The problem you can see may not be the problem you need to solve. Cameron Ferenchik's story offers a powerful lesson in looking beyond the obvious—from patient care to medical billing.
“Rational medicine can be defined as the practice of
medicine that maintains or improves the quality of healthcare whilst
controlling/driving down its cost.” — Dr.
Rahul Mukherjee,
What if your biggest billing problem isn't the denial?
Cameron Ferenchik was 24 years old and only two months into
her nursing career in Atlanta, Georgia, when she noticed swollen lymph nodes in
her neck.
She did what we tell patients to do.
She got checked.
The swollen lymph nodes were not the real problem.
An MRI revealed something nobody expected: a brain tumor
blocking the flow of spinal fluid.
Ferenchik soon found herself on the other side of
healthcare.
She wasn't the nurse.
She was the patient.
Her neurosurgeon, Dr. Jim Robinson, removed part of
the tumor.
The tumor wasn't cancerous.
But Dr. Robinson said it could have been fatal.
That is the part of the story that sticks with me.
Not simply the diagnosis.
The discovery.
Ferenchik went looking for one problem.
The medical team found another.
And that raises a provocative question for physicians and
clinic owners:
What if your biggest billing problem isn't the denial?
What if the denial is simply the thing you can see?
What if the real problem happened earlier?
At registration.
At eligibility verification.
During authorization.
In documentation.
During coding.
Inside a payer rule.
Or somewhere between your EHR and billing system.
We would never tell a physician:
“Don't worry about the abnormal finding. Just treat the
symptom.”
Yet that's essentially what many revenue-cycle workflows do.
Claim denied? Work the denial.
Another denial?
Work that one too.
Same denial next week?
Open another ticket.
Eventually, everyone becomes extremely good at fixing the
same problem over and over.
Congratulations.
You've built a very efficient treadmill.
You're still going nowhere.
Your denial team may be doing an excellent job hiding
your billing problem.
That sounds harsh.
But consider what happens when a practice becomes really
good at denial management.
Claims go out.
Claims come back.
Someone researches them.
Someone corrects them.
Someone appeals them.
Someone resubmits them.
Revenue eventually arrives.
The practice celebrates.
The workflow continues.
And nobody asks the dangerous question:
Why did this happen in the first place?
That's the question I want physicians and clinic owners to
start asking.
Because revenue-cycle management should not simply
recover lost money. It should learn how to lose less money.
Medicine has a concept billing needs more of: curiosity
Medicine is full of uncertainty.
A patient presents with chest pain.
The physician develops a differential diagnosis.
Something doesn't fit.
More information is gathered.
The diagnosis changes.
That's not failure.
That's medicine.
The dangerous physician is not the one who doesn't know the
answer.
It's the one who stops asking questions too early.
Billing has its own version of this problem.
A claim is denied for “medical necessity.”
Fine.
But what does that actually mean?
Was the documentation insufficient?
Was the diagnosis-code combination inappropriate?
Was an authorization missing?
Did the payer change its policy?
Was the claim submitted incorrectly?
Was the payer wrong?
Did something change upstream?
“Medical necessity” may be the label.
It isn't necessarily the root cause.
Labels are not explanations.
The MRI lesson for medical billing
Cameron Ferenchik's story is powerful because the first
clinical question wasn't the final answer.
That's exactly how sophisticated revenue-cycle management
should work.
The claim is the patient.
Not literally, of course.
But think about the claim as a case file.
It contains evidence.
Patient information.
Payer information.
Provider information.
Diagnosis.
Procedure.
Modifiers.
Documentation.
Authorization.
Place of service.
Historical outcomes.
Payer behavior.
Why would we throw all of that information into a black box,
wait for the payer to say “no,” and then begin investigating?
That's backward.
The better question is:
Can we identify the problem before the claim becomes a
problem?
That's where technology becomes interesting.
Not because it's AI.
Because it can potentially see patterns humans cannot
efficiently see across thousands of claims.
The billing industry's favorite four words
Here they are:
“That's just how billing works.”
You've heard them.
Maybe you've said them.
I have.
And sometimes they're true.
Payers have complicated rules.
Contracts differ.
Policies change.
Healthcare is messy.
But “that's just how billing works” can become a dangerous
organizational habit.
Because once a problem becomes familiar, people stop seeing
it as a problem.
The staff learns the workaround.
The biller knows the trick.
The office manager knows which phone number to call.
The physician knows which payer causes trouble.
Everyone survives.
Until the experienced employee leaves.
Then suddenly nobody knows why the mysterious spreadsheet
exists.
