Tuesday, September 15, 2026

Sophia Needed Someone to Connect the Dots. Your Revenue Cycle Does Too.

A five-year-old’s rare-disease journey exposes an uncomfortable truth about healthcare: the problem we see downstream often began much earlier.



“When practices cannot sustain themselves, patients have a harder time finding and keeping the physicians they need.” — Willie Underwood III, MD, MSc, MPH, AMA President, September 14, 2026

 


The Five-Year-Old Who Exposed a Revenue-Cycle Problem

Sophia was five years old.

She loved stickers.

Painting.

Bluey.

SpongeBob.

And, apparently, decorating anything within reach with enough stickers to make an adult wonder whether adhesive technology had finally gone too far.

But for almost half her life, Sophia had also been dealing with something much more serious.

Three years ago, she developed sudden, rapid weight gain.

Her parents, Kelly and Joseph, initially thought it might be related to the excitement and stress of becoming big siblings to twins.

They took her to her pediatrician in Western New York.

She was referred to an endocrinologist and a nutritionist.

At first, they were told everything seemed fine.

Then routine bloodwork showed elevated sodium levels.

Then came a seizure.

A week-long hospitalization followed.

Further testing showed that Sophia's oxygen levels were dropping.

Doctors recommended a sleep study.

Her parents began researching.

And eventually they recognized a disturbing pattern.

Sophia's symptoms matched ROHHAD syndrome, an extremely rare and life-threatening condition.

Her parents researched where to go.

They found Dr. Leslie Benson, co-director of Boston Children's Neuroimmunology Center and a leading expert in ROHHAD.

They traveled from Western New York to Boston.

And there, something changed.

Not because healthcare suddenly became simple.

It didn't.

Sophia still needed extensive testing, including a full-body MRI, more extensive sleep studies and a lumbar puncture. She required ongoing treatment and continued specialist care.

But her father, Joseph, described the experience this way:

“It was such a relief to arrive in Boston and be told that not only had they treated kids with ROHHAD before but that they also had a plan.”

That sentence stopped me.

They had a plan.

Not:

“They had another portal.”

Not:

“They had another dashboard.”

Not:

“They had another spreadsheet.”

A plan.

Because someone finally connected the pieces.

And that is where Sophia's story becomes surprisingly relevant to medical billing.


Your Denial May Not Be the Problem

Here is the contrarian idea:

The claim is often where the problem becomes visible—not where the problem began.

Yet much of revenue-cycle management is organized around what happens after the problem appears.

The claim is denied.

Someone investigates.

Someone calls.

Someone checks the payer portal.

Someone searches the chart.

Someone sends an email.

Someone corrects something.

Someone resubmits.

Someone waits.

Someone follows up.

Someone appeals.

Eventually, money arrives.

Everyone breathes.

Then three weeks later:

Congratulations. You're doing it again.

This is one of healthcare's strangest operational habits.

We become extraordinarily good at repairing problems that we never bothered to prevent.


The Billing Team Might Be the Victim

We often talk about revenue-cycle problems as if the billing department owns them.

That's convenient.

It's also frequently incomplete.

Imagine this:

A claim is denied because authorization was missing.

The natural reaction is:

“Billing needs to fix it.”

But let's walk backward.

Who scheduled the patient?

Who verified eligibility?

Who identified the authorization requirement?

Who requested authorization?

Who documented the approval?

Who knew the service had changed?

Who communicated the change?

Who captured the clinical information?

Who confirmed that the information required for the claim actually existed?

Suddenly, “the billing problem” has become a workflow problem.

And workflow problems have a nasty habit of crossing departmental boundaries.


Healthcare Has a Data Problem Disguised as a Billing Problem

Healthcare has no shortage of information.

We have EHRs.

Practice-management systems.

Clearinghouses.

Payer portals.

Authorization platforms.

Patient portals.

Analytics.

Automation.

AI.

And enough logins to qualify some employees for a second career in password management.

Yet information can still fail to reach the person who needs it.

That's the paradox.

Digital does not mean connected.

A fax converted into a PDF is still a fax.

A spreadsheet emailed between departments is still a handoff.

A portal that requires someone to remember to check it is still a manual workflow.

And AI sitting on top of fragmented information may simply become a very sophisticated way to misunderstand the same problem faster.


The Revenue Cycle Doesn't Start With the Claim

This is where I think the traditional mental model needs to change.

Revenue cycle is often perceived as:

Claim → Denial → Appeal → Payment

But the actual chain begins much earlier:

Patient registration

Scheduling

Eligibility

Authorization

Encounter

Documentation

Coding

Charge capture

Claim submission

Payer processing

Payment

Reconciliation

The claim is not the beginning.

It is a downstream event.

By the time a claim is denied, several opportunities to prevent that denial may already have passed.


The Denial Is a Lagging Indicator

This distinction matters.

A denial tells you something happened.

It does not necessarily tell you where the problem originated.

Think of a dashboard light in your car.

When it comes on, you don't congratulate yourself for having successfully identified a light.

You ask:

What caused it?

Healthcare should approach denials the same way.

Don't just ask:

“Why was this claim denied?”

Ask:

“What allowed this problem to survive every checkpoint before the claim?”

That is a much more interesting question.

And potentially a much more valuable one.


Here's the Really Uncomfortable Part

Your organization may be measuring the wrong success.

Suppose your billing team catches 500 errors before submission.

Great.

But what if those errors could have been prevented upstream?

The claim may still go out clean.

The denial rate may look fantastic.

The A/R may look healthy.

But your employees are spending hundreds of hours quietly cleaning up problems before they become visible.

You haven't eliminated the friction.

You've hidden it.

This is why clean claims alone do not tell the entire story.

A clean claim can be produced by a clean workflow.

Or by exhausted employees who manually scrub every problem before submission.

Those are not the same thing.


HFMA Has a Warning Hidden in Plain Sight

A 2026 HFMA Clean Claims study found that only 28% of survey respondents had fully automated claims submission, while 58% relied on a hybrid model of automation and manual intervention.

The study also identified front-end claim scrubbers as highly effective for improving claim accuracy and accelerating payment, while noting that changing payer requirements can undermine the effectiveness of static edits.

That tells us something important.

Automation exists.

But humans remain deeply involved.

The question is not whether humans should disappear.

They shouldn't.

The better question is:

What are humans spending their time doing?

Judgment?

