Wednesday, August 12, 2026

Ethan Hackney Is Six Years Old and Waiting for a Kidney. What His Story Reveals About the Healthcare Problem We Keep Missing

A six-year-old boy needs a kidney. Healthcare needs something else: fewer barriers between human need and human care.



“It felt in some way like it was this reminder that I existed and was seen at this time when so much of my experience with cancer had really been behind closed doors.” — Ashleigh Bell Pedersen, cancer survivor, speaking to NPR's My Unsung Hero, August 11, 2026.


The child behind the claim

Ethan Hackney is six years old.

He lives in Herring Cove, Nova Scotia.

And his family is searching for something no algorithm can manufacture, no billing company can process, and no healthcare executive can purchase off a shelf.

A kidney.

Ethan has lived with a serious kidney condition his entire life. His mother, Jenny Hackney, recently shared publicly that the family has reached the point where they are searching for a living kidney donor.

The request is painfully simple.

Someone, somewhere, may be able to help.

The family is asking people to share Ethan's story and help find that person. Public appeals identify Ethan by name and Herring Cove as his community.

There is something about this story that stops you.

Perhaps because Ethan is six.

Perhaps because every parent understands what six should look like.

School.

Friends.

Birthday parties.

Growing taller.

Learning things.

Making mistakes.

Getting annoyed with your parents.

Thinking the world is much bigger than it actually is.

Instead, Ethan's family is thinking about kidneys, transplant evaluation, donors, hospitals, medical decisions and whether a stranger might be willing to give part of themselves so their child can have more time.

And that is where this story becomes bigger than transplantation.

It becomes a story about what healthcare is really supposed to accomplish.

Healthcare is supposed to move a human being from need toward possibility.

Sometimes that requires a surgeon.

Sometimes a nurse.

Sometimes a physician.

Sometimes a caregiver.

Sometimes a donor.

And sometimes it requires hundreds of people working quietly behind the scenes to make sure that the care actually happens.

That last part is where physicians and clinic owners should pay attention.

Because we have created a healthcare industry that is remarkably good at solving complicated clinical problems while remaining strangely comfortable with unnecessary administrative ones.

We can perform extraordinary surgery.

Yet we still make physicians chase paperwork.

We can sequence genomes.

Yet we still make practices manually reconcile fragmented information.

We can build increasingly sophisticated artificial intelligence.

Yet a small clinic can still spend hours trying to understand why a legitimate claim was not paid.

That is not a technology problem.

At least, not entirely.

It is a workflow problem.

And workflow problems eventually become human problems.


The uncomfortable question

Here is the question I want physicians and healthcare leaders to sit with:

What if the biggest threat to better healthcare is not a lack of innovation, but the amount of human attention we waste?

We talk constantly about innovation.

Artificial intelligence.

Robotics.

Precision medicine.

Digital health.

Virtual care.

Genomics.

Remote monitoring.

Agentic systems.

Clinical decision support.

All important.

But innovation has a strange habit in healthcare.

We build something new.

Then we connect it to something old.

Then we create another login.

Another dashboard.

Another alert.

Another workflow.

Another administrative task.

Another exception.

Another person who has to make sense of it all.

Eventually, the physician is sitting at a computer after clinic, wondering why a profession built around human connection has become so dependent on human beings moving information between boxes.

That is the paradox.

We keep inventing tools to save time while designing workflows that consume it.

Ethan's story gives us a better way to think about healthcare.

The objective is not technology.

The objective is not efficiency.

The objective is not automation.

The objective is more life, better care, less unnecessary suffering, and more time for people to do the things only people can do.

Everything else is infrastructure.


What Ethan's story has to do with medical billing

At first glance, almost nothing.

Ethan needs a kidney.

A physician-owned clinic needs clean claims.

Those sound like completely different problems.

They are not.

Both are examples of the same fundamental healthcare challenge:

There is a human need on one side and a complicated system between that need and the resource required to meet it.

For Ethan, the system involves transplant medicine, donor evaluation, clinical coordination, family support and access to an appropriate donor.

For an outpatient practice, the system involves scheduling, registration, eligibility, authorization, documentation, coding, claims, adjudication, payment, denials and collections.

The clinical problem and financial problem are different.

But the operating principle is the same.

Every unnecessary barrier consumes human capacity.

And healthcare has a finite supply of that capacity.


Physicians do not have a time problem

They have a friction problem.

That distinction matters.

If you tell a physician:

“You need to manage your time better,”

you are often blaming the wrong person.

A physician cannot personally optimize away:

Payer portals.

Prior authorization.

Eligibility failures.

Duplicate documentation.

Unclear coding requirements.

Poorly integrated systems.

Manual claim corrections.

Denial follow-up.

Patient billing confusion.

Broken handoffs.

Credentialing delays.

Contract ambiguity.

Those are not personal productivity failures.

They are system design failures.

And physicians have been compensating for them for years.

They stay late.

They check messages after dinner.

They call payers.

They review charts.

They answer staff questions.

They solve problems that should never have reached them.

Then we wonder why burnout persists.

Perhaps we should stop asking:

“How can physicians work more efficiently?”

And start asking:

“Why are physicians doing this work in the first place?”

That is a much more uncomfortable question.

It is also a much more useful one.


The hidden economics of wasted attention

Suppose a physician spends 30 minutes every day dealing with billing problems.

That sounds small.

It isn't.

Thirty minutes a day becomes approximately 125 hours over a 250-day working year.

That is more than three full workweeks.

Now imagine a five-physician practice.

The number becomes roughly 625 physician hours per year.

That is not an abstract administrative burden.

That is clinical capacity.

And the calculation becomes even larger when you include:

Practice managers.

Billers.

Front-desk staff.

Nurses.

Medical assistants.

Coders.

Administrators.

Everyone who touches the problem.

This is why the cost of administrative complexity is larger than the invoice from a billing vendor.

The real cost includes the human time required to compensate for the system's weaknesses.


The statistic that should make practice owners uncomfortable

CMS has identified substantial administrative burden in prior authorization alone.

CMS has cited an estimate of approximately 13 hours per week spent by providers on prior authorization activities, equivalent to roughly 700 hours per year per provider.

That number should change the conversation.

Because 700 hours is not merely “administrative burden.”

It is time.

Time is the currency of medicine.

A physician has a limited number of hours.

A nurse has a limited number.

A practice manager has a limited number.

A patient has a limited number.

So when healthcare creates unnecessary administrative work, the system is not creating more capacity.

It is redistributing scarce human attention toward tasks that often create little clinical value.

That is expensive.

And sometimes the most expensive thing about a bad workflow is not the money.

It is what could have been done instead.


The contrarian view: stop measuring automation

This is where I disagree with much of the healthcare technology conversation.

We celebrate automation too quickly.

A company says it automated one million transactions.

Great.

But did patients get better care?

Did physicians regain time?

Did staff experience less stress?

Did preventable denials decline?

Did cash flow become more predictable?

Did the practice reduce its cost to collect?

Did patients understand their bills better?

Did the organization eliminate unnecessary work?