That's not a system.
That's institutional folklore.
The numbers are telling us something
The latest data should make physician owners uncomfortable.
The AMA reports that physicians complete an average of 40
prior-authorization requests each week. Nearly one-third report that
requests are often or always denied. 94% say prior authorization contributes
to burnout, while 74% report that prior-authorization denials have
increased over the past five years.
CMS estimates that prior authorization consumes roughly 13
hours per week and represents approximately $34,000 in annual
administrative cost per physician, using an estimated hourly cost of
$20–$50.
And MGMA's January 2026 revenue-cycle data found that 48%
of respondents identified denials and appeals as their largest source of
revenue-cycle leakage.
Those aren't just billing statistics.
They're capacity statistics.
Every unnecessary administrative hour is an hour that
doesn't go toward something else.
Patient care.
Staff retention.
Practice growth.
Clinical education.
Family.
Sleep.
Perhaps we should stop calling administrative burden
“overhead.”
Sometimes it's stolen capacity.
Here's where I disagree with conventional revenue-cycle
thinking
The industry often talks about:
clean claims.
denial rates.
days in A/R.
collections.
Important metrics.
But here's the problem:
A dashboard can tell you what happened without
telling you why it happened.
A denial rate of 8% doesn't tell me enough.
I want to know:
Which payer?
Which service?
Which provider?
Which location?
Which diagnosis?
Which modifier?
Which documentation issue?
Which workflow?
Which dollar amount?
Which claims were preventable?
And most importantly:
Which pattern is getting worse?
That's the difference between reporting and intelligence.
Three experts. Three lessons.
Dr. Willie Underwood III: healthcare should work for
people
Dr. Willie Underwood III became the 181st president of
the American Medical Association in June 2026. His leadership message
emphasizes physician leadership, patient care, access and confronting
persistent problems in healthcare.
The operational lesson for practice owners is simple:
A system should serve the clinician.
Not the reverse.
If your physician spends Friday afternoon fighting a payer
portal, your workflow has failed.
If your nurse spends hours chasing authorization paperwork,
your workflow has failed.
If your biller spends half the day correcting the same
preventable error, your workflow has failed.
Don't blame the person operating the system before examining
the system itself.
Dr. Mehmet Oz: administrative friction is becoming an
infrastructure problem
CMS Administrator Dr. Mehmet Oz has made electronic
prior authorization and interoperability important components of CMS's current
administrative modernization efforts.
CMS is moving toward more standardized electronic processes
that can help providers determine whether authorization is required, identify
documentation requirements and receive authorization decisions electronically.
That matters beyond prior authorization.
It points toward a larger change:
Healthcare administration is becoming increasingly
data-driven.
The question for independent practices is whether their
systems can keep up.
MGMA: stop treating leakage as one giant bucket
MGMA's recent revenue-cycle work emphasizes identifying
leaks across the entire revenue cycle rather than treating denials as one
homogeneous problem.
That's important.
Because “denial” is not a diagnosis.
It's a symptom category.
The useful question is:
What caused this particular dollar to become difficult to
collect?
Once you ask that question consistently, the revenue cycle
becomes much more interesting.
And much more manageable.
My favorite billing question
Here's one I would put on every practice manager's desk:
“Could we have known this before the claim was
submitted?”
If the answer is yes, you have an opportunity.
If the answer is no, investigate why.
If the same answer keeps appearing, automate the detection.
That's the loop.
Detect.
Understand.
Prevent.
Measure.
Learn.
Repeat.
Stop hiring people to compensate for broken workflows
This is another uncomfortable opinion.
Sometimes the answer to a billing problem is another
employee.
Sometimes it isn't.
If five people are manually checking the same thing because
the system cannot reliably identify it, hiring a sixth person may increase
capacity.
It does not necessarily solve the problem.
It's like adding another nurse to repeatedly rewrite the
same form.
At some point, somebody should ask:
Why does the form need to be rewritten?
This is where process improvement matters.
The biller is often not the problem
Physicians sometimes tell me:
“Our billing company isn't doing a good job.”
Maybe.
But before replacing the billing company, ask:
Does the billing team have the information it needs?
Can it see the clinical documentation?
Can it identify authorization requirements?
Can it see payer-specific patterns?
Does it receive timely eligibility information?
Can it connect denials back to their root causes?
Can it see what happened upstream?
If not, you may be asking the biller to perform detective
work with half the evidence missing.
That's not a people problem.
That's a data architecture problem.
The OnnX thesis
This is why I founded OnnX.
I don't believe small and medium-sized physician practices
need another complicated billing system.