Exception handling?

Patient communication?

Clinical reasoning?

Or repeatedly correcting predictable information problems?


The Patient Should Not Become the Middleware

This may be the most ridiculous part of fragmented healthcare.

Two organizations have information.

Their systems don't communicate properly.

So the patient gets asked to provide the information again.

Then again.

And sometimes again.

The patient becomes the integration layer.

Think about that.

The healthcare system has invested billions in technology, yet the human being receiving care sometimes becomes the API.

That is not sophisticated healthcare infrastructure.

That's a workaround with a pulse.


And Physicians Become the Backup System

The same thing happens internally.

The scheduler can't find something.

The biller needs clarification.

The authorization team needs documentation.

The payer needs information.

The physician gets a message.

Another message.

Another “quick question.”

Another inbox notification.

Another task.

Eventually, the physician becomes the human middleware connecting systems that were supposed to connect themselves.

And then we wonder why physicians feel buried in administrative work.


AMA's Warning Is Bigger Than Payment

That is why the AMA's latest warning about physician payment caught my attention.

On September 14, 2026, AMA President Willie Underwood III, MD, MSc, MPH, said that what began as a physician payment challenge had become a patient-access problem. The AMA argued that when practices cannot sustain themselves, patients have greater difficulty finding and keeping physicians.

That statement is important even if you set aside the broader policy debate.

Because it highlights a basic operational truth:

Financial friction eventually becomes healthcare friction.

When revenue is delayed, practices compensate.

When administrative work expands, practices compensate.

When staff spend hours on rework, practices compensate.

When physicians are pulled into billing problems, practices compensate.

Somebody always pays for broken workflow.

Sometimes it's the practice.

Sometimes it's the employee.

Sometimes it's the physician.

Sometimes it's the patient.

Usually, it's some combination.


Prior Authorization Is a Perfect Example

Prior authorization is one of the clearest demonstrations of what happens when information and workflow fail to connect.

CMS estimates that requesting prior authorization costs providers $20–$50 per hour and consumes an average of 13 hours per week per provider, or roughly 700 hours annually.

CMS is now pushing toward electronic prior authorization, with certain affected health plans required to implement and maintain APIs beginning January 1, 2027.

That is a major shift.

But here's the part worth watching:

Electronic does not automatically mean intelligent.

If you digitize a fragmented process without redesigning the process, you may simply replace:

Fax → Portal

with:

API → Different Portal

Congratulations.

The fax machine got promoted to software.


The Real Opportunity Isn't More Technology

This is where I differ from some of the healthcare technology conversation.

I don't think healthcare necessarily needs another shiny system.

It needs better system design.

Before buying another tool, ask:

Where does the problem begin?

Not:

Where does the problem become visible?

Those are different questions.

A denial may appear in billing.

But its root cause could be:

  • registration
  • eligibility
  • authorization
  • documentation
  • coding
  • scheduling
  • payer-rule changes
  • communication
  • system integration
  • unclear ownership

If you build your solution around the location where the problem appears, you may miss where it began.


Stop Celebrating Firefighters

Healthcare loves heroes.

The employee who stays late.

The biller who rescues a complicated claim.

The practice manager who calls the payer six times.

The physician who personally fixes the authorization.

The employee who remembers the weird payer rule nobody documented.

We celebrate these people.

And we should appreciate them.

But here's the uncomfortable question:

Why does the organization need so many heroes?

A healthy system should not depend on extraordinary effort to survive ordinary workflows.

If your best employee has become indispensable because they know all the workarounds, you may not have built a great system.

You may have built a system that requires one person to remember how broken it is.


The Most Expensive Employee May Be the One Doing Rework

This is not an argument against employees.

It's an argument for respecting their time.

Suppose an employee spends hours every week:

  • looking for missing information
  • correcting demographic data
  • checking authorization status
  • calling payers
  • reconciling mismatched information
  • searching through portals
  • correcting predictable claim errors

That labor is real.

But it may not create new value.

It is recovery work.

The strategic question becomes:

How much recovery work can we eliminate?

That is where technology becomes interesting.

Not because AI is fashionable.

Because unnecessary work is expensive.


AI Should Not Be the Hero of the Story

Here's another contrarian position:

“AI-powered” is not a strategy.

It is a description.

Maybe.

Sometimes it is marketing.

The question should be:

What does the AI actually prevent?

Does it identify missing information?

Does it detect inconsistencies?

Does it recognize patterns in recurring denials?

Does it surface authorization problems before service?

Does it route exceptions?

Does it reduce repetitive staff intervention?

Does it help the right person act at the right time?

If not, congratulations.

You may have purchased an AI-powered dashboard.

The dashboard will look fantastic.

Your staff will still be busy.


Machines Should Handle Patterns. Humans Should Handle Exceptions.

That is the model I find more compelling.

If something happens thousands of times and follows predictable rules, technology should help.

If something is unusual, ambiguous or clinically nuanced, humans should be involved.

The goal is not:

Human vs. AI.

The goal is:

Human attention where human attention matters.

Imagine a system that quietly handles predictable validation and routing while escalating the genuinely unusual cases.

The physician sees fewer interruptions.

The biller sees fewer repetitive tasks.

The practice manager sees fewer fires.

The patient experiences fewer administrative surprises.

That's a much better definition of automation.


What OnnX Is Trying to Rethink

This is the problem I am working on with OnnX.

The premise is simple:

Revenue problems often begin before the revenue cycle looks like a revenue-cycle problem.

OnnX is being built around an upstream approach to healthcare revenue operations.

That means paying attention to the information and workflow leading into the claim—not simply what happens after a payer rejects it.

The focus includes areas such as:

Eligibility

Authorization

Clinical context

Documentation

Charge capture

Information consistency

Workflow handoffs

Exception management

The goal isn't to promise that every denial disappears.

That would be unrealistic.

Healthcare is too complicated for that.

The goal is more practical:

Find preventable friction earlier.


The Sophia Test

Here's a test I would apply to healthcare technology.

Imagine Sophia's care journey.

Her parents had information.

Different physicians had information.

Specialists had information.

Eventually, the right team connected those pieces.

The result wasn't that the case became simple.

The result was that the family finally had a coherent plan.

Now apply the same test to your revenue cycle.

Ask:

Does this technology connect the information needed to make the next decision?

Or does it simply create another place to look?

That distinction is enormous.


