Crafting an AI terminology framework for the revenue cycle
New technologies are offering exciting capabilities for improving RCM, and it’s essential to know how they map to potential payoffs.
AI conversations in healthcare often move faster than operational teams can absorb them. Every conference, vendor meeting or product launch introduces a new term. Some sound familiar, others sound futuristic, and many leave revenue cycle leaders wondering which ones actually matter for the work inside their organizations.
Over the past year, I’ve watched AI shift from something conceptual to something that puts real pressure on RCM operations. Teams want clarity; CIOs want a consistent framework for evaluating technology; and vendors want to differentiate their capabilities.
Most confusion comes from vocabulary rather than function. The terms feel complex, but nearly all of them connect directly to the work that revenue cycle teams perform every day.
To help clients and internal teams make sense of the landscape, I created a simple way to anchor AI terms within common RCM workflows. It breaks down each concept into what it is, how it shows up in revenue cycle operations, and how a leader can assess its value.
The goal of this approach isn’t to make someone an expert in data science. The goal is to give teams a shared language so decisions about AI can be grounded, practical and operationally aligned.
Here is the framework on which I rely.
AI terms with RCM examples
Artificial intelligence (AI). Artificial intelligence refers to software that mimics aspects of human thinking to make decisions or predictions. In the revenue cycle, a common example is the automation of insurance discovery through scheduled 270 transactions, which removes the need for manual payer website checks or phone calls. Leaders usually measure the impact by evaluating reductions in manual effort, speed of active coverage identification and the accuracy of the information returned.
Machine learning (ML). Machine learning is a type of AI that learns from data rather than following rigid rules. In RCM environments, it might be used to predict denials or to identify payers that frequently cause rework. Success is typically measured through the accuracy of the model, improvements in denial rates and increases in first-pass yield.
Deep learning. Deep learning is a more advanced form of ML that uses multiple processing layers to recognize complex patterns. A practical RCM example is the interpretation of payer codes and Washington Publishing Company codes within 277 responses to classify denial reasons more precisely. Leaders usually look at the precision of denial interpretation and the model’s ability to resolve confusing or inconsistent payer codes.
Neural networks. Neural networks are the mathematical structures behind many deep learning systems. Their design is inspired by the human brain. In revenue cycle operations, they can be used to recognize patterns in revenue codes that predict payer reimbursement behavior. Performance is generally assessed by measuring the validation accuracy of denial predictions.
Natural language processing (NLP). Natural language processing helps computers understand and interpret human language. In the RCM world, it can extract payer names, denial categories and relevant text from PDFs or EDI documents. Leaders typically evaluate these tools by monitoring how accurately the system extracts information and how complete the extracted data is.
Generative AI. Generative AI creates new content such as text, summaries or code. In revenue cycle operations, it can write appeal letters, summarize payer policies into usable formats, compose mesage drafts indicating submission of medical records or generate communication templates. The value is usually determined by the quality of the generated content and the amount of time saved during human review.
Large language models (LLMs). Large language models are a specific type of generative AI trained on enormous collections of text. In RCM, they can power conversational interfaces that interpret 271 eligibility responses and determine whether coverage is active, while correctly separating medical benefits from dental or vision. Leaders assess these tools by checking the accuracy of the model’s interpretations and the correctness of its coverage classification.
Agentic AI. Agentic AI is designed to plan, reason and take action across multiple steps. A simple example in RCM would be an adaptive claim-resolution agent that determines whether to resubmit a claim electronically, initiate an appeal or escalate a case for human review. Leaders often measure task completion rates, manual effort reduction and the agent’s ability to resolve issues accurately.
Model context protocol (MCP). Model context protocol enables AI systems to share context so they work together rather than functioning as isolated tools. An RCM example is the linking of eligibility and claim status models so information remains synchronized even when 271 and 277 data conflict. Performance is usually monitored by tracking how often eligibility and claim status disagree and whether those disagreements decrease after context-sharing is enabled.
Artificial general intelligence (AGI). Artificial general intelligence is a theoretical concept describing an AI system capable of performing any intellectual task a human can. AGI does not yet have an application in the revenue cycle, but in the distant future, it may be capable of handling end-to-end workflows from eligibility checks to appeals. Because it is not yet real, it currently has no measurable operational metrics.
How RCM leaders can use this framework
This structure gives revenue cycle teams a practical way to interpret claims made by vendors or internal technology groups. Leaders can ask which type of AI is being used, how the capability supports a specific workflow and what metrics confirm that the solution is delivering value.
The most useful technologies are those that remove work, increase accuracy or improve financial yield. When an AI tool creates complexity rather than reducing it, its operational value is limited, regardless of the terminology attached to it.
AI is moving quickly into the daily work of revenue cycle organizations. Whether through document extraction, predictive models, conversational eligibility tools or early agent-based systems, these capabilities are becoming part of how teams operate. Leaders don’t need to understand every algorithm. They need a shared vocabulary and a grounded framework for evaluating solutions.
When AI terms are connected to familiar RCM tasks, the technology becomes easier to understand and far easier to evaluate. Clarity makes it possible to focus effort where AI can produce measurable and sustained improvement. This framework is meant to give revenue cycle teams a practical foundation as the technology continues to evolve.
Ken Poray is CEO of Integrex Health with 20 years of experience in working with claims status, including EDI and web portal transactions.