Wednesday, September 2, 2026

Dr. Paul Kalmansson, Leon Allan Loyd, and the Cello: The Five Minutes Healthcare Keeps Getting Wrong

What a physician, a veteran facing amputation, and a cello reveal about physician time, administrative burden, and the healthcare system we are building.



“The administrative burdens physicians shoulder each day directly affect the patient-physician relationship and unnecessarily interrupt the delivery of care.” American Medical Association (AMA)

 

Then came the cello.

On September 2, 2026, a remarkable human-interest story emerged from Loma Linda, California.

Leon Allan Loyd, a U.S. Army veteran, was facing another amputation.

His medical journey had already taken something enormous from him. Now another surgery was approaching.

The day before the operation, his physician, Dr. Paul Kalmansson, did something unusual.

He played the cello.

Not because music could reverse the disease.

Not because Bach could change the surgical plan.

Not because the hospital needed another intervention on the chart.

Kalmansson played because he wanted his patient to feel that someone was there.

Kalmansson had earned a master's degree in music before attending medical school. He chose Bach's Prelude from the G Major Cello Suite for Loyd.

Loyd later described what the music gave him:

more strength and more hope.

His wife, Sheri Loyd, said Leon called her afterward and became emotional.

He was grateful.

Very grateful.

And the story became public after the couple's granddaughter shared video of the performance on social media.

Think about that for a moment.

A physician.

A veteran.

An impending amputation.

A cello.

Bach.

And a few minutes in which nobody was talking about productivity.

Nobody was talking about throughput.

Nobody was talking about claims.

Nobody was talking about reimbursement.

Nobody was talking about artificial intelligence.

Someone was simply caring for another human being.

And that is where this story becomes much bigger than music.

Because I think healthcare has a time problem.

Not merely a staffing problem.

Not merely a burnout problem.

Not merely an administrative problem.

A time problem.

And we have been trying to solve it with more technology without first asking the uncomfortable question:

What if the technology is sometimes consuming the very time it was supposed to save?


The Contrarian Take

Here is my unpopular opinion:

Healthcare does not have a technology shortage.

We have a friction shortage.

Actually, let me say that differently.

We have too much friction.

Physicians have EHRs.

Patients have portals.

Staff have payer websites.

Billing teams have clearinghouses.

Administrators have dashboards.

Executives have analytics.

Everyone has another login.

Everyone has another password.

Everyone has another notification.

Everyone has another queue.

And somehow, after all this technology, someone still ends up calling the insurance company to ask:

“Where is the claim?”

That should make us laugh.

Except it doesn't.

Because somebody is paying for those minutes.

Usually with time.

And in healthcare, time is not a trivial resource.

Time is clinical capacity.

Time is attention.

Time is listening.

Time is explaining.

Time is thinking.

Time is teaching.

Time is reassuring.

Time is human connection.

Sometimes, apparently, time is also five minutes with a cello.


The Cello Is the Point

It would be easy to read the story of Dr. Kalmansson and Leon Allan Loyd and conclude:

“Isn't that nice?”

It is nice.

But I think that misses the point.

The more interesting question is:

Why does this moment feel so extraordinary?

Why does a physician sitting with a frightened patient and playing music feel almost surprising?

Because modern healthcare has become remarkably good at processing people.

We are less good at being with people.

We have become very sophisticated at moving information.

We are less sophisticated at protecting attention.

We can transmit a claim electronically.

We can route an authorization.

We can generate a note.

We can calculate a risk score.

We can predict a denial.

We can automate a message.

But none of those things can sit beside a human being who is about to lose a limb and say:

I am here.

That still requires a human.

And that is precisely why administrative efficiency matters.

Not because we need physicians to see more patients.

Not because every minute must produce another billable unit.

Not because the healthcare system needs to squeeze another percentage point of productivity from exhausted professionals.

But because human attention is finite.

If we waste it on work that machines and better-designed systems could handle, we are making a choice.

We are choosing administration over attention.

Even when nobody intended to.


The Healthcare Industry Has a Strange Definition of Efficiency

Healthcare loves the word efficiency.

But what does efficiency actually mean?

For some organizations, efficiency means:

More patients.

More encounters.

More claims.

More revenue.

Fewer employees.

Shorter cycle times.

Higher collections.

Lower cost per encounter.

All of those metrics can matter.

But there is a dangerous assumption hiding underneath them.

The assumption is that the purpose of efficiency is to create more throughput.

I disagree.

The highest form of healthcare efficiency should create more capacity for care.

Those are not the same thing.

Imagine a clinic saves two hours per physician every week.

There are two ways to use those two hours.

Option A:

Book more patients.

Option B:

Give physicians more time to think, communicate, review complex cases, call patients, mentor staff, coordinate care, or simply finish the day without taking another pile of work home.

We tend to assume Option A is automatically better.

Why?

Because Option A is easier to measure.

Revenue is measurable.

Claims are measurable.

Visits are measurable.

Hours saved are measurable.

But human connection is harder to put on a dashboard.

That doesn't make it less valuable.

It makes it easier to ignore.


The Administrative Tax Nobody Sees

The American Academy of Family Physicians has described administrative burden as a major problem for physicians, noting that administrative tasks can consume approximately half of physicians' time in some settings.

The AMA likewise describes administrative burden as something that consumes physician time and focus, interrupts patient care, and contributes to burnout.

That should change how we talk about medical billing.

Billing is often treated as something that happens after medicine.

I think that is outdated.

The revenue cycle starts much earlier.

It starts with the information captured during the encounter.

It starts with documentation.

It starts with coding.

It starts with payer requirements.

It starts with whether the right information is available at the right time.

It starts upstream.

By the time a claim is denied, the problem may have already existed hours, days, or weeks earlier.

And then we call someone in the billing department and ask them to fix it.

That is like discovering a leak in the basement and congratulating ourselves because we bought a better mop.

The mop matters.

But perhaps we should also fix the pipe.


Three Experts, One Bigger Message

Several physician leaders and professional organizations have been making versions of this argument for years.

Their approaches differ.

Their specialties differ.

Their technologies differ.

But the direction is remarkably similar.

1. Dr. Paul Kalmansson: Medicine Is Still Human

Kalmansson's contribution to this conversation is not a policy paper.

It is a cello.

His story reminds us that medicine is not simply the delivery of clinical interventions.

There is also the experience of being a patient.

Fear matters.

Uncertainty matters.

Dignity matters.

Hope matters.

Presence matters.

Kalmansson explained that he wanted his patient to know he cared and that music allowed him to communicate that more effectively than words.

That is a powerful lesson for technology leaders.

Technology should protect the conditions in which physicians can be human.

It should not make physicians more efficient at becoming machines.

 

2. Makrina Shanbour, MD: Fix the System, Not the Physician

Physician well-being discussions increasingly recognize that burnout cannot simply be solved by telling physicians to become more resilient.

That is important.

Because if a workflow is dysfunctional, giving the physician a meditation app does not repair the workflow.

If the EHR is unnecessarily complicated, telling doctors to “practice self-care” does not simplify the EHR.

If prior authorization is consuming hours, yoga does not complete the authorization.

If claims repeatedly fail because information is missing upstream, motivational speeches do not fix the claim.