The Five Questions Every Practice Should Ask

Before blaming billing, ask these five questions.

1. Where did the problem actually begin?

Not where it was discovered.

Where did it originate?

2. Who had the information first?

Was it registration?

Scheduling?

The clinical team?

Authorization?

The payer?

3. Why didn't the next person receive it?

Was the problem technical?

Procedural?

Human?

Organizational?

4. How many times have we fixed the same thing?

If the answer is “all the time,” stop calling it an isolated mistake.

It's a process.

5. Can we detect it earlier?

That's the question that moves the conversation from recovery to prevention.


A 30-Day Upstream Experiment

You don't need a multimillion-dollar transformation program.

Pick one service line.

One recurring problem.

One month.

Week 1: Follow the patient

Track 25 patient journeys from scheduling through payment.

Don't follow the organizational chart.

Follow the information.

Week 2: Find the friction

Look for:

Missing information

Incorrect information

Delayed information

Duplicate entry

Manual handoffs

Unclear ownership

Payer-rule changes

Documentation gaps

Week 3: Fix one upstream failure

Choose one.

Define:

Trigger → Owner → Action → Escalation → Resolution

Week 4: Measure recurrence

Track:

Denial frequency

Repeat denials

Staff touches

Authorization completion

Charge lag

Rework

Physician interruptions

The goal isn't to produce a beautiful report.

The goal is to discover whether one small upstream change reduces downstream chaos.


Measure What Happens Before the Denial

This is where I would change the dashboard.

Don't only show:

Denial rate

Also show:

Eligibility accuracy

Authorization completion before service

Registration error rate

Documentation completeness

Charge lag

Manual intervention rate

Repeat-error rate

Time to identify an upstream issue

Those metrics tell you something different.

They tell you whether the system is becoming more preventive.


A Warning About “Efficiency”

Efficiency can be deceptive.

Suppose your denial rate falls.

Great.

But staff hours increase.

Not so great.

Suppose A/R improves.

Great.

But physicians are spending more time answering billing questions.

Not so great.

Suppose claims go out clean.

Great.

But employees are manually fixing thousands of errors before submission.

Again, not exactly a victory parade.

Efficiency is not simply getting the right outcome.

It's getting the right outcome without unnecessary effort.


The Ethical Question

There is a human consequence to all of this.

Administrative friction doesn't remain administrative forever.

It leaks into the patient experience.

It leaks into physician attention.

It leaks into employee burnout.

It leaks into delayed care.

It leaks into practice economics.

And eventually it can influence whether a practice can continue offering services.

That is why revenue-cycle design isn't merely a finance issue.

It is part of healthcare infrastructure.


The Future Will Not Be “More Billing”

It will be less visible billing.

The best systems should increasingly recognize problems before humans have to chase them.

They should surface exceptions.

They should connect information.

They should make ownership obvious.

They should learn from recurring patterns.

They should reduce unnecessary handoffs.

They should allow humans to spend their time on the cases that actually require judgment.

The ideal outcome?

Nobody celebrates the technology.

Nobody talks about the automation.

Nobody says:

“Look how sophisticated our RCM platform is.”

They simply notice:

“Why are we getting fewer annoying problems?”

That's the point.


What I Think We Have Been Getting Wrong

We have spent years asking:

How do we collect more revenue?

I think the better question is:

How do we create fewer reasons for revenue to get stuck?

Those sound similar.

They aren't.

The first question is downstream.

The second is upstream.

The first asks how to recover.

The second asks how to prevent.

The first creates more work.

The second tries to remove work.

And that's the shift I believe healthcare needs.


Sophia Had a Plan. Your Revenue Cycle Should Too.

Sophia's story is not a metaphor for billing.

It is a reminder about something more fundamental:

Disconnected information creates uncertainty. Connected information creates the possibility of a plan.

Sophia's parents had been through uncertainty.

They had searched.

They had asked questions.

They had traveled.

They had pieced together clues.

When they reached a team experienced with ROHHAD, the pieces began to make sense.

They had a plan.

Healthcare revenue cycles deserve the same discipline.

Not because billing is as important as a child's health.

It isn't.

But because healthcare cannot separate clinical care from the systems that make care possible.

A physician cannot treat patients indefinitely in a practice that cannot sustain itself.

A staff cannot spend every day repairing avoidable workflow failures.

A patient should not have to become the messenger between disconnected systems.

And a billing team should not have to be the archaeological department of the entire practice.

The claim is where the problem becomes visible.

The workflow is where the problem begins.

That's where we should look.


The Question for Physicians and Clinic Owners

Here's the question I want to leave you with:

What problem does your billing team keep fixing that should never have reached billing in the first place?

Not the biggest problem.

Not the most expensive problem.

The recurring problem.

The one everyone has learned to tolerate.

The one someone says:

“That's just how our system works.”

Those words should make every practice leader nervous.

Because sometimes:

“That's just how our system works”

really means:

“We've become very good at living with a preventable problem.”

Tell me what yours is.

Leave a comment.

Share this with a physician, practice owner, administrator or biller who is tired of fixing the same problem twice.

And if you believe healthcare should spend less time compensating for broken workflows and more time preventing them, step into the conversation.


Free Resource

Want to think about your revenue cycle from the upstream rather than the downstream?

Check my Featured section on LinkedIn for a free resource.

No signup needed.

PS: Free resource in Featured on LinkedIn.


Continue the Conversation

I write about the intersection of medicine, healthcare operations, technology, revenue cycle and entrepreneurship.

Dr. Daniel Cham's website

Dr. Cham on Spotify

Dr. Cham on YouTube

Dr. Cham on X

Dr. Cham on Facebook


About the Author

Dr. Daniel Cham is a physician, medical consultant and entrepreneur working at the intersection of healthcare technology, medical practice management and medical billing.

As founder of OnnX, he focuses on practical ways to improve healthcare workflows, reduce administrative friction and help small and medium-sized medical practices build more transparent, efficient and data-driven revenue operations.

His work centers on one question:

How can healthcare organizations spend less time compensating for broken workflows and more time improving the work itself?

Connect with Dr. Daniel Cham on LinkedIn


Disclaimer

This article is for general educational and informational purposes only. It is not medical, legal, compliance, financial or professional advice. Healthcare organizations should consult appropriately qualified professionals regarding their specific clinical, operational, legal, regulatory and technology circumstances.


