Daniel Cham MD: Giovanni “Gio” Cruz Was Playing Baseball. Then a Rare Disease Changed Everything.

What one family’s journey through medical uncertainty can teach physicians, clinic owners, and healthcare leaders about the human cost of fragmented care—and why better information may be healthcare’s most overlooked innovation.

“The relationship between a patient and their primary care doctor is, at its best, one of the most protective forces in human health – and, right now, we’re systematically dismantling it.” — Lucy McBride, MD, The Guardian, How do you define good health – and achieve it in a broken US medical system?

There is a sentence in a recent healthcare story that I cannot stop thinking about.

Not because it involves artificial intelligence.

Not because it involves a billion-dollar healthcare company.

Not because another payer announced another policy.

It is much simpler than that.

It is about an 11-year-old boy named Giovanni “Gio” Cruz.

In May, Gio was dancing, smiling and playing baseball.

A few weeks later, he was in a children's hospital fighting a rare disease.

His mother, Patricia Cruz, was trying to understand what was happening to her son.

Gio developed severe chest pain. His symptoms escalated. He experienced sweating, back pain and vomiting. He was hospitalized, underwent testing and was eventually transferred to Banner Diamond Children's Medical Center.

Specialists diagnosed him with idiopathic multicentric Castleman disease, a rare immune disorder that can cause widespread inflammation and affect multiple organs.

He is reportedly receiving intensive treatment, including IL-6-directed therapy, high-dose steroids and dialysis.

But the part that stayed with me was not the name of the disease.

It was Patricia's description of uncertainty.

“They want answers from me when I don’t even know what to say or what’s going on.”

Think about that sentence.

Now think about your medical practice.

Because here is my contrarian view:

Healthcare does not have a billing problem.

Healthcare has an information problem.

Billing is simply where the problem becomes expensive.

The patient can live with uncertainty. Your revenue cycle shouldn't have to.

Medicine is complicated.

Rare diseases are complicated.

Patients do not always read the textbook.

Symptoms do not always arrive in neat diagnostic categories.

Sometimes the first test does not solve the mystery.

Sometimes the second test doesn't either.

Sometimes the physician has to say:

“We don't know yet.”

That is not necessarily bad medicine.

Sometimes it is honest medicine.

But imagine the administrative side of the practice saying:

“We don't know why this claim failed.”

Then:

“We don't know where the documentation went.”

Then:

“We don't know who is working on it.”

Then:

“We don't know why the payer didn't pay it.”

Then, six weeks later:

“Let's have a meeting about it.”

That is not clinical uncertainty.

That is operational failure.

And there is a difference.

A very expensive difference.

Here's the uncomfortable question

If a physician can manage a rare disease with a differential diagnosis, why can't the revenue cycle manage a claim with a risk score?

Think about it.

A physician doesn't look at a complex patient and say:

“Everything looks normal. Good luck.”

The physician asks:

What do we know?

What don't we know?

What is most likely?

What could be dangerous?

What information do we need next?

What should we do now?

What requires specialist input?

That is clinical reasoning.

Why shouldn't revenue-cycle technology work the same way?

Instead of:

Claim denied.

Imagine:

Potential authorization problem detected.

Documentation may not support the submitted service.

Payer-specific rule identified.

Missing information detected.

High-risk claim. Human review recommended.

That's a very different philosophy.

It moves billing from:

react → repair

to:

detect → prevent → learn

And that is where I believe healthcare technology is heading.

The great billing myth: “We just need to collect more.”

No.

We need to create fewer problems to collect in the first place.

This is where the healthcare industry sometimes makes me smile.

We build enormous machinery to repair mistakes we could have prevented.

A claim gets denied.

Someone opens it.

Someone researches it.

Someone calls someone.

Someone sends something.

Someone documents the phone call.

Someone updates the account.

Someone resubmits it.

Someone waits.

Someone checks again.

And then someone asks:

“Why is billing so expensive?”

Well...

Look at what we just did.

We turned one preventable problem into a small administrative opera.

Five people.

Three systems.

Two phone calls.

One spreadsheet.

And a partridge in a pear tree.

That's not innovation.

That's organized rework.

The hidden cost isn't the denial

This is the part I wish more physician-owners would measure.

The denial itself is not always the biggest problem.

The bigger problem can be everything surrounding it.

Staff time.

Rework.

Delayed cash.

Management attention.

Payer follow-up.

Appeals.

