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Intelligent Automation Vs. Artificial Intelligence

May 2, 2024

The term artificial intelligence (AI) is one of the most misused in the technological lexicon. Machine learning is frequently used interchangeably with it, resulting in an umbrella phrase that groups together unrelated technologies or confuses comparable technologies that are genuinely separate. Increased healthcare technology expenditures are creating misunderstanding about what defines artificial intelligence (AI) and intelligent automation (IA) in revenue cycle management (RCM). Consider artificial intelligence to be a subject, and intelligent automation to be its practical application.

RPA is often the first thing that comes to mind when people think of AI in RCM. The mere presence of a few bots does not equate to intelligent automation. While RPA is a fundamental component of intelligent automation, it lacks real "intelligence." Combining RPA with other disciplines of AI results in intelligent automation, for example, creating a digital worker who leverages RPA to decide which actions to take based on the results of a machine learning model.

Additionally, due to the massive amount of paper that still dominates the healthcare industry, computer vision/optical character recognition and natural language processing (NLP) are fundamental technologies for IA. As a result, digital workers will be able to automatically perform more tasks. Are bots capable of reading paper documents, for example? Computer vision-enabled digital workers can. At the transaction layer, it automatically fills in required fields from paper documents, which are extracted and validated. Utilizing workflow orchestration, digital workers can enable seamless handoffs between the human and digital workforces.

Automation in RCM is not meant to automate everything. A detailed review of processes within revenue cycle functions and domain expertise are essential to avoid automating tasks that are high-risk or overly complex. Although 80% to 90% of the volume flowing through certain paths can be automated, the effort to automate edge cases eventually outweighs the benefits.

Digital workers take care of redundant, error-prone tasks at scale, while people on the front and back ends are in charge of complex items. It is necessary for both to realize the full value of the automation ecosystem and to codify steps based on the specific needs of each healthcare system. As a result, humans are responsible for handling the exceptions, the part of the process subject to complex variations.

Intelligent automation is impossible without artificial intelligence. RCM cannot be transformed meaningfully or sustainably with AI alone. The creation of a modern, scalable revenue cycle requires the integration of digital and human workers, as well as deep expertise.

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