They need better visibility into what is happening
before, during and after the claim.
The central idea is straightforward:
Healthcare billing is often a data-quality problem
disguised as a billing problem.
If the information is incomplete, disconnected or
inconsistent, the claim inherits that weakness.
No software can magically turn bad input into perfect
output.
AI cannot change that.
Automation cannot change that.
A bigger billing department cannot permanently change that.
The solution starts earlier.
At the point where information enters the revenue cycle.
What should OnnX—or any intelligent billing
platform—actually do?
Forget the phrase “AI-powered” for a minute.
Ask what the software actually accomplishes.
It should help identify:
Potential eligibility problems.
Authorization risks.
Documentation gaps.
Coding inconsistencies.
Modifier issues.
Payer-specific patterns.
Recurring denial causes.
Potential underpayments.
A/R risks.
But there is an even more important requirement:
Explain the warning.
If software tells a biller:
“High-risk claim.”
That's not particularly useful.
Tell them:
“High risk because this payer has repeatedly denied this
service combination when authorization documentation is absent.”
Now the person has something to investigate.
Intelligence without explanation creates another black
box.
Healthcare already has enough of those.
AI should be the smoke detector, not the firefighter
Here's another analogy I like.
A firefighter is called after the fire.
A smoke detector is valuable because it warns you before
the house is fully involved.
AI in revenue-cycle management should increasingly behave
like a smoke detector.
Not:
“The claim was denied. Here's a summary.”
But:
“This claim has characteristics associated with previous
denials. Review it before submission.”
That is a much more interesting use of AI.
And much more useful.
But let's not worship AI
This is where I want to push back against my own industry.
Healthcare technology companies love AI.
Obviously.
I'm a healthcare technology founder.
But AI is not magic.
Sometimes the solution is:
better training.
Sometimes:
better documentation.
Sometimes:
a cleaner workflow.
Sometimes:
a payer policy update.
Sometimes:
structured data.
Sometimes:
a human conversation.
And sometimes:
AI.
The best healthcare technology isn't the technology with the
most impressive vocabulary.
It's the one that removes a real problem without creating
three new ones.
Practical playbook for physician owners
If I were sitting across from a physician-owner today, I'd
suggest starting here.
1. Pull 90 days of denials
Don't guess.
Get the data.
2. Rank by dollars
Not just frequency.
A hundred $25 denials may matter less than ten $5,000
denials.
3. Rank by repeatability
Ask:
Does this keep happening?
Recurring problems are where prevention has leverage.
4. Identify the upstream event
Ask:
Where did the problem begin?
Registration?
Eligibility?
Authorization?
Documentation?
Coding?
Submission?
Payer processing?
5. Create one prevention rule
Don't redesign the entire revenue cycle.
Fix one recurring problem.
6. Measure the result
Track:
denial rate
denial dollars
staff hours
days to resolution
appeal recovery
Then compare before and after.
7. Automate only after you understand it
This is important.
Do not automate confusion.
First understand the workflow.
Then automate the predictable parts.
Five billing metrics I would watch every month
1. Preventable denial rate
Not all denials are preventable.
Separate them.
2. Denial dollars
Revenue exposure matters.
3. First-pass payment rate
How often does the claim move through cleanly?
4. A/R over 90 days
Old money is expensive money.
5. Staff hours spent on rework
This one is frequently underestimated.
You aren't only losing revenue.
You're consuming labor.
The hidden cost nobody puts on the dashboard
Let's say a claim is denied.
The practice eventually gets paid.
Everyone celebrates.
But how many people touched it?
A biller.
A coder.
A physician.
A nurse.
A front-office employee.
Perhaps someone called the payer.
Perhaps someone appealed it.
Perhaps someone checked documentation.
Perhaps someone resubmitted it.
The practice recovered the money.
But it didn't recover the time.
That's an important distinction.
Revenue recovered is not the same thing as efficiency
achieved.
Legal and compliance considerations
This conversation also has a serious side.
Automated billing systems must operate within applicable
coding, reimbursement, privacy and compliance requirements.
A few principles are non-negotiable.
Never let technology justify unsupported coding.
Never alter documentation simply to obtain reimbursement.
Protect patient information.
Maintain appropriate human oversight.
Keep audit trails where appropriate.
Understand why an automated recommendation was made.
And remember:
A software recommendation does not transfer accountability
away from the healthcare organization.
Technology can assist.
Organizations remain responsible for their processes and
decisions.
For specific legal or compliance questions, practices should
obtain advice from qualified counsel and compliance professionals.