Patient confusion.

Employee frustration.

Opportunity cost.

And sometimes, the claim is never recovered.

Recent healthcare revenue-cycle analysis has continued to show pressure from denials and revenue leakage. Kodiak Solutions reported that provider organizations experienced a 25% increase in net revenue losses from final denials and bad debt in 2025 in its proprietary analysis.

The precise financial impact varies by organization.

But the direction is hard to ignore.

Revenue leakage is not merely a finance problem.

It is an operational problem.

And here's where small practices get hurt

A large health system can have departments for:

Revenue cycle.

Compliance.

Coding.

Analytics.

IT.

Contracting.

Denials.

Payer relations.

Authorization.

Data science.

A physician-owned clinic?

Maybe it has:

One office manager.

Two billers.

A front-desk team.

A physician who is already seeing patients.

And someone named Linda who somehow knows how the entire practice works.

Every practice has a Linda.

And when Linda leaves?

Suddenly everybody discovers that Linda was the EHR administrator, billing expert, payer encyclopedia, compliance historian and keeper of the sacred spreadsheet.

This is not a technology strategy.

It's institutional memory held together by one exhausted human being.

The future of medical billing isn't about hiring more people to chase more claims.

It is about building systems that require fewer people to chase preventable problems.

That is a very different goal.

And it changes what we should expect from software.

Traditional billing software asks:

What happened?

Modern systems should ask:

What is happening?

The next generation should ask:

What is likely to happen next?

And the really useful system should tell you:

What should we do about it?

That's the difference between a reporting system and an intelligent operating system.

Three experts. One surprisingly consistent lesson.

The most interesting part of the Gio Cruz story is not just the disease.

It is what rare disease teaches us about information.

1. David Fajgenbaum, MD: collaboration changes what is possible

Dr. David Fajgenbaum is a physician-scientist whose personal experience with Castleman disease helped drive the development of the Castleman Disease Collaborative Network.

His work illustrates a powerful principle:

Complex problems improve when information stops living in isolated silos.

Patients.

Physicians.

Researchers.

Families.

Clinical data.

Research data.

The goal is not simply to collect information.

It is to make the information useful to the people who need it.

That lesson translates directly to medical practices.

If your EHR knows something your billing system doesn't...

If your scheduling system knows something your authorization workflow doesn't...

If your billing system knows something your physician-owner doesn't...

You don't have an information system.

You have information islands.

And islands are beautiful on vacation.

They're terrible for healthcare operations.

2. Frits van Rhee, MD, PhD: complexity demands structure

Dr. Frits van Rhee is another major figure in Castleman disease research.

The broader lesson from rare-disease medicine is that complex clinical problems require structured approaches to diagnosis, treatment and collaboration.

The same principle applies to revenue cycle.

A complex claim should not simply be dumped into the same queue as everything else.

It should be recognized as complex.

It should receive appropriate attention.

It should be prioritized.

And, when appropriate, it should be escalated to a human.

Complexity should trigger intelligence, not confusion.

3. Andrew Knight and colleagues: you don't need perfect certainty to act

Recent rare-disease guidance emphasizes practical coordination and support for clinicians caring for patients with uncommon conditions.

That principle is important.

Healthcare often operates before complete certainty exists.

That's normal.

The mistake is believing that uncertainty means the system should become disorganized.

It doesn't.

A good system says:

We don't know everything yet.

But we know what we know.

We know what we don't know.

And we know what we're doing next.

That is exactly how good clinical teams operate.

It should also be how good administrative systems operate.

The lesson hidden inside Gio Cruz's story

Gio's physicians faced a difficult clinical problem.

The disease was rare.

His symptoms were serious.

The diagnosis was not immediately obvious.

The family was frightened.

Nobody could simply press a button and make certainty appear.

But there was still a process.

Evaluation.

Testing.

Specialist involvement.

Diagnosis.

Treatment.

Monitoring.

Adjustment.

That process matters.

Now compare that with a typical denied claim.

What happens?

Usually:

Denial.

Queue.

Research.

Correction.

Resubmission.

Wait.

Maybe payment.

Maybe another denial.

Where is the learning?

Often, it is buried somewhere in a report.

That is the missed opportunity.

A denial should be a teacher

This is one of my favorite contrarian ideas.

Stop treating every denial as a task.

Treat it as a lesson.

If one claim is denied because authorization was missing, that's a task.

