The rationale for increasing the rate of automation in healthcare claims processing is simple economics: a claim requiring human intervention costs approximately $4 to process, versus $1 for an auto-adjudicated claim.
Existing auto-adjudication platforms handle approximately 80 percent of claims, so increasing that percentage by 10 points or so would mean significant savings for insurers that process millions of claims a year.
One approach to extending auto-adjudication has been to add functionality to existing, decades-old auto-adjudication platforms. The problem with this approach is that modifying legacy systems is slow and expensive. And with constantly changing regulations and insurance guidelines, the updates can be out of date by the time they’re on line.
Another strategy is to invest in new auto-adjudication platforms that are more agile and effective, and can achieve auto-adjudication at rates close to 90 percent. The downside is that such new platforms are cost prohibitive to purchase and install, and they can take several years to implement.
Today many insurers are exploring a new alternative—robotic process automation applications that use rules-based logic to automate the review and resolution of common claims issues. Because they run as allocated resources on local servers and have minimal impact on underlying infrastructure, RPA tools can operate as an extension of existing adjudication systems.
Many insurers—including Blue Cross Blue Shield of North Carolina—have used RPA to extend the functionality of existing adjudication platforms and increase auto-adjudication by more than 10 percentage points.
Cost is another advantage. A digital robot that replaces five to 10 human claims processors costs as little as $10,000 to $15,000 a year, which includes license fees, deployment and maintenance.
Additionally, robotic automations can be developed, implemented and operational in a matter of weeks, providing the flexibility needed to adapt to frequent changes required by mandates and regulations.
RPA claims processing robots are built by writing a set of logical rules aligned to a set of review criteria related to a common claims review issue, such as determining whether or not a claim is a duplicate. Similar rules-based programs can be applied to resolve other common claims processing issues, such as authorization, medical review and billing.
A key to optimizing an RPA application is finding the sweet spot of where to draw the line. At some point, a claims issue becomes sufficiently unusual that writing a set of rules to automate its resolution isn’t economically viable. In other words, it’s more cost-effective to have a human specialist spend 30 minutes resolving an unusual exception that arises three or four times a month than it is to have a programmer spend a couple weeks designing, configuring and testing a system to automate that resolution. At the same time, it’s important to continually push the envelope and identify claims issues that, while not necessarily routine on a daily basis, are repeatable enough that automating them will yield a return on investment.
For insurers struggling to reduce operational costs, RPA represents a significant opportunity to cut administrative expenses and increase productivity.
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