Sources

Boston Children's Hospital — “They had a plan”: How Sophia's parents found care for ROHHAD syndrome. Published September 15, 2026.

Read the Boston Children's Hospital story

American Medical Association — “AMA, state societies back Patients First Act.” Published September 14, 2026. Quote from AMA President Willie Underwood III, MD, MSc, MPH.

Read the AMA statement

Healthcare Financial Management Association — 2026 Clean Claims Study Brief. Research on claims automation, front-end claim scrubbers and manual intervention.

Read the HFMA study brief

Centers for Medicare & Medicaid Services — Electronic Prior Authorization. Current CMS information on administrative burden, electronic prior authorization and upcoming API requirements.

Read the CMS overview


#Healthcare #MedicalBilling #RevenueCycleManagement #RCM #Physicians #ClinicOwners #PrivatePractice #HealthcareOperations #MedicalPracticeManagement #HealthcareTechnology #HealthcareAI #HealthTech #Automation #Interoperability #RevenueIntegrity #DenialManagement #PriorAuthorization #PatientExperience #HealthcareInnovation #PhysicianEntrepreneur #DigitalHealth #HealthcareLeadership #OnnX

 

Monday, September 14, 2026

Skylar Black Wasn't a Claim, a Code, or a Number

A three-year-old girl's story exposes an uncomfortable truth: healthcare has become remarkably good at recording patients while surprisingly bad at connecting the information that follows them.



“AI won't replace doctors — it will work alongside them.”John Whyte, MD, MPH, CEO of the American Medical Association

 

That idea is worth sitting with.

Because the future of healthcare should not be about making medicine less human.

It should be about using technology to remove the work that never required a human being in the first place.

And that distinction becomes much clearer when we look at one little girl.

On October 6, 2025, Tabitha Black lost her three-and-a-half-year-old daughter, Skylar Black, after a two-and-a-half-year battle with childhood cancer.

Her grandfather, Galen Stewart, described Skylar as “the light of our world.”

The story was reported this week by WAFF in Huntsville, Alabama, as Skylar's family turned their grief into advocacy during Childhood Cancer Awareness Month.

Skylar endured an extraordinary amount of treatment.

Chemotherapy.

Radiation.

Major surgeries.

Repeated sedation.

A stem-cell harvest.

A permanent chest tube.

Oxygen.

More medical appointments than most adults could imagine.

But those aren't the details that make the story stay with you.

Skylar wore a duck costume to chemotherapy.

She helped nurses access her port.

And when she heard another child crying, she would go comfort them.

Because she was immunocompromised, ordinary childhood experiences could become dangerous.

The clinic became part of her world.

The children there became her friends.

Think about that.

A place many adults associate with needles, waiting rooms, bad news and uncomfortable chairs became part hospital and part childhood for a little girl.

And that is where Skylar's story becomes bigger than childhood cancer.

Because healthcare has a peculiar habit.

We are very good at documenting what happened to a patient while sometimes losing the patient inside the documentation.

Diagnosis.

Procedure.

Medication.

Authorization.

Encounter.

Note.

Code.

Claim.

Payment.

Denial.

Appeal.

Each item may be correct.

And yet the collection can still fail to tell one coherent story.

Skylar wasn't a diagnosis.

She wasn't a claim.

She wasn't a billing code.

She was a daughter.

A granddaughter.

A friend.

A little girl in a duck costume trying to make another child feel better.

So here's the question I want physicians and clinic owners to consider:

When your healthcare system records a patient, does it actually remember the patient's journey—or does it simply remember the transactions generated by that journey?

Because those are not the same thing.

And that difference may explain more about healthcare's operational problems than we have been willing to admit.


The Patient Has One Story. Your Software Has Twelve.

Patients don't experience healthcare in modules.

Nobody wakes up thinking:

“Today I will interact with the eligibility subsystem.”

Nobody says:

“After lunch, I expect to encounter the authorization workflow.”

And no patient has ever happily announced:

“Excellent. My claim has entered the clearinghouse.”

Patients think:

I need help.

That's it.

Meanwhile, the healthcare organization starts opening tabs.

Scheduling.

Registration.

Eligibility.

Referral.

Authorization.

EHR.

Documentation.

Coding.

Clearinghouse.

Claim.

Payer portal.

Payment.

Denial.

Appeal.

Spreadsheet.

Email.

Phone call.

Another phone call.

And somewhere in the middle is usually a person named Susan who knows what happened.

Susan is extremely important.

Susan also has vacation days.

This is one of the hidden risks in healthcare operations:

Institutional knowledge often lives inside people instead of systems.

When Susan leaves, everyone discovers how much software they supposedly had.


We Didn't Build Healthcare Around the Patient. We Built It Around Transactions.

That may sound harsh.

But look at the structure.

A patient enters.

Information is captured.

That information is copied.

Then translated.

Then coded.

Then submitted.

Then interpreted.

Then paid.

Or denied.

Then somebody investigates why.

We call this a revenue cycle.

The patient calls it:

Tuesday.

The healthcare industry sees transactions.

The patient experiences a journey.

That distinction matters.

Because every time the journey is broken into disconnected transactions, somebody eventually has to reconstruct it.

And reconstruction is expensive.

Sometimes financially.

Sometimes operationally.

Sometimes emotionally.


Here's My Contrarian Take

Healthcare doesn't have a data shortage.

It has a data continuity problem.

We have enormous amounts of information.

What we often lack is the ability to answer five simple questions:

What happened?

When did it happen?

Why did it happen?

Who knew?

What happens next?

That sounds almost embarrassingly simple.

Which is probably why it gets overlooked.

Healthcare loves complexity.

Complexity sounds sophisticated.

Sometimes complexity is simply poor organization wearing a suit.


The Denial Isn't Always the Problem

This is where I disagree with a lot of traditional revenue-cycle thinking.

We tend to focus on the denial.

How many?

How fast can we resolve them?

How much can we recover?

What is the denial rate?

Important questions.

But they can also lead us into a trap.

Because a denial is often where the problem becomes visible—not where it began.

Maybe eligibility was wrong.

Maybe authorization was missed.

Maybe a referral wasn't complete.

Maybe documentation didn't support the eventual claim.

Maybe information changed.

Maybe the payer made a questionable determination.

Maybe the practice made an error.

Maybe nobody knows.

Those are completely different situations.

