Monday, September 21, 2026

Tim Phillips Walked Through the Door: What His Story Teaches Us About Friction in Healthcare

A man in Pontiac found help when he needed it. His story raises a much bigger question: why does healthcare make people work so hard to get something they already know they need?



“Health and research data are often fragmented across institutions, databases, and systems.”Venus Oliva Cloma-Rosales, MD, physician, public-health scientist and health-data researcher

 

There is something almost embarrassingly simple about what saved Tim Phillips.

He walked through a door.

Not into a futuristic hospital.

Not into a $500 million digital-health platform.

Not into an AI-powered clinical command center with seventeen dashboards.

He walked into a mental-health urgent-care facility in Pontiac, Michigan.

And someone helped him.

That sounds almost too ordinary to be news.

But perhaps that is exactly why it is worth paying attention to.


The Moment Before the Door

Tim Phillips is 48.

He was working as a live-in nanny caring for three children when anxiety, loneliness and the pressures of life became overwhelming.

About two years ago, he reached a moment when he was contemplating ending his life.

Then he thought about the children.

Instead, he went to CNS Healthcare's mental-health urgent-care facility in Pontiac.

Karla Heinig, the facility's mental-health urgent-care supervisor, described what happens after someone walks through the door: check-in, assessment, a nurse, vital signs, and a prescriber when needed.

Jennifer Shumaker, chief clinical officer of adult services at CNS Healthcare, identified something remarkably important.

The problem is not always the absence of care.

Sometimes the problem is the distance between needing care and actually getting it.

Wait times matter.

Access matters.

Timing matters.

Shumaker said walk-in and same-day treatment can help people get care earlier, before a crisis develops.

When asked how soon someone could be seen, her answer was strikingly simple:

Immediately.

Phillips later said:

“I’m happy.”

And then:

“I probably wouldn’t be here. I probably would have took my life.”

His message to other people is equally simple:

“Don’t be afraid to get the help you need. You’re not alone.”

That is a mental-health story.

It is also a healthcare operations story.

And that second part deserves more attention.

Healthcare Has a Friction Problem

We talk constantly about healthcare innovation.

AI.

Automation.

Interoperability.

Digital transformation.

Ambient documentation.

Remote monitoring.

Predictive analytics.

Agentic workflows.

The vocabulary gets more impressive every year.

But there is a less glamorous word that may explain more about healthcare than all of them:

Friction.

How much effort exists between a person needing something and actually getting it?

For Tim Phillips, the question was painfully personal:

Can I get help when I need it?

For a physician:

Can I get the authorization before the patient waits another week?

For a nurse:

Can I find the information I need without opening six systems?

For a front-desk employee:

Can I determine whether this patient's insurance is actually active?

For a coder:

Do I have the documentation required to submit this claim correctly?

For a practice owner:

Can I understand why revenue is leaking before another month closes?

Different problems.

Same architecture.

Friction.

And here is the uncomfortable part:

Healthcare has become very good at teaching people to tolerate it.

We call it workflow.

We call it administrative burden.

We call it payer complexity.

We call it documentation requirements.

We call it operational overhead.

Sometimes those descriptions are accurate.

But they can also become euphemisms.

A badly designed process is still a badly designed process even after we give it a sophisticated name.


The Strange Economics of Healthcare

Consider one of the strangest economic arrangements in modern healthcare.

We take highly trained professionals and ask them to compensate for systems that were poorly designed.

Physicians chase authorizations.

Nurses reconcile information.

Staff call insurance companies.

Coders correct incomplete documentation.

Billers appeal denials.

Practice managers build spreadsheets.

Someone inevitably becomes the unofficial expert in a process nobody else understands.

And every organization seems to have one.

You know the person.

They have been there for 19 years.

They know which payer portal actually works.

They know which phone number bypasses the automated system.

They know which modifier causes trouble.

They know which fax machine still receives something important.

Nobody remembers who gave them this knowledge.

Nobody has documented it.

And everyone becomes nervous when they take a vacation.

That is not institutional knowledge.

That is institutional dependency.

We have built healthcare systems where human beings become the middleware.


We Keep Fixing the Wrong End of the Problem

This is particularly obvious in medical billing and revenue cycle management.

A claim gets denied.

So we measure the denial.

We calculate the denial rate.

We create a denial work queue.

We assign somebody to investigate it.

Someone writes an appeal.

Someone submits the appeal.

Someone follows up.

Someone escalates it.

Someone updates the spreadsheet.

Then six weeks later, somebody asks:

Why did this happen?

That question should have come first.

A denial is rarely born a denial.

It usually has a history.

Maybe eligibility was never verified correctly.

Maybe the authorization was missing.

Maybe the authorization was obtained for the wrong service.

Maybe documentation did not support the billed service.

Maybe a modifier was missing.

Maybe information was entered differently in two systems.

Maybe the claim was submitted with a mismatch that should have been caught before submission.

The denial is the visible event.

The cause may have occurred much earlier.

That distinction matters.

Because if you only optimize the denial department, you can become extraordinarily efficient at processing problems you should have prevented.

That is not efficiency.

That is high-speed inefficiency.

The AI Paradox

And now we are adding AI.

This could be enormously valuable.

It could also create a spectacular new version of the same problem.

Imagine this:

AI generates the claim.

Another AI reviews the claim.

A payer AI denies the claim.

Your AI identifies the denial.

Another AI drafts the appeal.

The payer's AI reviews the appeal.

Your AI escalates it.

At some point, two artificial intelligences may be arguing about a modifier while a human employee sits nearby wondering whether lunch is still happening.

We should not automatically assume that adding intelligence to every step creates an intelligent system.

Sometimes it simply creates faster interaction between broken steps.

The important question is not:

Where can we put AI?

It is:

Which friction should disappear?

That is a very different question.


Fragmented Data Is Not Just a Technology Problem

This is where Dr. Venus Oliva Cloma-Rosales' comment this week is particularly relevant.

She described health and research data as fragmented across institutions, databases and systems, and argued that AI can help connect those sources and turn fragmented information into evidence for better decisions.

That idea extends well beyond research.

The same problem exists inside everyday medical practices.

A practice may have:

  • Patient demographics in one system.
  • Eligibility information somewhere else.
  • Authorization information in a payer portal.
  • Clinical documentation in the EHR.
  • Claims in a practice-management system.
  • Denials in an RCM platform.
  • Payments in another financial workflow.
  • Staff knowledge inside someone's head.

The data exists.

The problem is that the relationship between the data is often weak.

That is a crucial distinction.

The future of healthcare may not depend simply on collecting more data.

It may depend on making existing data understandable as a connected system.


The Rear-View-Mirror Problem

Most billing dashboards tell you what happened.

Claims.

Denials.

A/R.

Payments.

Collections.

Days in A/R.

Useful numbers.

But they are mostly rear-view mirrors.

The more interesting question is:

Why did it happen?

And then:

What is changing upstream?

Imagine a three-provider family practice.

1,200 visits.