If the answer is no, what exactly did we automate?

Activity is not improvement.

A faster bad process is still a bad process.

A beautiful dashboard does not create value by itself.

A sophisticated AI model does not create value by itself.

A million automated transactions do not create value by themselves.

The real measure is:

What changed for the human being at the end of the workflow?


Ethan's story gives us the right metric

Imagine measuring Ethan's healthcare journey by administrative activity.

Number of appointments.

Number of forms.

Number of phone calls.

Number of referrals.

Number of tests.

Number of records exchanged.

Number of messages.

Those numbers might tell us something.

But they would miss the most important question.

Did Ethan get the kidney he needed?

That is the outcome.

Healthcare should think about its administrative systems the same way.

Not:

How many claims did we submit?

But:

How many legitimate claims were paid accurately and promptly?

Not:

How many denial tasks did our team complete?

But:

How many preventable denials did we eliminate?

Not:

How many calls did staff make?

But:

How many problems did we prevent from requiring a call?

Not:

How much work did automation perform?

But:

How much unnecessary work disappeared?

That is the difference between measuring activity and measuring impact.


The revenue cycle is not a back-office problem

Physicians often hear the phrase “revenue cycle” and mentally place it somewhere behind the clinical operation.

Billing is over there.

Clinical care is over here.

I think that separation is increasingly dangerous.

The revenue cycle determines whether a practice gets paid for legitimate care.

That affects:

Staffing.

Equipment.

Technology.

Clinical programs.

Appointment availability.

Physician compensation.

Capital investment.

Access.

And ultimately, whether an independent practice remains independent.

A practice can provide excellent medicine and still become financially unstable.

That is not a contradiction.

It is an operational reality.

Clinical quality does not automatically produce financial sustainability.

And financial sustainability does not automatically produce clinical quality.

The two have to reinforce each other.


The mistake physicians make

Many physicians assume that if they hire a billing company, the billing problem becomes someone else's problem.

It doesn't.

The work may be outsourced.

The accountability is not.

A physician-owner should still know:

What is being billed?

What is being collected?

What is being denied?

Why are claims being denied?

How much money is sitting in A/R?

How much is over 90 days?

How much staff time is spent chasing payment?

Which payers create the most friction?

Which errors are preventable?

Which problems originate upstream?

If the answer to these questions is:

“I don't know. My billing company handles it,”

that should be a warning sign.

Outsourcing can be smart.

Outsourcing visibility is not.


The bigger mistake healthcare makes

We often treat the claim as the beginning of the revenue cycle.

It isn't.

The claim is the end product of dozens of earlier decisions.

The patient enters the practice.

Information is captured.

Insurance is entered.

Eligibility is checked.

Authorization may be obtained.

The encounter occurs.

Documentation is created.

Services are coded.

Charges are generated.

The claim is built.

Only then does the claim reach the payer.

By the time billing discovers the problem, the opportunity to prevent it may already be gone.

This is why I believe:

The future of medical billing is upstream.


A denial is often a symptom

Consider a denied claim.

The obvious question is:

“How do we get this paid?”

That is necessary.

But it is not enough.

The better question is:

“Why did this claim become deniable?”

Maybe the patient's insurance information was wrong.

Maybe authorization was missing.

Maybe the authorization was attached to the wrong service.

Maybe the documentation did not support the billed service.

Maybe the code was inconsistent.

Maybe the payer rule changed.

Maybe information was entered twice and one version was incorrect.

Maybe nobody knew who owned the exception.

The denial is where the problem became visible.

It may not be where the problem began.

That distinction is one of the most important ideas in revenue-cycle management.


The upstream data problem

This is the foundation of my thinking around OnnX.

Healthcare billing is not primarily a billing problem.

It is a data-quality problem that becomes a billing problem.

The information necessary for reimbursement is scattered across:

EHRs.

Practice-management systems.

Payer portals.

Clearinghouses.

Authorization systems.

Referral workflows.

Scheduling systems.

Clinical notes.

Eligibility databases.

Human memory.

The more fragmented the information, the more humans become the integration layer.

That is expensive.

It is also fragile.

People forget.

People misread.

People copy the wrong field.

People enter information twice.

People miss an update.

People get interrupted.

People leave.

A resilient system should not depend on perfect human memory.


The best billing system should be boring

This may sound strange coming from someone building an AI-powered billing company.

But I think it is important.

The best healthcare technology should not constantly demand attention.

It should quietly make the right thing easier.

It should surface exceptions.

It should prevent avoidable errors.

It should explain what happened.

It should tell the right person what needs attention.

Then it should get out of the way.

The goal is not to create an exciting billing experience.

Nobody wakes up excited about billing.

The goal is to create a predictable one.


What physicians actually want

Most physicians do not want another dashboard.

They want to know:

Are we getting paid?

Are we missing money?

Why?

What needs my attention?

What doesn't?

Can I trust the numbers?

Is my staff drowning in administrative work?

Can I stop thinking about this?

That last question may be the most important.

Good technology earns trust by reducing the number of things people have to think about.


Three expert lessons healthcare leaders should remember

Atul Gawande: reliability beats heroics

Dr. Atul Gawande's work has repeatedly examined how complex healthcare systems can improve reliability through checklists, standardization, teamwork and process design.

The lesson for revenue cycle is straightforward.

If a workflow only works when your best employee remembers every exception, you do not have a reliable workflow.

You have a hero-dependent workflow.

That is dangerous.

Build systems that make the correct action easier.

Do not build systems that depend on extraordinary employees rescuing ordinary processes.


Don Berwick: design around people

Dr. Don Berwick's work in healthcare quality has emphasized patient-centeredness and improvement of systems rather than blaming individuals.

That principle applies directly to administrative operations.

When a patient receives a confusing bill, do not simply ask why the patient is confused.

Ask why the system produced confusion.

When a physician spends an hour fixing a claim, do not simply congratulate the physician for being diligent.

Ask why the claim required physician intervention.

Good systems make good behavior easier.


The current CMS direction: reduce administrative friction

CMS continues to push toward electronic prior authorization, interoperability, standardized data exchange and changes to physician payment and administrative processes. CMS's current physician-fee-schedule materials include proposed 2027 payment policies, while its electronic-prior-authorization work reflects a broader move toward more standardized digital workflows.

That matters.

The government, payers, physicians, vendors and technology companies may disagree about almost everything else.

But one fact is increasingly difficult to ignore:

Administrative friction is a healthcare problem.


Recent News: the billing fight is not going away

Today's healthcare headlines continue to demonstrate how much financial friction remains between physicians, payers and patients.

On August 12, 2026, Axios reported renewed lobbying around the federal No Surprises Act and its independent dispute-resolution process. Providers and insurers remain sharply divided over how disputed payments should be calculated and whether current data accurately represent the system's impact.

This is not simply a political argument.

It is an operational signal.

When payment rules become contested, practices need stronger data.

When reimbursement rules change, practices need better visibility.

When payer behavior changes, practices need faster detection.

When regulations evolve, manual workflows become more expensive.

