Saturday, July 4, 2026

She Rowed 2,400 Miles Across the Pacific in 43 Days—Most Clinics Can’t Even Move Revenue Cleanly Through a Single Patient Visit





“She could see the ocean she was crossing. Most clinics cannot see the revenue they are losing.”

 “Tonight, hear the American woman making history.”

Kelsey Pfendler became the first American woman—and the youngest at 30—to row solo from California to Hawaii.

2,400 miles.
43 days.
17 hours. 55 minutes.

No team. No safety net. No second chances.

Every stroke mattered.

Every decision accumulated.

There was no billing department at the end to “fix” mistakes made mid-ocean.

And that is where healthcare quietly breaks.

Because most clinics are also crossing an ocean.

They just assume someone will fix the boat later.

THE TRUTH PHYSICIANS DON’T HEAR OFTEN

Most physicians believe:

Revenue problems happen in billing.

That belief is comfortable.

And wrong.

The real loss happens much earlier.

At the moment of:

  • clinical documentation
  • encounter structure
  • cognitive overload during care
  • fragmented data capture
  • unclear translation into coded reality

By the time billing “sees” the claim…

The outcome has already been decided.

Billing does not generate revenue. It only reveals what the system already failed to capture.

THE REAL PROBLEM IS NOT BILLING—IT IS VISIBILITY

Healthcare is not a financial problem first.

It is a signal integrity problem.

What happens in the clinic is rich, complex, and clinically meaningful.

But what gets captured is:

  • compressed
  • interpreted
  • fragmented
  • delayed
  • reconstructed

So the system behaves like this:

Clinical reality → translation loss → billing reconstruction → payer judgment

Every step reduces fidelity.

WHY THE SYSTEM FEELS LIKE IT IS BREAKING

Physicians feel it as:

  • “denials are increasing”
  • “billing is getting harder”
  • “we need better coders”
  • “RCM is broken”

But these are downstream symptoms.

The upstream truth is simpler:

Healthcare is trying to financially process unstructured human cognition in real time.

That mismatch does not scale.

THE ROWING METAPHOR ISN’T JUST STORYTELLING

Ocean rowing is not about strength.

It is about system discipline under isolation.

Every failure compounds:

  • navigation error → drift
  • energy miscalculation → exhaustion
  • delayed correction → compounding deviation

Now replace “ocean” with “clinical workflow.”

And the same logic applies.

But here is the difference:

In rowing, you see the drift immediately.

In healthcare, you see it weeks later in denied claims.

THE UNCOMFORTABLE NUMBER

Across independent clinics:

  • 15–30% revenue leakage is still common
  • not due to payer rejection alone
  • but due to preventable ambiguity at capture

And here is the part nobody says clearly:

You cannot fix what was never structured correctly in the first place.

WHY MOST “RCM IMPROVEMENTS” FAIL

Clinics keep investing in:

  • billing software
  • denial management
  • coding audits
  • outsourced RCM teams

But these tools assume a broken premise:

That downstream correction can fix upstream ambiguity.

It cannot.

It only organizes the cleanup.

THE REAL FAILURE POINT

Let’s name it clearly:

  • Physicians document for memory, not structure
  • Coders interpret intent after the fact
  • Billing reconstructs missing context
  • Payers adjudicate incomplete signals

Everyone is working hard.

No one owns data fidelity at the moment of care.

That is the gap.

WHY THIS IS NOW BECOMING MORE EXPENSIVE

Healthcare is shifting toward:

  • value-based reimbursement
  • automated claim validation
  • AI-driven audits
  • real-time compliance systems

Which means:

ambiguity is no longer just inefficient—it is financially punishable.

The system is becoming less forgiving.

Not more.

THE AI MISUNDERSTANDING

A growing assumption:

“AI will fix billing.”

No.

AI does not fix ambiguity.

It scales it.

If the input is unclear:

  • AI makes it faster
  • more consistent
  • and harder to detect

So the real question is not:

Can we use AI?

It is:

Can we structure clinical reality before AI touches it?

THE ONNX SHIFT

At OnnX OnnX, the thesis is simple:

Revenue is not collected after care. It is designed during care.

That means moving focus upstream:

  • structured clinical capture
  • real-time documentation intelligence
  • reduced interpretive loss
  • direct alignment between care and coding logic

Not faster billing.

Not better denial recovery.

But preventing ambiguity from entering the system.

THE NEW DEFINITION OF REVENUE CYCLE

Old model:

Care → documentation → coding → billing → denial → correction

New reality:

Care → structured capture → validated logic → clean claim → minimal friction

Everything else is compensation for upstream failure.

THE 5 HIDDEN FAILURE MODES

Most clinics don’t see these clearly:

  1. Documentation built for humans, not systems
  2. Clinical nuance lost in translation layers
  3. Coding treated as interpretation instead of alignment
  4. Revenue measured after leakage occurs
  5. Tools added without removing structural friction

Each one compounds silently.