1,150 claims.

14% initial denial rate.

42 days in A/R.

$7,500 a month in billing expense.

The dashboard can tell the owner that something is wrong.

But suppose the real problem is not "billing."

Suppose 40% of the denials trace back to three upstream issues:

Eligibility verification is inconsistent.

Authorization information is not consistently captured.

Documentation requirements are being interpreted differently by different clinicians.

Now the denial rate is not the diagnosis.

It is the symptom.

The practice doesn't need another report saying:

You have denials.

The owner already knows.

The owner needs the system to say:

Here is where the pattern begins.

That is a fundamentally different kind of intelligence.


A Practice Is a Chain, Not a Collection of Departments

Revenue cycle management is often treated as a department.

That is a mistake.

It is a chain.

Registration → Eligibility → Authorization → Documentation → Coding → Charge Capture → Claims → Denials → A/R → Payments

The financial outcome at the end of the chain is heavily influenced by decisions made near the beginning.

Yet organizations often manage each segment separately.

That creates a strange situation.

Everyone optimizes their own box.

Nobody owns the entire journey.

The registration team says:

"We entered the patient correctly."

The authorization team says:

"The authorization was obtained."

The clinician says:

"I documented the encounter."

The coder says:

"I coded what was documented."

The biller says:

"I submitted the claim."

The payer says:

"The claim does not meet requirements."

Technically, everyone may be right.

Financially, the system can still be wrong.

That is the problem with fragmented optimization.

The Hidden Cost Isn't Always the Denial

Imagine a $200 claim that gets denied.

People often focus on the $200.

But the real cost may be much larger.

Someone investigates it.

Someone calls.

Someone searches the chart.

Someone checks the payer portal.

Someone sends a message.

Someone corrects the claim.

Someone resubmits it.

Someone tracks the result.

Someone follows up again.

The organization has now consumed human time to recover money that should not have required recovery.

So perhaps the more useful metric is not simply:

How many claims were denied?

It is:

How much unnecessary work did those claims create?

That metric changes the conversation.

Because a healthcare organization can have acceptable financial performance while quietly exhausting its workforce.


The Physician Is Not the Backup System

This problem extends beyond billing.

Physicians are often the final workaround for broken systems.

A task does not get completed?

Ask the physician.

A form is confusing?

Ask the physician.

A prior authorization needs clarification?

Ask the physician.

A patient cannot navigate the portal?

Ask the physician.

An alert fires?

Ask the physician.

Another message arrives?

Ask the physician.

Eventually, the physician becomes the human equivalent of a software patch.

That is dangerous.

Not because physicians cannot do these things.

They can.

But because every workaround consumes attention.

And attention is one of the scarcest resources in healthcare.

A physician who spends ten minutes solving a systems problem is not simply spending ten minutes.

That is ten minutes removed from clinical reasoning, patient communication, teaching, recovery, or another task that actually requires a physician.

We should stop treating physician availability as infinite infrastructure.

The Physician Burnout Conversation Needs a New Question

We talk about physician burnout as if the solution is mostly personal resilience.

Sleep.

Mindfulness.

Boundaries.

Wellness programs.

Those things can matter.

But there is another question:

How much unnecessary work are we asking physicians to absorb?

A September 2026 article on healthcare AI adoption noted that physician skepticism toward new technology is understandable after years of systems that promised efficiency while adding clicks, alerts and administrative responsibilities.

That is an important observation.

Physicians are not necessarily resistant to technology.

They may be resistant to more work disguised as technology.

There is a difference.


Prior Authorization Is the Perfect Example

The American Medical Association's latest physician survey found that physicians reported an average of 40 prior authorizations per week. Nearly one-third said requests are often or always denied. The survey also found that 94% said prior authorization contributes to burnout, while physician and staff time devoted to prior authorization averaged 13 hours per week.

Those numbers are not merely an argument for faster authorization.

They are an argument for asking why so much human labor is required in the first place.

And there is an important distinction:

Automating a burden is not the same as removing the burden.

If a bad process takes 13 hours and automation reduces it to six, that is an improvement.

But the better question is:

Why are we spending six hours?

That question is harder.

It requires redesign.

It requires standardization.

It requires better information.

It requires changing incentives.

And sometimes it requires admitting that a process everyone has accepted for years is simply not very good.


The New Healthcare Arms Race May Be AI Versus AI

There is already growing use of AI in administrative healthcare workflows.

Recent CAQH findings reported by AJMC indicate that AI adoption is increasingly concentrated in high-burden administrative functions, including prior authorization, fraud detection and documentation.

That trend makes sense.

Administrative work is repetitive.

It generates data.

It follows patterns.

It is expensive.

It is therefore attractive territory for automation.

But there is a trap.

If providers automate against payer automation, and payers automate against provider automation, we could create an administrative arms race.

The humans may disappear from the middle.

The complexity will not.

That is not the future we should aim for.

The objective should be fewer unnecessary interactions, not simply faster interactions.


What Tim Phillips Teaches Us About Healthcare Design

This is where his story becomes unexpectedly powerful.

Tim Phillips did not need healthcare to become more technologically sophisticated in that moment.

He needed healthcare to become accessible.

The difference between:

"I need help"

and

"I can actually get help"

is friction.

That same principle applies across healthcare.

A patient experiences:

Need → access

A physician experiences:

Clinical decision → action

A nurse experiences:

Problem → information

A practice experiences:

Care delivered → payment received

Whenever unnecessary steps sit between those points, friction grows.

And friction has consequences.

Sometimes the consequence is frustration.

Sometimes it is lost productivity.

Sometimes it is delayed care.

Sometimes it is burnout.

Sometimes it is lost revenue.

Sometimes, as Phillips' story reminds us, the stakes can be much more serious.


The Contrarian Idea

Here is the contrarian idea I keep coming back to:

Healthcare may not need more efficiency nearly as much as it needs less unnecessary work.

Those sound similar.

They are not.

Efficiency asks:

How can we do this faster?

Elimination asks:

Why are we doing this at all?

Healthcare technology has spent decades getting better at the first question.

The next generation should become much better at the second.

What Should We Actually Measure?

If I were auditing a medical practice today, I would look beyond traditional revenue-cycle metrics.

I would still measure:

  • Initial denial rate
  • Final denial rate
  • Days in A/R
  • Clean claim rate
  • Net collection rate
  • Authorization turnaround
  • Eligibility failure rate
  • Claim correction rate
  • Payment variance
  • Revenue leakage

But I would add another category:


Friction Metrics

1. Rework rate

How often does the same case have to be touched again?

2. Manual intervention rate

How many cases require a human workaround?

3. Information retrieval time

How long does staff spend finding information that should already be available?

4. Exception rate

How often does the standard workflow break?

5. Repeat-contact rate

How many times must someone call, message or resubmit?

6. Unnecessary work hours

How much staff time is consumed correcting preventable problems?

7. Upstream error rate

How many downstream failures can be traced to an earlier stage?

Those measurements tell a different story.