The system needs work.

The AMA has repeatedly emphasized organizational approaches to physician well-being and the need to remove obstacles that interfere with patient care.

That leads to a provocative principle:

Do not train physicians to tolerate broken systems. Fix the systems.

 

3. Steven Waldren, MD, MS: Technology Can Help — If We Use It Correctly

Dr. Steven Waldren, Chief Medical Informatics Officer at the American Academy of Family Physicians, has written extensively about administrative burden, documentation and the promise and limitations of AI in primary care.

His work reflects an important reality:

Technology can reduce burden.

But technology can also create burden.

AI is not automatically the answer simply because it has “AI” in the name.

A poorly designed AI workflow can become one more system clinicians have to monitor.

One more dashboard.

One more alert.

One more verification step.

One more thing to learn.

The objective should therefore not be:

“Where can we put AI?”

The better question is:

“Where is human time being wasted, and what is the safest way to remove that waste?”

That distinction matters.


The AI Industry Has a Problem

Here is another contrarian thought:

The best AI in healthcare may be the AI nobody notices.

We have become fascinated with spectacular demonstrations.

AI writes a note.

AI summarizes a chart.

AI generates an answer.

AI produces an image.

AI writes an email.

Fine.

But what if the most valuable healthcare AI is much less glamorous?

What if it quietly prevents a claim error before submission?

What if it identifies missing documentation before the billing team sees the claim?

What if it understands payer-specific rules?

What if it checks eligibility?

What if it recognizes that a seemingly minor documentation issue is likely to create a denial?

What if it helps structure information before the downstream mess happens?

Nobody will make a viral video about that.

There will be no robot dancing.

No cinematic soundtrack.

No dramatic product launch.

Just fewer problems.

And fewer problems are often more valuable than impressive demonstrations.


We Have Been Automating the Wrong End

Much of healthcare technology has historically focused on what happens after information is created.

The claim is generated.

Then someone checks it.

The claim is denied.

Then someone works the denial.

The authorization is rejected.

Then someone appeals it.

The chart is incomplete.

Then someone chases the physician.

The payer changes a rule.

Then someone discovers it.

The patient receives a bill.

Then someone calls.

This is downstream automation.

It can help.

But it is still reactive.

The bigger opportunity is upstream.

Prevent the problem before it becomes a workflow.

That means asking:

  • Was the right information captured?
  • Was it structured correctly?
  • Does it support the intended code?
  • Does it satisfy payer requirements?
  • Is something missing?
  • Is something contradictory?
  • Is this likely to trigger a denial?
  • Can the problem be corrected while the encounter is still fresh?

This is where clinical-to-claims intelligence becomes interesting.

The objective is not to turn physicians into coders.

Quite the opposite.

The objective is to keep physicians focused on medicine while the infrastructure quietly makes the administrative consequences of that medicine more reliable.


The Five-Minute Test

Here is a simple test I would give every healthcare technology company.

Ask this:

“What will the physician do with the five minutes your product saves?”

Not:

“How many clicks did you remove?”

Not:

“How many claims did you process?”

Not:

“What is the accuracy rate?”

Those metrics matter.

But ask the next question.

What happens to the five minutes?

If the answer is:

“Now the physician can see another patient,”

that's useful.

But it is not the only possible answer.

Maybe the physician calls a patient who received a frightening diagnosis.

Maybe they review a complicated case.

Maybe they explain a treatment plan properly.

Maybe they mentor a younger clinician.

Maybe they eat lunch.

Yes.

Lunch.

Healthcare has somehow reached a point where eating lunch can sound like a technology use case.

Maybe they go home fifteen minutes earlier.

Maybe they play with their child.

Maybe they sleep.

Maybe they simply stop working.

Those are not failures of productivity.

They are evidence that time was returned to a human being.


The Great Healthcare Productivity Trap

There is a dangerous cycle:

  1. Technology saves time.
  2. Organizations discover the time.
  3. The organization fills the time with more work.
  4. Productivity rises.
  5. The time savings disappear.
  6. Everyone asks for another technology solution.

Repeat.

This is the productivity treadmill.

We keep making people more efficient at doing more work.

At some point we should ask:

What if efficiency is supposed to make work more humane rather than merely more abundant?

That is a radical idea in modern healthcare.

But perhaps it should not be.


Medical Billing Is Not the Enemy

I want to be careful here.

Medical billing is not evil.

Revenue matters.

Claims matter.

Accurate coding matters.

Compliance matters.

Physician practices cannot survive if they do not get paid.

A clinic with beautiful patient relationships and terrible cash flow eventually becomes a clinic that closes.

That helps nobody.

So the answer is not:

“Forget billing and focus only on patients.”

That's romantic nonsense.

The answer is:

Build billing infrastructure that respects the clinical mission.

A physician should not have to choose between caring for patients and running a financially viable practice.

A clinic should be able to do both.

That requires better infrastructure.


Why Small and Independent Practices Matter

This conversation becomes particularly important for small and medium-sized physician practices.

Large health systems can have entire departments dedicated to revenue cycle management.

They can employ analysts.

They can negotiate contracts.

They can build internal teams.

They can absorb implementation costs.

A small physician practice has a different reality.

The office manager may be handling five different functions.

The physician may still be involved in billing decisions.

The staff may be switching between the EHR, payer portals, clearinghouse systems, spreadsheets, phone calls and email.

The technology stack becomes a patchwork.

One vendor handles one problem.

Another vendor handles another.

A third vendor promises to integrate them.

Eventually the practice needs a consultant to explain why the integrations don't integrate.

We laugh because it is absurd.

Then somebody gets another denial.


This Is Why I Believe the Future Is Upstream

My own interest in this problem led me to build OnnX around a simple idea:

Revenue-cycle intelligence should begin closer to the point where clinical information is created.

Not after the claim fails.

Not after the payer rejects it.

Not after the billing team spends an hour figuring out why.

Earlier.

Much earlier.

The vision is not “AI replaces the billing department.”

It is not “AI replaces physicians.”

It is not “AI replaces staff.”

That would miss the point.

The goal is to create infrastructure that helps humans make fewer preventable administrative mistakes.

The system should do the repetitive work.

Humans should handle judgment, exceptions, relationships and accountability.

That is a much more interesting future than simply replacing people.


Human-in-the-Loop Is Not a Weakness

There is a strange obsession in technology with eliminating humans from workflows.

I think that is backwards in healthcare.

Healthcare is full of ambiguity.

Clinical judgment is contextual.

Patient preferences matter.

Exceptions happen.

Payers behave inconsistently.

Documentation can be nuanced.

Rules change.

Data can be incomplete.

So the best system is often not:

Human → machine disappears

It is:

Human → machine assists → human verifies when needed

That is not failure.

That is good system design.

The machine handles scale.

The human handles judgment.

The machine remembers rules.

The human understands context.

The machine watches the queue.

The human decides what deserves attention.

That division of labor is much more realistic.


What We Should Stop Automating

Here is where I will be deliberately contrarian.

We should not automate something simply because it is technically possible.

Some things should remain deeply human.

1. Empathy

Don't automate the moment when someone is scared.

2. Difficult conversations

A chatbot should not become the default interface for every emotionally significant conversation.