LEGAL AND COMPLIANCE REALITY

This is no longer just operational inefficiency.

It is increasingly:

  • audit exposure risk
  • documentation liability
  • reimbursement defensibility issue
  • compliance traceability requirement

Because payers and regulators are shifting toward:

  • algorithmic claim validation
  • structured data review
  • automated anomaly detection

Unstructured documentation becomes a liability surface.

ETHICAL LAYER MOST PEOPLE MISS

This is not about maximizing reimbursement.

It is about:

  • accurate representation of care
  • preserving clinical intent
  • ensuring fair system translation
  • maintaining trust in medical records

Bad structure is not just inefficient.

It distorts reality downstream.

PRACTICAL SHIFT: WHAT HIGH-PERFORMING CLINICS DO DIFFERENTLY

They stop asking:

“How do we fix billing?”

They start asking:

“How do we eliminate ambiguity before it exists?”

That single shift changes:

  • revenue consistency
  • operational stress
  • coding accuracy
  • denial volume
  • staff cognitive load

STEP-BY-STEP SHIFT FRAMEWORK

  1. Map where documentation becomes billing data
  2. Identify ambiguity points in encounters
  3. Standardize clinical capture structure
  4. Align coding logic earlier in workflow
  5. Measure revenue integrity, not just denial rates

TOOLS AND METRICS THAT ACTUALLY MATTER

Forget vanity metrics.

Focus on:

  • clean claim rate
  • first-pass acceptance rate
  • documentation completeness
  • coding variance
  • revenue per encounter stability

These expose system health.

Not symptoms.

FUTURE OUTLOOK

Within 3–5 years:

  • claims will be validated before submission
  • documentation will be AI-assisted by default
  • real-time revenue feedback loops will emerge
  • coding will shift upstream into care workflows
  • RCM will merge with clinical intelligence systems

The separation between “care” and “billing” will collapse.

FINAL INSIGHT

If your system needs correction after the fact…

It was never designed correctly at the source.

Healthcare is not failing because people are careless.

It is failing because:

  • clinical reality is rich
  • financial systems are rigid
  • and the translation layer between them is outdated

Until that gap is fixed upstream:

  • denials will persist
  • margins will tighten
  • complexity will grow
  • physicians will absorb system friction

The solution is not more correction.

It is less ambiguity.

CALL TO ACTION

Here is the real question:

Where is your revenue actually being lost?

At billing?

Or at the moment of documentation?

Comment with what you see in your practice.

Share this with a physician still optimizing the wrong layer.

And consider this:

  • Are you reacting to revenue loss?
  • Or designing systems where loss cannot occur?

Get involved. Get on board. Step into the conversation. Start your journey. Be part of something bigger. Engage with the community. Raise your voice. Be the change. Take the first step. Make your move. Ignite your momentum. Start here. Build your knowledge base. Explore the insights. Help shape the future.

ABOUT THE AUTHOR

Dr. Daniel Cham is a physician and medical consultant specializing in healthcare systems, medical technology, and revenue cycle transformation. He focuses on practical, system-level insights that help clinics improve operational clarity and financial integrity.

Connect with Dr. Cham on LinkedIn to learn more.

DISCLAIMER

This article is for informational purposes only and does not constitute medical or legal advice. Professional consultation is recommended for specific operational decisions.

CONTINUE THE CONVERSATION

Explore practical insights, evidence-based strategies, and behind-the-scenes perspectives that help physicians and clinic leaders navigate complex challenges.

Knowledge drives progress — start your journey today.

FEATURED RESOURCE

Check the LinkedIn Featured section for your free download—no signup required.

If this perspective resonates, consider resharing to help other physicians rethink how revenue is actually created—not just collected.

REFERENCES

1. CMS Improper Payment Data (FY 2025 Report)

The Centers for Medicare & Medicaid Services reports a 6.55% improper payment rate (~$28.8B) in Medicare Fee-for-Service, with a significant portion linked to documentation and coding gaps.

2. Revenue Leakage in Healthcare (Industry Analysis)

Industry benchmarks show healthcare organizations lose approximately 4–5% of net revenue annually due to documentation gaps, coding errors, and denied claims—highlighting upstream workflow failures as the root cause.

3. Clinical Documentation as Revenue Cycle Risk

Revenue cycle surveys show 84% of healthcare finance leaders identify clinical documentation and coding as major revenue vulnerabilities, directly linking documentation quality to denial rates and reimbursement accuracy.