They begin to reveal the cost of complexity.


A 30-Day Friction Audit

You do not need a massive transformation project to begin.

Start with 30 days.

Pick five recurring problems.

For each one, ask:

Where did the problem first appear?

Not where was it discovered.

Where did it begin?

Then ask:

What information was missing?

Who had to compensate?

How many times was the case touched?

What could have prevented it?

What could be standardized?

What should never require human intervention again?

That last question is particularly important.

Because the goal of automation should not be to create a robot that performs unnecessary work.

The goal should be to make the unnecessary work disappear.


What Not to Automate

This may be the most important technology lesson.

Do not automatically automate a broken process.

First understand it.

Then simplify it.

Then standardize it.

Then automate what remains.

Otherwise you risk building:

Garbage in → faster garbage out.

Or, in the healthcare version:

Ambiguity in → automated ambiguity out.

AI is extraordinarily good at scaling processes.

That is precisely why we should be careful about what we ask it to scale.

If the underlying process is undefined, AI can scale confusion.


The OnnX Question

This is where my thinking about OnnX has changed.

The opportunity is not simply to build another billing tool.

There are already plenty of tools that tell practices what happened.

The more interesting opportunity is to understand why it happened.

And then connect:

Problems → Causes → Actions → Outcomes

That means looking across the entire revenue cycle.

Eligibility.

Authorization.

Documentation.

Coding.

Claims.

Denials.

A/R.

Payments.

Not as eight isolated modules.

As one connected system.

The goal is a living Practice Intelligence layer.

Something that can say:

"Your denial rate increased."

Useful.

But incomplete.

A more valuable system would say:

"Your denial rate increased 3.2 percentage points over the last 60 days. Most of the increase traces to eligibility mismatches in two payer categories. Those errors began upstream at registration. Here is the recurring pattern. Here is the action. Here is whether the action worked."

Now we are moving from reporting to intelligence.

From rear-view mirror to windshield.


The Difference Between a Dashboard and Intelligence

A dashboard displays information.

Intelligence creates context.

A dashboard says:

Denials: 14%

Intelligence asks:

Why?

A dashboard says:

A/R: 42 days

Intelligence asks:

Which patients, payers, services or upstream failures are driving the increase?

A dashboard says:

Authorization delays: 9 days

Intelligence asks:

Which step creates the delay, how often does it occur, and what can prevent it?

The distinction sounds subtle.

It is not.

One tells you what happened.

The other helps you understand what to do next.


The Ethical Question

There is also an ethical dimension to all of this.

Whenever someone says:

"We made the process more efficient," I want to ask:

Efficient for whom?

The payer?

The physician?

The practice?

The patient?

The billing company?

The employee?

The algorithm?

Those interests do not always align.

A system that reduces payer administrative cost by creating more work for a physician has not necessarily created healthcare efficiency.

A system that reduces staff time but makes patients navigate three additional portals has moved friction.

It has not eliminated it.

Healthcare needs to measure friction from the perspective of the entire system.

Especially the person at the receiving end.


The Legal Question

There is also a legal and operational reality.

Automation does not eliminate accountability.

If software makes a mistake involving eligibility, authorization, coding, documentation or claims, "the algorithm did it" is unlikely to be a satisfying operational answer.

Healthcare organizations still need:

  • Appropriate human oversight
  • Clear responsibility
  • HIPAA-compliant handling of protected health information
  • Appropriate business associate agreements where required
  • Audit trails
  • Access controls
  • Validation of automated outputs
  • Defined escalation procedures
  • Coding and billing oversight
  • Clear documentation of system limitations

The more consequential the decision, the more important the human accountability layer becomes.

The objective is not to remove humans from healthcare.

It is to stop wasting human intelligence on work machines and better-designed processes can handle.


Three Experts. Three Lessons.

Venus Oliva Cloma-Rosales, MD

Her observation this week about fragmented health and research data points toward a larger opportunity: connect fragmented information so it becomes useful for decision-making.

Lesson: Data becomes more valuable when relationships between data become visible.

Ryan Sadeghian, MD

In a recent Healthcare IT News discussion about clinical AI adoption, Sadeghian argued that physician skepticism toward technology is understandable after years of tools that promised efficiency while adding clicks and administrative work.

Lesson: The best technology does not ask physicians to tolerate another layer of technology.

Stephen J. Morgan, MD

Morgan emphasized involving clinicians in the design and implementation of new tools and listening to the actual problems users encounter.

Lesson: If you want adoption, start with the problem—not the technology.

Put those three lessons together and something interesting emerges:

Connect the data. Start with the real problem. Keep humans involved in judgment.

That is a much more useful AI strategy than simply asking where the next chatbot belongs.


The Future of Medical Billing May Be Less Billing

That sounds strange coming from someone building a medical billing company.

But I believe it.

The future of revenue cycle management should not be about becoming better and better at recovering preventable mistakes.

It should be about preventing the mistakes.

The best denial is not the denial that gets appealed successfully.

It is the denial that never happens.

The best A/R intervention is not the one that collects a 120-day-old balance.

It is the one that prevents the balance from becoming 120 days old.

The best automation is not the robot that works all night.

It is the process that no longer needs the work.

That is a very different definition of innovation.


The Real AI Opportunity

The healthcare AI conversation often starts with:

"What can AI do?"

I think we should start somewhere else.

What should no longer require human effort?

That question is harder.

It forces us to understand workflows.

It forces us to understand incentives.

It forces us to understand clinical reality.

It forces us to understand data quality.

It forces us to understand where errors originate.

And it forces founders to spend time with the people actually doing the work.

That is less glamorous than launching another AI demo.

It is also where some of the most valuable healthcare companies may be built.


One Open Door

Go back to Tim Phillips.

He was overwhelmed.

He needed help.

The healthcare system could have told him to call someone else.

It could have given him a number.

It could have asked him to complete another form.

It could have told him the next appointment was three weeks away.

Instead, he walked through a door.

Someone assessed him.

Someone listened.

Someone connected him with care.

That is not a small thing.

It is a design principle.

When someone is ready to take the next step, the system should not make that step unnecessarily difficult.

That principle applies to mental healthcare.

Primary care.

Emergency care.

Specialty care.

Prior authorization.

Medical records.

Patient access.

Revenue cycle management.

Everything.


The Question I Would Ask Every Practice Owner

If Tim Phillips walked through the door of your practice today, what would happen next?

Not just clinically.

Operationally.

Could you find his information?

Could you verify his coverage?

Could you determine what he needed?

Could you document it correctly?

Could you obtain anything required before care?

Could you submit the resulting claim cleanly?

Could you explain what happened if payment was delayed?

Could your team do all of that without relying on one person who "just knows how things work"?

That is the real test.

Because a healthcare system is not defined only by what it can do.

It is defined by how much unnecessary friction stands between a person and what they need.


Final Thoughts

We do not have a shortage of healthcare technology. We have a shortage of healthcare systems designed around the elimination of unnecessary work.

