Beyond The Buzz: Practical applications of artificial intelligence in revenue cycle management

For the past few years, if not longer, artificial intelligence has been promising to revolutionize the hospital revenue cycle. But the reality of applying AI to RCM has been less transformational. That’s not to say progress hasn’t been made, says Derek Morkel, CEO of HealthTech and GAFFEY Healthcare, which provides revenue cycle automation technology and services to healthcare providers. “At the present time, we know that most of what is described as AI is really process automation,” he says.

“It is effectively reducing non-value added processes in RCM.”


Morkel points to transcription automation, computer assisted coding, and automated claim status updating as three such examples. 

They might not be show-stopping examples of AI, but when it comes to a hospital’s bottom line, solutions like these can turn heads. 

That’s because 70% of all human activities during the lifecycle of a claim add no value to the process.

“Many processes and decisions that could be automated are still being done manually or in many cases not done at all,” he says.

With hospitals forever on the hunt for ways to reduce costs while improving quality of care, AI represents a way to lower labor costs while 

boosting revenue. GAFFEY Healthcare, in fact, has a pair of intelligent revenue cycle tools that were in use long before AI was a buzzword. 

AlphaCollector is an intelligent collections workflow system that helps hospitals streamline key RCM processes and reduce the steps to collect.

AutoStatus, meanwhile, is an automated claim statusing engine built into AlphaCollector that automatically gets the current status of a claim from 

a payor and then based on the rules for that status, will hold the claim back from being touched unless a collector can add value to that claim. 

That data is also incorporated with payor payment history, age and balance to create a risk matrix that then determines when a claim should be 

touched and by what skill level. Limitations in current EMR technology can prevent hospitals from being able to take advantage of tools like these.


This is especially true for smaller providers, Morkel says. “The main issue that we see is that the core patient accounting platforms in EMRs do not 

have the capability to incorporate AI technology,” he explains. “They were not built to accommodate it. EMRs for smaller providers, especially, struggle 

to extract the data necessary to run the required applications.” Take automated status updates, for example: “Patient accounting systems are built on 

when a collector should touch an account, not on when they shouldn’t,” he says. “Automated claim status platforms hold back what should not be touched 

until the data would indicate there is an issue. Core EMRs are built where all accounts should be touched at some point. It’s a different approach.”


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