#HealthcareLeadership #MedicalBilling #RevenueCycleManagement #PhysicianEntrepreneurs #HealthTech #DigitalHealth #ClinicalOperations #MedicalPracticeManagement #HealthcareInnovation #AIinHealthcare #HealthcareStrategy #IndependentPhysicians #HealthcareSystems #MedTech #FutureOfHealthcare #ClinicalDocumentation #PracticeEfficiency #HealthcareFinance #PhysicianLeadership #HealthcareTransformation

  

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WHEN “DEAD” IS NOT THE FINAL WORD: WHAT A TODDLER CASE REVEALS ABOUT THE BROKEN DATA PIPELINE IN HEALTHCARE

 



“The patient is the center of the medical experience.” — Abraham Verghese

A CHILD WAS DECLARED DEAD… THEN FOUND ALIVE HOURS LATER

An 18-month-old boy is brought into an emergency department after a drowning incident. Resuscitation begins. The team works under pressure. Emotions are high. Time is collapsing.

At one point, a physician leaves the room to prepare for a death notification.

Then something changes.

A nurse detects a pulse.

The team continues efforts.

But the child is still pronounced dead.

Hours later, during post-procedure handling, the child is found to be alive.

He survives.

The hospital launches a review. Questions emerge around communication, timing, documentation, and decision confirmation loops.

No single actor “failed.”

The system did.

And this is where most physicians quietly nod—because they recognize this pattern immediately.

Not just in emergency medicine.

But in documentation. Billing. Coding. Claims. Prior authorizations. Revenue cycles.

THE UNSEEN PARALLEL: CLINICAL ERROR AND ADMINISTRATIVE ERROR ARE THE SAME PROBLEM

Most physicians think of mistakes as clinical events.

But modern healthcare has two parallel systems:

  • Clinical care system
  • Financial + documentation system

And both depend on the same fragile input:

HUMAN DATA UNDER PRESSURE

When that data breaks, consequences differ—but the root cause is identical:

Broken capture → delayed correction → systemic distortion

In clinical care, it may affect outcomes.

In billing, it affects revenue, compliance, and sustainability.

WHY THIS MATTERS FOR PHYSICIANS AND CLINIC OWNERS

Let’s translate the hospital story into clinic reality:

In your clinic, this looks like:

  • Missing documentation leading to denied claims
  • Upcoded services rejected weeks later
  • Undercoded visits silently draining revenue
  • Prior authorization delays impacting patient care
  • Staff rework cycles consuming 15–30% of admin time

The outcome is not dramatic like a headline.

But it is financially and operationally devastating over time.

THE REAL PROBLEM: HEALTHCARE DATA IS NOT CAPTURED IN REAL TIME

Most clinics still rely on:

  • Post-visit documentation
  • Manual coding workflows
  • Fragmented billing vendors
  • Delayed claims validation

This creates a dangerous gap:

Clinical reality ≠ documented reality ≠ billed reality

And when those three diverge, you get:

  • Revenue leakage
  • Compliance exposure
  • Staff burnout
  • Physician frustration
  • Patient friction

KEY STATISTICS EVERY CLINIC OWNER SHOULD KNOW

Revenue Leakage & Billing Inefficiency

  • Up to 15–25% of clinic revenue is lost due to coding and billing inefficiencies (MGMA benchmarks)
  • Denial rates commonly range between 5–10% of all claims
  • Nearly 40% of denied claims are never reworked

Administrative Burden

  • Physicians spend an estimated 15–20% of their time on documentation tasks
  • Billing-related admin costs account for 25–30% of total practice overhead

Error Propagation

  • Documentation errors propagate into billing errors in 1 in 5 outpatient encounters

INSIGHT: THE SYSTEM DOES NOT FAIL AT COLLECTION — IT FAILS AT CAPTURE

The healthcare industry is obsessed with:

  • Faster billing
  • Better collections
  • Outsourcing revenue cycle management

But the real bottleneck is earlier:

DATA CAPTURE AT THE POINT OF CARE

If the input is wrong, everything downstream becomes optimization theater.

PRACTICAL PERSPECTIVE: WHAT HIGH-PERFORMING CLINICS DO DIFFERENTLY

Top-performing clinics do not “work harder.”

They design systems around three principles:

1. Real-time documentation capture

Reduce memory dependency post-visit.

2. Automated coding intelligence

Reduce human interpretation variability.

3. Closed-loop billing feedback

Every denial is traced to its origin, not patched at the surface.

EXPERT ROUND-UP: WHAT LEADING VOICES IN HEALTHCARE ARE SAYING

Dr. Robert Wachter (UCSF – Digital Health Leader)

Emphasizes that healthcare failure is increasingly a systems design problem, not individual incompetence.

Dr. Eric Topol (Scripps Research)

Highlights the need for AI-assisted clinical workflows that reduce cognitive overload and documentation burden.

Dr. Ziad Obermeyer (Health AI Researcher, UC Berkeley)

Focuses on how data quality determines downstream AI and operational outcomes in healthcare systems.

MYTH BUSTERS IN MEDICAL BILLING

Myth 1: “Good coders fix bad documentation.”

