Why What We Learn About Claims andPayers in RCM Gets Forgotten
Every revenue cycle leader has had the samequiet realization.
A denial comes back that looks familiar.Someone remembers dealing with this before. There was a workaround. A nuance inhow the payer interpreted a modifier. A small adjustment that made the claim gothrough the second time.
But no one can quite recall what it was.
So the team works it again. Calls the payer,tries a variation, waits, and learns, once more, what they already knew.
And then, somewhere down the line, forgetsit again.
This is the rhythm of much of revenue cyclemanagement. Not steady improvement, but repetition. Not compounding knowledge,but episodic rediscovery.
It feels, at times, like the movieGroundhog Day.
Where the Memory Breaks
Inside provider organizations, knowledge isconstantly leaking out of the system.
Turnover ispart of it. Experienced billers leave, taking years of accumulated judgmentwith them. New staff step in, often capable but undertrained in edge cases,unwritten rules or payer-specific quirks that never made it into a manual.
Even when people stay, knowledge tends toconcentrate. A handful of “go-to” individuals become the living memory of theoperation. When volume spikes or those individuals are unavailable, theopportunity to apply that expertise is often missed.
There is rarely time to formalize what theyknow. Documentation is partial, outdated, or too generic to be useful. Trainingfocuses on process, not on pattern recognition.
The result is a kind of institutionalamnesia. The organization works hard and resolves issues, but it does notreliably retain what it learns.
The Same Problem, Outsourced
Vendors are often assumed to solve this. Inpractice, they mirror it.
Offshore teams, in particular, can facehigh turnover and constant onboarding cycles. Knowledge is transferred throughshadowing and repetition, not through durable systems. Playbooks exist, butthey are rarely living documents and it’s easy for them to lag behind the paceof payer change.
Most importantly, there is littlesystematic capture of what actually works.
A team may learn that a specific payerresponds better to a certain phrasing in an appeal, or that a claim with aparticular combination of codes needs to be sequenced differently. But thatknowledge stays local; it is not aggregated, tested or propagated across thebroader system.
So your vendor does more work over time,but does not necessarily get better at it.
A Moving Target
Part of the challenge is structural. Therules themselves do not stand still. Payers update policies. Plans changereimbursement logic. Edits appear and disappear. What worked last quarter maynot work today. {In fact, in some cases the target isn’t just ‘moving’, it maybe deliberately hiding, as payers can be gaming the system.)
This creates a constant sense of urgency.Teams are always catching up. There is little space to step back, synthesize,and improve.
But the deeper issue is not just change. Itis context. The difference between a paid claim and a denied one often hingeson small, situational factors such as the combination of codes, the patient’splan variant, the ordering of actions, the timing of submission, or the historyof prior interactions with that payer.
These are not easily captured in staticrules: they require a system that understands probability, not just policy. Asystem that can say not only what should work, but what is most likely to workhere, now, given everything that has come before.
Why Experience Doesn’t Compound
In most industries, repetition leads tolearning. Over time, systems improve. In RCM, however, repetition instead oftenleads to fatigue.
There are several reasons:
- Feedback loops are weak. It can take weeks to learn whether a change worked. By then, the context has shifted
- Outcomes are not consistently linked back to actions. It is hard to know which specific intervention made the difference.
- Data is fragmented. Information sits across PMS, clearinghouses, payer portals, and spreadsheets, rarely unified in a way that supports learning.
- Success is not standardized. One team’s workaround is another team’s exception.
- Without tight feedback and sharedvisibility, learning remains local and temporary.
What an Intelligence Engine Would Require
If the problem is memory, the solution isnot more documentation. It is a different kind of system.
An effective intelligence engine for RCMwould need to do several things at once.
- Capture not just the basics ofbilling rules, but the full context of each interaction. What was submitted,how it was submitted, what happened next, and what ultimately led to payment.
- Connect actions to outcomeswith precision. Not in aggregate, but at the level of individual claims anddecisions.
- Learn continuously. As payerbehavior shifts, the system would need to update its understanding in near realtime, not months later.
- Express its knowledge in termsof likelihoods. Given this scenario, what is the probability of success witheach available action? And which path optimizes both speed and yield?
- Operationalize those insights.Recommendations are not enough. The system would have to influence or executethe next step in the workflow, ensuring that what has been learned is actuallyapplied.
And it would have to do all of this acrossa hybrid environment that includes automation tools and human billers.
From Repetition to Compounding
The promise of such a system is not justefficiency. It is compounding.
Each claim processed would make the nextone slightly smarter. Each denial resolved would reduce the likelihood of thenext. Over time, the organization would not just do more work. It would dobetter work.
The Groundhog Day effect would begin tofade.
Not because the system has eliminatedcomplexity, but because it has learned to remember, and to act on what itknows.
And in revenue cycle management, that maybe the difference between running in place and finally moving forward.