3. Clinical judgment

Decision support can be valuable.

Decision abandonment is not.

4. Accountability

If an automated process causes a serious problem, someone must own the outcome.

5. Trust

Patients should understand when technology is involved in their care.

6. Exceptions

The strangest cases are often the ones that require human attention.

7. The physician-patient relationship

If technology gives physicians more time with patients, excellent.

If technology becomes another barrier between them, we have solved the wrong problem.


What We Should Automate Aggressively

Now the other side.

There are tasks where healthcare should be much more aggressive.

Automate repetitive work.

Automate data checking.

Automate routine status checks.

Automate predictable workflows.

Automate duplicate entry.

Automate claim-quality checks.

Automate administrative reminders.

Automate repetitive payer-rule matching where appropriate.

Automate the hunting.

Automate the copying.

Automate the reconciliation.

Automate the things humans hate doing and machines are good at doing.

Then give the human back the time.

That's the deal.


A Practical Five-Step Test for Clinic Owners

If I were running a physician practice today, I would not begin by asking:

“What AI should we buy?”

I would ask five questions.

Step 1: Find the Time Leaks

Track where physicians and staff spend time for one week.

Not estimates.

Actual time.

Phone calls.

Portal checks.

Claim follow-up.

Prior authorization.

Documentation.

Coding questions.

Denial work.

Eligibility.

Faxing.

Data entry.

Find the leaks.

 

Step 2: Separate Clinical Work From Administrative Work

Create two buckets.

Only a human should do this.

And:

A machine should probably do this.

You may be surprised how much work sits in the second bucket.

 

Step 3: Attack the Upstream Cause

Don't only count denials.

Ask why the denial happened.

Was documentation incomplete?

Was coding wrong?

Was eligibility incorrect?

Was the payer requirement misunderstood?

Was information entered inconsistently?

Was the claim scrubbed properly?

Was the problem detectable earlier?

Move upstream.

 

Step 4: Measure Time Returned

Do not measure only revenue.

Measure:

  • Physician minutes returned
  • Staff minutes returned
  • Denial rate
  • Clean-claim rate
  • Days in A/R
  • First-pass resolution
  • Administrative touches per claim
  • Manual payer interactions
  • Rework
  • After-hours administrative work

And then ask the most important metric:

What did people do with the time we returned?

 

Step 5: Protect the Savings

This is the step most organizations skip.

If automation saves five hours, don't automatically schedule five more hours of work.

Decide what the recovered capacity is for.

Patient communication.

Complex cases.

Staff development.

Quality improvement.

Professional development.

Or simply sustainability.

Otherwise, automation becomes a machine for creating more work.


The ROI We Don't Put on the Spreadsheet

Suppose an administrative improvement saves a physician one hour a week.

The obvious calculation is financial.

But there are other returns.

One hour may mean:

One difficult patient conversation.

One family phone call.

One chart reviewed more carefully.

One trainee taught.

One staff member supported.

One less late night.

One less weekend spent catching up.

One additional moment of presence.

Not everything valuable in healthcare appears as revenue.

Some things appear as capacity.

Some appear as trust.

Some appear as better decisions.

Some appear as less exhaustion.

And some appear as a physician sitting beside a frightened patient with a cello.


The Most Dangerous Word in Healthcare Technology

The word is:

More.

More patients.

More claims.

More automation.

More dashboards.

More data.

More alerts.

More productivity.

More throughput.

More revenue.

More features.

More integrations.

More AI.

More.

More.

More.

Perhaps the healthcare technology industry should become obsessed with another word:

Enough.

Enough alerts.

Enough clicks.

Enough duplication.

Enough portals.

Enough manual reconciliation.

Enough administrative noise.

Enough work that nobody should have been doing manually in the first place.

Because sometimes the greatest technology achievement is not producing more.

It is eliminating what never needed to happen.


A Myth We Need to Kill

Myth:

“If AI makes physicians more productive, healthcare automatically improves.”

No.

Not automatically.

Productivity without purpose can make a bad system worse.

If AI helps a physician complete documentation faster and the organization immediately fills the recovered time with more appointments, the patient may not experience the benefit.

If AI helps billing process twice as many claims but creates another verification queue, staff may not experience the benefit.

If an AI tool saves ten minutes but requires twenty minutes of setup, training and monitoring, congratulations:

You invented a new job.

Technology should be judged by net burden, not theoretical efficiency.


Another Uncomfortable Question

Why do we celebrate technology that saves a physician ten minutes...

and then use those ten minutes to schedule another patient?

Maybe that's appropriate.

Sometimes it is.

But sometimes the better question is:

What would happen if we used those ten minutes to make the existing patient experience better?

Healthcare has spent decades optimizing volume.

Perhaps the next era should optimize attention.


The Cello and the Claim

This brings us back to Leon Allan Loyd.

Imagine two versions of healthcare.

In Version One, Dr. Kalmansson finishes his administrative work late.

He has charts.

Messages.

Documentation.

Billing questions.

Administrative interruptions.

He is exhausted.

There is no cello.

There is no extra time.

There is no special moment.

In Version Two, the infrastructure around him works better.

The repetitive administrative work is reduced.

The physician has more capacity.

And one day, instead of clicking through another queue, he sits down with his patient.

He plays Bach.

The disease is still there.

The operation is still coming.

The medicine has not changed.

But the experience of medicine has changed.

That difference matters.


Recent Healthcare News Is Telling Us Something

The broader healthcare conversation is moving in the same direction.

The AMA continues to focus on reducing administrative burden and identifying workflow and technology barriers that prevent physicians from spending more time with patients.

The AAFP has similarly emphasized administrative burden as a major threat to the physician experience and has highlighted technologies that can reduce burdens such as documentation and prior authorization.

And current physician discussions around AI increasingly recognize that its value is not simply generating impressive outputs. The real opportunity is reducing work that consumes professional time without adding equivalent clinical value.

The trend is clear.

The question is whether we will use technology to give time back or simply to fill every empty minute with more work.


Three Things I Would Change Tomorrow

If I had the authority to redesign a physician practice tomorrow, I would start with three rules.

Rule #1: Protect Physician Attention

Do not allow low-value administrative interruptions to compete constantly with clinical work.

Attention is a scarce resource.

Treat it like one.

Rule #2: Move Problems Upstream

Don't celebrate fixing a denial.

Celebrate preventing the denial.

Don't celebrate correcting missing information.

Celebrate capturing it correctly the first time.

Don't celebrate finding a billing error.

Celebrate never creating it.

Rule #3: Measure Human Capacity

Revenue matters.

Productivity matters.

But measure physician time and staff time too.

Because a healthcare system that collects more money while consuming every remaining minute of its people may be financially efficient and humanly bankrupt.


The Legal and Ethical Question

There is also an important compliance issue here.

Automation does not eliminate responsibility.

Healthcare organizations still need appropriate controls around:

HIPAA and privacy.

Data security.

Access controls.

Auditability.

Coding compliance.

Documentation integrity.

Payer requirements.

Human oversight.

Transparency.

Vendor accountability.

A system that automatically makes a decision should not become a black box that nobody understands.

The more consequential the decision, the more important explainability and oversight become.

The goal is not:

“The AI did it.”