Reality: They can only interpret what exists. They cannot reconstruct missing clinical context.

Myth 2: “Billing is back-office work.”

Reality: Billing is a direct extension of clinical documentation quality.

Myth 3: “More staff solves revenue issues.”

Reality: More staff amplifies broken workflows unless the system itself changes.

PITFALLS CLINICS FALL INTO

  • Over-reliance on third-party billing vendors
  • Lack of real-time audit visibility
  • No structured feedback loop from denials
  • Physician burnout leading to documentation shortcuts
  • Fragmented software stack (EHR ≠ billing intelligence)

LEGAL AND COMPLIANCE IMPLICATIONS

Billing inaccuracies are not just financial issues.

They carry regulatory exposure:

  • False Claims Act risk
  • Audit penalties
  • Credentialing risk
  • Payer contract termination

Even unintentional errors can escalate when systemic.

ETHICAL CONSIDERATIONS

Healthcare documentation is not just administrative work.

It is:

  • A legal record
  • A financial instrument
  • A clinical communication tool

When documentation is inaccurate:

  • Patients are misrepresented
  • Physicians are exposed
  • Systems lose trust

STEP-BY-STEP: HOW MODERN CLINICS SHOULD REBUILD BILLING WORKFLOWS

Step 1: Capture structured data at point of care

Reduce narrative-only dependency.

Step 2: Standardize coding logic across providers

Reduce variability in interpretation.

Step 3: Automate claim validation before submission

Prevent downstream denial loops.

Step 4: Integrate feedback from payers into workflow

Close the loop between denial and documentation.

Step 5: Use AI-assisted billing intelligence

Shift from reactive billing to predictive billing.

TOOLS, METRICS, AND OPERATIONAL DASHBOARD INDICATORS

Clinics should actively track:

  • Clean claim rate (%)
  • Denial rate (%)
  • Days in A/R
  • Revenue per encounter
  • Documentation completion time
  • Coding variance across providers

If you cannot measure it, you cannot optimize it.

RECENT INDUSTRY CONTEXT (EMERGING THEMES IN 2026)

Across healthcare systems, three shifts are accelerating:

1. AI documentation assistance is becoming standard

Reducing physician typing burden.

2. Payers are tightening real-time validation

Claims are being evaluated more strictly at submission.

3. Small clinics are consolidating tech stacks

Moving away from fragmented billing vendors.

FUTURE OUTLOOK: WHERE THIS IS HEADED

Healthcare billing is moving toward:

  • Real-time claim validation
  • AI-assisted documentation correction
  • Autonomous coding suggestions
  • End-to-end revenue intelligence systems

The next generation of clinics will not “submit claims.”

They will operate in a continuous billing validation environment.

THE BIG IDEA

The toddler case is not just a medical anomaly.

It is a reminder that:

When systems rely on delayed confirmation, errors persist longer than they should.

The same is true in billing.

And the cost is silent—but cumulative.

WHY ONNX EXISTS

OnnX was built around a simple premise:

Eliminate middlemen and reduce friction between clinical reality and financial reality.

We focus on:

  • AI-powered medical billing automation
  • Real-time coding intelligence
  • Reduction of administrative overhead
  • Direct clinic control over revenue workflows

Not as theory.

But as operational infrastructure.

FREQUENTLY ASKED QUESTIONS (FAQ)

Q1: Is AI billing replacing medical coders?

No. It is augmenting them by reducing repetitive interpretation work.

Q2: What causes most claim denials?

Documentation gaps, coding mismatch, and missing clinical specificity.

Q3: Can small clinics benefit from automation?

Yes. Smaller clinics often benefit the most due to limited administrative staff.

Q4: Is billing really a clinical issue?

Yes. Billing accuracy is directly tied to documentation quality.

Q5: How fast can clinics see impact from optimization?

Typically within one billing cycle when workflows are corrected.

FINAL THOUGHTS

Healthcare is not broken in one place.

It is fragmented across many small delays:

  • A delayed chart entry
  • A missed code
  • A denied claim
  • A rework cycle
  • A lost payment

Individually small.

Collectively expensive.

The question is not whether systems need improvement.

The question is:

How long can modern clinics afford to operate with disconnected systems?

ABOUT THE AUTHOR

Dr. Daniel Cham is a physician and medical consultant specializing in healthcare technology, medical billing systems, and operational optimization for clinical practices. He focuses on translating complex healthcare workflows into practical systems that improve efficiency, compliance, and financial performance.

Connect with Dr. Cham on LinkedIn to learn more.

DISCLAIMER

This article is intended to provide an overview of healthcare operational and billing concepts and does not constitute medical or legal advice. Readers should consult appropriate professionals for specific guidance.

CONTINUE THE CONVERSATION

Explore insights, practical strategies, and behind-the-scenes perspectives designed to improve clinical operations, financial performance, and healthcare innovation.

