An 11-year-old’s remarkable journey reveals what great care looks like. It also raises an uncomfortable question: why is healthcare so much better at treating complex problems than preventing avoidable administrative ones?
“Patient safety, physician expertise and the humanity of
clinical practice must guide how these tools are developed and used.” — Joint
statement from six U.S. medical societies, reported October 1, 2026.
The boy who wanted to run the hospital
Most children leaving a hospital after a life-threatening
illness have a simple wish: go home, see their friends, return to school, and
reclaim the ordinary childhood that illness interrupted.
Peyton Stewart had those dreams, too.
But the 11-year-old from Gulfport, Mississippi, had one
additional ambition. After surviving a rare genetic disorder and receiving a
life-saving transplant, he announced that he wanted to become the future CEO of
the hospital that helped save his life.
Naturally, he planned to play in the NBA first.
That is a career plan with a rather ambitious succession
strategy.
Behind the humor is a remarkable story about family,
medicine, and the kind of human connection that can make a frightening
healthcare experience feel a little less frightening.
Peyton began experiencing recurring chalazions, or eyelid
cysts, when he was just two years old. Even after surgery, the problems
returned. In late 2023, while fighting multiple infections, his family learned
that his hemoglobin level was dangerously low.
Then, in February 2024, a sinus infection was followed by
seizures. Peyton was taken to a local hospital, where clinicians recognized the
need for specialized care. He was transferred to Manning Family Children’s
Hospital in New Orleans.
There, physicians eventually identified a rare condition
called STAT1 gain-of-function, which caused his immune system to attack
his own red blood cells.
The treatment journey was demanding. One crucial part of the
solution came from an extraordinary source: Peyton’s older brother, Acie
Stewart, who was a suitable match to donate bone marrow for the transplant.
Their mother, Laurie Stewart, faced the emotional burden of
having two children involved in a serious medical journey at the same time.
Their father, Christopher Stewart, and grandmother, Yvonne Hill, also watched
Peyton navigate an experience no child should have to endure.
The transplant ultimately succeeded.
After spending months in the hospital, Peyton celebrated his
recovery with a bell-ringing ceremony marking the anniversary of his
transplant. His family and hospital staff gathered to celebrate a future that
had once seemed uncertain.
Peyton thanked Acie for giving him a second chance at life.
He also thanked the hospital staff for helping him through
difficult days, making him smile, and supporting his mother.
One detail stood out. Peyton remembered how Lucio “Lou”
Fragoso, president and CEO of Manning Family Children’s Hospital, visited him
and played basketball with him.
Fragoso did not simply appear as a distant executive. He
became someone Peyton could look forward to seeing.
Peyton told PEOPLE that the visits cheered him up on his
hardest days. He also noticed that the CEO visited other children, not just
him.
That mattered.
Peyton wanted to do the same one day: help children smile on
difficult days, laugh on good ones, and stay healthy.
His ambition to lead the hospital was not about an
impressive title. It was about becoming the kind of leader who made a
difference to people facing frightening circumstances.
As Peyton put it, he wanted to become the hospital’s CEO
someday, but only after his NBA career.
Fair enough. Even healthcare leadership could use a little
more long-range planning.
The real lesson is not that healthcare needs more heroes
Peyton’s story deserves to be celebrated for what it is: a
child’s recovery, a brother’s generosity, a family’s endurance, and a medical
team’s work.
It would be wrong to use his experience to suggest that
billing problems affected his treatment. The reporting does not establish that.
But his story does invite a broader question about
healthcare.
What would happen if the systems surrounding
compassionate care were designed to protect more of the time, attention, and
resources that compassionate care requires?
A physician can make the right diagnosis and still spend
hours completing administrative work. A nurse can deliver excellent care and
then spend time chasing missing information. A practice administrator can
coordinate a complex visit only to discover that a document, authorization, or
data field is missing when the claim is submitted.
The patient sees the appointment, the treatment, and the
people who help.
The practice also sees the paperwork, portals, forms,
handoffs, corrections, and follow-up work that keep the operation moving.
These are two views of the same healthcare system.
And sometimes, the second view is a lot less inspiring.