The goal is:

“The system assisted, the workflow was auditable, and the appropriate human remained accountable.”

That distinction will become increasingly important as AI moves deeper into healthcare operations.


The Five Questions Every Healthcare Founder Should Ask

If you are building healthcare technology, ask yourself:

1. What human problem am I actually solving?

Not what feature are you building.

What problem?

2. Am I removing work or moving work?

This is a killer question.

If you eliminate one task but create three verification tasks, you haven't eliminated burden.

You've redistributed it.

3. Does my technology protect attention?

If not, why not?

4. Can the user understand why the system made a recommendation?

If not, what happens when it is wrong?

5. What does the human do with the time I give back?

If you cannot answer that question, you may be optimizing the wrong thing.


The Future of Healthcare May Be Less About AI Than We Think

The future will certainly contain more AI.

But I don't think the defining question will be:

“How intelligent is the AI?”

It may be:

“How intelligently did we redesign the work?”

AI inside a broken workflow can simply create a faster broken workflow.

AI inside a well-designed workflow can remove friction.

That is a much bigger opportunity.

The winners may not be the companies with the most impressive demonstrations.

They may be the companies that quietly remove thousands of tiny frustrations every day.

No applause.

No headlines.

No futuristic robot.

Just fewer interruptions.

Fewer errors.

Fewer denials.

Fewer clicks.

Fewer calls.

Fewer unnecessary handoffs.

More attention.

More capacity.

More human work.


And That Is Why I Keep Thinking About the Cello

Dr. Paul Kalmansson could not save Leon Allan Loyd from the reality he was facing.

But he could change how Leon experienced that reality.

That distinction is enormous.

Medicine cannot always change the outcome.

But physicians can influence the experience.

And technology should help them do that.

Not by pretending machines can replace compassion.

Not by turning every human interaction into a workflow.

Not by measuring every minute solely by revenue.

But by removing unnecessary work around the people doing necessary work.

That is the promise I see in healthcare technology.

Not a hospital full of robots.

Not physicians replaced by algorithms.

Not endless automation.

Something much more practical.

A physician with enough time to be a physician.


My Hot Take

We have spent years asking:

“How can we make doctors more productive?”

I think we should ask:

“How can we make doctors less interrupted?”

Those are not the same thing.

A more productive physician can see more patients.

A less interrupted physician may listen better.

Think more clearly.

Notice something subtle.

Explain something better.

Call someone back.

Teach.

Rest.

Or sit down and play music for a patient who is afraid.

Maybe that is the future we actually want.


What I Got Wrong About Efficiency

For years, healthcare has trained us to think about efficiency as doing something faster.

But speed is only one dimension of efficiency.

The deeper question is whether the work should exist at all.

A five-minute task that should never have existed is not efficient just because we completed it in four minutes.

That is not efficiency.

That is optimized waste.

And healthcare has plenty of it.

The future belongs to organizations willing to ask:

What should disappear?


The OnnX Thesis

This is ultimately what drives my work with OnnX.

I don't believe physicians need another piece of software demanding their attention.

They need infrastructure that quietly removes friction.

I don't believe AI should replace the people who understand patients.

I believe AI should help remove repetitive administrative work that keeps those people away from patients.

I don't believe the objective of medical billing technology should be to make healthcare feel more transactional.

I believe it should help make the administrative side of medicine less visible to the people who are trying to practice medicine.

That means moving from:

reactive → predictive

downstream → upstream

manual → intelligent

fragmented → connected

transactional → contextual

more work → more capacity

That is a different vision of healthcare technology.

And I think it is worth pursuing.


A 30-Day Experiment for Any Practice

You don't need to buy a new AI system tomorrow.

Start with observation.

For 30 days, track five things:

1. Administrative interruptions per physician

2. Manual billing touches per claim

3. Preventable denials

4. Time spent on payer-related work

5. After-hours administrative work

Then ask:

What are the top three sources of wasted human attention?

Don't start with technology.

Start with the waste.

Then decide whether technology, process redesign, staffing, training or policy change is the best solution.

Sometimes the answer will be AI.

Sometimes it won't.

That's okay.

Good healthcare innovation is not about selling AI.

It is about solving problems.


Metrics That Actually Matter

Healthcare leaders should continue tracking traditional financial metrics.

But add human-capacity metrics.

Consider measuring:

  • Physician administrative minutes
  • Staff administrative minutes
  • Denial rate
  • Clean-claim rate
  • Days in accounts receivable
  • Claim rework
  • Number of manual touches
  • Payer portal interactions
  • Prior-authorization turnaround
  • After-hours work
  • Physician satisfaction
  • Staff satisfaction
  • Patient communication time

The goal is not to make every metric rise.

The goal is to understand where the system is consuming human capacity.


Questions Worth Asking Your Team

At your next practice meeting, don't ask only:

“Are we collecting enough?”

Ask:

What administrative task annoys everyone?

What task do we repeat every day?

What information do we enter twice?

What do we manually check that should be automatically checked?

What causes the most rework?

What creates the most interruptions?

What keeps physicians after hours?

What could we prevent rather than repair?

And finally:

If we gave everyone five hours back next month, what would we want them to do with it?

That answer tells you what your technology strategy should actually be.


The Bigger Lesson

The story of Leon Allan Loyd and Dr. Paul Kalmansson is emotionally powerful because it contains something technology cannot manufacture on command.

Presence.

A physician saw a human being.

Not a case.

Not a diagnosis.

Not a claim.

Not an encounter number.

A person.

And for a few minutes, he gave that person something medicine sometimes struggles to find:

time.

That is why the story matters.

Because healthcare technology should ultimately be judged by what happens to the human beings on the other side of the screen.

If technology gives physicians more time to care, it is doing something valuable.

If it gives staff more capacity to solve problems, it is doing something valuable.

If it reduces errors before they become expensive downstream problems, it is doing something valuable.

If it allows a frightened patient to receive more attention from the person caring for them, it is doing something valuable.

But if technology merely creates more screens, more alerts, more workflows, more verification and more work...

we shouldn't call that innovation.

We should call it administrative inflation.


Three Things I Want Healthcare Leaders to Remember

Protect physician attention.

Move administrative problems upstream.

Measure success by the human capacity your technology creates—not simply the transactions it processes.


Get Involved

Here is the uncomfortable question I want to leave with physicians, clinic owners, healthcare executives and healthcare technology builders:

If we could give every physician five additional minutes with every patient, what would they do with those five minutes?

Would they listen?

Explain?

Think?

Teach?

Reassure?

Call a family member?

Or perhaps, once in a while, do something completely unexpected.

Like play Bach.

I would genuinely like to hear your answer.

Comment below: What is the single administrative task that steals the most valuable time from you or your clinical team?

And if this perspective resonates with you, share or repost it.

The conversation about healthcare technology should not be limited to what AI can do.

It should include what humans can finally do when unnecessary work gets out of their way.


Final Thoughts

Technology should not make physicians better machines. It should give physicians more room to be human.

The best administrative system is not the one that processes the most work. It is the one that prevents unnecessary work from reaching the people who should be caring for patients.

The ultimate ROI of healthcare technology may not be another claim processed. It may be another human moment made possible.