Ethical considerations
Here's the ethical question:
Are we using technology to help patients—or simply to
collect faster?
Those goals can overlap.
They should not be confused.
A financially healthy practice is important.
But aggressive billing without appropriate clinical and
compliance safeguards is not innovation.
The goal should be:
accurate claims
appropriate reimbursement
fewer preventable errors
less administrative waste
better patient access
sustainable practices
That is a much healthier definition of revenue-cycle
innovation.
The five biggest mistakes I see
Mistake 1: Looking only at denials
The denial is downstream.
Look upstream.
Mistake 2: Measuring percentages without dollars
A percentage can look impressive while thousands of dollars
disappear.
Mistake 3: Treating every payer the same
They aren't.
Mistake 4: Blaming staff before studying workflow
People often create workarounds because the system requires
them.
Mistake 5: Buying AI before fixing data
AI cannot compensate indefinitely for poor data quality.
Myth busters
Myth: “More billing staff means fewer problems.”
Sometimes.
But if the root problem is workflow or data quality, you may
simply be adding people to the leak.
Myth: “Zero denials is the goal.”
No.
Zero preventable denials is a much more sensible
aspiration.
Myth: “AI will replace billers.”
Not necessarily.
The more useful future is likely to be AI handling
repetitive detection while experienced professionals handle exceptions and
judgment.
Myth: “Outsourcing eliminates responsibility.”
It doesn't.
The practice still needs visibility, oversight and
accountability.
Myth: “The payer is always the problem.”
Sometimes the payer is.
Sometimes the provider is.
Sometimes both sides are operating from different
information.
The useful question is:
What does the evidence show?
A 30-day challenge for your practice
Try this.
For the next 30 days, don't ask:
“How many denials did we work?”
Ask:
“How many denials did we prevent?”
Then pick one category.
Maybe authorization.
Maybe eligibility.
Maybe coding.
Maybe documentation.
Measure it.
Fix it.
Measure again.
If the number improves, repeat the process.
You don't need a billion-dollar transformation.
You need a feedback loop.
A little humor from the revenue-cycle trenches
Healthcare has an unusual talent.
We can build a six-figure clinical system and then send
someone a fax.
We can perform extraordinarily complex procedures and then
spend 45 minutes trying to determine which payer portal password still works.
We can produce terabytes of healthcare data and then email a
spreadsheet called:
FINAL_FINAL_v7_REAL_FINAL.xlsx
And somehow everyone accepts this as normal.
Maybe it's time to stop.
The future doesn't have to be more complicated.
It can actually be simpler.
The real opportunity for healthcare founders
Healthcare founders should pay attention to a larger shift.
The next generation of healthcare infrastructure won't
simply move information around.
It will increasingly interpret information and identify
what deserves attention.
That's where the opportunity gets interesting.
Imagine a revenue-cycle system that doesn't merely store
historical denials.
It learns from them.
Imagine it doesn't merely tell a practice:
“You lost $18,000.”
It says:
“Here are the three recurring patterns responsible for
most of that exposure.”
Then:
“Here are the claims currently showing those
characteristics.”
Then:
“Here is what changed.”
That is not just billing software.
That's operational intelligence.
The future: from reactive to predictive
The revenue cycle has traditionally been reactive.
Claim submitted.
Claim denied.
Problem investigated.
The next phase is more predictive.
Claim prepared.
Risk identified.
Human reviews.
Claim corrected.
Claim submitted.
Then the next level:
Outcome recorded.
Pattern learned.
Workflow improved.
That's a continuous learning system.
Not perfect.
Not autonomous.
But increasingly intelligent.
And this brings us back to Cameron Ferenchik
A young nurse in Atlanta noticed something unusual.
She sought care.
The obvious explanation wasn't the important one.
The MRI revealed something else.
Dr. Jim Robinson acted.
And a potentially dangerous problem was found before it
became something worse.
There is a lesson here that extends beyond medicine.
Don't confuse the first visible signal with the
underlying problem.
In medicine, that can matter enormously.
In healthcare operations, it matters too.
Your denial report may be telling you something.
Your A/R may be telling you something.
Your payer mix may be telling you something.
Your staff turnover may be telling you something.
Your authorization backlog may be telling you something.
Your claim data may be telling you something.
The question is:
Are you listening?
The biggest billing question of all
Maybe we have been asking the wrong question.
Instead of:
“How do we collect more?”
Ask:
“Why didn't we collect correctly the first time?”
Instead of:
“How do we work more denials?”
Ask:
“Why did these claims become denials?”
Instead of:
“How can we hire more people?”