If 37 claims are denied because authorization was missing, that's a process failure.

If the same payer creates the same authorization problem every month, that's a systems problem.

And if you continue fixing each claim individually?

Congratulations.

You have built a very efficient machine for repeating the same mistake.

The real question is:

Where did the error begin?

Maybe it started at scheduling.

Maybe eligibility.

Maybe authorization.

Maybe documentation.

Maybe coding.

Maybe charge capture.

The denial is simply where the problem became visible.

Visibility is not origin.

That distinction is crucial.

The billing equivalent of a differential diagnosis

Here is a framework I think physician-owned practices should adopt.

Instead of looking at a denial as:

“Why didn't they pay?”

Ask:

1. What happened?

The claim was denied.

2. What are the possible causes?

Eligibility?

Authorization?

Coding?

Documentation?

Payer policy?

Timely filing?

Technical rejection?

3. What evidence supports each possibility?

Look at the data.

4. What is the most likely root cause?

Find the pattern.

5. What action should happen next?

Assign responsibility.

6. How do we prevent recurrence?

Fix the upstream process.

That's basically clinical reasoning applied to operations.

And frankly, healthcare should be better at this than almost any other industry.

We invented the differential diagnosis.

Why are we still treating billing problems like random acts of nature?

Statistics: don't fall in love with the dashboard

Healthcare leaders love dashboards.

I understand why.

Dashboards look intelligent.

Lots of numbers.

Lots of graphs.

Lots of green.

Very reassuring.

Until the cash doesn't arrive.

A dashboard should not exist to make executives feel informed.

It should exist to help someone make a better decision.

For a physician-owned practice, I would focus on a relatively small group of operational signals:

First-pass acceptance

Denial rate

Denial reason

Days in A/R

A/R aging

Net collection rate

Charge lag

Payment lag

Underpayments

Rework hours

Exception volume

And I would add one more:

Preventable error rate

Because that's where the future is.

Not:

“How many problems did we fix?”

But:

“How many problems did we prevent?”

The metric nobody puts on the wall

Here's another contrarian metric:

Human touches per claim.

How many times does a human have to touch a claim?

One?

Two?

Five?

Ten?

Twenty?

Nobody celebrates this number.

Maybe we should.

Because every human touch has a cost.

Not just payroll.

Attention.

Context switching.

Fatigue.

Opportunity cost.

And error risk.

If a routine claim requires six human touches, don't immediately ask:

“How can we make our staff faster?”

Ask:

“Why does this claim need six human touches?”

That's a better question.

AI is not the answer

Now I am going to disappoint the AI crowd.

AI is not the answer.

There.

I said it.

AI is a tool.

A potentially powerful one.

But if you put AI on top of a broken workflow, you may simply create a faster broken workflow.

That's not transformation.

That's turbocharged dysfunction.

The useful question is:

Where does intelligence actually belong?

AI can help with:

Pattern recognition

Anomaly detection

Claim validation

Risk prediction

Denial classification

Work prioritization

Documentation comparison

Payer-rule analysis

Workflow recommendations

But AI should not become an excuse to remove human accountability.

The ideal relationship is simple:

AI finds.

AI explains.

AI prioritizes.

Human validates.

Human decides when judgment matters.

The most dangerous AI in healthcare

It isn't necessarily the hallucinating chatbot.

It's the AI that looks confident.

Imagine a system that says:

“This claim is fine.”

And everyone believes it.

That's dangerous.

A better system might say:

“This claim appears low risk based on the available information. Here are the validation checks completed.”

And:

“These two elements remain uncertain.”

That's much more useful.

Healthcare does not need artificial confidence.

It needs transparent assistance.

Why I built OnnX

This is the problem that led me to build OnnX.

I am a physician.

I have spent enough time around healthcare to see how often good clinicians are forced to operate inside bad administrative systems.

The problem is not that people don't care.

Usually, they care enormously.

The problem is that the workflow asks them to compensate for system weaknesses manually.

My philosophy is simple:

Capture better information.

Validate earlier.

Surface risk sooner.

Reduce repetitive work.

Give physicians visibility.

Keep people involved where judgment matters.

OnnX is being built around that idea.

Not:

“Let's put AI on billing.”

But:

“Let's rethink what billing should have been doing all along.”

That's a much bigger ambition.

The industry asks the wrong question

The usual question is:

“How do we get more claims paid?”

I think the better question is:

“Why are we creating claims that need fixing?”