Continue the Conversation

Explore practical insights, evidence-based strategies, and behind-the-scenes perspectives that help physicians and clinic leaders navigate complex challenges.

Knowledge drives progress. Start your journey here.

Check the Featured section on LinkedIn for a free resource—no signup required.

CALL TO ACTION

What breaks first in your practice: documentation, coding, or billing?

Drop a comment with your experience.

Share this post with a physician or clinic owner who is tired of revenue leakage.

Get involved. Step into the conversation. Start here. Make your move. Take action today. Be part of shaping a more efficient healthcare system.

References

1. AI in Healthcare Revenue Cycle Transformation (Industry Analysis)

Healthcare systems are rapidly deploying AI to reduce revenue leakage, coding errors, and administrative burden, especially in billing and denial management workflows. However, most gains are still limited to partial automation rather than full system transformation.

Source: Healthcare Finance News — “The AI arms race in the revenue cycle”

2. AI Increasing Revenue Cycle Efficiency but Raising System Complexity (Recent Industry Report)

AI adoption in healthcare billing is expanding quickly, but organizations still report challenges around data accuracy, compliance risk, and integration with existing workflows, limiting full ROI realization.

Source: Experian Health — “AI in healthcare RCM: 2026 opportunities and insights”

3. Leading Healthcare Systems Expanding AI in Billing & Coding Workflows (Case Study)

Major institutions like Mayo Clinic are actively exploring AI in coding, documentation, and revenue cycle management, but emphasize that full automation remains constrained by clinical complexity and documentation variability.

Source: MedCity News — “How Is Mayo Clinic Using AI in Its Revenue Cycle?”

#HealthcareInnovation #MedicalBilling #PhysicianBurnout #HealthTech #AIinHealthcare #RevenueCycleManagement #HealthcareOperations #ClinicalWorkflow #DigitalHealth #MedTech #PracticeManagement #HealthcareEfficiency #OnnX

 

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Friday, July 3, 2026

The Heatwave Didn’t Break Healthcare. It Revealed What Was Already Failing Quietly.

 



“Systems don’t fail when they are tested. They fail when their hidden assumptions are exposed.”

What the news is showing—and what it is missing

New York City just hit 100°F for the first time in over a decade.

Across the United States, 180 million people are under extreme heat alerts.

Emergency departments are reporting spikes in heat stroke, dehydration, and collapse events. Paramedics are seeing a sharp rise in calls. Hospitals are stretched. Power grids are strained.

The public story is simple:

It’s the heat.

But that explanation is incomplete.

Because heat is not the cause of failure.

Heat is the trigger that exposes existing system fragility.

And healthcare already operates with that same hidden fragility every day.

Contrarian truth most physicians won’t hear in training

Healthcare systems do not collapse from rare events.

They collapse from normal conditions that were never truly stable to begin with.

The heatwave didn’t create new problems.

It revealed:

  • Operational inefficiencies already present
  • Capacity assumptions that were never realistic
  • Workflow gaps that were always there
  • Invisible bottlenecks tolerated as “normal”

This is not an emergency medicine lesson.

It is a healthcare operations lesson.

The uncomfortable parallel: your clinic is the same system

What is happening in emergency departments right now is structurally identical to what happens in outpatient clinics—just slower and less visible.

Think in systems terms:

  • Heat stroke cases → sudden demand spikes
  • ED overcrowding → clinic backlog accumulation
  • Staffing strain → administrative overload
  • Delayed care → delayed claims
  • Resource saturation → revenue leakage

Different environment.

Same architecture.

The real failure point is not clinical—it is structural

Most physicians assume the breakdown happens at the point of care.

It doesn’t.

The breakdown happens earlier:

At the moment information is first created.

That includes:

  • Clinical documentation
  • Intake data capture
  • Coding interpretation
  • Workflow transitions
  • Billing handoffs

If the input is incomplete, everything downstream becomes correction work.

And correction work is expensive.

Why revenue loss feels invisible in most clinics

Most clinics do not “see” revenue loss in real time.

They feel it later as:

  • Denials
  • Delayed payments
  • Lower-than-expected collections
  • Staff burnout
  • Increasing administrative burden

But by then, the loss has already occurred.

Because healthcare revenue loss is not linear.

It is cumulative and delayed.

A clinic can feel 90% efficient while operating at 70–85% actual revenue capture.

Not because of incompetence.

Because friction compounds silently.

The heatwave analogy physicians should actually care about

Heatwaves don’t kill systems directly.

They expose what cannot tolerate stress.

In healthcare systems:

  • Small documentation inconsistencies become coding errors
  • Minor workflow gaps become claim failures
  • Slight delays become cash flow disruption
  • Minor inefficiencies become structural breakdowns under volume

The system does not fail because something new happens.

It fails because something already weak gets stressed.

