RPA Use Case: Monitor Insurer Requirements

It’s not easy to work in revenue cycle management. In addition to every medical practice having a long list of services they provide, whether or not those services are covered by insurance and how much the practice can charge is determined by what insurance plan the customer happens to have. For this RPA Use Case, let’s examine bots that monitor insurer requirements.

For medical practices, keeping up with insurance plan changes is challenging. After all, as of a few years ago there were nearly one thousand different insurance companies in the United States. When we consider that each of those has multiple plans with different levels of coverage, it becomes clear that the average medical office could easily deal with thousands of different plans over the course of a year.

Assuming that wasn’t a large challenge, plans are constantly being updated. What a plan specifically covers and what documentation is required for payment are factors that frequently change. 

As there is no standard for how or when insurance companies report plan changes, medical practices instead find themselves only aware of a problem when a claim is rejected. Worse, it may take several rejections for an office to fully understand that the insurance company has changed the requirements for paying a claim.

This is a solvable problem; one that robotic process automation (RPA) is uniquely capable of fixing. Let’s examine how bots can monitor insurer requirements and the benefits that happen when they do.

Too Much Information

Beyond thousands of providers offering multiple insurance plans is the fact that each has its own documentation requirements, many of which are changing on a weekly, if not daily, basis. As practices look to keep up with the myriad of changes, few can stay ahead and instead find themselves unable to monitor changing insurer requirements.

To be clear, a failure to keep up with insurance claims rates goes directly to a practice’s Clean Claim Rate. As we’ve written about before, Clean Claim Rate (CL-1) is one of the most important KPIs to monitor. If your CL-1 is less than 90%, perhaps this bot can help.

And because unclean claims often go unpaid, this bot should also be considered if your uncompensated care (FM-4) rate is excessive.

Cluster Analysis Through Bots

As we’ve stated, documentation changes happen frequently. And the more common a procedure, the more likely one should expect a claim to be rejected for faulty documentation.

Where documentation requirements have changed, many medical practices would need to encounter multiple rejections before becoming aware that, in fact, requirements have changed. 

With technology, however, a bot can leverage results from cluster analysis. Cluster analysis is a process by which commonalities can be established within data sets. If the same rejection code for the same diagnosis code is observed within a predetermined amount of time, the bot can recognize the pattern and make it aware to the RCM team. Though analysis will be required to find the root cause of the rejection, the repetition of the error becomes a useful tool that points analysts in the proper direction.

Always Working, Even Silently

The type of bot described above would be identified as a monitoring bot. Though it would be impractical for a human to consistently determine commonalities among rejection codes, monitoring bots are running at all times. They evaluate claims 24/7, attempting to see if they’ve been rejected and if those rejections are for a common reason.

In some cases, RPA exists to simplify tedious tasks performed by humans. Monitoring bots, in comparison, are different. They run quietly in the background, only making themselves known when a monitored behavior has been recognized.

Swift recognition of new documentation requirements are key to keeping a low clean claim rate (CL-1) and minimizing the level of uncompensated care (FM-4). For medical practices, a bot that can monitor insurer requirements offers the opportunity for cleaner claims and therefore, faster revenue.