A claim can be rejected because information is incomplete. A
prior authorization can require additional documentation. A payer may request
clarification. A staff member must locate the information, correct the record,
resubmit the claim, and monitor the result.
One task becomes three. Three become six. Six become an
inbox that appears to reproduce overnight.
The frustrating part is that some of this work is necessary.
Healthcare is complicated, clinical documentation must be accurate, and payers
have legitimate reasons to verify coverage and medical necessity.
But necessary verification and avoidable rework are not
the same thing.
The industry should not confuse the two.
The contrarian argument: a denial may be the last visible
symptom, not the first failure
Medical billing discussions often begin at the point where a
claim is rejected, delayed, underpaid, or denied.
That is understandable. A denial is visible. It has a
status, a dollar amount, a deadline, and often a person assigned to fix it.
But visibility does not tell us where the problem began.
Consider a hypothetical example.
A patient’s insurance information changes. The update is
recorded in one system but does not reach another. An authorization requirement
is missed. The clinical encounter takes place, but a necessary detail is not
documented in the expected location. The billing team later discovers the gap
and begins a chain of corrections.
The denial or delay may appear at the end of the process.
The conditions that made it likely may have appeared much earlier.
That distinction matters because a practice can become
remarkably efficient at fixing the same problem repeatedly without becoming any
better at preventing it.
Imagine a restaurant that burns every tenth order.
Management buys a faster fire extinguisher, trains staff to respond more
quickly, and celebrates a shorter recovery time.
Those improvements may be useful.
But eventually someone should ask why the kitchen keeps
catching fire.
Healthcare billing deserves the same question.
Are we improving the process, or merely improving our
ability to recover from a process that keeps breaking?
Not every denial is preventable. Some result from payer
decisions, coverage exclusions, medical-necessity disputes, policy changes, or
legitimate documentation requirements. No software can eliminate every
disagreement or guarantee payment.
But that does not mean all administrative rework should be
accepted as inevitable.
When the same categories of errors recur, practices should
examine where information first becomes incomplete, inconsistent, inaccessible,
or disconnected.
The goal is not to blame the person who submits the claim.
It is to understand why that person is repeatedly asked to repair a problem
that may have started somewhere else.
The hidden cost of “just one more correction”
In a busy independent clinic, administrative friction rarely
arrives as one enormous crisis.
It arrives as small interruptions.
A missing insurance detail.
A chart note waiting for clarification.
An authorization that needs another attachment.
A claim that must be corrected.
A payer portal that does not agree with the practice
management system.
A message asking someone to confirm what was already entered
somewhere else.
Each interruption seems manageable. Collectively, they
consume attention, delay work, and create opportunities for further mistakes.
There is a hidden economic cost, too.
Staff time spent correcting preventable errors is time
unavailable for other work. Delayed claims can complicate cash-flow planning.
Repeated follow-up increases the cost of collecting revenue. Physician time
spent navigating administrative tasks is time not spent on clinical work,
practice development, or simply going home on time.
That last point deserves more attention.
A clinic can appear busy, productive, and fully staffed
while quietly relying on people to compensate for weaknesses in its systems.
The practice may not be short of effort. It may be short of
continuity.
And effort is expensive when the same work must be done
twice.
The prior authorization problem is a warning sign
Prior authorization illustrates the scale of the
administrative burden, although it is not identical to medical billing and
should not be treated as the cause of every claim denial.
The American Medical Association has reported substantial
physician concerns about prior authorization, including treatment delays,
administrative workload, and effects on physician burnout.
In its 2024 survey of physicians, the AMA reported that 94%
of respondents said prior authorization delays care, 78% said it sometimes
leads patients to abandon treatment, and physicians and their staff spent an
average of about 13 hours per week completing prior authorization work. These
findings reflect surveyed physicians’ experiences, not a universal measurement
of every practice.
The broader point is hard to ignore: administrative
processes can consume meaningful clinical capacity even when the people doing
the work are competent and committed.
Policy changes are also moving toward greater electronic
access to prior authorization information and more transparency around payer
decisions. The Centers for Medicare & Medicaid Services has established
interoperability and prior authorization requirements intended to improve
processes and reduce burden for affected payers and providers.