The lesson for clinic owners is not to predict which side of every policy debate will win.

It is to build a practice capable of adapting when the rules change.

Flexibility is now a revenue-cycle capability.


Another warning: reimbursement pressure does not disappear

CMS's current physician-fee-schedule process is already looking ahead to 2027, with proposed policies open for comment.

That means physician practices should not build their economics around the assumption that reimbursement will always rise enough to compensate for inefficiency.

It may not.

If payment pressure increases, the practice has two broad choices.

Work harder.

Or reduce waste.

The first approach has a ceiling.

The second has an opportunity.


The hidden threat to independent medicine

Independent practices do not necessarily lose because they provide inferior care.

They can lose because the economics of running the practice become too complicated.

A physician can be clinically excellent and operationally overwhelmed.

That creates an opening for consolidation.

When independent practices cannot manage:

Administrative costs.

Payer complexity.

Technology costs.

Staffing.

Collections.

Compliance.

Contracting.

Documentation.

They may eventually decide that selling is easier than surviving.

That is not necessarily a clinical failure.

It is an infrastructure failure.

And if enough independent practices reach that point, patients lose choices.

That is why revenue-cycle efficiency is not merely about increasing practice profit.

It can also be about preserving independent access to care.


Five things I would change tomorrow

1. Stop asking only about collections

Ask about collection friction.

How much work does it take to collect each dollar?

 

2. Stop celebrating low denial volume without examining cause

A low denial rate can still hide serious financial leakage.

Look at dollars.

Look at preventability.

Look at repeat causes.

 

3. Stop hiring around broken workflows

Before adding another employee, identify why the work exists.

Sometimes the answer is genuinely “we need more people.”

Sometimes it is:

“We created this work ourselves.”

 

4. Stop measuring software activity

A system completing 10,000 tasks is not automatically valuable.

Measure what happened because of those tasks.

 

5. Stop treating physician time as free

This may be the most important.

If a physician spends an hour fixing an administrative problem, that hour has economic value.

It also has human value.

Do not hide it.


The 30-day practice reset

You do not need a massive transformation project.

Start small.

Days 1–5: follow one dollar

Take a claim.

Follow it from:

Patient registration.

To encounter.

To documentation.

To coding.

To claim.

To payer.

To payment.

To posting.

To reconciliation.

Document every handoff.

You will learn more from one claim's journey than from another generic billing presentation.

 

Days 6–10: find your recurring failures

Look at your last several months.

Identify the five most common denial causes.

Then identify the five most expensive.

They may not be the same.

 

Days 11–15: calculate preventability

For each major problem, ask:

Could we have prevented this?

If yes, where?

Registration?

Scheduling?

Authorization?

Documentation?

Coding?

Claim creation?

Payer configuration?

 

Days 16–20: calculate human cost

Estimate:

Staff hours.

Physician hours.

Manager hours.

Phone calls.

Portal logins.

Manual corrections.

Appeals.

Follow-ups.

Then put a dollar value on that time.

 

Days 21–25: fix one upstream problem

Do not fix everything.

Pick the highest-leverage issue.

Build a new workflow.

Assign ownership.

Define exceptions.

 

Days 26–30: measure again

Compare:

Denials.

Cash.

A/R.

Staff time.

Physician time.

Errors.

Patient complaints.

Then decide what to scale.


The metrics I would put on the wall

Every clinic is different.

But I would start with:

Clean claim rate

Preventable denial rate

Denial dollars

Days in A/R

A/R over 90 days

Net collection rate

Cost to collect

Claim submission lag

Payment turnaround

Administrative hours per 100 claims

And one metric most practices rarely track:

Physician hours spent on revenue-cycle work.

That number belongs on the dashboard.


The metric that matters most

If I could add only one question to every revenue-cycle dashboard, it would be:

How much human attention did this process consume?

Because money tells you what happened financially.

Time tells you why.

A claim that generates $500 but consumes three hours of staff work may be less valuable operationally than a $300 claim that requires almost no intervention.

This is where healthcare needs to mature.

We need to measure friction, not just transactions.


Myth Buster

Myth: “The billing company owns the problem.”

No.

The billing company may manage the workflow.

The practice owns the outcome.

 

Myth: “More automation automatically means better billing.”

No.

Bad automation can scale bad decisions.

 

Myth: “Every denial should be appealed.”

Not necessarily.

Sometimes the better investment is preventing the next 100 similar denials.

 

Myth: “AI will eliminate billing.”

Probably not.

The more realistic future is AI handling increasingly complex administrative tasks while humans supervise exceptions, compliance, judgment and relationships.

 

Myth: “Revenue cycle is purely financial.”

No.

It affects staffing, access, patient communication, physician time, and practice sustainability.

 

Myth: “Independent practices cannot compete with large health systems.”

They can.

But they need leverage.

Technology can provide some of that leverage if it is designed around the realities of smaller practices.


The ethical line AI billing must not cross

There is a temptation in revenue-cycle technology to optimize relentlessly.

More revenue.

Fewer write-offs.

Higher collections.

Faster payment.

But healthcare cannot reduce everything to optimization.

A legitimate claim should be paid accurately.

An illegitimate claim should not be manufactured into legitimacy.

AI should never be used to justify:

Upcoding.

Unsupported documentation.

Unbundling.

Manipulation.

Misrepresentation.

Aggressive patient collection.

The goal is not to extract every possible dollar.

The goal is:

Accurate reimbursement for legitimate care.

That distinction protects patients.

It protects physicians.

And it protects the credibility of healthcare technology.


Legal and compliance considerations

Medical billing technology operates in a high-risk environment.

A clinic considering automation should evaluate:

HIPAA compliance

Data security

Access controls

Business associate relationships

Audit trails

Coding compliance

Documentation integrity

Payer contracts

Fraud-and-abuse risk

Patient financial communications

State requirements

Human oversight

AI should not become a black box.

A practice needs to understand:

What information entered the system?

What rule was applied?

What recommendation was generated?

Who approved the action?

Can the decision be audited?

Can an error be corrected?

What happens when the system is uncertain?

Those questions should be answered before deployment, not after an incident.


Health equity is hiding in the billing workflow

Here is another uncomfortable truth.

Administrative friction does not affect every patient equally.

A financially comfortable patient may absorb an unexpected $200 bill.

Another patient may not.

A patient with a flexible job may spend an hour on the phone.

Another patient may lose wages by doing so.

A patient who speaks English fluently may navigate a confusing explanation of benefits.

Another patient may struggle.

A patient with stable housing may receive every letter.

Another may not.

So when we improve billing, we should not only ask:

“Can we collect faster?”

We should also ask:

“Can we make the financial experience clearer and fairer?”

That is where operational efficiency and patient-centered care meet.


Why the best healthcare technology may become invisible

The most useful technology in medicine may not be the technology physicians talk about most.

It may be the technology they stop noticing.

A system that quietly verifies information.

A workflow that catches an error before submission.

A system that identifies a payer change.

A tool that routes an exception to the right person.

A platform that tells a manager where cash is stuck.