That sounds like a small difference.

It isn't.

The first question creates a reactive organization.

The second creates a preventive organization.

The first rewards recovery.

The second rewards learning.

The first measures output.

The second measures system quality.

And that is a fundamental shift.

Five “best practices” I would challenge

Best practice #1: “Denials are inevitable.”

Some are.

Many patterns are not.

If the same preventable denial happens repeatedly, calling it “inevitable” is just a polite way of saying we stopped looking for the cause.

Best practice #2: “Just hire another biller.”

Sometimes staffing is absolutely necessary.

But if the workflow is broken, adding people can hide the problem.

Before adding labor, ask:

Can we eliminate the work?

Then:

Can we automate the work?

Then:

Can we simplify the work?

Only then:

Do we need more people?

Best practice #3: “Billing starts after the encounter.”

No.

Billing starts when information enters the system.

Registration matters.

Eligibility matters.

Scheduling matters.

Authorization matters.

Documentation matters.

Coding matters.

Charge capture matters.

The claim is the final product of a much larger information chain.

Best practice #4: “The physician doesn't need to know.”

The physician doesn't need to know every claim.

But the physician-owner needs to know whether the practice is leaking money.

That's not micromanagement.

That's ownership.

Best practice #5: “More software means better technology.”

Absolutely not.

Sometimes the best technology is the technology that removes a screen.

Or removes a spreadsheet.

Or removes a phone call.

Or removes a manual handoff.

The best workflow is often the one you no longer need.

The front desk may be your most important revenue-cycle department

Here's another uncomfortable truth.

Some billing problems are born before the biller ever sees the claim.

An incorrect demographic field.

An outdated insurance card.

A missing authorization.

A scheduling mismatch.

A payer-specific requirement nobody noticed.

By the time the biller receives the claim, the mistake has already matured.

The biller is now asked to perform archaeology.

This is why I believe revenue-cycle technology needs to move upstream.

Don't wait until the claim becomes a problem.

Catch the problem when it is still cheap to fix.

Fix problems where they begin

This principle is simple:

The farther downstream a mistake travels, the more expensive it becomes.

Consider a missing authorization.

At scheduling:

Easy to correct.

At check-in:

Still manageable.

After the encounter:

More complicated.

After claim submission:

More expensive.

After denial:

Now someone has a work queue.

After appeal:

Now management may be involved.

After timely filing expires:

Maybe the revenue is simply gone.

Same mistake.

Different price.

That's why prevention is so powerful.

The 20-minute physician-owner audit

You don't need a consulting firm.

Start with 20 minutes.

Ask your team:

Question 1

What are our three most common denials?

Question 2

Where does each one actually begin?

Question 3

How many staff hours do we spend fixing them?

Question 4

Which one could we prevent first?

Question 5

What information would have allowed us to catch it earlier?

Question 6

Can technology help us catch it?

Question 7

Who owns the fix?

That's it.

You don't need a 74-slide PowerPoint.

You need answers.

A seven-step revenue-cycle reset

Step 1: Map the journey

Patient registration.

Scheduling.

Eligibility.

Authorization.

Encounter.

Documentation.

Coding.

Charge.

Claim.

Adjudication.

Payment.

Denial.

Appeal.

Do not assume everyone sees the same journey.

Map it.

Step 2: Identify the friction

Where are people copying information?

Where are they switching systems?

Where are they waiting?

Where are they calling?

Where are they manually checking?

Those are opportunities.

Step 3: Find the repeat offender

Which denial keeps coming back?

That's your first target.

Step 4: Trace it upstream

Find where the problem began.

Do not stop at the denial code.

Step 5: Create a rule

If a predictable problem can be identified before submission, create a validation rule.

Step 6: Create an exception path

Not every claim needs human attention.

Not every claim should bypass human review.

Build a smart middle.

Step 7: Measure prevention

Track whether the same problem actually declines.

If it doesn't, your intervention didn't work.

That's okay.

Learn.

Change it.

Try again.

Legal and compliance reality

Here is where enthusiasm needs a seatbelt.

Healthcare technology cannot turn compliance into a checkbox.

Practices must consider applicable requirements involving:

HIPAA

Privacy

Security

Coding

Documentation

Payer contracts

Fraud and abuse

False claims risk

Auditability

Vendor agreements

AI governance

AI does not transfer responsibility to the algorithm.

If an automated recommendation is wrong, the practice still needs appropriate controls.