The next generation of healthcare AI should not merely make broken workflows faster; it should make some workflows unnecessary.

And perhaps the simplest measure of innovation is this: How much easier did we make it for the next person to get what they need?

Your Turn

What is the most unnecessary piece of work your medical practice performs every week?

Not the biggest problem.

Not the most expensive problem.

The most unnecessary one.

The task everyone accepts because "that's just how healthcare works."

I would genuinely like to hear it.

Leave it in the comments.

And if this made you think differently about healthcare friction, medical billing, revenue cycle management, or AI, share it with someone who works inside the system.

Sometimes the most valuable healthcare innovation begins with one uncomfortable question:

Why are we still doing this?


A Practical Resource for Independent Practices

If you run an independent medical practice, start with one exercise:

Write down the five tasks your staff complain about most.

Then ask one question about each:

Could we prevent this problem instead of becoming better at fixing it?

That question may reveal more about your practice than another dashboard.


Recent Healthcare News Behind the Discussion

The Tim Phillips story was reported by WXYZ Detroit on September 21, 2026. Phillips described how accessing walk-in mental-health care at CNS Healthcare in Pontiac became a turning point, while CNS Healthcare's Jennifer Shumaker emphasized the importance of same-day access before a crisis develops.

At the same time, healthcare AI continues moving deeper into administrative workflows. Current reporting shows increasing use of AI in areas such as prior authorization, documentation and other high-burden administrative processes.

That makes the central question even more important:

Are we eliminating friction—or simply automating it?


FAQ

Is healthcare's biggest problem really technology?

No single explanation captures healthcare's complexity. Technology can solve specific problems, but poorly designed workflows, fragmented information, conflicting incentives and administrative requirements can create friction that technology alone does not eliminate.

Why focus on upstream problems?

Because downstream problems are often symptoms.

A denial is discovered at billing, but its cause may have occurred during registration, eligibility, authorization, documentation or coding.

Is AI the solution?

AI can be part of the solution. But AI is not automatically a solution simply because it is automated.

The quality of the underlying process and data still matters.

Does automation eliminate administrative work?

Sometimes.

But automation can also move work from one person or organization to another. The right question is whether total unnecessary work has actually decreased.

Why does this matter to independent medical practices?

Smaller practices often have fewer people available to absorb operational complexity. One broken workflow can therefore consume a disproportionate amount of staff time and directly affect cash flow.

What should a practice measure beyond denial rates?

Consider rework, manual intervention, exception rates, information-retrieval time, repeat contacts and upstream error rates.

Those metrics help expose the hidden cost of complexity.


Myth Buster

Myth: A lower denial rate automatically means a healthier revenue cycle.

Not necessarily.

A practice may have a reasonable denial rate while losing significant time to eligibility problems, authorization work, documentation corrections, claim edits or payment follow-up.

Myth: More automation automatically means more efficiency.

No.

Automation can accelerate a bad process.

Myth: Physician resistance means physicians dislike technology.

Not necessarily.

Physicians may be rejecting technology because previous technology increased clicks, alerts and administrative work.

Myth: The billing department owns the revenue cycle.

The billing department owns an important part of the revenue cycle.

But revenue-cycle performance begins much earlier.


The Bigger Idea

Healthcare has spent years building systems that answer:

What happened?

The next generation should become much better at answering:

Why did it happen?

And eventually:

How do we prevent it from happening again?

That is the difference between reporting and intelligence.

Between reaction and prevention.

Between treating symptoms and redesigning systems.

And perhaps between adding another healthcare tool and actually building a better healthcare system.


One Last Thing

Tim Phillips did something incredibly difficult.

He asked for help.

Healthcare's job should be to make that decision easier—not harder.

Whether the person is a patient walking into a mental-health clinic, a physician trying to obtain authorization, a nurse searching for information, or a practice owner trying to understand missing revenue, the principle is the same:

Remove the unnecessary obstacle.

Structure the information.

Let people spend their time on the work that actually requires them.

That is what better healthcare should feel like.


About the Author

Dr. Daniel Cham, MD is a physician, healthcare entrepreneur and medical consultant focused on the intersection of healthcare operations, medical billing, technology and practical AI. His work explores how better information structure can reduce administrative friction and help independent medical practices operate more intelligently.

His current work with OnnX focuses on a simple premise:

Most revenue-cycle problems begin before the billing department ever sees them.


Disclaimer

This article is for educational and informational purposes only. It does not constitute medical, legal, financial, coding or billing advice. Healthcare organizations should evaluate technology, workflow changes and automation in the context of their own clinical, regulatory, contractual and operational requirements.

The discussion of Tim Phillips' experience is based on publicly reported information and is intended to highlight healthcare-access and systems-design lessons, not to provide clinical commentary on his individual circumstances.


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References

  1. WXYZ Detroit — Tim Phillips and CNS Healthcare, September 21, 2026
    The primary source for the human story about Tim Phillips, Karla Heinig, Jennifer Shumaker, and same-day mental-health access. WXYZ/BHNet — Tim Phillips’ story
  2. Dr. Venus Oliva Cloma-Rosales — Voice of Asia, September 21, 2026
    Current physician/public-health scientist discussing fragmented health data and the role of AI in connecting information for better decisions. Voice of Asia — Dr. Venus Oliva Cloma-Rosales
  3. American Medical Association — 2026 Prior Authorization Physician Survey
    Survey of 1,000 practicing physicians: 40 prior authorizations per week on average, 13 hours of physician/staff time, 94% reporting that prior authorization contributes to burnout, and only 33% believing the latest insurer pledge will make a meaningful difference. AMA — Prior Authorization Physician Survey

 

#Healthcare #HealthcareAI #MedicalBilling #RevenueCycleManagement #HealthcareOperations #PhysicianBurnout #PriorAuthorization #HealthTech #IndependentPractice #PracticeManagement #HealthcareInnovation #AIinHealthcare

 

Sunday, September 20, 2026

Brie Morgan Bauer Lost Four Limbs. Her Brother Gave Her a Kidney. What Her Story Reveals About Medical Billing

A mother survived sepsis, lost all four limbs, went on dialysis—and then received a kidney from her brother. Her story exposes a problem hiding in plain sight across healthcare: the crisis we see is often only the final symptom.



“We’re trying to get out of the theoretical, and into real clinical impact.”Sammy Chouffani El Fassi, Duke University, quoted in the Financial Times

 


THE HUMAN STORY

Brie Bauer's Life Changed in Hours

Brie Morgan Bauer was pregnant with her third child when everything changed.

She was just twenty-seven weeks pregnant.

Then came an emergency C-section.

Then infection.

Then septic shock.

Then multiple organ failure.

Brie spent ten days in a coma.

Doctors ultimately had to amputate both arms and both legs to save her life.

Her baby survived.

Brie survived.

But survival came with an extraordinary price.

Her kidneys had been severely damaged.

She became dependent on dialysis.

For hours every week, a machine became part of her life.

Then came another problem.

She needed a kidney transplant.