An automated process that eliminates three manual steps.

None of these sound revolutionary.

That is the point.

The best infrastructure disappears into the workflow.

The patient sees a smoother experience.

The physician gets time back.

The staff sees fewer exceptions.

The owner sees more predictable operations.

That is innovation.


Why I founded OnnX

My interest in this problem comes from seeing the disconnect between clinical work and administrative infrastructure.

Physicians create enormous value in the examination room.

But the information generated there has to travel through an administrative maze before the practice gets paid.

Every handoff creates an opportunity for error.

Every disconnected system creates uncertainty.

Every preventable denial creates additional work.

My thesis behind OnnX is simple:

Healthcare billing should become more deterministic by improving the quality and intelligence of the information upstream.

The goal is not to create another layer between physicians and payers.

It is to reduce unnecessary layers.

For small and medium-sized physician-owned practices, that distinction matters.

They do not need more complexity.

They need leverage.


The opportunity for healthcare founders

There is a lesson here for entrepreneurs.

Do not ask:

“Where can I put AI?”

Ask:

“Where is a human being repeatedly compensating for a broken workflow?”

That is where the opportunity often lives.

Look for:

Repeated data entry.

Repeated verification.

Repeated phone calls.

Repeated portal searches.

Repeated corrections.

Repeated denials.

Repeated escalations.

Repeated reconciliation.

Repeated confusion.

Those repetitions are signals.

But there is another question founders should ask:

Why does the repetition exist?

If you do not understand that, you may automate the symptom.

The better company fixes the underlying workflow.


The future is not autonomous billing

At least, not in the simplistic sense.

The future is context-aware revenue infrastructure.

Systems that understand:

The patient.

The encounter.

The payer.

The contract.

The documentation.

The code.

The authorization.

The claim.

The denial.

The payment.

The exception.

And the next best action.

But sophisticated automation must be coupled with something equally important:

knowing when to stop.

When confidence is low, escalate.

When information is missing, ask.

When a decision carries significant risk, require review.

When the data conflict, do not pretend they agree.

The future of healthcare AI will not be defined only by how much it can do.

It will be defined by how intelligently it knows what it should not do alone.


A different definition of innovation

Innovation is not always invention.

Sometimes innovation is subtraction.

Removing a form.

Removing a login.

Removing a handoff.

Removing duplicate entry.

Removing a denial.

Removing a phone call.

Removing an unnecessary approval.

Removing an unnecessary report.

Removing the need for the physician to intervene.

That is the kind of innovation independent practices need.

Less work. More care.


The question every physician-owner should ask

Not:

“What billing software are we using?”

Ask:

“How much of our billing process exists only because our systems do not communicate?”

That question opens a very different conversation.

If the answer is “a lot,” you have found an opportunity.


The question every healthcare founder should ask

Not:

“What can our AI automate?”

Ask:

“What human attention can we return to healthcare?”

That is the better north star.


The question every healthcare executive should ask

Not:

“How much did we spend on administrative infrastructure?”

Ask:

“What did our administrative infrastructure make possible?”

Did it give clinicians time?

Did it reduce patient friction?

Did it improve financial reliability?

Did it protect access?

Did it reduce preventable work?

If not, the system may be consuming resources rather than creating capacity.


Three practical rules for 2026

Rule one: fix upstream

Do not wait for the denial.

Find the first place the information became wrong.

 

Rule two: automate the mechanical work

Let machines handle repetitive, rules-based processes where appropriate.

Let humans handle judgment, exceptions and relationships.

 

Rule three: measure time

If technology saves $50,000 but creates 500 hours of new work, the business case is incomplete.

If technology costs money but returns thousands of hours of clinical and administrative capacity, the economics may look very different.

Time belongs on the balance sheet of healthcare innovation.


The story we should remember

Ethan Hackney is not a metaphor.

He is a real six-year-old boy whose family is asking for help.

That distinction matters.

We should not use a child's medical situation simply as a marketing device.

His story deserves to remain about him.

About Ethan.

About Jenny.

About a family facing something no parent wants to face.

About the possibility that a stranger may become the person who changes a child's future.

But his story can also remind us why healthcare exists.

Not to create more workflows.

Not to generate more claims.

Not to produce more dashboards.

Not to maximize administrative activity.

To help people.

Every system we build should be judged against that standard.


Final Thoughts: We are solving the wrong problem

Healthcare does not have a shortage of intelligence.

It has a shortage of well-directed human attention.

We have brilliant physicians.

Dedicated nurses.

Exceptional technicians.

Committed practice managers.

Hard-working billers.

Caregivers who sacrifice sleep.

Families who search the country for donors.

Communities that share a child's story.

And increasingly powerful technology.

Yet we still make these people spend too much of their limited time navigating systems that were supposed to help them.

That is the contradiction.

Ethan's family is looking for a kidney.

A kidney cannot be automated into existence.

But the coordination surrounding care can be improved.

A physician cannot be replaced by a billing algorithm.

But unnecessary billing work can be reduced.

A nurse cannot be cloned.

But repetitive administrative tasks can be redesigned.

A caregiver cannot be given more hours in the day.

But healthcare can stop wasting the hours they already have.

That is the real opportunity.

Do not ask how much technology healthcare can absorb.

Ask how much unnecessary work healthcare can eliminate.

Do not ask how many transactions your system can process.

Ask how much human attention it can return.

Do not build technology merely to make healthcare more digital.

Build it to make healthcare more human.**


Get Involved: Change the Conversation

Here is the question I want to leave with physicians and clinic owners:

What is the one administrative task you would eliminate tomorrow if you could?

Prior authorization?

Eligibility?

Coding?

Denial management?

Patient collections?

Payer follow-up?

Referral coordination?

Something else?

Tell me in the comments.

Your answer may reveal a problem another physician is struggling with right now.

Share this article with a physician, practice owner, administrator, or healthcare founder who should be part of this conversation.

And if this perspective resonates, repost it.

Not to promote another technology.

But to start a better conversation about what healthcare should be optimizing for.

Get involved. Raise your hand. Step into the conversation. Share what you have learned. Help shape the future of independent medicine.

Because healthcare does not need another generation of people working harder to compensate for broken systems.

It needs better systems.


About the Author

Dr. Daniel Cham is a physician, entrepreneur, and healthcare technology consultant focused on the intersection of medical practice, healthcare operations, medical billing, artificial intelligence, and innovation.

He is the founder of OnnX, an AI-powered medical billing SaaS initiative designed around a simple premise: small and medium-sized physician-owned practices should not need layers of unnecessary administrative complexity to receive accurate and timely reimbursement for legitimate care.

Dr. Cham writes about the practical side of healthcare transformation, with particular interest in physician time, revenue-cycle performance, workflow design, healthcare AI, and the future of independent medical practice.

His perspective is shaped by the belief that technology should serve clinicians rather than create another job for them.

Connect with Dr. Daniel Cham on LinkedIn:

Dr. Daniel Cham on LinkedIn


Disclaimer

This article is intended solely for general educational and informational purposes. It does not provide medical, legal, coding, compliance, reimbursement, or financial advice.

