There are plenty of examples of artificial intelligence being harnessed on the clinical side of healthcare—from detecting cancers faster to finding the right candidates for developmental drugs to interpreting medical imaging results. But when it comes to a hospital’s financial operations, AI has lagged—or has it?

Unfortunately, there isn’t a singular, universally accepted definition of AI, explains this intro piece by Built In, a tech networking site and service. And the absence of one makes it hard to decide if the ways in which the revenue cycle has harnessed new technology can really be considered AI.

We like the definition that was given by DataRobot CEO Jeremy Achin, keynote speaker at the 2017 Japan AI Experience conference: “AI is a computer system able to perform tasks that ordinarily require human intelligence.… Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules.”

Using Achin’s definition, the hospital revenue cycle has started to embrace AI, because intelligent automation, and the more old-fashioned sounding robotic process automation, refer to software that follows rules.

An example is GAFFEY Healthcare’s AutoStatus. Just as the name suggests, this intelligent automation tool automatically queries payer databases for claims status and updates the information in the patient’s accounting and billing record.

Not only does AutoStatus eliminate the need for revenue cycle employees to manually check for claims status, it identifies denials and problem accounts earlier in the collection process—on average, 14 days or sooner than waiting for the 835 information—giving your staff a jumpstart on resolving denials. We think it’s pretty neat, but it isn’t exactly thinking for itself. It is, however, following the rules.

Another example is AlphaCollector, GAFFEY Healthcare’s intelligent collections workflow system that help revenue cycle departments streamline key processes and dramatically reduce the steps to collect. The system also can prioritize claims on collectors’ worklists so that they focus on high-dollar account balanced and aged claims first.

True artificial intelligence, on the other hand, uses massive amounts of data to mimic, or in some cases, exceed, human intelligence. It looks for patterns, learns from experiences, and makes decisions based on those inputs, with the goal being to solve complex problems quickly and to scale.

Revenue cycle departments can use AI to provide more accurate estimates of patient out-of-pocket costs, using physician-specific historical data or to predict denials earlier in the process, allowing an organization to resolve issues before claims are submitted which not only reduces denial rates but also increases revenue. AI and process automation are both hot topics and are items that everyone says they want; however, organizations should make sure they analyze their current state and identify specific processes internally that would benefit most from implementing these strategies. While both AI and process automation can lead to significant cost savings and increased efficiencies, each also requires time and effort from many stakeholders to ensure these strategies are implemented appropriately and effectively.