Her family began looking for a donor.

Her older brother, George Morgan, was tested.

He was a perfect match.

George donated his kidney to Brie in March twenty twenty-six.

The transplant was successful.

Brie was able to stop dialysis and focus on rehabilitation, her children and rebuilding her life.

The story is extraordinary.

But there is another lesson hiding inside it.

The most visible crisis was not the beginning of the story.

It was the consequence of everything that came before it.

That distinction matters enormously in healthcare.

It also matters in medical billing.


THE CONTRARIAN TAKE

The Denial May Be the Crime Scene

A denial appears at the end of a process.

That makes it visible.

It does not make it the cause.

A claim is denied.

The billing team sees it.

Someone opens the account.

Someone checks the payer portal.

Someone looks at the documentation.

Someone calls the payer.

Someone sends a corrected claim.

Someone asks the physician for additional information.

Someone enters something again.

Someone waits.

Someone follows up.

Someone documents the follow-up.

Eventually, someone gets paid.

Everyone celebrates.

But here is the uncomfortable question:

Why did the claim become a problem in the first place?

Maybe eligibility information was wrong.

Maybe authorization was missing.

Maybe the authorization existed but did not match the service.

Maybe documentation did not contain the information the payer required.

Maybe the clinical note was incomplete.

Maybe the wrong payer information moved through the system.

Maybe a handoff failed.

Maybe nobody owned the transition between scheduling, authorization, clinical documentation and billing.

The denial was merely where the failure became visible.

The fire may have started much earlier.

The denial is the smoke. The fire started somewhere else.


The Hidden Lesson in Brie’s Story

Brie's story is not a metaphor for billing.

It is a reminder about systems.

A visible crisis can be the final manifestation of a much longer chain of events.

In medicine, clinicians are trained to ask:

Where did this problem begin?

Not merely:

Where do I see it now?

That distinction is fundamental to diagnosis.

Yet healthcare administration often does the opposite.

The denial arrives.

The denial team handles the denial.

The problem disappears from the dashboard.

The organization calls that improvement.

But did the underlying process improve?

Or did the organization simply become better at cleaning up after itself?

That is the question worth asking.


THE HEALTHCARE FIX-IT ECONOMY

The Denial Is Not Your Enemy

Here is the contrarian part.

Stop treating denials as merely a nuisance.

A denial can be valuable information.

It can tell you where your system is leaking.

It can reveal a broken handoff.

It can expose inconsistent documentation.

It can identify payer-specific friction.

It can show that your staff is repeatedly performing the same corrective action.

It can reveal that physicians are being pulled into administrative work that should never have reached them.

In other words:

Your denial queue may be an operational X-ray.

The mistake is assuming that the X-ray is the disease.

It is not.

It is evidence.

The question is what you do with the evidence.


Welcome to the Healthcare Fix-It Economy

Healthcare has become extraordinarily good at fixing things.

We have denial teams.

Appeals teams.

Coding teams.

Authorization teams.

Eligibility teams.

Revenue-cycle consultants.

Outsourced billing companies.

Clearinghouses.

Scrubbers.

Dashboards.

Work queues.

Payer portals.

Fax systems.

Spreadsheets.

And now AI.

We have built an enormous ecosystem around correcting information after it has already gone wrong.

Sometimes those tools are necessary.

Sometimes they are essential.

But there is an uncomfortable possibility:

We may be getting very good at managing the consequences of bad system design.

Healthcare has created an entire economy around the sentence:

“Something went wrong. Who can fix it?”

What if the better question is:

“Why does this keep going wrong?”


The Invisible Employee

There is another cost hiding inside every correction loop.

Human time.

A claim does not repair itself.

Someone has to touch it.

Someone has to investigate it.

Someone has to search for information.

Someone has to make a phone call.

Someone has to open a portal.

Someone has to upload a document.

Someone has to send a fax.

Someone has to wait for a response.

Someone has to follow up.

That person is often invisible in the financial analysis.

The organization sees the denial.

It sees the dollar amount.

It sees the eventual payment.

It does not always see the labor required to make the payment happen.

That labor is real.

And it is expensive.


WHERE THE MONEY REALLY DISAPPEARS

The Claim That Cost More Than It Earned

Imagine a relatively modest claim.

The clinical work is complete.

The patient was treated.

The physician documented the encounter.

The claim is submitted.

Then something goes wrong.

A missing authorization triggers a denial.

The biller investigates.

The front desk is contacted.

The physician's office is contacted.

A document is located.

The claim is corrected.

It is resubmitted.

The payer asks for something else.

The process repeats.

Eventually, the claim gets paid.

The accounting system records revenue.

But the revenue-cycle team sees something else.

Work.

The payment may look successful.

The process may still be economically irrational.

This is why simply measuring collections can be misleading.

You should also measure the human effort required to collect.


The Hidden FTE Problem

Many practices have what could be called a hidden staffing problem.

Not because the organization officially employs another person.

Because enough administrative friction has accumulated to create the equivalent workload of another employee.

A few minutes here.

A correction there.

An authorization phone call.

A portal message.

A documentation request.

A claim resubmission.

A physician signature.

A follow-up.

Then another follow-up.

None of these tasks looks enormous.

Together, they become a job.

And sometimes that job does not exist because the practice needs more people.

It exists because the system creates unnecessary work.

That distinction matters.

Adding another person can increase capacity.

It does not necessarily reduce friction.


THE PATIENT JOURNEY

The Patient Doesn't Experience Your Org Chart

Patients do not experience your departments.

They experience your system.

They do not care whether the problem belongs to:

Scheduling.

Front desk.

Clinical staff.

Prior authorization.

Coding.

Billing.

Revenue cycle.

IT.

Compliance.

The payer.

The clearinghouse.

From the patient's perspective, it is simply:

“The healthcare system.”

That is why internal boundaries can become invisible sources of patient frustration.

The organization says:

“That isn't our department.”

The patient hears:

“Nobody owns this.”


The Front Desk Is Part of Revenue Cycle

This is one of the most underappreciated ideas in independent practice.

Revenue cycle does not begin when the claim reaches billing.

It begins when patient information enters the system.

Insurance.

Demographics.

Coverage.

Referral requirements.

Authorization requirements.

Scheduling details.

Clinical information.

Documentation.

Every downstream transaction inherits the quality of the information that came before it.

A billing department can be extraordinarily competent and still spend its day correcting problems created upstream.

That does not mean the front desk is doing a bad job.

It means the system may be asking humans to compensate for variability that the system itself should prevent.


The Biller Is Not the Problem

This point deserves emphasis.

Your biller may be your most reliable employee.

That does not mean your process is reliable.

In fact, exceptional billers can sometimes hide broken systems.

They remember payer quirks.

They know which portal to use.

They know who to call.

They know which physician needs which reminder.

They know how to reconstruct missing information.

They know the workaround.

They carry institutional memory inside their heads.

That makes them valuable.

It also creates risk.

Because when the hero leaves, the system suddenly appears broken.