Healthcare regulations, payer requirements, contracts, and billing practices can change and may differ according to specialty, jurisdiction, payer, and individual circumstances.

Readers should consult appropriately qualified professionals for advice relating to their specific clinical, legal, compliance, operational, or financial situation.


Continue the Conversation

Healthcare is changing quickly, but the most important questions remain remarkably human.

How do we give physicians more time?

How do we reduce unnecessary administrative work?

How do we make healthcare easier to navigate?

How do we build technology that strengthens rather than weakens human connection?

Explore more perspectives, practical strategies, and conversations about healthcare operations, innovation, medical technology, entrepreneurship, and the future of medicine.

Knowledge is the beginning. Understanding creates leverage. Action creates change.

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No complicated funnel. No unnecessary barrier. Just practical information you can use.

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If this perspective resonates, repost it.

Another physician may be struggling with the same administrative problem.

Another clinic owner may be questioning whether there is a better way.

Another healthcare founder may be building the technology that makes the better way possible.

One conversation can expose the problem. Thousands of conversations can change the standard.


Three References

1. Ethan Hackney and his family's public donor appeal. Jenny Hackney has publicly shared that her six-year-old son Ethan has lived with a serious kidney condition throughout his life and that the family is now seeking a living kidney donor; community posts identify Ethan as being from Herring Cove, Nova Scotia.

Public appeal and family statement

2. CMS electronic prior authorization initiative. CMS describes its continuing work toward electronic prior authorization and standardized digital information exchange, reflecting the broader movement to reduce administrative friction in healthcare.

CMS Electronic Prior Authorization

3. Current physician payment and administrative policy environment. CMS's current Physician Fee Schedule materials show that physician payment policy and administrative requirements remain active areas of change heading into 2027.

CMS Physician Fee Schedule

#HealthcareInnovation #HumanCenteredHealthcare #PatientExperience #PhysicianLeadership #MedicalBilling #HealthcareAI #RevenueCycleManagement #HealthcareOperations #IndependentPractice #HealthcareTechnology #OnnX

Tuesday, August 11, 2026

Destiny Moore Wouldn’t Stop Asking Questions. What Legaci Harris-Moore’s Rare Diagnosis Can Teach Us About the Future of Medical Billing

What if the biggest problem in healthcare isn't a lack of data—but our inability to recognize the signal hiding inside it?



“Nobody sees you as a whole person.” — Lucy McBride, MD, Beyond the Prescription, a Washington, D.C.-based, board-certified internal-medicine physician and patient-advocacy writer. She is known for emphasizing whole-person, patient-centered care and better communication between doctors and patients.


At two months old, Legaci Harris-Moore began doing something that frightened her mother, Destiny Moore.

Her eyes sometimes turned inward.

Later, they rolled upward and appeared to get stuck.

Legaci was also struggling with developmental milestones.

Destiny was a first-time mother.

She did what many parents do.

She asked questions.

Some tests came back normal.

But the answers didn't explain what Destiny was seeing.

So she kept asking.

Eventually, genetic testing revealed that Legaci had ELP2-related disorder, an extraordinarily rare genetic neurological condition with fewer than 30 reported cases in the medical literature, according to reporting by Signal Akron. There is no established cure, and physicians are still learning about the condition.

At Akron Children's, Dr. Carrie Costin, director of genetics, had never personally treated a patient with the disorder.

Dr. Matthew Ginsberg, a pediatric neurologist, became part of the multidisciplinary care team.

Rachel Larkin, a physical therapist, worked with Legaci as she began early intervention.

And Destiny kept advocating.

At eight months, Legaci was gaining better head control, nearing independent sitting and beginning to hold her bottle.

There is a lesson here that has almost nothing to do with rare disease.

It is about signals.

Destiny saw one.

The healthcare system initially didn't know what it meant.

That distinction matters.

Because modern healthcare has no shortage of data.

It has a shortage of meaningful signal detection.

And nowhere is that more obvious than in medical billing.


The uncomfortable question physicians should be asking

What if your practice is already telling you where it is losing money?

What if the clues are sitting inside your claims?

What if your denials are not random?

What if your payer behavior has changed?

What if your documentation patterns are producing predictable financial consequences?

What if your staff knows exactly where the friction is—but nobody has connected all the pieces?

And what if the problem isn't that your practice needs more billing reports?

What if it needs someone—or something—to notice the pattern?

That is a very different way to think about medical billing.

And it is why I believe the next generation of revenue-cycle technology should not begin with:

“How can we automate billing?”

It should begin with:

“What is the practice failing to see?”


Medical billing has been asking the wrong question

For decades, the dominant question has been:

Did we get paid?

That question is necessary.

It is not sufficient.

A physician-owned practice should also ask:

Why did we get paid?

Why weren't we paid?

Why was the payment different?

Why did the claim require rework?

Why did this payer behave differently?

Why did this happen again?

Those questions move billing from transaction processing to intelligence.

And that shift is overdue.


Medical billing is not primarily a billing problem.

It is an information problem disguised as a billing problem.

Think about what happens after a patient encounter.

The physician creates documentation.

Someone translates that documentation into codes.

A claim is created.

The claim moves through a clearinghouse.

A payer adjudicates it.

A remittance comes back.

Someone posts the payment.

A denial may appear.

Someone works the denial.

An appeal may be filed.

Eventually, money arrives.

That sounds like one process.

It isn't.

It is a chain of disconnected information systems.

And every handoff creates an opportunity for information to disappear.

That is where money leaks.


The hidden irony of healthcare technology

Healthcare has spent billions digitizing information.

Yet physicians still spend enormous amounts of time trying to find the information they need.

We have EHRs.

Practice-management systems.

Clearinghouses.

Payer portals.

Revenue-cycle platforms.

Analytics tools.

Eligibility systems.

Prior-authorization platforms.

Coding software.

Denial-management systems.

And now AI.

Yet many practices still cannot answer a basic question:

Where exactly are we losing revenue?

That should bother us.

Because if we cannot see the leak, we cannot reliably fix it.


The industry loves dashboards

I am increasingly skeptical of dashboards.

Not because dashboards are bad.

Because a dashboard can create the illusion of control.

A practice receives a beautiful report.

Denial rate: 6.4%.

Days in A/R: 47.

Net collection rate: 94%.

Clean claim rate: 96%.

Everyone nods.

Then someone asks:

“Why?”

Silence.

The numbers are there.

The explanation isn't.

That's the difference between data visibility and decision intelligence.

A dashboard tells you what happened.

Intelligence helps you understand what deserves attention.


The Legaci lesson applies here

Destiny Moore didn't need another dashboard.

She needed someone to recognize that the available information did not explain what she was seeing.

That is what made her persistence valuable.

She wasn't simply asking:

“Is this test normal?”

She was asking:

“Does this explanation make sense?”

Physicians do this constantly.

You see the patient.

You review the labs.

You consider the history.

You notice something doesn't fit.

You investigate.