Frequently Asked Questions

Is this article arguing against medical billing technology?

No.

Quite the opposite.

Accurate, efficient billing is essential to practice sustainability. The argument is that billing technology should be designed around the clinical mission rather than operate as an isolated financial function.

 

Is AI going to replace medical billing staff?

Some repetitive tasks will increasingly be automated.

That does not necessarily mean humans disappear.

The more likely model is that technology handles repetitive work while people handle exceptions, judgment, communication, compliance and accountability.

 

Why connect a cello performance to medical billing?

Because both involve the same scarce resource:

human time.

The cello story demonstrates what can happen when a physician has the time and inclination to provide meaningful human presence.

Administrative inefficiency can reduce that available capacity.

 

Isn't physician productivity important?

Absolutely.

But productivity should not automatically mean seeing more patients.

It can also mean spending more attention on complex patients, improving communication, reducing errors, mentoring staff or sustaining a healthier clinical practice.

 

What is the upstream revenue-cycle problem?

Many downstream billing problems originate earlier in the workflow.

Documentation, coding, eligibility, payer requirements and data quality can influence what happens when a claim is eventually submitted.

The earlier a problem can be identified, the less expensive and disruptive it may be to correct.

 

Should every medical practice adopt AI?

No.

A practice should adopt technology when it solves a clearly identified problem better than the alternatives.

Sometimes that means AI.

Sometimes it means workflow redesign.

Sometimes it means better training.

Sometimes it means hiring the right person.

Technology should serve the problem, not the other way around.

 

What should physicians look for in healthcare AI?

Look beyond the demo.

Ask:

Does it reduce net workload?

Does it integrate into the existing workflow?

Can users understand its recommendations?

Does it preserve human oversight?

Is patient data protected?

Can the organization audit what happened?

Does it actually save time?

And perhaps the most important question:

What will the physician do with the time saved?


Three References From This Week

1. Dr. Paul Kalmansson and Leon Allan Loyd: The Cello Before Amputation

ABC7 Los Angeles reported on September 2, 2026, how Dr. Paul Kalmansson played Bach on the cello for U.S. Army veteran Leon Allan Loyd before Loyd underwent amputation surgery, giving him strength and hope during an extraordinarily difficult moment.

ABC7 Los Angeles — Loma Linda doctor's cello performance gives veteran patient hope

2. AMA: Reducing Administrative Burden

The American Medical Association continues to emphasize that administrative burden consumes physician time and focus and can interfere with patient care, reinforcing the need to redesign workflows rather than simply ask physicians to work harder.

AMA — Reducing Administrative Burden

3. AAFP: Relieving Administrative Burden

The American Academy of Family Physicians identifies administrative work as a major burden on physicians and highlights technology and workflow innovations designed to reduce documentation, prior authorization and other administrative demands.

AAFP — A Guide to Relieving Administrative Burden


Tools and Resources

For practice leaders evaluating administrative technology, start with the fundamentals:

Workflow mapping — identify where information enters, moves and breaks.

Denial analysis — determine the actual causes of denials rather than simply counting them.

Time tracking — measure administrative minutes rather than relying on assumptions.

Payer-rule monitoring — identify changes before they create downstream problems.

Documentation review — find recurring information gaps.

Human-in-the-loop AI — automate repetitive work while preserving appropriate human oversight.

Audit trails — make important automated decisions traceable.

Security controls — protect patient information throughout the workflow.


The Future Outlook

Healthcare is not heading toward a world where machines simply replace people.

It is heading toward a much messier and more interesting world.

Humans and machines will work together.

The challenge will be deciding which work belongs to which.

Machines are exceptionally good at repetition.

Humans are exceptionally good at context.

Machines can monitor thousands of transactions.

Humans can recognize when something doesn't feel right.

Machines can remember rules.

Humans understand relationships.

Machines can identify patterns.

Humans decide what those patterns mean in context.

The future belongs to systems that understand this division of labor.

And the best healthcare technology may ultimately be almost invisible.

Patients may never know it exists.

Physicians may barely notice it.

Staff may simply wonder why the day feels less chaotic.

Claims may move more cleanly.

Errors may be caught earlier.

Denials may decline.

Workflows may become quieter.

And somewhere in that quieter system, a physician may have enough time to sit beside a patient.

Maybe talk.

Maybe listen.

Maybe hold a hand.

Maybe play a cello.

That is not a productivity failure.

That is the point.


About the Author

Dr. Daniel Cham is a physician and medical consultant whose work spans medical technology consulting, healthcare management and medical billing. His focus is practical: helping healthcare professionals and organizations navigate complex challenges at the intersection of medicine, technology, operations and the business of practice.

Connect with Dr. Cham on LinkedIn:

Dr. Daniel Cham on LinkedIn

This article represents the author's professional perspective and is intended for educational and informational purposes. It does not constitute legal, medical, coding, billing or compliance advice. Healthcare organizations should obtain appropriate professional guidance for decisions involving their specific circumstances.


Continue the Conversation

More healthcare insights, commentary and practical ideas:

Website: Dr. Daniel Cham

Spotify: Dr. Daniel Cham on Spotify

YouTube: Dr. Daniel Cham on YouTube

X: Dr. Daniel Cham on X

Facebook: Dr. Daniel Cham on Facebook

Knowledge drives progress. Start your journey here.


Free Resource

Check the Featured section of my LinkedIn profile for a free download.

No signup required.


One Last Thought

If this perspective resonates with you, consider reposting it.

Not to promote a product.

Not to promote a technology.

But to help other physicians, clinic owners and healthcare leaders rethink a deceptively simple question:

What would healthcare look like if we stopped asking humans to do work machines could safely do—and started giving humans back the time to do what only humans can do?

Because Leon Allan Loyd did not need another transaction.

He needed a physician.

And for a few minutes, Dr. Paul Kalmansson gave him exactly that.

#Healthcare #PhysicianLeadership #MedicalBilling #HealthcareAI #RevenueCycleManagement #PhysicianBurnout #AdministrativeBurden #HealthcareTechnology #DigitalHealth #PatientCare #MedicalPractice #PhysicianWellbeing #AIinHealthcare #HealthcareInnovation #IndependentPractice #OnnX

 

Tuesday, September 1, 2026

Cameron Ferenchik Went to the Doctor in Atlanta for Swollen Lymph Nodes. What Dr. Jim Robinson Found Has a Lesson for Every Physician

The problem you can see may not be the problem you need to solve. Cameron Ferenchik's story offers a powerful lesson in looking beyond the obvious—from patient care to medical billing.



“Rational medicine can be defined as the practice of medicine that maintains or improves the quality of healthcare whilst controlling/driving down its cost.”Dr. Rahul Mukherjee,

 

What if your biggest billing problem isn't the denial?

Cameron Ferenchik was 24 years old and only two months into her nursing career in Atlanta, Georgia, when she noticed swollen lymph nodes in her neck.

She did what we tell patients to do.

She got checked.

The swollen lymph nodes were not the real problem.

An MRI revealed something nobody expected: a brain tumor blocking the flow of spinal fluid.

Ferenchik soon found herself on the other side of healthcare.

She wasn't the nurse.

She was the patient.

Her neurosurgeon, Dr. Jim Robinson, removed part of the tumor.