Most RCM strategies optimize the wrong layer

The healthcare industry is heavily focused on:

  • Denial management
  • Coding optimization
  • Claim scrubbing tools
  • Outsourced billing services
  • AI-assisted coding tools

These are all downstream interventions.

They assume the data is already correct.

But in reality:

If upstream clinical data is inconsistent, downstream optimization is just expensive cleanup.

This is why many clinics feel like they are “fixing billing” without actually improving financial performance.

They are polishing output built on unstable input.

What actually drives revenue stability

Revenue stability is not a billing function.

It is a data design function at the point of care.

High-performing clinics share one hidden trait:

They reduce ambiguity early.

That means:

  • Standardized documentation patterns
  • Structured intake workflows
  • Reduced interpretation variability
  • Clear clinical-to-billing mapping
  • Fewer assumptions in coding handoffs

This is not administrative work.

It is system architecture.

What AI in healthcare is missing

AI is often positioned as the solution to billing inefficiency.

But AI cannot fix:

  • Missing clinical context
  • Ambiguous documentation
  • Inconsistent inputs
  • Fragmented workflows

AI does not correct structure.

It scales whatever structure exists.

If the system is weak, AI makes it faster—not better.

Why clinics fail under pressure (and don’t realize it)

Just like emergency departments during heatwaves:

Everything feels manageable—until it isn’t.

Under increased load:

  • Documentation shortcuts increase
  • Cognitive overload rises
  • Coding becomes less precise
  • Follow-up gaps widen
  • Revenue capture becomes inconsistent

The failure is not sudden.

It is progressive and hidden.

Key insight most physicians overlook

Healthcare does not have a complexity problem.

It has a friction tolerance problem.

Small inefficiencies are tolerated because individually they seem harmless.

But collectively, they define system performance.

The real question clinic owners should be asking

Not:

  • “How do we improve billing?”

But:

“Where is revenue leaking before billing ever begins?”

Because once revenue reaches billing, the system has already decided its fate.

Step change in thinking

Old model:

Care → Documentation → Billing → Correction → Payment

New model:

Care → Structured Data → Deterministic Billing → Clean Payment

The difference is not software.

It is design philosophy.

Final perspective

The heatwave is temporary.

But what it revealed is not.

Healthcare systems are not fragile because they are complex.

They are fragile because they tolerate unseen inefficiencies for too long.

And eventually, conditions expose them.

Closing challenge

If your clinic depends on downstream correction…

You do not have a billing system problem.

You have a design problem.

And design problems do not resolve themselves.

They only get exposed.

Call to Action

Ask yourself:

  1. Where in your clinic workflow does information first become “uncertain”?
  2. How much revenue depends on correction instead of precision?
  3. What would change if revenue capture was built upstream—not repaired downstream?

Comment your biggest operational friction point.

Share this with a physician who still believes billing is the problem.

Because it isn’t.

It never was.

About the Author

Dr. Daniel Cham is a physician and founder of OnnX, an AI-powered medical billing platform focused on eliminating friction in healthcare revenue systems. His work focuses on upstream system design, clinical documentation structure, and revenue integrity for independent clinics.

Connect with Dr. Cham on LinkedIn to learn more.

Continue the Conversation

Explore practical insights, evidence-based strategies, and behind-the-scenes perspectives that help physicians and clinic leaders navigate complex challenges.

Knowledge drives progress — start your journey today.

Disclaimer

This content is for informational and educational purposes only and does not constitute medical, legal, or financial advice. Readers should consult appropriate professionals for specific guidance.

References

1. CDC – Heat-Related Illness & Emergency Department Surveillance
The CDC tracks rising emergency department visits and hospitalizations linked to extreme heat, highlighting heat waves as a major and growing public health threat in the United States.

2. CDC MMWR – Heat-Related Emergency Department Visits in the United States
A national surveillance report showing significant increases in heat-related emergency visits across regions, especially among working-age adults during extreme heat seasons.

3. CDC Heat & Health Tracker (National Syndromic Surveillance Program)
A real-time public health monitoring system that uses emergency department data to track heat-related illness trends and support community response during extreme heat events.

#HealthcareLeadership #HealthcareInnovation #PhysicianEntrepreneur #HealthcareSystems #MedicalBilling #RevenueCycleManagement #HealthcareAI #DigitalHealth #ClinicalOperations #HealthcareStrategy #MedTech #HealthcareFinance #HospitalOperations #PrimaryCare #PhysiciansOfLinkedIn #HealthcareTransformation #SystemDesign #OperationsStrategy #HealthTech #HealthcareEfficiency #ClinicManagement #AIinHealthcare

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Wednesday, July 1, 2026

They Climbed the Empire State Building in Plain Sight. Healthcare Billing Works the Same Way.

 


“Every system is perfectly designed to get the results it gets.” — W. Edwards Deming (quality systems pioneer)

Most people will see the Empire State Building stunt and think: “security failure.”