Yet they can all end up as:

DENIED.

That's like diagnosing a patient with “fever.”

Technically accurate.

Clinically inadequate.


Stop Celebrating Faster Failure Recovery

This may be unpopular.

But I don't think the ultimate goal of revenue-cycle technology should be:

“Let's become really fast at fixing things after they break.”

Imagine a restaurant where 20% of the meals are routinely burned.

Management proudly announces:

“We hired a faster waiter to handle customer complaints.”

The waiter deserves a raise.

The kitchen needs an investigation.

Healthcare sometimes does the opposite.

We build:

A denial queue.

An escalation queue.

A work queue.

A dashboard.

A tracking spreadsheet.

A task list.

A reminder.

A second reminder.

Then we measure how quickly people move through the queues.

Wonderful.

We have become extremely efficient at cleaning up messes.

But who is studying the kitchen?


The Most Expensive Word in Healthcare May Be “Later”

Eligibility problem?

We'll find out later.

Authorization issue?

We'll deal with it later.

Documentation problem?

Billing will catch it later.

Claim problem?

The payer will tell us later.

Denial?

We'll work it later.

Appeal?

Later.

Follow-up?

Later.

Eventually, later becomes expensive.

The earlier a problem is detected, the cheaper it usually is to correct.

That is not uniquely a healthcare principle.

It is common sense.

Yet healthcare frequently discovers problems at the most expensive point in the workflow.

Why?

Because that's where the system finally has enough information to notice them.

That's backwards.


What If the System Knew Sooner?

Imagine a practice where the system could connect:

Patient.

Insurance.

Eligibility.

Referral.

Authorization.

Encounter.

Documentation.

Coding.

Claim.

Payer.

Payment.

Exception.

Resolution.

Not merely store them.

Connect them.

Now imagine the system noticing:

“This authorization requirement may create a problem.”

Before the appointment.

Not after the claim.

Or:

“This information conflicts with what was previously captured.”

Before submission.

Not after denial.

Or:

“This claim resembles a previous exception.”

Before somebody spends 45 minutes researching it.

That's a different philosophy.

It's not:

How quickly can we clean up the mess?

It's:

How early can we see the mess forming?


That Is Where AI Gets Interesting

Healthcare AI has become obsessed with intelligence.

Can it write?

Can it summarize?

Can it code?

Can it predict?

Can it answer?

Those capabilities matter.

But I think we are asking the wrong question.

The better question is:

Can AI understand what needs to happen next?

Healthcare isn't just an information business.

It is a coordination business.

Someone has to do something.

At the right time.

With the right information.

For the right patient.

And somebody needs to know whether it happened.

That's workflow.

That's state.

That's ownership.

That's accountability.

And that's where AI becomes much more interesting than a chatbot.


The Future of Healthcare AI Might Be Boring

I actually hope it is.

The most valuable AI in a clinic might never generate a viral demo.

It might simply prevent:

One missed authorization.

One eligibility surprise.

One unnecessary phone call.

One duplicate entry.

One preventable denial.

One forgotten follow-up.

One hour of physician administrative work.

Nobody will clap.

There will be no dramatic music.

No robot walking into the exam room.

No humanoid announcing:

“Doctor, I have analyzed your dashboard.”

The claim will simply get paid.

And honestly?

That's pretty impressive.


Physicians Don't Need Another Dashboard

This is another hill I'm willing to stand on.

Healthcare has enough dashboards.

We have dashboards about dashboards.

If another screen appears asking physicians to monitor the dashboard that monitors the dashboard, someone should probably call a meeting.

Actually, don't.

That's how we got here.

The goal shouldn't be more visibility for its own sake.

The goal should be:

Useful visibility at the moment a decision matters.

A physician doesn't need to know everything.

A practice manager doesn't need to know everything.

A biller doesn't need to know everything.

They need to know what matters now.


Physician Time Is Not Free

Recent AMA data reinforces something physicians already know from experience: the workday doesn't end when the patient schedule ends. Physicians reported substantial weekly hours devoted to indirect patient care and administrative work, alongside continued burnout concerns.

And prior authorization is an especially obvious example.

Recent physician survey data reported by healthcare organizations based on AMA research shows that practices handle roughly 39–40 prior authorization requests per physician per week, with about 13 hours of physician and staff time devoted to the process.

Let's translate that.

Thirteen hours isn't “administrative overhead.”

It's thirteen hours.

It is payroll.

It is physician attention.

It is staff capacity.

It is delayed work.

It is patient frustration.

It is opportunity cost.

And it is time that cannot be recovered by telling a physician to “work smarter.”

At some point, the system needs to stop asking the human to absorb the inefficiency.


Don't Blame the Biller

This is important.

When a process is broken, the person closest to the problem often becomes the person blamed for the problem.

That's backwards.

Your biller didn't create the payer ecosystem.

Your biller didn't design the EHR.

Your biller didn't create fragmented data.

Your biller didn't decide that five systems should each contain a different version of the same patient.

And your biller shouldn't have to become a human search engine.

Experienced billing professionals are valuable precisely because they know how to navigate complexity.

The opportunity is to stop wasting that expertise on work a system should handle.


Don't Fire Your Biller. Change the Job.

Good automation should move humans toward higher-value work.

Less:

Searching.

Copying.

Re-entering.

Reconciling.

Chasing.

Remembering.

More:

Judgment.

Exceptions.

Analysis.

Patient communication.

Payer strategy.

Quality control.

Process improvement.

Training.

The goal isn't:

Human versus AI.

The goal is:

Human judgment plus machine consistency.


A More Useful Definition of Automation

Here's my definition:

Automation is successful when the organization no longer needs a human to repeatedly perform a low-value step.

Not:

“An AI generated something.”

Not:

“We added a chatbot.”

Not:

“We have a new dashboard.”

The question is:

Did the work disappear?

That's the test.


The Skylar Test

Now return to Skylar.

Imagine trying to reconstruct her healthcare journey.

Could you answer:

What happened?

When?

Why?

Who knew?

What was waiting?

What was missing?

Who owned the next step?

What changed?

What was communicated?

What happened afterward?

If the answer is:

“Probably. We'd have to check the EHR, billing system, email, payer portal, authorization system and ask a few people.”

Then you don't have one connected patient journey.

You have fragments.

And fragments create work.

Skylar's story reminds us why that matters.