The tumor wasn't cancerous.

But Dr. Robinson said it could have been fatal.

That is the part of the story that sticks with me.

Not simply the diagnosis.

The discovery.

Ferenchik went looking for one problem.

The medical team found another.

And that raises a provocative question for physicians and clinic owners:

What if your biggest billing problem isn't the denial?

What if the denial is simply the thing you can see?

What if the real problem happened earlier?

At registration.

At eligibility verification.

During authorization.

In documentation.

During coding.

Inside a payer rule.

Or somewhere between your EHR and billing system.

We would never tell a physician:

“Don't worry about the abnormal finding. Just treat the symptom.”

Yet that's essentially what many revenue-cycle workflows do.

Claim denied? Work the denial.

Another denial?

Work that one too.

Same denial next week?

Open another ticket.

Eventually, everyone becomes extremely good at fixing the same problem over and over.

Congratulations.

You've built a very efficient treadmill.

You're still going nowhere.


Your denial team may be doing an excellent job hiding your billing problem.

That sounds harsh.

But consider what happens when a practice becomes really good at denial management.

Claims go out.

Claims come back.

Someone researches them.

Someone corrects them.

Someone appeals them.

Someone resubmits them.

Revenue eventually arrives.

The practice celebrates.

The workflow continues.

And nobody asks the dangerous question:

Why did this happen in the first place?

That's the question I want physicians and clinic owners to start asking.

Because revenue-cycle management should not simply recover lost money. It should learn how to lose less money.


Medicine has a concept billing needs more of: curiosity

Medicine is full of uncertainty.

A patient presents with chest pain.

The physician develops a differential diagnosis.

Something doesn't fit.

More information is gathered.

The diagnosis changes.

That's not failure.

That's medicine.

The dangerous physician is not the one who doesn't know the answer.

It's the one who stops asking questions too early.

Billing has its own version of this problem.

A claim is denied for “medical necessity.”

Fine.

But what does that actually mean?

Was the documentation insufficient?

Was the diagnosis-code combination inappropriate?

Was an authorization missing?

Did the payer change its policy?

Was the claim submitted incorrectly?

Was the payer wrong?

Did something change upstream?

“Medical necessity” may be the label.

It isn't necessarily the root cause.

Labels are not explanations.


The MRI lesson for medical billing

Cameron Ferenchik's story is powerful because the first clinical question wasn't the final answer.

That's exactly how sophisticated revenue-cycle management should work.

The claim is the patient.

Not literally, of course.

But think about the claim as a case file.

It contains evidence.

Patient information.

Payer information.

Provider information.

Diagnosis.

Procedure.

Modifiers.

Documentation.

Authorization.

Place of service.

Historical outcomes.

Payer behavior.

Why would we throw all of that information into a black box, wait for the payer to say “no,” and then begin investigating?

That's backward.

The better question is:

Can we identify the problem before the claim becomes a problem?

That's where technology becomes interesting.

Not because it's AI.

Because it can potentially see patterns humans cannot efficiently see across thousands of claims.


The billing industry's favorite four words

Here they are:

“That's just how billing works.”

You've heard them.

Maybe you've said them.

I have.

And sometimes they're true.

Payers have complicated rules.

Contracts differ.

Policies change.

Healthcare is messy.

But “that's just how billing works” can become a dangerous organizational habit.

Because once a problem becomes familiar, people stop seeing it as a problem.

The staff learns the workaround.

The biller knows the trick.

The office manager knows which phone number to call.

The physician knows which payer causes trouble.

Everyone survives.

Until the experienced employee leaves.

Then suddenly nobody knows why the mysterious spreadsheet exists.

That's not a system.

That's institutional folklore.


The numbers are telling us something

The latest data should make physician owners uncomfortable.

The AMA reports that physicians complete an average of 40 prior-authorization requests each week. Nearly one-third report that requests are often or always denied. 94% say prior authorization contributes to burnout, while 74% report that prior-authorization denials have increased over the past five years.

CMS estimates that prior authorization consumes roughly 13 hours per week and represents approximately $34,000 in annual administrative cost per physician, using an estimated hourly cost of $20–$50.

And MGMA's January 2026 revenue-cycle data found that 48% of respondents identified denials and appeals as their largest source of revenue-cycle leakage.

Those aren't just billing statistics.

They're capacity statistics.

Every unnecessary administrative hour is an hour that doesn't go toward something else.

Patient care.

Staff retention.

Practice growth.

Clinical education.

Family.

Sleep.

Perhaps we should stop calling administrative burden “overhead.”

Sometimes it's stolen capacity.


Here's where I disagree with conventional revenue-cycle thinking

The industry often talks about:

clean claims.

denial rates.

days in A/R.

collections.

Important metrics.

But here's the problem:

A dashboard can tell you what happened without telling you why it happened.

A denial rate of 8% doesn't tell me enough.

I want to know:

Which payer?

Which service?

Which provider?

Which location?

Which diagnosis?

Which modifier?

Which documentation issue?

Which workflow?

Which dollar amount?

Which claims were preventable?

And most importantly:

Which pattern is getting worse?

That's the difference between reporting and intelligence.


Three experts. Three lessons.

Dr. Willie Underwood III: healthcare should work for people

Dr. Willie Underwood III became the 181st president of the American Medical Association in June 2026. His leadership message emphasizes physician leadership, patient care, access and confronting persistent problems in healthcare.

The operational lesson for practice owners is simple:

A system should serve the clinician.

Not the reverse.

If your physician spends Friday afternoon fighting a payer portal, your workflow has failed.

If your nurse spends hours chasing authorization paperwork, your workflow has failed.

If your biller spends half the day correcting the same preventable error, your workflow has failed.

Don't blame the person operating the system before examining the system itself.


Dr. Mehmet Oz: administrative friction is becoming an infrastructure problem

CMS Administrator Dr. Mehmet Oz has made electronic prior authorization and interoperability important components of CMS's current administrative modernization efforts.

CMS is moving toward more standardized electronic processes that can help providers determine whether authorization is required, identify documentation requirements and receive authorization decisions electronically.

That matters beyond prior authorization.

It points toward a larger change:

Healthcare administration is becoming increasingly data-driven.

The question for independent practices is whether their systems can keep up.


MGMA: stop treating leakage as one giant bucket

MGMA's recent revenue-cycle work emphasizes identifying leaks across the entire revenue cycle rather than treating denials as one homogeneous problem.

That's important.

Because “denial” is not a diagnosis.

It's a symptom category.

The useful question is:

What caused this particular dollar to become difficult to collect?

Once you ask that question consistently, the revenue cycle becomes much more interesting.

And much more manageable.


My favorite billing question

Here's one I would put on every practice manager's desk:

“Could we have known this before the claim was submitted?”

If the answer is yes, you have an opportunity.

If the answer is no, investigate why.

If the same answer keeps appearing, automate the detection.

That's the loop.

Detect.

Understand.

Prevent.

Measure.

Learn.

Repeat.


Stop hiring people to compensate for broken workflows

This is another uncomfortable opinion.

Sometimes the answer to a billing problem is another employee.

Sometimes it isn't.

If five people are manually checking the same thing because the system cannot reliably identify it, hiring a sixth person may increase capacity.