That is why explainability, audit trails, access controls and human oversight matter.

The goal is not:

“The AI told us to do it.”

The goal is:

“The system identified an issue, showed us why, and the appropriate person made the decision.”

That's defensible.

Ethical question: what happens when revenue optimization wins?

This deserves more discussion.

Healthcare organizations have a legitimate responsibility to collect appropriate payment for services provided.

But optimization can become dangerous when the financial objective becomes more important than clinical truth.

Technology should never encourage:

Unsupported coding.

Misleading documentation.

Unnecessary services.

Aggressive interpretation of clinical facts.

Gaming payer rules.

The goal is not:

maximize the bill.

The goal is:

accurately represent the care that was provided and get appropriately reimbursed for it.

That's a very different philosophy.

Myth Buster

Myth: AI will eliminate billing staff.

Reality: The better objective is to eliminate unnecessary billing work.

Myth: A low denial rate means you're doing great.

Reality: Look at underpayments, aging, write-offs, rework and net collections too.

Myth: The billing department owns the revenue cycle.

Reality: Revenue-cycle performance is influenced by the entire practice.

Myth: Every denial should be appealed.

Reality: The right response depends on the reason, documentation, economics and likelihood of recovery.

Myth: Small practices can't use sophisticated technology.

Reality: Small practices may benefit disproportionately because they have less administrative capacity to absorb inefficiency.

Myth: AI makes billing objective.

Reality: AI reflects the data, rules and assumptions built around it. Human oversight still matters.

The future is not “AI billing”

I don't think that's the right phrase.

I think the future is:

Predictive revenue-cycle management.

The system should know enough to say:

“This looks normal.”

Or:

“This looks unusual.”

Or:

“This claim is likely to encounter a problem.”

Or:

“Here is the reason.”

Or:

“Here is what you should check.”

Or:

“This problem has happened 18 times this month. You should probably fix the process rather than the claims.”

Now we're getting somewhere.

The real competitive advantage for physician-owned practices

It won't necessarily be having the largest staff.

It won't necessarily be having the most software.

It won't necessarily be having the fanciest AI.

It may be something much less exciting:

Knowing what is happening sooner.

That sounds boring.

Good.

Boring is underrated.

Predictable cash flow is boring.

Accurate claims are boring.

Fewer denials are boring.

Employees not spending Friday afternoon fixing the same error for the 400th time is boring.

Boring is wonderful.

Healthcare has enough excitement.

What healthcare founders should learn

If you're building healthcare technology, here's my challenge.

Stop asking:

“Where can we add AI?”

Start asking:

“Where is a human repeatedly compensating for a system failure?”

That's the opportunity.

Find the spreadsheet.

Find the workaround.

Find the person everyone calls when something breaks.

Find the task employees complain about but have accepted as normal.

Find the process that requires three systems.

Find the task someone does every morning because “that's just how we've always done it.”

That's where the innovation is hiding.

A word about middlemen

I am deliberately provocative here.

Healthcare has accumulated layers.

EHR.

Practice management.

Clearinghouse.

Billing company.

Consultant.

RCM vendor.

Coding vendor.

Analytics platform.

Payer portal.

Another portal.

Another login.

Another spreadsheet.

Every layer may have a legitimate purpose.

But every layer also creates a question:

Who actually owns the outcome?

Physician-owned practices should have more visibility and control.

That does not mean every practice must insource everything.

It means outsourcing should not require surrendering intelligence.

If a vendor is managing your revenue cycle, you should still be able to understand:

What is happening.

Why it is happening.

What is being done.

What is being recovered.

What keeps recurring.

And what is being done to prevent it.

Transparency should not be an upgrade.

It should be the baseline.

The OnnX thesis

My thesis behind OnnX is simple:

Healthcare billing is a data-quality problem before it is a billing problem.

Bad information creates bad claims.

Bad claims create denials.

Denials create rework.

Rework consumes labor.

Labor increases cost.

Delayed payment affects cash flow.

And the cycle repeats.

So why start at the end?

Start upstream.

Capture.

Validate.

Predict.

Submit.

Monitor.

Learn.

That's the loop.

What I would do if I owned a small practice tomorrow

Monday morning, I would ask for the last 90 days of denial data.

Not the dashboard.

The actual reasons.

Then I would sort them.

Top 10 causes.

Then I would ask:

Which three are preventable?

Then:

Which one costs us the most?