Ask:
“Why does this workflow require so much human rework?”
Instead of:
“Where can we add AI?”
Ask:
“Where would better intelligence prevent avoidable work?”
Those questions lead to different businesses.
Different workflows.
Different technology.
And potentially different outcomes for physicians.
Final Thoughts: Look for the problem behind the problem
Cameron Ferenchik went looking for an explanation.
She found something she wasn't expecting.
That's what good healthcare does.
It stays curious.
It investigates.
It doesn't stop at the first convenient answer.
Physician practices deserve the same discipline in their
financial operations.
A denial is information.
A billing error is information.
An unusual A/R pattern is information.
A recurring payer problem is information.
The smartest practices won't necessarily be the ones with
the biggest billing departments.
They may be the ones that learn fastest from the information
already sitting inside their systems.
That is the opportunity I see for OnnX.
Not another system that simply helps you work harder.
A smarter approach to identifying problems before they
become expensive.
Because the best claim is not the one you successfully
appeal.
It's the one that never needed the appeal.
The best billing workflow isn't the one that handles a
mountain of denials efficiently.
It's the one that makes the mountain smaller.
And the best technology isn't the technology that says it
uses AI.
It's the technology that gives a busy physician one less
problem to worry about.
Get Involved: What Do You Think?
Here's my question for physicians, practice owners and
revenue-cycle leaders:
If your billing data could warn you about one problem
before a claim was submitted, what would you want it to catch?
Tell me in the comments.
What is the most frustrating recurring billing problem in
your practice—and what have you tried to fix it?
And if this perspective made you rethink the way you look at
denials, repost this article and start the conversation with another
physician or clinic owner.
Don't wait for the denial to tell you something went
wrong.
Find the signal earlier.
Let's build revenue cycles that learn instead of repeat.
About the Author
Dr. Daniel Cham is a physician, medical consultant
and healthcare entrepreneur working at the intersection of medical
technology, healthcare management and medical billing.
As founder of OnnX, he focuses on practical
approaches to helping small and medium-sized physician practices improve
visibility, reduce preventable billing problems and build more intelligent
revenue-cycle workflows.
His perspective comes from looking at healthcare not only as
a clinical environment, but also as an operational system where data,
people, technology and financial sustainability have to work together.
Connect with Dr. Cham on LinkedIn:
Dr.
Daniel Cham on LinkedIn
Continue the Conversation
The healthcare conversation doesn't end with one article.
Explore additional perspectives on healthcare operations,
medical billing, technology, entrepreneurship and innovation, including
practical lessons that can be applied inside real-world medical practices.
Listen to the
podcast on Spotify
Knowledge only creates value when it changes what we do.
Find one problem. Follow the signal. Take one practical
step.
Then share what you learned so someone else doesn't have
to learn it the hard way.
Free Resource
Looking for a practical starting point?
Visit the Featured section of my LinkedIn profile to find
the free resource. No signup is required.
Start there.
Use what is useful.
Test it in your practice.
And keep the conversation going.
If this article resonates with you, consider reposting it
so another physician or clinic owner can see the problem differently.
References
CBS News Atlanta — Cameron Ferenchik's unexpected
brain-tumor diagnosis. The human-interest story that inspired this article
follows a 24-year-old Atlanta nurse whose evaluation for swollen lymph nodes
unexpectedly revealed a potentially life-threatening brain tumor.
Read the CBS News Atlanta report
American Medical Association — 2026 prior-authorization
survey. The AMA's latest physician data highlights the continuing
administrative burden associated with prior authorization, including the
reported volume of requests, time burden, denials and burnout.
Read the AMA report
CMS — Electronic prior authorization. CMS outlines
current federal efforts to make prior authorization more electronic,
standardized and transparent, including requirements and implementation
timelines affecting certain payers.
Read the CMS guidance
Disclaimer / Note
This article is intended solely for general
educational and informational purposes. It does not provide medical, legal,
coding, reimbursement, compliance or financial advice. Specific circumstances
can vary considerably. Healthcare professionals and organizations should
consult appropriately qualified medical, legal, compliance and financial
professionals before making decisions based on the information discussed here.
#MedicalBilling #RevenueCycleManagement #Healthcare
#Physicians #MedicalPractice #PhysicianPractice #HealthcareInnovation
#HealthcareTechnology #AIinHealthcare #MedicalBillingSoftware #DenialPrevention
#DenialManagement #PriorAuthorization #HealthcareAI #PracticeManagement
#PhysicianEntrepreneur #HealthTech #RevenueCycle #PatientCare
#HealthcareLeadership