It does not necessarily solve the problem.

It's like adding another nurse to repeatedly rewrite the same form.

At some point, somebody should ask:

Why does the form need to be rewritten?

This is where process improvement matters.


The biller is often not the problem

Physicians sometimes tell me:

“Our billing company isn't doing a good job.”

Maybe.

But before replacing the billing company, ask:

Does the billing team have the information it needs?

Can it see the clinical documentation?

Can it identify authorization requirements?

Can it see payer-specific patterns?

Does it receive timely eligibility information?

Can it connect denials back to their root causes?

Can it see what happened upstream?

If not, you may be asking the biller to perform detective work with half the evidence missing.

That's not a people problem.

That's a data architecture problem.


The OnnX thesis

This is why I founded OnnX.

I don't believe small and medium-sized physician practices need another complicated billing system.

They need better visibility into what is happening before, during and after the claim.

The central idea is straightforward:

Healthcare billing is often a data-quality problem disguised as a billing problem.

If the information is incomplete, disconnected or inconsistent, the claim inherits that weakness.

No software can magically turn bad input into perfect output.

AI cannot change that.

Automation cannot change that.

A bigger billing department cannot permanently change that.

The solution starts earlier.

At the point where information enters the revenue cycle.


What should OnnX—or any intelligent billing platform—actually do?

Forget the phrase “AI-powered” for a minute.

Ask what the software actually accomplishes.

It should help identify:

Potential eligibility problems.

Authorization risks.

Documentation gaps.

Coding inconsistencies.

Modifier issues.

Payer-specific patterns.

Recurring denial causes.

Potential underpayments.

A/R risks.

But there is an even more important requirement:

Explain the warning.

If software tells a biller:

“High-risk claim.”

That's not particularly useful.

Tell them:

“High risk because this payer has repeatedly denied this service combination when authorization documentation is absent.”

Now the person has something to investigate.

Intelligence without explanation creates another black box.

Healthcare already has enough of those.


AI should be the smoke detector, not the firefighter

Here's another analogy I like.

A firefighter is called after the fire.

A smoke detector is valuable because it warns you before the house is fully involved.

AI in revenue-cycle management should increasingly behave like a smoke detector.

Not:

“The claim was denied. Here's a summary.”

But:

“This claim has characteristics associated with previous denials. Review it before submission.”

That is a much more interesting use of AI.

And much more useful.


But let's not worship AI

This is where I want to push back against my own industry.

Healthcare technology companies love AI.

Obviously.

I'm a healthcare technology founder.

But AI is not magic.

Sometimes the solution is:

better training.

Sometimes:

better documentation.

Sometimes:

a cleaner workflow.

Sometimes:

a payer policy update.

Sometimes:

structured data.

Sometimes:

a human conversation.

And sometimes:

AI.

The best healthcare technology isn't the technology with the most impressive vocabulary.

It's the one that removes a real problem without creating three new ones.


Practical playbook for physician owners

If I were sitting across from a physician-owner today, I'd suggest starting here.

1. Pull 90 days of denials

Don't guess.

Get the data.

 

2. Rank by dollars

Not just frequency.

A hundred $25 denials may matter less than ten $5,000 denials.

 

3. Rank by repeatability

Ask:

Does this keep happening?

Recurring problems are where prevention has leverage.

 

4. Identify the upstream event

Ask:

Where did the problem begin?

Registration?

Eligibility?

Authorization?

Documentation?

Coding?

Submission?

Payer processing?

 

5. Create one prevention rule

Don't redesign the entire revenue cycle.

Fix one recurring problem.

 

6. Measure the result

Track:

denial rate

denial dollars

staff hours

days to resolution

appeal recovery

Then compare before and after.

 

7. Automate only after you understand it

This is important.

Do not automate confusion.

First understand the workflow.

Then automate the predictable parts.


Five billing metrics I would watch every month

1. Preventable denial rate

Not all denials are preventable.

Separate them.

2. Denial dollars

Revenue exposure matters.

3. First-pass payment rate

How often does the claim move through cleanly?

4. A/R over 90 days

Old money is expensive money.

5. Staff hours spent on rework

This one is frequently underestimated.

You aren't only losing revenue.

You're consuming labor.


The hidden cost nobody puts on the dashboard

Let's say a claim is denied.

The practice eventually gets paid.

Everyone celebrates.

But how many people touched it?

A biller.

A coder.

A physician.

A nurse.

A front-office employee.

Perhaps someone called the payer.

Perhaps someone appealed it.

Perhaps someone checked documentation.

Perhaps someone resubmitted it.

The practice recovered the money.

But it didn't recover the time.

That's an important distinction.

Revenue recovered is not the same thing as efficiency achieved.


Legal and compliance considerations

This conversation also has a serious side.

Automated billing systems must operate within applicable coding, reimbursement, privacy and compliance requirements.

A few principles are non-negotiable.

Never let technology justify unsupported coding.

Never alter documentation simply to obtain reimbursement.

Protect patient information.

Maintain appropriate human oversight.

Keep audit trails where appropriate.

Understand why an automated recommendation was made.

And remember:

A software recommendation does not transfer accountability away from the healthcare organization.

Technology can assist.

Organizations remain responsible for their processes and decisions.

For specific legal or compliance questions, practices should obtain advice from qualified counsel and compliance professionals.


Ethical considerations

Here's the ethical question:

Are we using technology to help patients—or simply to collect faster?

Those goals can overlap.

They should not be confused.

A financially healthy practice is important.

But aggressive billing without appropriate clinical and compliance safeguards is not innovation.

The goal should be:

accurate claims

appropriate reimbursement

fewer preventable errors

less administrative waste

better patient access

sustainable practices

That is a much healthier definition of revenue-cycle innovation.


The five biggest mistakes I see

Mistake 1: Looking only at denials

The denial is downstream.

Look upstream.

Mistake 2: Measuring percentages without dollars

A percentage can look impressive while thousands of dollars disappear.

Mistake 3: Treating every payer the same

They aren't.

Mistake 4: Blaming staff before studying workflow

People often create workarounds because the system requires them.

Mistake 5: Buying AI before fixing data

AI cannot compensate indefinitely for poor data quality.


Myth busters

Myth: “More billing staff means fewer problems.”

Sometimes.

But if the root problem is workflow or data quality, you may simply be adding people to the leak.

Myth: “Zero denials is the goal.”

No.

Zero preventable denials is a much more sensible aspiration.

Myth: “AI will replace billers.”

Not necessarily.

The more useful future is likely to be AI handling repetitive detection while experienced professionals handle exceptions and judgment.

Myth: “Outsourcing eliminates responsibility.”

It doesn't.

The practice still needs visibility, oversight and accountability.

Myth: “The payer is always the problem.”

Sometimes the payer is.

Sometimes the provider is.

Sometimes both sides are operating from different information.

The useful question is:

What does the evidence show?


A 30-day challenge for your practice

Try this.

For the next 30 days, don't ask:

“How many denials did we work?”

Ask:

“How many denials did we prevent?”

Then pick one category.

Maybe authorization.

Maybe eligibility.

Maybe coding.

Maybe documentation.

Measure it.

Fix it.

Measure again.

