ve seen situations where the largest payer might be more than double the next closest payer in terms of volume for that practice. Considering this payer to be the “worst” then maybe very misleading just because it has the most denials since it also has overwhelmingly the most number of claims.

There are other metrics than denials of course, like how long they take to pay, how many claims they “lose” to no claim on file, and so on.  There are far too many to list and cover in detail in one article, so will focus here on denials.

Denials Analysis

In simple terms, one could always say that the most denials you have determines your personal “worst” payer.  However, an alternative method that may provide more insight and that is more capable of being benchmarked across providers is to look at the percentage of claims that are denied. And since we have seen analysts get this wrong, we do not mean the percent of denials you receive since #1 would still be #1 always, but looking at the percent of claims sent to an individual payer that are denied.  Specifically, what percent of the time they deny when they receive a claim.  We’ve seen a lot of examples where a payer has denied 100% of the claims.

Sometimes, that’s hundreds of claims. Hopefully, it’s not thousands of claims, but if you have five claims that were denied and five that were submitted, that’s a 100% denial rate. Since 100% is as high as you can go, you might consider that your “worst” payer.  The goal of doing an analysis to determine your “worst” would presumably be to lead to some action.  Otherwise what point is there in the analysis?  But it isn’t worth you or your team’s time focusing on that payer with 5 claims obviously, since that’s so far down the list in volume of denials.  Therefore, categorizing this as your worst would not be useful.  A better way is needed.

We suggest looking at multiple variables in the analysis. The two variables that you could combine would be the volume of denials and the percentage of denied claims. A small but important nuance is that we do not suggest looking at the percentage of claims denied but the percentage of line items denied.  This is because there are many situations where if you submit multiple CPT codes, one or more of them are paid, and then some number of them are not paid. You have to have a more nuanced approach to that. It isn’t just, “Did the claim get denied?”  Dollar charges is a good proxy for this also as long as it is done at a line item level and not at a claim level.

When looking at a combination of variables of the volume of denials and the percentage of claims denied, one possible method is to determine the top quartile (that’s the top 25%) in terms of percentage of claims denied and then overlay that cutoff onto the top payers by volume of denials in descending order. Some may deny at a 2% rate, some 10%, some 25%. Find that cutoff, maybe it’s 11% for your practice for the top quartile.  Once you have a threshold, you apply this to your top payers and identify who are the “worst” to target for improvement.  Following is an example table with real data (slightly anonymized) illustrating this:

Notice that this type of analysis might have you skip payer #4 in the list in favor or trying to solve problems with payer #6 or #7.

Payer Policies

One of the crucial things to do is to distinguish between the payer’s fault versus the provider’s fault in denials. If the payer is denying because the provider is not complying. With that payer’s written payer policies, then it’s pretty much the provider’s fault. That is hardly the fault of the payer.

We’ve seen games where payers sometimes don’t publish all the information or the criteria for which they are adjudicating.   Other times they are not consistent with their own policies or seem to adjudicate for random reasons. If there are a clear written policy and your practice is not following it and you get denials. That doesn’t make them a bad payer…but it does mean there’s something you need to solve.

Keeping a running list of payer policies that impact the provider is vital. There needs to be a whole series of steps and, in fact, an entire process around where there are not only policies that are captured, but then somebody translates those policies into rules. Those rules get translate into business rules that get entered into a billing system to prevent claims from going out the door that we know will never get paid.

If you don’t have a sophisticated system that can do all of those kinds of things, that’s fine. There are workarounds for this. You can batch them and group and check them, or export to excel and run some rules against them. There are ways to accomplish this manually. There are also external systems that will do this and you can interface. Them together, Although that tends to be relatively expensive and you typically need some scale to do that.

Not everybody will do all the heavy and constant manual research. To identify all the new issues and keep up with all the payer policies. Perhaps your denials management process. Your team is drilling into individual denials and researching them. To determine why they are not getting paid. And what to do to resolve or appeal those denials successfully. In that case, people in your organization will find de facto new payer policies. But de facto payer policies still cause adjudication issues. That will translate into creating new rules in the system and having the system update. Even if no one is proactively doing research.  While it might cause some claims that were already submitted. To be unpayable, at least future claims will have been solved through this reactive process.

Monitor payers policies

Not everyone is going to be constantly monitoring payer policies that may affect them. And it’s tough to do that once you get past your top few payers in terms of concentration. Our general suggestion is that for your top couple payers you should research regularly to keep track of the policies. You don’t want a large volume of claims. To go unpaid because you didn’t realize something had changed and it’s too late to solve the problem after the fact. If you violated the policies.

The smaller payers are not as big of an issue if you capture those on the back-end and then identify them. Those de facto policies through the denials management process. But you can change your policy internally so that you don’t have any more going out the door.

We also suggest looking at denials patterns across all payers. Meaning look for common problems in terms of certain types of procedures or diagnosis. Codes or ages or places of service or other things. That may not pop up with an individual payer because there are only a few of them. Once you aggregate it across all payers, a policy may emerge. Therefore, now, you can solve that problem and make sure those get paid. If you’re a billing company, do this across all clients, it gives you a much larger data volume.

While the average for denials is probably in the high-single digits, and it very significantly depends. On the type of provider and all kinds of other factors, don’t use that as a particular benchmark. We have seen some payers that have a 40% denial rate with a specific provider, and that’s obviously somewhat catastrophic.

What to do about bad payers?

Why come up with a bad payers list or a problematic payers list like we’ve been talking about here? Well, the question is, “What are you gonna do about it?” There should be some purpose to coming up with this list, doing this analysis, and not just, “We’re labeling payers as a good payer or a bad payer.”  Shaming them will have no effect.  They have no conscience.

Denials management, appeals, and problem prevention is a giant subject and necessitates not only its own article but probably several just to scratch the surface.  Suffice it to say that one of the primary benefits of coming up with a bad payers list is to prioritize the denials management process, set targets, and track improvement over time.  If you can effectively show that your work in denials brought down the average denials rate for the “worst payers”, that is money in the bank.

One little-discussed solution for improving denials is better marketing to attract patients with a better payer mix.  If you don’t like WC for example, market to referral sources that drive less WC and more PPO. The last recourse is don’t take those patients. That involves marketing to ensure that you’re attracting those and giving that information to marketing sales organizations that you have for helping you do your marketing or sales. And it means have an effective and coordinated front desk team that uses data effectively.  Data is critical to help profitability.


This is just one analytical tool in your toolbox to be sure.  We are not suggesting this is the only method or that it solves all problems.  The reality is that ideally you would benchmark to find out where you have the greatest opportunity of improvement.  For example one might find out that effectively no one ever gets better than 15% from that particular BCBS and so there is no point in trying to improve it, while it is possible to get down to 1% for UHC and therefore there is more opportunity for improvement counterintuitively if you focus on one with a lower rate of denials.  While you may contact us to get data for benchmarking, most practices do not benchmark and therefore this is a great option that yields practical benefits.


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