If Your Revenue Cycle Depends on Post-It Notes, AI Is Coming for You
Every kitchen has one. The junk drawer. It’s where everything ends up that doesn’t quite belong anywhere else, spare batteries, random keys, rubber bands, a half-used roll of tape, mysterious cables from devices you no longer own. Somewhere in that chaotic mix is a tool you actually need. Maybe a tiny screwdriver. But when you need it, you have to rummage.
You may have seen this in action in a billing office, and it’s represented by Post-It stickers on multiple surfaces. You dig through things you don’t need. You move post-its around. You try a few alternatives. Eventually, you either find what you’re looking for or settle for something that kind of works.
Searching for patient data inside most Electronic Health Records (EHRs) and billing systems feels exactly the same.
Healthcare organizations sit on enormous mountains of data. EHRs contain clinical records, notes, orders, results, documentation, timestamps, and metadata. Revenue cycle systems track authorizations, claims, eligibility, remittances, coding logic, and denial workflows. There is a lot of useful information in those systems.
But when someone working in revenue cycle management (RCM) needs one specific piece of information, a documentation detail to support medical necessity, a missing lab result, a timestamp for observation status, or confirmation of a prior authorization, they often have to dig through layers of screens, post-it notes, attachments, and fragmented records.
It’s rummaging in the junk drawer.
And the cost of that inefficiency is enormous. RCM teams spend countless hours manually searching for information needed to support claims, respond to payer denials, or prepare for peer-to-peer reviews. Highly trained staff end up acting less like analysts and more like digital archaeologists, digging through data artifacts hoping to uncover the one piece that unlocks payment.
But the real problem goes deeper than messy data.
RCM Isn’t Broken. It’s Just Buried Under Data Nobody Can Find
Much of the information needed to support a claim isn’t even visible to the RCM team.
Payers often have access to far broader datasets than providers do. Information that spans systems, health plans, and sometimes even state-level data networks. Test results, prior encounters, imaging reports, and authorization histories may exist in systems the hospital never sees. From the physician’s perspective, it’s like knowing you have the perfect tool somewhere… but you loaned it to your neighbor.
You remember exactly what it looks like. You know it would solve the problem instantly. But it’s not in your drawer anymore. You could knock on the neighbor’s door and ask for it but they may take forever to answer. And frankly, they’re not very interested in sharing because the tool has now become an essential part of their own toolkit. So instead, you grab something else.
A larger screwdriver. A knife. A coin.
It sort of works. But it’s inefficient, frustrating, and sometimes damages the screw in the process. That’s how healthcare RCM operates today. Providers work with incomplete information, fragmented systems, and slow communication loops with payers. Denials happen not because the care was inappropriate, but because the right information wasn’t accessible at the right time. Enter Agentic AI. Traditional automation and analytics tools can help organize the junk drawer. They add dashboards, reporting, and rule-based workflows that make it easier to find certain items.
Agentic AI goes much further.
Instead of waiting for humans to rummage through data, Agentic AI systems actively search across clinical records, billing platforms, payer rules, historical claims, contract data, and external data sources to locate the exact information needed to resolve a problem like payer contract rates.
Think of it less like a smarter drawer and more like a digital assistant that:
- Knows where everything is stored
- Understands what tool is needed for the job
- Retrieves it instantly
- And if necessary, it gets it from the neighbor automatically
In the context of revenue cycle management, that means AI agents capable of:
- Investigating denials and assembling supporting documentation
- Identifying missing clinical evidence before a claim is submitted
- Navigating EHRs and billing systems to extract the precise data needed
- Dynamically interpreting payer policies at lightspeed and matching them to clinical documentation
- Preparing peer-to-peer summaries automatically for physician advisors
The impact will be profound.
Denials Aren’t Always About Care, they’re About Who Has the Data. Instead of RCM teams spending hours hunting for data, AI agents could assemble complete, payer-ready claim packages in seconds. Denials could be predicted before submission. Missing documentation could be flagged in real time. Even more importantly, the balance of information asymmetry between payers and providers begins to shift.
For decades, payers have had the advantage of scale, data aggregation, and analytical infrastructure. Providers have been left trying to defend claims using fragmented systems and manual processes.
Agentic AI changes that equation.
It turns the provider’s messy junk drawer into an intelligent toolkit. The screwdriver isn’t lost anymore. The system knows exactly where it is even if the neighbor has it, and it goes and gets it. Healthcare doesn’t need more dashboards. It needs systems that can act. And in the complex, data-heavy world of revenue cycle management, Agentic AI may finally be the tool that stops us rummaging through the drawer.
If Your Revenue Cycle Depends on Post-It Notes, AI Is Coming for You