Because behind every fragmented record is a person whose life is not fragmented.


The Data Is Not the Patient

This distinction sounds philosophical.

It isn't.

A diagnosis is data.

A claim is data.

An authorization is data.

A denial is data.

A clinical note is data.

But none of those things is the patient.

They are representations of the patient's experience.

The danger comes when the representation becomes more visible than the person.

Healthcare starts optimizing:

The claim.

The code.

The metric.

The queue.

The dashboard.

The productivity number.

And eventually someone asks:

How did we improve the metric while making the experience worse?

That's not a technology failure.

That's a measurement failure.


The Metric Problem

Healthcare loves measurable things.

That's understandable.

But measurable doesn't automatically mean meaningful.

Claims processed?

Easy.

Tasks completed?

Easy.

Messages answered?

Easy.

Denials resolved?

Easy.

But ask:

How much unnecessary work did we prevent?

Suddenly things become more interesting.

I would rather know:

How many exceptions never happened?

That is a harder metric.

It is also potentially more valuable.


The Metric I Want More Practices to Track

Preventable Exception Rate

Take the problems that required human intervention.

Then ask:

How many could reasonably have been prevented upstream?

Classify them.

Preventable.

Probably preventable.

Payer-driven.

Unknown.

Then track the percentage.

Because there is a huge difference between:

“We resolved 500 problems.”

and:

“We prevented 200 problems from being created.”

The first measures recovery.

The second measures system improvement.


Healthcare's Favorite Workaround

You know the one.

A spreadsheet.

Every organization has one.

Somewhere there is an Excel file named:

FINAL.xlsx

Then:

FINAL2.xlsx

Then:

FINAL_NEW.xlsx

Then:

FINAL_NEW_USE_THIS_ONE.xlsx

Then someone emails:

“Please don't edit the original.”

That spreadsheet is not the problem.

The spreadsheet is evidence.

It is a tiny protest against a system that doesn't quite work.

Employees create workarounds because they are trying to get the job done.

Instead of asking:

“Why is Susan using a spreadsheet?”

Ask:

“What unmet need is Susan solving with that spreadsheet?”

That question can reveal your real product requirement.


Start With the Work, Not the Software

If you're a physician-owner, don't begin with:

“What AI should we buy?”

Start with:

Where are we losing time?

Then:

Where are we losing information?

Then:

Where are we discovering problems too late?

Then:

Where does one person have to remember something the system should remember?

Then:

Where are we doing the same thing twice?

Those questions are far more valuable than starting with a vendor demo.


A 30-Day Practice Experiment

Days 1–5: Follow One Patient

Pick a common patient journey.

Follow it from scheduling through payment.

Watch the actual process.

Don't rely on the policy manual.

Reality is usually more creative.

Document every handoff.


Days 6–10: Count the Systems

How many systems does the patient journey touch?

EHR.

Scheduling.

Eligibility.

Payer portal.

Authorization.

Clearinghouse.

Billing.

Payment.

Email.

Spreadsheet.

Count them.

Then ask:

Why?


Days 11–15: Follow 25 Problems Backward

Take 25 denials, delays or exceptions.

Don't start at the denial.

Start at the beginning.

Trace backward.

Where did the problem actually begin?

You may discover the billing department is where the problem became visible—not where it originated.


Days 16–20: Identify Preventable Work

Label every problem:

Preventable.

Possibly preventable.

Payer-driven.

Unknown.

This alone can expose patterns.


Days 21–25: Assign Ownership

Every important exception should have:

An owner.

A next action.

A deadline.

A dependency.

An escalation path.

If nobody owns the next step, the workflow owns nobody.

That's not a workflow.

That's a hope.


Days 26–30: Measure Again

Track:

Clean-claim rate

First-pass acceptance

Denial rate

Days in A/R

Manual touches

Exception-resolution time

Preventable exception rate

Staff hours spent on rework

Don't create another 40-metric dashboard.

Nobody needs that.

Pick the metrics that explain the economics and the patient experience.


What OnnX Is Trying to Change

This is the thinking behind OnnX.

The core belief is simple:

Healthcare billing is often treated as a downstream billing problem when much of the opportunity exists upstream.

The objective isn't another dashboard.

It isn't another chatbot.

It isn't another system that gives staff one more place to look.

The larger opportunity is to connect the events that already happen:

Patient → Insurance → Eligibility → Referral → Authorization → Encounter → Documentation → Coding → Claim → Payer → Payment → Exception → Resolution → Feedback

The patient experiences one journey.

The organization should be able to understand one journey.

That's the idea.

Not magic.

Not perfect prediction.

Connected context.


The OnnX Thesis

Most of the problem starts upstream.

If information is incomplete upstream, somebody downstream eventually pays for it.

Sometimes the payer.

Sometimes the practice.

Sometimes the physician.

Sometimes the staff.

Sometimes the patient.

And sometimes everybody.

So instead of asking:

“How do we fix the denial?”

Ask:

“How could we have known sooner?”

That is a much more interesting question.

It shifts the conversation from recovery to prevention.

From transactions to relationships.

From documentation to context.

From reactive work to intelligent orchestration.

From:

Data → Work → Problem

toward:

Data → Context → Decision → Action → Outcome


The AI Test for Healthcare

Before buying another AI product, ask seven questions.

1. What does it actually know?

Not what the marketing page says.

What data does it really receive?

2. Does it understand sequence?

Healthcare events have order.

Context without sequence can be misleading.

3. Can it explain itself?

If the system recommends something important, can people understand why?

4. What happens when it is wrong?

Every system will be wrong sometimes.

The question is whether failure is visible and manageable.

5. Who owns the decision?

AI should not become an accountability escape hatch.

6. Can the organization reconstruct what happened?

Auditability matters.

Especially when money, compliance or patient care is involved.

7. Does it remove work?

This is the big one.

If AI creates another queue, another dashboard and another login, congratulations.

You may have automated the creation of more work.


The Legal Question Nobody Should Skip

AI doesn't magically transfer responsibility.

If a system touches protected health information, healthcare organizations still need appropriate privacy, security and contractual safeguards.

Depending on the application, that can involve:

HIPAA.

Business associate agreements.

Access controls.

Audit logs.

Data minimization.

Security monitoring.

Vendor oversight.

Retention policies.

Incident response.

And if AI participates in a consequential workflow, organizations should know:

What information it uses.

What it is allowed to do.