Then:

Where does it begin?

Then:

Can we catch it before the claim is submitted?

Then:

Can technology help?

And finally:

Who owns the change?

That's it.

No massive transformation project.

No six-month committee.

No 300-page strategy.

Just one problem.

Then another.

The human lesson

Let's return to Gio.

Because this article should not lose the human being who started it.

Giovanni “Gio” Cruz is not a billing problem.

He is not a statistic.

He is not a workflow.

He is not a claim.

He is a child.

His mother, Patricia Cruz, is not a data point.

She is a mother trying to understand what is happening to her son.

The medical team is dealing with a rare and serious condition.

There is uncertainty.

There are questions.

There are decisions.

There is fear.

Healthcare cannot eliminate all of that.

But it can decide how much additional friction to create.

That matters.

Because every unnecessary administrative problem consumes someone's attention.

And attention is one of the most valuable resources in healthcare.

The bigger idea

Maybe the real measure of healthcare technology isn't how much technology we deploy.

Maybe it is how much unnecessary work disappears.

How many clicks?

How many calls?

How many spreadsheets?

How many duplicate entries?

How many preventable denials?

How many hours spent searching?

How many times does a patient have to repeat the same information?

How many times does a physician have to explain something already documented?

How many times does a biller have to repair something that could have been prevented?

That is the technology scorecard I care about.

Not:

How intelligent does the software look?

But:

How much unnecessary friction did it remove?

The next healthcare advantage may be boring

Healthcare loves breakthroughs.

The new drug.

The new device.

The new AI model.

The new platform.

The new billion-dollar startup.

But some of the biggest improvements may look remarkably ordinary.

A claim that never becomes a denial.

A prior authorization caught before the appointment.

A missing field identified before submission.

A physician who can see the practice's financial health in five minutes.

A biller who no longer spends half the day fixing the same error.

A patient who receives a clear statement.

A small practice that keeps more of the revenue it legitimately earned.

None of that makes a flashy conference keynote.

But it matters.

Three questions every physician-owner should ask

1. What problem are we repeatedly fixing instead of preventing?

If you don't know, find out.

2. Where does that problem actually begin?

The denial may not be the origin.

3. What information would have allowed us to catch it earlier?

That question points directly toward better workflow and better technology.

Three actions for this week

Action 1: Find your most expensive recurring problem.

Not the most annoying.

The most expensive.

Action 2: Trace it upstream.

Find the first point where the problem could have been detected.

Action 3: Ask whether the next occurrence can be prevented.

If yes, build the rule.

If no, build the exception workflow.

Then measure what happens.

Final Thoughts: Medicine can tolerate uncertainty. Systems shouldn't manufacture it.

Gio Cruz's story began with uncertainty.

A child became sick.

His family needed answers.

The diagnosis was difficult.

The road ahead remained unclear.

That's medicine.

We should not pretend otherwise.

But there is another kind of uncertainty that physicians and clinic owners do not have to accept.

Where is the claim?

Why was it denied?

Who is fixing it?

Why does this keep happening?

How much money are we losing?

Which problem should we address first?

Those are not mysteries.

They are information problems.

And information problems can be designed better.

That's where I believe healthcare technology has an enormous opportunity.

Not to replace physicians.

Not to replace billers.

Not to make another dashboard.

Not to sprinkle AI dust over an old workflow and call it innovation.

But to make the right information available earlier, to the right person, with the right next action.

That is what intelligent healthcare operations should look like.

And if we get that right, something interesting happens.

The technology becomes almost invisible.

The physician gets more time.

The staff gets less rework.

The practice gets better visibility.

The patient gets a smoother experience.

And healthcare becomes just a little more human.

That is the kind of innovation worth building.

Get Involved: Don't Just Read This. Challenge It.

Here is my question for physicians and clinic owners:

If your practice could identify a preventable billing problem before the claim was submitted, why would you wait for the denial?

Maybe you disagree.

Good.

Tell me in the comments.

What is the biggest source of administrative friction in your practice today?

Denials?

Authorization?

Coding?

Eligibility?

Documentation?

Payer rules?

Or something nobody is talking about?

Daniel Cham MD

Sunday, August 30, 2026

Giovanni “Gio” Cruz Was Playing Baseball. Then a Rare Disease Changed Everything.

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Giovanni “Gio” Cruz Was Playing Baseball. Then a Rare Disease Changed Everything.

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