That’s the wrong lesson.

The real lesson is more uncomfortable:

The system didn’t fail. It behaved exactly as designed—just not for the outcome everyone assumed.

Two climbers reach over 1,400 feet, scale a globally recognized landmark, and unfurl a banner in full public view.

Security existed. Protocols existed. Surveillance existed.

And yet—someone still made it to the top.

Not because there was no system.

But because the system was optimized for routine threats, not edge-case behavior.

Now translate that into healthcare.

The Uncomfortable Parallel Physicians Don’t Want to Admit

Most independent clinics assume:

  • “Our billing is handled.”
  • “Our RCM vendor is managing it.”
  • “Denials are just part of the game.”
  • “This is just how healthcare works.”

But that mindset is exactly like standing at the base of the building saying:

“Security is present, so nothing can go wrong.”

Meanwhile, the real leakage is happening somewhere between floors 10 and 102.

Quiet. Distributed. Normalized.

The Real Stunt Wasn’t on the Roof

The stunt wasn’t climbing the Empire State Building.

The stunt was how easily complexity disguised itself as safety.

Because what looked like “controlled systems” was actually:

  • layered responsibility
  • fragmented accountability
  • delayed detection
  • and assumptions that someone else is watching the critical point

That is also modern medical billing.

Just replace:

  • rooftop access → claim submission
  • security guards → coding teams
  • building surveillance → clearinghouse edits
  • police response → denial management

And the pattern becomes uncomfortably familiar.

Healthcare Billing Isn’t Broken

It’s stable.

And that’s the problem.

Because it is stable in the same way an old bridge is stable:

  • It holds under normal conditions
  • It passes inspections
  • It appears “good enough”
  • Until load patterns change slightly

Then the weak joints reveal themselves.

In healthcare, those “load changes” are:

  • payer rule updates
  • documentation variability
  • staff turnover
  • EHR templating drift
  • coding interpretation gaps

And suddenly:

15–30% revenue leakage becomes “normal variance.”

The Hidden Truth About Middlemen

Every layer added to “improve billing” does two things at once:

  1. It reduces local workload
  2. It increases system distance from truth

So clinics end up with:

  • clinicians documenting one reality
  • coders translating another
  • billing teams submitting a third
  • payers adjudicating a fourth

By the time money moves, no one is looking at the same system anymore.

That’s not efficiency.

That’s distributed misunderstanding at scale.

Why Physicians Feel Like Things Are Getting Worse (Even When Revenue Is “Stable”)

This is the paradox:

Revenue cycle reports often show stability.

But physicians feel instability.

Why?

Because stability is being maintained through:

  • more rework
  • more appeals
  • more staffing
  • more back-and-forth corrections

So the system doesn’t collapse.

It absorbs friction.

Silently.

Expensively.

Continuously.

What the Empire State Incident Actually Reveals

The climbers didn’t break physics.

They exploited blind spots between enforcement layers.

Not one failure.

A chain of acceptable tolerances.

That’s the same structure inside most clinic billing systems:

  • Each step is “acceptable”
  • Each vendor is “doing their job”
  • Each denial is “normal”
  • Each correction is “handled downstream”

Until you zoom out and realize:

No one owns the full outcome.

The Real Question Physicians Should Be Asking

Not:

  • “Why are we getting denials?”

But:

“Why does our system require interpretation at every step before we get paid?”

Because interpretation is where revenue dies.

Not in coding.

Not in billing.

In translation.

Between:

clinical intent → structured data → payer logic

Every translation step introduces variance.

And variance is where revenue leakage hides.

A More Dangerous Insight

Most clinics are not underperforming.

They are over-mediated.

Meaning:

They don’t have a performance problem.

They have a distance-to-truth problem.

What High-Performing Systems Actually Do Differently

Whether in aviation, finance, or logistics, high-performance systems share one principle:

Reduce the number of human interpretations between action and outcome.

Healthcare did the opposite for decades.

We added interpreters:

  • coders
  • auditors
  • billing vendors
  • clearinghouses
  • prior auth intermediaries

Each one necessary in isolation.

But collectively:

they create latency where accuracy should live.

The Real Fix Is Not “Better Billing”

This is where most solutions go wrong.

They say:

  • improve coding accuracy
  • improve denial management
  • improve RCM workflows

But that is like adding more cameras after someone already reached the rooftop.

You don’t need more observation.

You need fewer ambiguous transitions.

Expert Perspectives on System Failure in Healthcare

To understand why these “silent failures” persist in healthcare billing, it helps to look at how leading voices in medicine and health systems think about complexity.

 

1. Dr. Atul Gawande — Complexity vs. Reliability

A consistent theme in Dr. Atul Gawande’s work is that modern healthcare does not fail because of lack of knowledge, but because of lack of reliable systems.

His core idea is simple:

High performance in medicine comes from reducing unnecessary variation, not increasing effort.