Maybe the system was always broken.

The employee was simply compensating for it.


DENIALS AS DIAGNOSTIC DATA

Your Denial Queue Is an Operational X-Ray

Start looking at denials differently.

Do not merely ask:

“How much did we recover?”

Ask:

“Where did this problem originate?”

That single question changes the conversation.

Suppose the same denial appears repeatedly.

Do not celebrate the team's recovery rate.

Investigate the pattern.

Was eligibility wrong?

Was authorization incomplete?

Was the wrong information captured?

Was documentation missing?

Was a payer requirement misunderstood?

Was a clinical workflow disconnected from an administrative workflow?

Was information entered multiple times?

Was information copied manually?

Was the same information requested by multiple people?

Those questions turn the denial queue into an improvement system.


The Question That Changes the Meeting

The typical revenue-cycle meeting asks:

“How many denials did we clear?”

Try asking:

“How many of these denials should have existed at all?”

That is a very different question.

It moves the organization from productivity to prevention.

It changes the conversation from:

“Are our people working hard enough?”

to:

“Why are our people having to work this hard?”

That is where meaningful operational improvement begins.


The Denial Autopsy

Every recurring denial deserves an autopsy.

Not blame.

Not finger-pointing.

An autopsy.

Trace the information backward.

Where did the patient enter the system?

Where was insurance captured?

Where was eligibility checked?

Where was authorization determined?

Where was clinical information documented?

Where did the information change hands?

Where was information re-entered?

Where did someone have to make a judgment manually?

Where did someone have to search for information that should already have been available?

Where did the correction begin?

Then ask the most important question:

Could the system have prevented this?

If yes, you have found an upstream opportunity.


WHAT CURRENT HEALTHCARE NEWS IS TELLING US

Insurance Denials Are More Complicated Than the Number Suggests

The problem is not theoretical.

Current healthcare reporting continues to show how much administrative friction exists between clinical decisions and actual access to care.

Recent reporting from the Los Angeles Times found that patients who appeal insurance denials frequently prevail, with some plans showing very high reversal rates. The reporting also highlighted substantial prior-authorization denial activity across major insurers.

The important operational lesson is not that every denial is wrong.

It is more basic.

A denial creates work.

Someone has to interpret it.

Someone has to decide whether to appeal.

Someone has to gather information.

Someone has to communicate.

Someone has to wait.

That friction eventually reaches patients and clinicians.


Prior Authorization Has Become a Workflow Problem

Prior authorization is often discussed as an insurance problem.

It is also a workflow problem.

A physician decides treatment is appropriate.

The patient needs the treatment.

The practice needs authorization.

Information must be assembled.

The information must reach the payer.

The payer evaluates it.

The payer responds.

The response must return to the practice.

The practice must act.

Every handoff creates an opportunity for friction.

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

That is expensive.

And it is exhausting.


Some Payers Are Beginning to Simplify the Journey

There are also signs of movement in the other direction.

Recent reporting indicates that some insurers are experimenting with reducing or bundling certain authorization requirements.

Aetna, for example, announced plans involving bundled cancer-treatment authorizations beginning in twenty twenty-seven.

The concept is straightforward.

Instead of repeatedly asking for approval throughout a treatment pathway, simplify the administrative journey for certain patients and treatments.

That points toward a larger principle:

Good healthcare administration should reduce unnecessary decisions, not multiply them.

The fewer unnecessary handoffs, the fewer opportunities for information to disappear.


THE AI PROBLEM

AI Won't Save a Broken Workflow

Now we arrive at the favorite word in healthcare technology:

AI.

AI can be useful.

AI can extract information.

AI can summarize documentation.

AI can identify missing data.

AI can classify claims.

AI can prioritize work.

AI can automate repetitive tasks.

But AI can also make a broken process faster.

That is not necessarily improvement.

If you automate a bad workflow, you may simply produce bad outcomes more efficiently.

The Financial Times recently examined this problem in healthcare AI, highlighting the gap between technical validation and evidence of real-world clinical impact.

The same principle applies to administrative AI.

Do not ask only:

“Can the model do this?”

Ask:

“Does doing this actually improve the system?”


Stop Asking Whether AI Works

The question is too vague.

Works for what?

Works where?

Works under what conditions?

Works for whom?

Works with what data?

Works compared with what?

Works after implementation?

Works without creating new review work?

Works without increasing the number of exceptions?

Works without forcing staff to monitor another dashboard?

Works without creating another correction loop?

The useful question is:

What measurable work disappears because this technology exists?

That is a much harder question.

It is also a much better one.


AI Should Remove Work, Not Merely Move It

Imagine a system that claims to automate claims.

But staff still review every AI recommendation.

Someone still checks the data.

Someone still corrects the output.

Someone still moves information between systems.

Someone still handles exceptions.

Someone still watches another dashboard.

Is the work gone?

Or did the work move?

Technology should not receive credit simply because the human task changed location.

The goal is less unnecessary work.

Not more sophisticated work queues.


THE ONNX THESIS

Medical Billing Is Not Really a Billing Problem

This is where the OnnX thesis begins.

Medical billing appears to be about claims.

But claims are downstream.

The actual problem often begins earlier.

Patient information.

Insurance verification.

Authorization.

Documentation.

Coding.

Claim creation.

Payment.

Each stage depends on the quality of what came before it.

If information is incomplete upstream, downstream systems compensate.

If information is inconsistent, downstream systems compensate.

If information is fragmented, downstream systems compensate.

If information has to be manually re-entered, downstream systems compensate.

Eventually someone says:

“We need better billing software.”

Maybe.

But perhaps the deeper problem is:

The organization is asking billing to repair information that should have been correct before billing ever saw it.


The Upstream Revenue Cycle

Think of the revenue cycle as a chain:

Patient information → insurance verification → authorization → documentation → claim → payment

Every arrow matters.

Most organizations spend enormous energy optimizing the final arrow.

But a weak early arrow can contaminate everything downstream.

This is why OnnX starts upstream.

Not because downstream billing does not matter.

It does.

But because the cheapest error to fix is often the one you prevent before it travels through the organization.


Eliminate the Correction Loop

Healthcare contains countless correction loops.

Enter.

Review.

Correct.

Re-enter.

Submit.

Reject.

Investigate.

Correct again.

Submit again.

Wait.

Follow up.

Repeat.

Every loop creates cost.

Every loop creates delay.

Every loop creates another opportunity for human error.

The strategic goal should therefore be simple:

Reduce the number of times information has to be corrected after it enters the system.

That is not glamorous.

It is not futuristic.

It is operationally important.

And boring is underrated.

Boring is what reliable systems feel like.


THE PRACTICAL PLAYBOOK

Start With a Representative Denial Sample

You do not need a massive transformation program.

Start small.

Take a representative sample of recent denials.

Do not begin by buying software.

Do not begin by blaming the payer.

Do not begin by blaming billing.

Trace the cases.

Ask where each problem began.

Look for patterns.

Maybe most problems originate in eligibility.

