RCM Automation: Bots vs. Brains

There are many solutions out there intended to automate parts of the revenue cycle, but revenue cycle automation is a complex process, so the solution that will simplify this process has to be more advanced than one that, say, enters names in a database.

 

This blog will detail two types of automation solutions and their differences. For our purposes, we’ll call them bots and brains.

 

Check out this video for an overview on the differences between Bots and Brains! 

 

 

Bots

Bots are systems using robotic process automation, or RPA. RPA is software with simple artificial intelligence (AI) and machine learning capabilities to handle high-volume, repeatable tasks that humans would otherwise be required to perform.

 

Bots can automate common (simple) human tasks, such as:

  • Logging into software

  • Reading and/or downloading data (running a report in a software system or scanning individual data on a single screen)

  • Writing and/or entering data into systems from a file or other location

  • Basic rules-based decision making (IF-THEN-DO or IF-THEN-ELSE)

 

Because revenue cycle automation is complex, the simple RPA bots perform is not enough to automate this in an efficient way that actually saves your organization time. Bots are useful for tasks like entering thousands of records in a database, or folding shirts, even, but not for revenue process automation.

Brains 

When we say “brains” here, we’re not referring to human brains, but rather advanced machine learning, using more complex artificial intelligence (AI) than bots. This AI simulates human intelligence processes by systems, including learning, reasoning, and self-correction. These “brains” are, essentially, systems that can think, learn, and become increasingly capable as they are used. They help us learn from the past.


Brains can automate more complex human tasks, saving more time and money than bots. There are two different types of brains:

  • Supervised machine learning—maps an input to an output using decision tree-based rules where an expert is managing the system and telling the machine learning which connections to make and how to structure the output

  • Unsupervised machine learning—finds previously unknown patterns in a data set by running the AI and identify its own correlations (also known as deep learning, allows for complex decision making)

How can this help my organization?

Because brains can handle more complex tasks, and can even identify previously unknown patterns with unsupervised machine learning, they can help you reduce staffing and right-size your organization by allowing staff to focus on fallout management tasks (coding, AR follow-up, reconciliation, appeal, rejection). Brains are actually smart enough to increase efficiency and cut down on staff needed for revenue cycle management, while bots would cause you to hit a wall.

 

Check out this guide today to start having conversations with your team about the benefits of RCM AI!

Making the Business Case for RCM AI Today