That is clinical reasoning.

Why don't we use the same mindset in practice operations?


Your billing data has a clinical history

Imagine treating your revenue cycle like a patient.

The claim is the symptom.

The denial is a finding.

The payer response is another finding.

The payment is an outcome.

The historical claim data is the longitudinal record.

The question becomes:

What is the diagnosis?

Maybe it is a documentation problem.

Maybe it is a payer-policy issue.

Maybe it is a workflow failure.

Maybe it is an authorization problem.

Maybe it is a coding inconsistency.

Maybe it is an underpayment pattern.

Maybe it is a combination.

The point is this:

You shouldn't treat every denial as an isolated event.

Some denials are symptoms of a larger condition.


The biggest billing mistake may be fixing the claim instead of fixing the cause

Let's say 100 claims are denied for the same reason.

Your team works all 100.

Ninety are eventually paid.

The practice celebrates a 90% recovery rate.

But nobody asks why the 100 claims were denied.

Next month, another 100 appear.

This is what I call administrative Groundhog Day.

The organization gets better at recovering from the same failure.

It never becomes better at preventing the failure.

That distinction could be worth more than another percentage point in collection rate.


Revenue recovery versus revenue intelligence

These are not the same thing.

Revenue recovery asks:

“How do we get this claim paid?”

Revenue intelligence asks:

“Why did this claim fail?”

Revenue recovery is reactive.

Revenue intelligence is preventive.

Revenue recovery measures effort.

Revenue intelligence measures patterns.

Both matter.

But if your organization spends most of its energy recovering from problems it could have prevented, you have an efficiency problem.


The statistics tell a bigger story

Administrative burden is not a minor annoyance.

The American Medical Association has repeatedly documented the burden physicians face from prior authorization and other administrative requirements.

The AMA has reported that physicians complete approximately 40 prior authorization requests per week, with significant physician and staff time consumed by the process. Previous AMA surveys found 95% of physicians reporting that prior authorization contributes to burnout.

The AMA's more recent survey also found limited physician confidence that insurer commitments to improve prior authorization would substantially reduce the burden.

These numbers are often presented as an argument for administrative reform.

They should also be understood as an argument for better information architecture.

Because administrative burden grows when humans repeatedly perform tasks that machines could organize, prioritize, or detect.

The goal should not be to eliminate humans.

It should be to stop wasting them.


The physician attention tax

We talk about taxes on income.

We should talk more about the attention tax.

Every unnecessary:

Phone call.

Portal login.

Claim review.

Documentation clarification.

Denial appeal.

Payer follow-up.

Spreadsheet.

Email.

Manual reconciliation.

takes something from the practice.

Sometimes it takes money.

Sometimes it takes staff morale.

Sometimes it takes physician time.

Sometimes it takes attention away from patients.

The last one is the most expensive.

Because attention is not infinitely scalable.


The true cost of a denial is not the dollar amount of the denial.

It is:

lost revenue + staff time + physician time + delay + opportunity cost + future repetition.

A $150 denial that takes 30 minutes to resolve may cost more than $150.

And if it happens 500 times a year, the real cost becomes substantial.

Yet most systems measure the denial as a transaction.

They do not measure the organizational friction around it.

That is a blind spot.


Why “AI will replace billing” is the wrong pitch

I understand why companies make the pitch.

It is catchy.

It sounds transformative.

It attracts attention.

But I don't think it is the right vision.

Healthcare is too complicated.

Coding has exceptions.

Documentation has context.

Payer policies differ.

Clinical judgment matters.

Compliance matters.

Contracts matter.

And occasionally the machine will simply be wrong.

The better question is:

Where should AI assist, and where should humans remain accountable?

That is a much more interesting question.


AI should find the needle

Not become the doctor.

Not become the coder.

Not become the compliance officer.

Not become the practice manager.

AI should help identify:

What deserves attention?

That could mean:

“This claim looks unusual.”

“This payer's denial behavior changed.”

“This procedure is being reimbursed differently than expected.”

“These claims share the same failure pattern.”

“This documentation issue is recurring.”

“This A/R category is deteriorating.”

“This payment appears inconsistent with historical behavior.”

The system surfaces the signal.

The human investigates.

That is a far safer and more useful model.


The human should decide what the signal means

This is especially important in healthcare.

A machine can recognize a pattern.

A human must understand context.

That is why human-in-the-loop design is not a temporary compromise.

It is likely to remain essential.

Recent research on automated medical coding illustrates this point. AI systems can be useful for extracting and structuring coding information, but generating highly accurate and specific codes remains challenging, particularly across complex clinical situations. Human oversight remains important.

That should not discourage innovation.

It should make us more disciplined about where we deploy it.


The future isn't autonomous billing

I don't think the future of medical billing is:

AI does everything.

I think it is:

AI watches everything. Humans decide what matters.

That is a subtle difference.

But it could change the economics of practice management.


What physicians should measure instead

Forget the temptation to track everything.

Start with a handful of questions.

1. What is our denial rate?

Useful.

But incomplete.

2. What causes our denials?

Much more useful.

3. Which causes repeat?

Now we are getting somewhere.

4. Which payer creates the most friction?

Useful.

5. Which services create the largest payment variance?

Very useful.

6. Which problems require physician intervention?

Extremely useful.

7. Which problems could have been prevented upstream?

That is the question I would build a company around.


The five signals every practice should watch

Signal 1: Repeated denials

One denial is an event.

Repeated denials are a pattern.

 

Signal 2: Unexpected payment variation

If the same service produces materially different payment outcomes, investigate.

Do not assume.

Measure.

 

Signal 3: Documentation friction

If the same type of clarification repeatedly reaches the same physician, something upstream may need attention.

 

Signal 4: Growing aged A/R

A/R aging is not merely a finance metric.

It can be a signal of workflow breakdown.

 

Signal 5: Staff rework

This may be the most overlooked metric.

Ask your billing staff:

“What task do you hate doing because you have to do it over and over?”

Then listen.

That answer may reveal your next automation opportunity.


Three expert lessons

Dr. Carrie Costin: Rare disease requires collaboration

The Legaci story shows what happens when a physician encounters something rare.

The answer may not exist inside one clinician's memory.

It may require literature.

Research networks.

Specialists.

Genetic information.

Therapists.

And the patient's family.

Lesson for medical billing:

When a problem crosses organizational boundaries, no single system may contain the answer.

Integration matters.


Dr. Matthew Ginsberg: Think beyond the individual encounter

Multidisciplinary care recognizes that one clinician cannot solve every aspect of a complex patient's needs.

Lesson for operations:

Your billing problem may not actually belong to billing.

It may originate in scheduling.

Authorization.

Documentation.

Coding.

Payer configuration.

Or contracting.

If you only look at the billing department, you may miss the cause.


Destiny Moore: Listen to the person closest to the problem

Destiny's experience may be the most important lesson of all.

The person closest to the problem often sees something others cannot.

In medicine, that person may be a parent.

A patient.

A nurse.

A front-desk employee.

A biller.

A practice manager.

A physician.