What it cannot do.

How confidence is handled.

When a human reviews the result.

How an error is corrected.

“Because the AI said so” is not a governance model.


The Ethical Question Is Bigger

Healthcare leaders should also ask:

What are we optimizing for?

Speed?

Revenue?

Staff productivity?

Patient access?

Clinical quality?

Convenience?

Sometimes those objectives align.

Sometimes they don't.

An efficient system can still produce a bad experience.

A fast authorization can still be wrong.

A perfectly optimized claim can still represent poor care.

Technology should reduce unnecessary friction without reducing human judgment.

Efficiency is not the same thing as humanity.


The Real AI Debate Isn't Human Versus Machine

That debate is already becoming boring.

The better question is:

What should humans stop doing?

Physicians should spend more time exercising judgment.

Nurses should spend more time caring.

Staff should spend more time solving real problems.

Billers should spend more time on complex exceptions.

Practice owners should spend more time making decisions.

Machines are very good at repetitive pattern recognition.

Machines are very good at monitoring.

Machines are very good at routing.

Machines are very good at remembering.

Humans are very good at context, judgment, relationships and responsibility.

The opportunity is obvious.

Automate the friction surrounding humanity.

Don't automate humanity itself.


What Healthcare Leaders Should Stop Saying

“That's just how insurance works.”

Maybe.

But which part?

Payer policy?

Practice workflow?

Bad information?

Missing documentation?

Unclear ownership?

Don't use “insurance” as a universal explanation.


“Our biller catches it.”

Maybe.

But why does the biller have to catch it?

The question isn't whether Susan can fix it.

The question is whether Susan should have to.


“We have all the data.”

Great.

Now answer:

Can you connect it?


“We just need better AI.”

Maybe.

Or maybe you need a better workflow.

AI cannot fix a process nobody understands.


The Healthcare Innovation Trap

There is a strange phenomenon in healthcare technology.

We build software to solve a problem.

The software creates a new workflow.

The workflow creates new tasks.

The tasks create new notifications.

The notifications create alert fatigue.

Then someone builds AI to summarize the alerts.

Eventually we need AI to explain the AI.

At some point, perhaps we should stop and ask:

What if we simply removed the original problem?

That is not anti-technology.

It is pro-design.


The Best Technology May Be the Technology Nobody Notices

Imagine a practice where:

An authorization problem is identified before the appointment.

An eligibility conflict is caught before the claim.

A missing piece of information is surfaced before submission.

A likely exception is routed to the right person.

A staff member knows what needs attention without searching five systems.

A physician doesn't discover an administrative problem three days later.

A patient doesn't have to explain the same situation four times.

Nobody calls it revolutionary.

That's okay.

Healthcare doesn't need more impressive technology.

It needs more technology that quietly works.


Why Skylar Belongs at the Center of This Conversation

Skylar's story is not a metaphor for billing.

It shouldn't be turned into one.

Her story belongs here for a different reason.

It reminds us of the thing every healthcare system is supposed to protect:

the person behind the information.

Skylar's medical record could contain thousands of pieces of information.

But none of them alone explains who she was.

Her mother, Tabitha Black, knew her differently.

Her grandfather, Galen Stewart, knew her differently.

The nurses knew her differently.

The children around her knew her differently.

They knew the little girl.

The systems knew the data.

Healthcare needs both.

But the data must serve the person.

Never the other way around.


The Most Important Question Isn't “Can AI Do It?”

That's the question everybody asks.

Can AI code?

Can AI document?

Can AI authorize?

Can AI predict?

Can AI answer?

I think there is a better question:

Should a human have to do this at all?

If the answer is no, automate it.

If the answer is yes, make the human's job easier.

If the answer is unclear, investigate.

That's a much healthier AI strategy.


The Future of Healthcare Will Be Won Upstream

I believe the next major opportunity in healthcare operations won't come from building a better cleanup crew.

It will come from preventing the mess.

Better information at capture.

Better connections between events.

Better understanding of workflow state.

Better ownership.

Better timing.

Better feedback.

The goal is not merely:

Fix the denial.

It is:

Understand why the denial happened.

Then:

Prevent the next one.

That's how systems learn.


Five Questions Every Practice Owner Should Ask This Month

1. Where are we repeatedly doing the same work?

Repetition is a signal.

2. Where do we discover problems too late?

Late discovery is expensive.

3. Where does one employee carry knowledge that the system should carry?

That is institutional risk.

4. Which exceptions could have been prevented?

That's where improvement begins.

5. What does the patient experience while all of this is happening?

Because operational efficiency that creates patient frustration isn't really efficiency.

It's just shifting the cost.


What If Healthcare's Biggest Problem Is Not Documentation?

Here's another uncomfortable thought.

We often blame documentation.

Too much documentation.

Too little documentation.

Wrong documentation.

Incomplete documentation.

Poor documentation.

And yes, documentation matters.

But perhaps documentation is sometimes just the most visible symptom.

The deeper problem may be that healthcare is not operating as a real-time connected system.

The patient has an encounter today.

Documentation may happen later.

Coding may happen later.

Claims may be submitted later.

The payer may respond later.

The denial may arrive weeks later.

Then someone tries to reconstruct what happened.

That's not a data problem alone.

It's a feedback-loop problem.

The system learns too slowly.


Healthcare Should Learn While the Work Is Happening

Imagine a system that doesn't wait for the denial to teach you.

It learns from:

Eligibility.

Authorization.

Documentation.

Coding.

Claims.

Payments.

Denials.

Appeals.

Resolutions.

Then feeds those lessons upstream.

That creates a loop.

Capture → Detect → Decide → Act → Measure → Learn → Improve

That's much closer to how modern intelligent systems should work.

Not:

Work → Wait → Denial → Panic → Spreadsheet

Although, to be fair, the second system has had an impressive run.


Proof Doesn't Have to Mean a Perfect AI Model

Healthcare leaders should also rethink what “AI accuracy” means.

A model can be statistically accurate and operationally useless.

What matters is whether the organization can understand:

What the system saw.

What it inferred.

What it recommended.

What happened next.

Whether the recommendation was correct.

What changed.

That is reconstructability.

And I think reconstructability will become increasingly important as AI moves deeper into healthcare operations.

Because when something goes wrong, “the model predicted it” isn't an explanation.


A Better Healthcare AI Scorecard

Don't ask only:

How accurate is it?