If the number improves, repeat the process.

You don't need a billion-dollar transformation.

You need a feedback loop.


A little humor from the revenue-cycle trenches

Healthcare has an unusual talent.

We can build a six-figure clinical system and then send someone a fax.

We can perform extraordinarily complex procedures and then spend 45 minutes trying to determine which payer portal password still works.

We can produce terabytes of healthcare data and then email a spreadsheet called:

FINAL_FINAL_v7_REAL_FINAL.xlsx

And somehow everyone accepts this as normal.

Maybe it's time to stop.

The future doesn't have to be more complicated.

It can actually be simpler.


The real opportunity for healthcare founders

Healthcare founders should pay attention to a larger shift.

The next generation of healthcare infrastructure won't simply move information around.

It will increasingly interpret information and identify what deserves attention.

That's where the opportunity gets interesting.

Imagine a revenue-cycle system that doesn't merely store historical denials.

It learns from them.

Imagine it doesn't merely tell a practice:

“You lost $18,000.”

It says:

“Here are the three recurring patterns responsible for most of that exposure.”

Then:

“Here are the claims currently showing those characteristics.”

Then:

“Here is what changed.”

That is not just billing software.

That's operational intelligence.


The future: from reactive to predictive

The revenue cycle has traditionally been reactive.

Claim submitted.

Claim denied.

Problem investigated.

The next phase is more predictive.

Claim prepared.

Risk identified.

Human reviews.

Claim corrected.

Claim submitted.

Then the next level:

Outcome recorded.

Pattern learned.

Workflow improved.

That's a continuous learning system.

Not perfect.

Not autonomous.

But increasingly intelligent.


And this brings us back to Cameron Ferenchik

A young nurse in Atlanta noticed something unusual.

She sought care.

The obvious explanation wasn't the important one.

The MRI revealed something else.

Dr. Jim Robinson acted.

And a potentially dangerous problem was found before it became something worse.

There is a lesson here that extends beyond medicine.

Don't confuse the first visible signal with the underlying problem.

In medicine, that can matter enormously.

In healthcare operations, it matters too.

Your denial report may be telling you something.

Your A/R may be telling you something.

Your payer mix may be telling you something.

Your staff turnover may be telling you something.

Your authorization backlog may be telling you something.

Your claim data may be telling you something.

The question is:

Are you listening?


The biggest billing question of all

Maybe we have been asking the wrong question.

Instead of:

“How do we collect more?”

Ask:

“Why didn't we collect correctly the first time?”

Instead of:

“How do we work more denials?”

Ask:

“Why did these claims become denials?”

Instead of:

“How can we hire more people?”

Ask:

“Why does this workflow require so much human rework?”

Instead of:

“Where can we add AI?”

Ask:

“Where would better intelligence prevent avoidable work?”

Those questions lead to different businesses.

Different workflows.

Different technology.

And potentially different outcomes for physicians.


Final Thoughts: Look for the problem behind the problem

Cameron Ferenchik went looking for an explanation.

She found something she wasn't expecting.

That's what good healthcare does.

It stays curious.

It investigates.

It doesn't stop at the first convenient answer.

Physician practices deserve the same discipline in their financial operations.

A denial is information.

A billing error is information.

An unusual A/R pattern is information.

A recurring payer problem is information.

The smartest practices won't necessarily be the ones with the biggest billing departments.

They may be the ones that learn fastest from the information already sitting inside their systems.

That is the opportunity I see for OnnX.

Not another system that simply helps you work harder.

A smarter approach to identifying problems before they become expensive.

Because the best claim is not the one you successfully appeal.

It's the one that never needed the appeal.

The best billing workflow isn't the one that handles a mountain of denials efficiently.

It's the one that makes the mountain smaller.

And the best technology isn't the technology that says it uses AI.

It's the technology that gives a busy physician one less problem to worry about.


Get Involved: What Do You Think?

Here's my question for physicians, practice owners and revenue-cycle leaders:

If your billing data could warn you about one problem before a claim was submitted, what would you want it to catch?

Tell me in the comments.

What is the most frustrating recurring billing problem in your practice—and what have you tried to fix it?

And if this perspective made you rethink the way you look at denials, repost this article and start the conversation with another physician or clinic owner.

Don't wait for the denial to tell you something went wrong.

Find the signal earlier.

Let's build revenue cycles that learn instead of repeat.


About the Author

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

As founder of OnnX, he focuses on practical approaches to helping small and medium-sized physician practices improve visibility, reduce preventable billing problems and build more intelligent revenue-cycle workflows.

His perspective comes from looking at healthcare not only as a clinical environment, but also as an operational system where data, people, technology and financial sustainability have to work together.

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


Continue the Conversation

The healthcare conversation doesn't end with one article.

Explore additional perspectives on healthcare operations, medical billing, technology, entrepreneurship and innovation, including practical lessons that can be applied inside real-world medical practices.

Visit Dr. Cham's website

Listen to the podcast on Spotify

Watch on YouTube

Follow Dr. Cham on X

Follow Dr. Cham on Facebook

Knowledge only creates value when it changes what we do.

Find one problem. Follow the signal. Take one practical step.

Then share what you learned so someone else doesn't have to learn it the hard way.


Free Resource

Looking for a practical starting point?

Visit the Featured section of my LinkedIn profile to find the free resource. No signup is required.

Start there.

Use what is useful.

Test it in your practice.

And keep the conversation going.

If this article resonates with you, consider reposting it so another physician or clinic owner can see the problem differently.


References

CBS News Atlanta — Cameron Ferenchik's unexpected brain-tumor diagnosis. The human-interest story that inspired this article follows a 24-year-old Atlanta nurse whose evaluation for swollen lymph nodes unexpectedly revealed a potentially life-threatening brain tumor.
Read the CBS News Atlanta report

American Medical Association — 2026 prior-authorization survey. The AMA's latest physician data highlights the continuing administrative burden associated with prior authorization, including the reported volume of requests, time burden, denials and burnout.
Read the AMA report

CMS — Electronic prior authorization. CMS outlines current federal efforts to make prior authorization more electronic, standardized and transparent, including requirements and implementation timelines affecting certain payers.
Read the CMS guidance


Disclaimer / Note

This article is intended solely for general educational and informational purposes. It does not provide medical, legal, coding, reimbursement, compliance or financial advice. Specific circumstances can vary considerably. Healthcare professionals and organizations should consult appropriately qualified medical, legal, compliance and financial professionals before making decisions based on the information discussed here.


#MedicalBilling #RevenueCycleManagement #Healthcare #Physicians #MedicalPractice #PhysicianPractice #HealthcareInnovation #HealthcareTechnology #AIinHealthcare #MedicalBillingSoftware #DenialPrevention #DenialManagement #PriorAuthorization #HealthcareAI #PracticeManagement #PhysicianEntrepreneur #HealthTech #RevenueCycle #PatientCare #HealthcareLeadership

 

 

Megan White Was a 2-Pound NICU Baby. Dr. Stephen Ragatz Helped Care for Her. 42 Years Later, She Came Back as His Colleague.

One Patient Became a Nurse. Why Can’t Healthcare Systems Learn the Same Way? “Although we are in different systems, none of us compete in ...