The hierarchy of healthcare should never become the hierarchy of information.

The person with the signal deserves to be heard.


The question most CEOs don't ask their billing staff

If I were walking into a physician-owned practice tomorrow, I wouldn't start with the CEO.

I'd start with the person doing the work.

I'd ask:

“What keeps breaking?”

Then:

“What do you keep fixing manually?”

Then:

“What do you know that the software doesn't?”

Those answers could be more valuable than a six-month consulting engagement.

Because frontline workers live inside the exceptions.


The technology gap is often a workflow gap

A practice might say:

“We need better AI.”

Maybe.

But sometimes the real problem is simpler.

Nobody owns the process.

Nobody reviews payer trends.

Nobody compares expected versus actual reimbursement.

Nobody analyzes denial root causes.

Nobody follows up on recurring problems.

Nobody turns lessons into workflow changes.

Buying technology before fixing ownership is like buying a faster ambulance without deciding where the hospital is.


What OnnX is trying to change

The idea behind OnnX is not that physicians need another billing dashboard.

They need a better way to understand what is happening between the clinical encounter and the payment.

The long-term vision is an intelligent layer connecting:

Clinical documentation → coding → claims → payer behavior → denials → payments → compliance → forecasting

The purpose is not to remove people.

It is to reduce unnecessary friction between them.

The technology should help answer:

What happened?

Why did it happen?

Is it recurring?

How much does it matter?

Who should look at it?

What can we do next?

That is a much more useful form of AI.


The practice should become a learning system

Imagine if every denial taught your organization something.

Imagine if every underpayment became a data point.

Imagine if every successful appeal updated your understanding of payer behavior.

Imagine if documentation issues became visible before claims were submitted.

Imagine if your practice could identify a deteriorating trend before it showed up in the quarterly financial statement.

That is what a learning revenue cycle looks like.

It does not merely process transactions.

It learns from them.


But there is a warning

Don't confuse intelligence with automation.

A system can be highly automated and completely unintelligent.

It can move bad information faster.

It can generate thousands of alerts nobody reads.

It can produce beautiful reports nobody acts on.

It can automate the wrong process.

Automation without judgment is just faster confusion.

That may be the most important sentence in this entire article.


The legal line cannot be ignored

Any serious discussion of AI-powered billing must include compliance.

Healthcare organizations must consider:

HIPAA

Accurate coding

Medical necessity

Documentation integrity

Payer contracts

False Claims Act risk

Anti-kickback rules

Stark Law where applicable

State-specific requirements

Data security

AI governance

The worst possible AI billing system would be one that makes aggressive recommendations without adequate controls.

A system should never encourage unsupported coding.

It should never manufacture documentation.

It should never confuse optimization with compliance.

The objective is:

appropriate reimbursement for appropriate care, supported by appropriate documentation.

Nothing more.

Nothing less.


The ethical question

Here is the question I want healthcare founders to ask:

If the AI makes the practice more profitable but makes the physician less attentive, did we actually improve healthcare?

I don't think so.

Technology should ultimately protect the human relationship at the center of medicine.

That includes protecting physician attention.

It includes reducing unnecessary administrative work.

It includes helping practices remain financially sustainable.

And it includes respecting the patient.

The patient should never become secondary to the revenue cycle.


What I would do in a practice tomorrow

Not next year.

Tomorrow.

Step 1: Pull the last 90 days of denials.

Rank them.

 

Step 2: Identify the top five causes.

Ignore the long tail for now.

 

Step 3: Find the repetition.

Which denial occurs again and again?

 

Step 4: Trace each denial upstream.

Where did the failure originate?

 

Step 5: Quantify the cost.

Include:

Lost reimbursement

Staff time

Physician time

Delay

Appeal effort

Opportunity cost

 

Step 6: Fix one problem.

Not ten.

One.

 

Step 7: Measure the result.

Did the problem decline?

If yes, standardize the improvement.

If no, investigate again.


Don't start with AI

This may sound strange coming from an AI founder.

But here it is:

Don't start with AI.

Start with the problem.

Then ask whether AI is the best tool.

Sometimes it is.

Sometimes a better workflow is enough.

Sometimes a rules engine is better.

Sometimes a human needs to make the decision.

Sometimes the problem is simply bad data.

Technology should serve the workflow.

Not the other way around.


The emerging opportunity: predictive billing

Today, much of revenue-cycle management is reactive.

A claim is denied.

Then someone acts.

The next generation should be more predictive.

Before submitting a claim, the system could potentially identify patterns suggesting elevated risk.

Before a payment is posted, it could identify unusual variance.

Before A/R becomes a problem, it could identify deterioration.

Before a physician repeatedly receives the same query, the organization could identify the pattern.

The goal is simple:

Move intervention upstream.

That is where the real leverage exists.


From reactive billing to preventive billing

Traditional:

Encounter → claim → denial → work

Better:

Encounter → intelligence → exception → intervention → claim

Even better:

Encounter → learning → prevention → clean claim → appropriate payment

That is the direction I believe the industry should move.


Why independent practices need this more than large systems

Large health systems can absorb inefficiency.

They have departments.

Analytics teams.

Revenue-cycle executives.

IT resources.

Consultants.

Independent practices usually don't.

A physician-owned clinic may have:

One practice manager.

A small billing team.

A front desk.

Several clinicians.

And a mountain of payer rules.

The smaller the organization, the more valuable attention efficiency becomes.

That is why intelligent automation could matter enormously for independent medicine.

Not because small practices need more technology.

Because they need less wasted effort.


The biggest opportunity may not be more revenue

This is another point I would challenge.

Healthcare technology companies often sell revenue growth.

But physician owners may care just as much about something else:

predictability.

Knowing what is coming.

Knowing where the problems are.

Knowing which payer is changing.

Knowing which workflow is failing.

Knowing how much cash is likely to arrive.

Knowing where the practice is exposed.

Predictability creates confidence.

Confidence changes decisions.

That may be more valuable than chasing another percentage point of collections.


A better definition of practice growth

Growth is not simply:

More patients.

More visits.

More revenue.

A healthier definition is:

More value created with less unnecessary friction.

That can mean:

Better patient access.

Better physician time.

Better staff retention.

Better documentation.

Better collections.

Better predictability.

Better margins.

Better patient experience.

Technology should support all of those.


The future belongs to connected information

The next major healthcare advantage may not come from another isolated application.

It may come from connecting information that already exists.

Clinical.

Financial.

Operational.

Payer.

Patient.

The winner will not necessarily be the company with the most AI.

It may be the company that creates the clearest context.

Because context turns information into decisions.


A final lesson from Destiny and Legaci

Destiny Moore didn't give up when the first answer was “normal.”

She recognized that normal was not the same thing as explained.

That distinction should be printed on the wall of every medical practice.

A clean claim is not necessarily a healthy revenue cycle.

A high collection rate is not necessarily an efficient practice.

A low denial rate is not necessarily proof that nothing is wrong.

A dashboard is not necessarily insight.

An AI model is not necessarily intelligence.