Maybe authorization is the largest source.

Maybe documentation creates the bottleneck.

Maybe one payer creates a disproportionate amount of manual work.

Maybe the same physician receives repeated requests.

Maybe one workflow creates dozens of downstream corrections.

The pattern is the prize.


Measure the Human Cost

Most practices measure dollars.

Also measure minutes.

How much staff time does a denial consume?

How many people touch it?

How many systems are opened?

How many phone calls occur?

How many messages are exchanged?

How many times is the same information entered?

How many times does the physician become involved?

How many days does the issue remain unresolved?

The organization should know not only the financial cost of failure but also the human cost of failure.


Introduce the Preventable Work Ratio

One useful internal metric is the Preventable Work Ratio:

Hours spent correcting preventable problems ÷ total revenue-cycle labor hours

You do not need an industry benchmark to make this useful.

You need consistency.

Track it over time.

If the ratio falls, your system may be getting better.

If your staff becomes faster at correcting the same mistakes while the ratio stays high, you may have improved productivity without improving the underlying process.

That distinction is critical.


Measure Touches Per Claim

Another simple metric:

Touches per claim.

How many people interact with the claim?

How many times does the claim move between queues?

How many times does information get re-entered?

How many manual decisions occur?

A claim that moves cleanly through the system is fundamentally different from a claim that becomes an administrative relay race.

Your goal should not merely be faster touches.

It should be fewer unnecessary touches.


THE UPSTREAM AUDIT

Map the Journey

Write down the actual workflow.

Not the workflow in the policy manual.

The real workflow.

What happens when a patient schedules?

What happens when insurance information arrives?

What happens before the visit?

What happens during documentation?

What happens after the visit?

What happens before billing?

Where does information move?

Where does it get copied?

Where does someone make a manual decision?

The truth usually lives in the gaps between departments.


Identify the Handoffs

Every handoff is a potential failure point.

Front desk to clinical staff.

Clinical staff to authorization.

Authorization to scheduling.

Clinical documentation to coding.

Coding to billing.

Billing to payer.

Payer back to billing.

The more handoffs you have, the more important information continuity becomes.


Find Repeated Corrections

Look for repetition.

Same payer.

Same physician.

Same denial type.

Same missing field.

Same documentation request.

Same portal.

Same manual correction.

Repetition is evidence.


Separate Preventable From Unavoidable

Not every denial is preventable.

Not every payer rule is unreasonable.

Not every administrative problem can be eliminated.

That distinction matters.

The goal is not to pretend healthcare can become frictionless.

The goal is to identify friction that should not exist.


Fix One Thing

Do not redesign everything at once.

Pick one recurring failure.

Fix the upstream cause.

Measure the result.

Then move to the next.

That is how operational improvement becomes sustainable.


THE HUMAN COST

Healthcare's Hidden Currency Is Time

Money is visible.

Time is not.

A physician loses fifteen minutes.

A biller spends twenty minutes chasing a document.

A front-desk employee spends ten minutes on a payer portal.

A practice administrator spends an hour investigating a recurring issue.

Multiply those moments across weeks and months.

Suddenly the organization has created an invisible department.

Except nobody budgeted for it.

The work simply appeared.


Administrative Burden Becomes Patient Burden

Administrative friction does not stay administrative.

It can delay care.

It can delay payment.

It can delay scheduling.

It can frustrate clinicians.

It can frustrate patients.

It can create additional calls.

It can create additional paperwork.

It can consume time that could have been spent with patients.

That is why revenue-cycle improvement is not merely a financial exercise.

It can also be a patient-experience exercise.


WHAT WE GET WRONG

Myth: More Billers Will Fix It

More staff can help when volume is the problem.

But if the process itself creates unnecessary work, more staff can simply increase the capacity to process unnecessary work.

You have built a faster conveyor belt for the wrong boxes.

The better question is:

What work should disappear?


Myth: A Clean Claim Means a Clean System

A clean claim is good.

It is not proof of upstream excellence.

You can have a high clean-claim rate while staff spend enormous amounts of time creating those clean claims.

The final output does not always reveal the complexity required to produce it.


Myth: Outsourcing Solves the Problem

Outsourcing can be useful.

Specialization can create efficiencies.

But moving a broken process outside the organization does not automatically fix the process.

Sometimes the logo changes.

The problem does not.

If information is poor before it reaches the outsourced partner, someone still has to deal with that problem.


Myth: More Technology Means Better Operations

Technology is a tool.

It is not a strategy.

A practice can have sophisticated technology and terrible workflows.

It can have basic technology and excellent operational discipline.

The question is not:

“How much technology do we have?”

The question is:

“How much unnecessary work does our technology eliminate?”


ETHICS, COMPLIANCE AND ACCOUNTABILITY

Technology Does Not Transfer Responsibility

Automation does not eliminate accountability.

If software makes a recommendation, someone must understand how that recommendation is used.

If AI extracts information, someone must know whether the extraction is reliable enough for the decision being made.

If a workflow is automated, the practice still owns the consequences.

Technology can change who performs the task.

It does not automatically change who is responsible for the outcome.


The Ethical Question

Healthcare administration has an ethical dimension.

When administrative friction delays care, the burden does not fall equally on everyone.

Patients with more time, knowledge, confidence or resources may be better positioned to navigate complexity.

Others may simply give up.

That makes administrative simplicity more than an efficiency goal.

It can also be a question of access.

The ethical goal is not to eliminate every control.

Controls exist for legitimate reasons.

The goal is to distinguish necessary friction from unnecessary friction.


FAILURE IS INFORMATION

Stop Hiding Workarounds

Here is another uncomfortable idea.

Your workarounds are data.

When employees create spreadsheets, sticky notes, personal checklists and private tracking systems, do not immediately tell them to stop.

Ask why they created them.

The workaround may be compensating for a missing capability in the official workflow.

The spreadsheet is not necessarily the problem.

It may be the evidence.


Heroic Employees Can Hide Broken Systems

Every practice has someone who knows how everything works.

The person who remembers every payer rule.

The person who knows which number to call.

The person who can fix almost any claim.

The person everyone asks when something breaks.

These people are invaluable.

But organizations should be careful about confusing heroic performance with system quality.

A great system does not require heroes every afternoon.

A great system makes ordinary work ordinary.


A LITTLE HEALTHCARE HUMOR

The Seven-Step Solution to a Four-Step Problem

Healthcare sometimes solves a simple problem with a committee.

Then the committee creates a workflow.

The workflow creates an exception.

The exception requires a spreadsheet.

The spreadsheet creates a meeting.

The meeting creates a dashboard.

The dashboard requires a new employee.

The new employee asks why the process exists.

Someone replies:

“That is just how we've always done it.”

And somewhere, quietly, another fax machine turns on.

We laugh because it is familiar.

But the joke contains a serious operational lesson.

Complexity has a way of becoming institutionalized.

Eventually nobody remembers the original reason.

They only remember the workaround.


