How a practical AI use case supports the healthcare revenue cycle
Discussions about artificial intelligence often remain theoretical. Concrete examples of use move it from alphabet soup to operational reality.
Healthcare executives sit through meeting after meeting in which artificial intelligence is described in terms that sound impressive but land without meaning.
Agentic AI. Orchestration layers. Multi-agent systems. Sub-agents. The vocabulary keeps growing, and the gap between the language and the actual work keeps widening.
I want to close that gap with a real example – not a hypothetical, not a vendor pitch, but a specific operational problem that revenue cycle teams face every day and a practical description of how AI solves it.
The problem is national provider identifier (NPI) enrollment. The solution is a coordinated team of AI agents working together. The result is faster client onboarding, accelerated revenue and a readiness dashboard that replaces the old five- to 10-day waiting game to begin ubmitting 270 requests. The result is less frustration, as provider ineligible responses from Blues plans disappear overnight.
The problem that slows everyone down
When a health system or physician group implements an eligibility or insurance discovery solution, one of the first operational hurdles is NPI enrollment. To submit eligibility transactions to payers, particularly state Medicaid plans, the provider's NPIs must first be enrolled with each payer. A large client may bring hundreds of NPIs that each need to be verified across more than 20 payers.
The industry standard for this work has been to quote clients a five- to 10-day turnaround. Organizations manually track which NPIs have been submitted for enrollment, wait for payer responses and follow up on any that haven’t been processed. The process is slow, labor-intensive and difficult to communicate clearly to clients who are eager to go live.
Beyond the timeline, there is a transparency problem. Clients often cannot tell which providers are ready to submit and which are still waiting on payer approval. The entire implementation stalls while teams work through a process that was designed for a different era.
What the Orchestration Layer Actually Means
When vendors and consultants talk about an orchestration layer, they are describing the part of an AI system that manages coordination. It is the logic that decides which agent handles which task, how information flows between them, and how results get interpreted and acted upon.
Think of it less like software and more like a capable manager who assigns work, reads the results and decides what happens next. In the NPI enrollment use case, the orchestration layer is what makes it possible to process hundreds of NPIs intelligently rather than treating each one identically.
The orchestration layer does not work alone. It directs a team of specialist agents, each with a defined role. Together, they handle a workflow that would otherwise require significant manual coordination.
The team of agents and what they do
In this use case, four agents work together to assess NPI readiness across payers.
The orchestrator. This agent serves as the operating system for the entire workflow. It receives a file of NPIs, manages the logic of which NPI gets tested against which payer, interprets the responses that come back and determines what action to take next.
The NPI coordinator. This agent knows the NPI registry. It maps each provider's business address to the correct state Medicaid payer, which means the system targets only the relevant plans for each NPI rather than submitting blindly across all 50 states. This precision prevents wasted transactions and keeps the workflow efficient.
The EDI submitter. This technology serves as the technical engine of the workflow. It formats outbound transactions and processes inbound payer responses, delivering structured results to the orchestrator for routing and decision-making.
The enrollment tracker. This agent maintains the readiness matrix. This is a continuously updated database showing which NPI and payer combinations have been confirmed as ready for eligibility verification and insurance discovery, which are pending a simple digital enrollment, and which require a more involved process, such as the wet signature and mail-in enrollment that New York Medicaid requires.
How the workflow runs
The orchestrator receives the full list of NPIs at the start of the engagement. The NPI coordinator immediately begins categorizing each one by state, so the system knows which payers are relevant for each provider. This targeting ensures that only the appropriate payers are included in the workflow for each NPI, eliminating unnecessary work from the start.
The EDI submitter handles the technical formatting and transmission of payer communications while the orchestrator evaluates the responses that come back. Based on what each payer returns, the orchestrator classifies each NPI as either ready for eligibility verification and insurance discovery, or whether additional enrollment steps are necessary before it can be used.
For those requiring further action, the orchestrator determines the complexity of the requirement. Some payers need only a digital checkbox through Availity. Others require an online form or PDF upload. A smaller number require physical paperwork with wet signatures, such as New York Medicaid. Knowing which category applies to each NPI enables the team to sequence work appropriately and give clients a clear and honest picture of what comes next.
Changing the client conversation
Instead of generically telling a client that NPI enrollment will take 10 days, this workflow produces a readiness dashboard that is generated by the enrollment tracker and updated in real time as results come in.
For example, a client with 600 NPIs might learn within hours of go-live that 450 are immediately ready for eligibility verification and insurance discovery. Another 100 need a digital enrollment that the team is processing. The remaining 50 require physical signatures from the client's authorized personnel.
That level of specificity is valuable. It turns a vague five- to 10-day wait to start a structured action plan with clear ownership and measurable progress. Revenue begins flowing on validated NPIs immediately rather than waiting for the full enrollment process to complete. Provider ineligible messages disappear overnight.
Why this use case matters
As chair of the AI community of Practice at the American College of Health Data Management, I see a consistent pattern in how healthcare organizations approach AI. The concepts are interesting. The terminology is accessible in the abstract. But leaders want to know where AI solves a real problem and they want to see it described in operational terms they recognize.
The NPI enrollment use case is a strong example because it is concrete and the results are near immediately measurable. The orchestration layer is not a theoretical construct. It is the logic that routes each NPI to the right payer, reads the response and decides what happens next. The specialist agents are not abstractions. They are defined functions that handle specific tasks in a coordinated sequence.
When AI terminology is attached to a familiar workflow, it becomes a framework for evaluating whether a solution actually does what it claims. Revenue cycle leaders do not need to understand every model or algorithm. They need to see how the technology connects to the work and they need to know what to measure results.
In this case, the measurement is straightforward. How quickly can NPIs be submitted for 270 requests? This is an operational question. AI answers them in this use case. That is the kind of clarity that moves organizations forward.
Ken Poray is CEO of Integrex Health and Chair of the AI Community of Practice at the American College of Health Data Management. He has 20 years of experience working with payers, status, EDI transactions and most recently with AI workflows.