And a normal number is not necessarily the end of the investigation.

Sometimes the most important question is simply:

“Does this make sense?”

That is where curiosity begins.

That is where clinical reasoning begins.

And perhaps that is where the next generation of healthcare operations should begin too.


Final Thoughts: Stop Looking at the Number. Look for the Signal.

The story of Destiny Moore and Legaci Harris-Moore is ultimately a story about persistence.

A mother noticed something.

She questioned the explanation.

She kept looking.

Eventually, the system found the signal.

Physician-owned practices face a different version of the same challenge every day.

The signal is already there.

It may be in your denials.

Your payments.

Your A/R.

Your payer behavior.

Your documentation.

Your staff's frustration.

Your physician's inbox.

Your patients' administrative experience.

The question is whether you are looking for it.

Because the future of medical billing is not about processing claims faster.

It is about understanding the practice better.

It is about moving from:

transactions → patterns

patterns → insight

insight → action

action → prevention

That is where technology becomes genuinely useful.

And that is where I believe physician-led healthcare has an opportunity to take back something it cannot afford to lose:

attention.

Attention to the patient.

Attention to the practice.

Attention to the signals that tell us when something isn't working.


Get Involved: Ask the Question Others Aren't Asking

Here is my question for physicians and clinic owners:

What is your practice's “something doesn't make sense” moment?

The denial that keeps returning.

The payer that suddenly behaves differently.

The procedure that is consistently underpaid.

The workflow your staff has learned to work around.

The administrative task everyone accepts because “that's just how healthcare works.”

Tell me about it in the comments.

What are you seeing that the reports aren't telling you?

If you've solved one of these problems, share how.

If you're still trying to solve it, say so.

And if this article makes you think differently about medical billing, repost it so another physician or clinic owner can join the conversation.

Healthcare improves when we stop normalizing problems simply because they've been around for a long time.

Question the process. Find the signal. Fix the system.


Three Actions for Physician Leaders

Look closer. Your billing data may contain information your practice is not using.

Ask why. Don't stop at the denial, the payment, or the number. Find the pattern behind it.

Start upstream. The best revenue-cycle problem may be the one you prevent before the claim is ever submitted.


Frequently Asked Questions

Is AI going to replace medical billers?

Probably not in the way the marketing suggests.

AI is more useful as a force multiplier for experienced professionals than as a wholesale replacement for human judgment.

Should every medical practice adopt AI billing?

No.

The first question should be whether there is a measurable problem worth solving.

What should a practice automate first?

Start with high-volume, repetitive, rules-based tasks that consume significant staff time and carry relatively low clinical risk.

What should remain human?

Complex coding, compliance-sensitive decisions, ambiguous documentation, unusual cases, and situations requiring clinical or professional judgment should have appropriate human oversight.

What is revenue intelligence?

It is the ability to turn claims, documentation, payer, payment, and operational information into actionable understanding of how the practice is performing.

Why aren't denial reports enough?

Because reports tell you what happened.

They often don't tell you why it happened, whether the pattern is recurring, or what should happen next.

Should physicians care about medical billing?

Yes, but not by becoming billers.

Physicians should understand the major financial and administrative patterns affecting the sustainability of their practices.

What is the biggest mistake practices make?

Treating recurring operational failures as individual incidents.

If the same problem keeps appearing, it deserves a root-cause analysis.


Myth Busters

MYTH: A low denial rate means your billing operation is healthy.

Not necessarily. You may still have underpayments, missed charges, aged A/R, or other leakage.

MYTH: AI accuracy is the only thing that matters.

No. Workflow fit, explainability, security, compliance, escalation, and human oversight matter too.

MYTH: More automation is always better.

No. Bad automation can amplify bad processes.

MYTH: The billing department owns every revenue-cycle problem.

Often false. Problems can originate in scheduling, authorization, documentation, coding, contracting, or payer configuration.

MYTH: Financial optimization conflicts with patient care.

It can if done badly. But a financially healthy practice can sustain staff, technology, access, and patient care. The key is keeping clinical appropriateness at the center.


Practical Resources

Start with what you already have:

EHR data

Practice-management reports

Clearinghouse reports

ERA information

Denial reports

Payer portals

A/R aging

Contract schedules

Provider productivity reports

Then create a simple weekly review.

Ask five questions:

What went wrong?

How often did it happen?

How much did it cost?

Why did it happen?

What will we change?

You don't need a sophisticated AI system to begin thinking this way.

You need curiosity.


The Future Outlook

The next decade of medical billing will probably not be defined by one magical AI system.

It will be defined by the gradual movement from reactive administration to predictive intelligence.

Claims will become more connected to clinical context.

Payer behavior will become more measurable.

Documentation and coding workflows will become more integrated.

Exceptions will become easier to identify.

Human review will become more targeted.

Revenue forecasting will become more dynamic.

And physician-owned practices may finally gain something they have historically lacked:

a clear view of what is happening between the exam room and the bank account.

That is a worthwhile goal.

Not because money is more important than medicine.

Because financial clarity helps physicians keep practicing medicine on their own terms.


About the Author

Dr. Daniel Cham is a physician, healthcare technology consultant, and entrepreneur focused on the practical intersection of medical technology, healthcare management, medical billing, and practice operations.

As founder of OnnX, an AI-powered medical billing SaaS concept, Dr. Cham is exploring how intelligent technology can help small and medium-sized physician practices reduce administrative friction, improve revenue-cycle visibility, identify recurring problems, and make more informed operational decisions.

His perspective is grounded in a simple principle:

Healthcare technology should give physicians more clarity, not more complexity.

Connect with Dr. Cham on LinkedIn to learn more.


Disclaimer

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

Healthcare laws, regulations, payer policies, contracts, coding requirements, and reimbursement methodologies can change and may vary by circumstance.

Practices should consult appropriately qualified professionals before making decisions involving patient care, billing, coding, compliance, contracts, technology implementation, or legal risk.


Continue the Conversation

The most useful healthcare ideas often emerge where medicine, operations, technology, and human experience intersect.

For additional perspectives on healthcare innovation, medical billing, practice management, entrepreneurship, and the changing business of medicine:


Knowledge Drives Progress

Knowledge is useful when it changes what we notice.

What we notice changes the questions we ask.

Better questions create better decisions.

Start looking for the signals your practice has been generating all along.


A Resource for Physicians and Clinic Owners

I've placed a free resource in the Featured section of my LinkedIn profile.

No complicated funnel.

No unnecessary signup.

Just a practical resource you can use to think differently about practice operations, billing, and healthcare technology.

PS: Check the Featured section of my LinkedIn profile for the free resource and start there.


Join the Conversation

If this perspective resonates, repost the article so other physicians, clinic owners, and healthcare leaders can consider the same question:

What is your practice trying to tell you that you haven't noticed yet?

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Ethan Hackney Is Six Years Old and Waiting for a Kidney. What His Story Reveals About the Healthcare Problem We Keep Missing

A six-year-old boy needs a kidney. Healthcare needs something else: fewer barriers between human need and human care. “It felt in some way...