THE FUTURE

The Future of Medical Billing Is Less Billing

The future should not be about creating increasingly sophisticated people to chase increasingly sophisticated problems.

It should be about reducing the number of problems that reach them.

That means better data capture.

Better information continuity.

Better eligibility verification.

Better authorization workflows.

Better documentation alignment.

Better handoffs.

Better exception management.

Better visibility into where errors originate.

And yes, better technology.

But technology should support the architecture.

Not substitute for it.


From Reactive to Deterministic

A reactive revenue cycle asks:

“What went wrong?”

A more deterministic revenue cycle asks:

“How do we make this class of problem less likely to happen?”

That is a profound difference.

Reactive systems depend on people noticing problems.

Deterministic systems reduce the number of problems that require noticing.

The first model needs more firefighters.

The second model needs fewer fires.

The goal is not better firefighters. Fewer fires.


WHAT BRIE'S STORY REALLY TEACHES US

The Visible Crisis Is Rarely the Whole Story

Brie Bauer's story is ultimately about something much larger than transplantation.

It is about the difference between treating the visible crisis and understanding the chain that produced it.

Her medical team had to respond to an emergency.

They had to save her life.

Then they had to deal with the consequences.

Then rehabilitation.

Then dialysis.

Then transplantation.

Then recovery.

Each stage inherited circumstances created by what happened earlier.

That is how complex systems work.

And that is how revenue cycle works.

The denial at the end of the process inherits decisions made much earlier.

The billing department inherits the quality of the information that entered the system.

The physician inherits documentation requirements.

The patient inherits administrative friction.

The organization inherits all of it.


Stop Celebrating the Wrong Victory

A claim gets paid.

Great.

But ask:

How many people touched it?

How much time did it consume?

How many corrections were required?

How many messages were exchanged?

How many payer interactions occurred?

Did anyone have to involve the physician?

Could the problem have been prevented?

If the answer is yes, then payment was the end of the story.

Not the beginning of improvement.


QUESTIONS FOR EVERY CLINIC OWNER

Where Does the Work Really Begin?

Where does your billing team spend time fixing information that should have been correct earlier?

That question can reveal more than another denial report.

 

Which Problems Are Actually Upstream?

Which recurring denials are symptoms of upstream workflow failures?

Look beyond the final denial code.

Look at the journey that produced it.

 

What Work Could Disappear?

What administrative work could disappear if you redesigned the process instead of adding another person to it?

That may be the most important operational question of all.


ACTIONS FOR TOMORROW

Pull the Denials

Pull a representative sample.

Trace each case backward.

Do not begin with blame.

Begin with evidence.


Find the Origin

Identify the earliest point where the problem could have been prevented.

That is where improvement begins.


Calculate the Human Cost

Measure more than dollars.

Measure minutes.

Touches.

Calls.

Messages.

Corrections.

Physician interruptions.

Days delayed.

That is your real operational picture.


FINAL THOUGHTS

Stop Admiring the Fire Department

Healthcare loves heroes.

The physician who stays late.

The nurse who finds the missing information.

The biller who rescues the claim.

The administrator who knows exactly who to call.

The staff member who somehow makes the impossible happen.

We should appreciate those people.

But we should also ask a harder question.

Why does the system require so many heroes?

The strongest organization is not the one with the most heroic employees.

It is the one that makes heroics less necessary.

That is what good systems do.

They absorb complexity so humans do not have to.


What Should We Remember?

Don't just work the denial. Find where the problem began.

Don't just automate the workflow. Question whether the workflow deserves to exist.

Don't measure how hard your team works. Measure how much unnecessary work your system creates.


GET INVOLVED

The Provoking Question

What if your biggest revenue-cycle problem is not the denial?

What if the denial is simply the first place your organization can finally see the problem?

That changes everything.

Because once you stop treating the denial as the disease, you can start using it as evidence.

And evidence can lead you upstream.


Share the Conversation

If you are a physician, clinic owner, practice administrator or medical biller, look at your recent denials.

Do not start with the dollar amount.

Start with the origin.

Ask where the information first went wrong.

Then ask how many people had to compensate for that mistake.

What did you find?

Share your experience in the comments.

If you know someone running an independent medical practice who is buried in denials, prior authorization, payer portals or administrative correction loops, share this article with them.

The conversation should not be about how to become better at fixing broken workflows.

It should be about how to build fewer broken workflows.


ABOUT THE AUTHOR

Dr. Daniel Cham

Dr. Daniel Cham is a physician, healthcare strategist and founder of OnnX, an AI-powered medical billing SaaS designed to eliminate middlemen and reduce administrative correction loops for small and medium-sized physician-owned clinics.

His work focuses on the intersection of clinical operations, healthcare administration, revenue cycle management and practical AI.

The central OnnX thesis is simple:

Most of the problem starts upstream.

When clinical and operational information is structured correctly at the point of capture, downstream billing becomes more predictable.

The objective is not to replace physicians.

It is to reduce the friction surrounding them.


CONTINUE THE CONVERSATION

Stay ConnectedLinkedIn: Connect with Dr. Daniel Cham

Website: Dr. Daniel Cham

Spotify: Listen to the podcast

YouTube: Watch on YouTube

X: Follow on X

Facebook: Follow on Facebook

A free resource, Revenue Cycle Made Simple for Independent Clinics, is available in the Featured section of LinkedIn.

No signup required.


REFERENCES AND FURTHER READING

Human Story

The reporting on Brie Morgan Bauer's medical journey and kidney donation from her brother George Morgan provides the human foundation for this article.

People — Brie Bauer receives kidney from her brother

KCTV — Living organ transplants give new hope

 

Insurance Denials and Prior Authorization

Current reporting continues to document the administrative complexity surrounding insurance denials and prior authorization.

Los Angeles Times — Patients who fight health insurance denials often win

KFF — Medicare Advantage prior authorization data

 

Practice Administrative Burden

MGMA reporting illustrates the continuing difficulty practices face with prior-authorization turnaround despite requirements intended to accelerate decisions.

MGMA — Prior authorization turnaround

 

Medical AI

Recent Financial Times reporting examines the gap between technical validation and real-world healthcare impact.

Financial Times — Medical AI has a proof problem


IMPORTANT NOTE

This article is educational and informational.

It does not provide legal, medical, coding, billing or reimbursement advice.

Healthcare reimbursement rules, payer requirements, documentation standards and authorization policies vary by payer, jurisdiction, specialty and patient circumstances.

Practices should verify applicable requirements with appropriate clinical, legal, compliance, coding and revenue-cycle professionals.

The operational concepts described here are intended to encourage process analysis and discussion, not to replace professional advice.


HASHTAGS

#MedicalBilling #RevenueCycleManagement #HealthcareOperations #HealthcareAI #MedicalPracticeManagement #PriorAuthorization #ClaimsDenials #PhysicianPractice #IndependentClinics #HealthcareInnovation #HealthcareAutomation #ClinicalWorkflow #HealthcareLeadership #PracticeManagement #HealthTech

 

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