In the context of billing systems, this translates directly:

When every clinic, coder, and payer interprets the same event differently, the system becomes unpredictable—even if each actor is “doing their job.”

 

2. Dr. Donald Berwick — Systems Over Blame

Dr. Donald Berwick, former CMS administrator and founder of the Institute for Healthcare Improvement, has long emphasized that healthcare outcomes are determined more by system design than individual performance.

His central principle:

“Every system is perfectly designed to get the results it gets.”

Applied to revenue cycle management:

If denials, delays, and leakage are common, it is not a staffing issue.

It is a design outcome.

Not a failure of people—
a reflection of architecture.

 

3. Dr. Ezekiel Emanuel — Administrative Burden as Structural Cost

Health policy expert Dr. Ezekiel Emanuel has repeatedly highlighted that administrative complexity is one of the largest hidden cost drivers in U.S. healthcare.

His perspective reinforces a key insight:

Administrative layers do not just manage care—they reshape it.

In billing systems, each added intermediary:

  • increases transaction cost
  • slows feedback loops
  • and distances clinicians from financial truth

Over time, this creates a system where compliance replaces clarity.

 

Synthesis: What These Perspectives Converge On

Across all three viewpoints, one pattern emerges:

Healthcare does not suffer from a lack of effort.

It suffers from excess interpretation layers between intent and outcome.

That is exactly where modern revenue cycle systems break:

  • not at execution
  • but at translation
  • not at performance
  • but at handoffs

And this is why clinics can feel “stable on paper” while financially leaking in practice.

The OnnX Perspective (What This Actually Means)

This is the problem OnnX was built around:

Not to “optimize billing.”

But to reduce the number of moments where:

clinical reality must be reinterpreted before it becomes revenue

Because every reinterpretation step introduces:

  • delay
  • error
  • dependency
  • and leakage

The goal is not faster billing.

The goal is fewer chances for billing to become guesswork.

Myth That Needs to Die

“More RCM sophistication improves revenue.”

In reality:

More sophistication often means:

  • more layers
  • more dashboards
  • more exceptions
  • more specialists interpreting other specialists

Sophistication feels like control.

But often it is just structured confusion with better reporting.

What Clinics Should Start Paying Attention To

Not just:

  • collections
  • A/R days
  • denial rates

But:

  • where interpretation is introduced
  • where handoffs occur
  • where documentation becomes subjective
  • where decisions are delayed for validation

Because those are the real revenue inflection points.

Not the billing department.

The interfaces between departments.

Final Insight

The Empire State Building wasn’t “breached.”

It was navigated.

Step by step.

Layer by layer.

Within acceptable assumptions.

That is exactly how revenue leakage works in healthcare.

Not as a failure.

But as a sequence of acceptable decisions that no one re-examines end-to-end.

Until someone finally asks:

“Why does this system need so many people to explain what already happened?”

Closing Thought

If your billing system requires constant interpretation to function, it is not a system.

It is a conversation between disconnected parts.

And conversations are expensive when they determine revenue.

Call to Action

Where do you see the most unnecessary interpretation in your revenue cycle?

Comment your experience.

Because the real problem is not disagreement.

It’s distance.

Share this if you think healthcare doesn’t have a billing problem—but a systems design problem.

And if you’re building or running a clinic, start asking a harder question:

What would break if we removed one layer of interpretation?

About the Author

Dr. Daniel Cham is a physician and healthcare founder focused on rebuilding revenue cycle systems from the ground up through clinical data design and AI-native workflows. His work centers on reducing structural friction in independent medical practice operations.

Connect with Dr. Cham on LinkedIn to learn more.

Continue the Conversation

Explore practical insights, evidence-based strategies, and behind-the-scenes perspectives that help physicians and clinic leaders navigate complex challenges.

Knowledge drives progress — start your journey today.

1. CMS – National Health Expenditure Data

A foundational source showing how administrative complexity and system design contribute to rising healthcare costs in the U.S., including billing and overhead burdens.

2. American Medical Association (AMA) – Administrative Burden in Healthcare

This report highlights how excessive administrative work, including billing and prior authorization processes, contributes to physician burnout and inefficiency in care delivery.

3. HFMA (Healthcare Financial Management Association) – Revenue Cycle Insights

HFMA provides ongoing analysis of revenue cycle inefficiencies, denial management, and structural leakage in provider reimbursement systems.

#HealthcareInnovation #RevenueCycleManagement #MedicalBilling #PhysicianEntrepreneurs #HealthcareOperations #MedTech #HealthTech #PrimaryCare #IndependentPractice #HealthcareLeadership #ClinicalOperations #AIinHealthcare #HealthcareEfficiency #PracticeManagement #HealthcareSystems #ValueBasedCare #PhysicianBurnout #HealthcareStrategy #StartupHealthcare #OnnX

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