Using Process Automation to Improve Auto-adjudication


The need to measure success — or the lack thereof — is critical to any business. A healthcare payer is no exception, and typically looks at its auto-adjudication (AA) rate for medical claims. This refers to the percentage of claims that pass automatically through the payer’s claims system with no human intervention. Some payers use different terms — such as pass-through rate, or first-pass rate, or operational first-pass rate.

This is an important number for various reasons. First, it’s a key performance indicator (KPI) that measures the efficiency of the company’s paying the claims. Many believe that having a high AA rate means the organization is providing efficient services to its customers with a high quality standard and for the lowest price possible. In fact, this number is so important for some organizations that it has become a KPI for executive bonuses.

Still, as advances continue in medicine and medical care, and medical codes and regulations become more complex, there’s an increasing likelihood of over- or under-paying claims through AA. Do we really know the amount of money wasted because the system auto-adjudicated claims? And what about the reverse of that — claims that the company pended for review by a human, especially an expensive human (nurse, doctor) through medical review, when the answer was obvious? I have seen situations where the cost of a person reviewing a claim was more than the cost of just paying the claim!

How Some Use the AA Rate

Some payers use the AA rate as a measure of potential cost savings for planned improvements. Yes, this can help determine which cost-cutting projects to approve. But you have to be careful how you use this, because it’s usually not as accurate as you might think.

For example, let’s say a company wants to reduce operational costs in its claims department. A standard way to incorporate the AA rate into the cost calculation is to divide the remaining AA rate into the operational budget. This will give you a cost-per-AA percentage. So, if your budget is $6 million per year and your AA rate is 85%, then your cost-per- AA percentage remaining is $6 million divided by 15, or $400,000. I have seen cost-per-AA percentages as low as $200K and as high as $2.5 million. One would tend to think that each percentage of improvement will yield that amount of cost savings; so, in this example, it would mean each percentage point of improvement would save $400,000.

But Not So Fast . . .

However, it’s not that simple. This is especially true if you’re using process automation, or robotic process automation (RPA), to help improve your AA rate.

Let’s say your improvement project is to automate the processing of pended claims. You need to identify claims that can give you the biggest impact on your AA rate. Typically, this is done by identifying the top errors — that is, the top edit codes. The natural thing to do would be to choose the edit codes that are on the most claims. So, perhaps you choose the most common edit codes, and the total number of claims with those edit codes is enough to provide a potential improvement of 1%. But there are several things you still need to consider:

  • First, what is the success rate you expect? In other words, of the [x] thousands of claims that the automation solution handles, what percent will it actually finalize and count toward the AA rate?
  • Second, are there edit codes you cannot automate? These tend to land with some of the edit codes you have marked for automation, If there are such codes, you may want to reevaluate your automation opportunities. Any claim that contains an edit code that can’t be automated will still have to be touched by a human. That claim therefore won’t count toward AA improvement. After doing your analysis to determine potential success rate, you will most likely end up with a success rate of somewhere between 30% and 50%. This means you need to identify another set of edit codes to get back to the expected 1% improvement.
  • Another issue with this approach is that you really won’t get a 1% improvement. The reason is that some of the edit codes you choose could be very simple to work, and therefore doesn’t take a lot of FTEs. As a result, your cost savings calculation is going to be short of expected.

An Alternate Method

There is another way. Instead of measuring your success by your AA rate, measure it by the average cost to process a claim.

This approach will allow you to reduce costs in several ways — including automating claims processing — but without the constraint of improving the AA rate. In the long run, you reach the same goal as AA improvement: cost savings. However, now you can automate errors with no regard to the potential improvement in AA. You no longer worry about other errors that might be on a claim. After all, any work being done by the automation solution reduces the time a human spends on the claim.

You can also implement workforce improvements using analytics tools to help your users be more productive. In turn, this reduces the cost of a claim, because users are able to work more claims in the same amount of time. Instead of measuring the cost per claim (which could be difficult), perhaps you can measure the cost per percentage point of AA rate. Through automation and/or enhanced workforce productivity, you could reduce costs. This means the cost of each percentage of AA will go down — even if you don’t improve the AA itself.

A Win-win Way to Measure Success

Here’s the bottom line. If your overall goal is to reduce costs, then you should look at alternatives to improving your AA rate. If you choose to implement the claim cost or cost-per-AA percentage, you will still improve your AA rate; but you also will save a lot more money and become more efficient and accurate, which also will reduce rework.