New technologies offer exciting capabilities to improve RCM, and it is important to know how they align with the potential benefits.
AI conversations in healthcare often move faster than operations teams can absorb them. At every conference, vendor meeting, or product launch, new terminology is introduced. Some sound familiar, others sound futuristic, and many revenue cycle leaders are left wondering which ones actually matter to the work within their organization.
Over the past year, I have seen AI move from conceptual to putting real pressure on RCM operations. The team wants clarity. CIOs want a consistent framework for evaluating technology. And vendors want to differentiate their capabilities.
Most of the confusion stems from vocabulary rather than functionality. The terminology may seem complex, but almost all of it relates directly to the work that revenue cycle teams perform every day.
We've created an easy way to anchor AI terminology within common RCM workflows to help clients and internal teams understand the situation. We'll break down each concept into what it is, how it's reflected in revenue cycle operations, and how leaders can assess its value.
The goal of this approach is not to make someone an expert in data science. The goal is to provide teams with a common language so they can align their AI decisions in an evidence-based, practical, and operational manner.
This is the framework I depend on.
AI terminology and RCM examples
Artificial intelligence (AI). Artificial intelligence refers to software that mimics aspects of human thinking to make decisions and make predictions. A common example in the revenue cycle is insurance discovery automation with 270 scheduled transactions. This eliminates the need for manual verification on payment processor websites or phone calls. Leaders typically measure impact by evaluating manual effort reduction, speed of identification of active coverage, and accuracy of information returned.
Machine learning (ML). Machine learning is a type of AI that learns from data rather than following strict rules. In an RCM environment, it may be used to predict denials or identify payers that frequently cause rework. Success is typically measured by model accuracy, improved rejection rate, and increased 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 interpreting payer codes and Washington publisher codes within 277 responses to more accurately categorize denial reasons. Leaders typically focus on the accuracy of denial interpretation and the model's ability to resolve confusing or inconsistent payer codes.
Neural network. Neural networks are the mathematical structures behind many deep learning systems. Their designs are inspired by the human brain. Revenue cycle operations can be used to recognize patterns in revenue codes that predict payer repayment behavior. Performance is typically evaluated by measuring the validation accuracy of rejection predictions.
Natural language processing (NLP). Natural language processing helps computers understand and interpret human language. In the RCM world, payer names, denial categories, and related text can be extracted from PDF 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, and code. Revenue cycle operations can write appeal letters, summarize payer policies into a usable format, draft messages to indicate medical record submissions, and generate communication templates. Its value is usually determined by the quality of the content produced and the time saved during human review.
Large-Scale Language Models (LLM). Large-scale language models are a specific type of generative AI trained on large collections of text. RCM can power a conversational interface that interprets eligibility responses and determines whether coverage is valid, while correctly separating medical benefits from dental and vision. Readers evaluate these tools by checking the accuracy of the model's interpretation and the accuracy of its coverage classification.
Agent AI. Agentic AI is designed to plan, reason, and take action across multiple steps. A simple example of RCM is an adaptive claims resolution agent that decides whether to electronically resubmit a claim, initiate a dispute, or escalate a case for human review. Leaders often measure task completion rates, manual effort reduction, and agents' ability to accurately solve problems.
Model Context Protocol (MCP). Model context protocols allow AI systems to share context and work together instead of acting as isolated tools. An example of RCM is to link eligibility and claims status models to keep information synchronized even when 271 and 277 data conflict. Performance is typically monitored by tracking how often eligibility and billing status mismatch and whether those mismatches decrease after context sharing is enabled.
Artificial General Intelligence (AGI). Artificial general intelligence is a theoretical concept that describes an AI system that can perform any intellectual task that a human can perform. Although AGI does not yet have applications in the revenue cycle, it may be able to handle end-to-end workflows from eligibility checks to appeals in the distant future. This is not yet a reality, so there are currently no measurable operational metrics.
How RCM readers use this framework
This structure gives revenue cycle teams a practical way to interpret claims from vendors and internal technology groups. Leaders can ask what type of AI is being used, how its capabilities support specific workflows, and what metrics ensure the solution is delivering value.
The most useful technologies are those that reduce work, increase accuracy, and increase revenue. If an AI tool creates complexity rather than reducing it, its operational value is limited, regardless of the terminology that accompanies it.
AI is rapidly permeating the daily operations of revenue cycle organizations. Through document extraction, predictive models, conversational eligibility tools, early agent-based systems, and more, these capabilities are becoming part of how teams operate. Leaders don't need to understand every algorithm. We need a shared vocabulary and a grounded framework for evaluating solutions.
When AI terminology is associated with familiar RCM tasks, the technology becomes easier to understand and much easier to evaluate. Clarity allows you to focus your efforts on areas where AI can create measurable and lasting improvements. This framework is intended to provide revenue cycle teams with a practical foundation as technology continues to evolve.
Ken Poray is the CEO of Integrex Health and has 20 years of experience in processing claims status, including EDI and web portal transactions.