Ask:

How much work did it eliminate?

How early did it detect the problem?

How often did humans override it?

Why did humans override it?

How many preventable exceptions disappeared?

Did staff trust it?

Could the organization audit it?

Did the patient experience improve?

Those questions measure whether AI is actually improving the system.


Final Thoughts: Stop Building Faster Band-Aids

Skylar Black's story began with something no healthcare dashboard can fully represent.

A little girl.

A mother.

A grandfather.

A family.

A community.

A child who, despite everything she was going through, still found the instinct to comfort another child.

Her family is now turning grief into purpose and asking people not to look away from children still fighting cancer.

That message applies beyond childhood cancer.

Healthcare leaders should not look away from the things their organizations have quietly normalized.

The spreadsheet.

The duplicate entry.

The forgotten authorization.

The recurring denial.

The mysterious payer portal.

The physician doing administrative work at night.

The employee who knows everything because the software knows almost nothing.

Those are not just inconveniences.

They are clues.

They tell us where the system is compensating for itself.

And perhaps that is the real opportunity.

Not to build another layer on top of healthcare's complexity.

But to remove some of the complexity underneath it.


The Question I Want You to Answer

Think about your practice.

What is the administrative problem everyone has accepted as normal?

Not the biggest problem.

Not the most expensive problem.

The one everyone simply shrugs at and says:

“That's just how we do it.”

Maybe it's a spreadsheet.

Maybe it's a payer portal.

Maybe it's an authorization.

Maybe it's a recurring denial.

Maybe it's a staff member who has become the unofficial operating system.

Maybe it's you.

Now ask:

What would happen if we stopped accepting it?

Not:

“What software should we buy?”

Not:

“Can AI fix it?”

First ask:

Why does this problem exist?

That's where the interesting work begins.


Three Things I Hope You Remember

The patient has one story. Your systems should be able to connect it.

The denial is often where the problem becomes visible—not where it began.

The best healthcare technology doesn't replace human connection. It creates more room for it.


Get Involved

I want to hear from physicians, clinic owners, practice managers, billers and healthcare operators.

What is the strangest workaround your practice has accepted as normal?

Tell me in the comments.

If you've discovered a way to eliminate one of these problems, share it.

If your practice is still fighting one, share that too.

Because there is a good chance your “unique” problem isn't unique at all.

Comment with the workaround.

Challenge the assumption.

Repost this for someone who needs to see it.

Healthcare improves when people stop quietly compensating for broken systems and start asking better questions.


Continue the Conversation

Healthcare innovation should be practical.

It should make work clearer.

It should reduce unnecessary friction.

And it should give physicians, staff and patients more room to focus on what matters.

For more perspectives on healthcare operations, medical billing, medical technology, practice management and intelligent automation, continue the conversation through Dr. Cham's online channels.

Dr. Daniel Cham's website

Dr. Cham on Spotify

Dr. Cham on YouTube

Dr. Cham on X

Dr. Cham on Facebook

Knowledge creates possibility. Applied knowledge creates progress.

Start with one question.

Challenge one assumption.

Improve one workflow.

Then share what you learn.


Free Resource for Physicians and Clinic Owners

Want a practical place to start?

Visit the Featured section of my LinkedIn profile for a free resource.

No signup required.

Read it.

Question it.

Use it with your team.

And tell me what you discover.


About the Author

Dr. Daniel Cham is a physician, medical consultant and healthcare technology entrepreneur with experience spanning medical practice, healthcare management, medical technology and medical billing.

His work focuses on the practical intersection of healthcare operations, medical billing, data, workflow and intelligent automation.

As founder of OnnX, he explores how healthcare organizations can move from fragmented, reactive processes toward more connected and predictable systems.

His perspective comes from seeing healthcare from multiple sides: as a physician, as a practice operator and as a technology entrepreneur.

The question behind much of his work is simple:

Can healthcare technology make the system easier for humans to operate without making healthcare less human?

Connect with Dr. Cham on LinkedIn:
Dr. Daniel Cham on LinkedIn


Disclaimer

This article is provided for general educational and informational purposes only. It does not constitute medical, legal, financial, compliance or other professional advice.

Healthcare organizations should consult appropriately qualified professionals regarding their individual clinical, legal, regulatory, privacy, security, payer and operational circumstances.


References and Further Reading

1. WAFF — “They don't deserve for us to look away”: Family honors three-year-old for Childhood Cancer Awareness Month

The September 14, 2026 report by Sarah Grace Kennedy tells the story of Skylar Black, her mother Tabitha Black, grandfather Galen Stewart, and the family's effort to turn grief into advocacy for children with cancer.

2. STAT — AMA CEO John Whyte on AI and the future of medicine

A current September 2026 perspective from AMA CEO John Whyte, MD, MPH, examining the distinction between automating medical tasks and automating medicine itself.

3. American Medical Association — Physician workload, technology and administrative burden

AMA research documents the continuing amount of physician time devoted to indirect clinical and administrative work and the need to make technology an asset rather than another burden.

4. American Medical Association / 2026 prior-authorization research

Recent 2026 reporting on physician prior-authorization burden highlights the significant amount of time practices spend managing authorization requirements and the continuing effect on physician and staff workload.


One Last Thought

Skylar's family is asking people not to look away.

Maybe healthcare leaders should take that seriously.

Don't look away from the patient behind the data.

Don't look away from the employee who has created a workaround because the system failed to.

Don't look away from the physician who is doing administrative work at 9 p.m.

Don't look away from the recurring denial everyone has decided is inevitable.

And don't look away from the possibility that the problem isn't the person trying to fix the system.

Maybe the system is asking too much of the person.

That is the opportunity.

Not to build a machine that replaces the human.

But to build a system that finally remembers what the human is there to do.

Care.

Think.

Decide.

Connect.

And, occasionally, go home on time.

#Healthcare #Physicians #MedicalPractice #MedicalBilling #RevenueCycleManagement #HealthcareLeadership #HealthcareInnovation #HealthcareAI #HealthTech #PracticeManagement #PatientExperience #PhysicianEntrepreneur #IndependentPractice #HealthcareTransformation #OnnX

 

Sophia Needed Someone to Connect the Dots. Your Revenue Cycle Does Too.

A five-year-old’s rare-disease journey exposes an uncomfortable truth about healthcare: the problem we see downstream often began much earli...