Those changes matter. But a new electronic pathway does not
automatically create a clean, complete, and consistent set of information at
the point where a clinical or administrative decision begins.
Digitizing a fragmented process can make it faster. It can
also make the fragmentation harder to see.
Automation is not the same as prevention.
A faster way to submit incomplete information is still a
faster way to submit incomplete information.
The uncomfortable question: why do we fix the claim
instead of examining the chain?
Think about the journey from scheduling to payment.
Insurance details must be collected and verified.
Eligibility must be understood. Referrals and authorizations may be required.
The clinical encounter must be documented. Coding must accurately reflect the
services and applicable rules. The claim must be assembled and submitted.
Payment must be reconciled, and denials or underpayments must be addressed.
These steps are connected in theory.
In practice, the information may pass between people,
software systems, payer portals, and workflows that do not always communicate
cleanly.
A front-desk employee may enter information that a clinician
cannot easily see. A clinician may document a relevant detail that the billing
team struggles to locate. A biller may discover a missing item only after the
encounter is complete. A practice administrator may spend the afternoon
coordinating a correction that nobody intended to create.
Each person can do their job correctly within the boundaries
of their own system while the overall process still fails.
That is the problem with looking only at individual
performance.
Sometimes the person is not the problem. The handoff is.
And sometimes the handoff is not even the first problem. The
information may have been captured incompletely at the beginning.
This suggests a more useful way to think about revenue-cycle
performance:
- Data
quality: Was the information accurate, complete, and current when it
was first collected?
- Continuity:
Did the information reach the people and systems that needed it?
- Timing:
Was a problem detected before it caused downstream work, or only after a
claim was submitted?
- Ownership:
Was it clear who needed to act when information was missing or
inconsistent?
- Learning:
Did the organization prevent recurrence, or simply close the latest
ticket?
These questions shift attention from the final claim to the
entire chain of events.
They also reveal why adding another dashboard may not be the
answer.
A dashboard can tell a practice that denials are rising. It
may not explain why the same missing information keeps recurring across the
front desk, clinical documentation, and billing.
A report can identify a backlog. It may not remove the
underlying cause.
And another alert can make a problem more visible without
making the work any easier.
Healthcare does not necessarily need more notifications
telling people that they have work to do. It needs better ways to make sure the
right information is available before that work becomes a correction.
The next breakthrough may be upstream, not downstream
Much of healthcare technology focuses on what happens after
information has been captured: reviewing notes, suggesting codes, processing
claims, identifying denials, or automating follow-up.
These capabilities can be valuable. They can reduce
repetitive work and help teams manage complex operations.
But there is another opportunity.
What if the system could help prevent incomplete or
inconsistent information before it enters the downstream billing process?
That does not mean forcing clinicians to complete endless
forms or turning every patient encounter into a data-entry exercise.
In fact, that would be precisely the wrong lesson.
Physicians should not have to become full-time clerks to
make software work. A technology designed to reduce administrative burden
should not create a new administrative burden in the process.
The better goal is to make the right information easier to
capture, structure, validate, and share at the appropriate point in the
workflow.
For example, a well-designed process might flag a missing
required field before a claim is submitted, identify inconsistent insurance
information before it propagates, or make relevant documentation easier for
authorized team members to locate.
The system should distinguish between information that is
genuinely missing and information that simply resides somewhere different than
expected.
It should also respect clinical judgment. Automation should
not invent documentation, alter clinical meaning, or pressure clinicians to
record something that did not occur.
A useful system helps people complete the work accurately.
It does not encourage them to manufacture a cleaner record.
This is where responsible AI may have a meaningful role: not
as a magical machine that guarantees reimbursement, but as a tool that can
support data validation, identify patterns in recurring errors, surface missing
information, and reduce avoidable manual handoffs.
The important question is not whether AI can generate
another output.
It is whether the output helps prevent the next unnecessary
correction.
A better test for healthcare AI
Healthcare has no shortage of impressive demonstrations.
A tool summarizes a chart. Another drafts a note. Another
predicts a denial. Another generates a report that explains why last month’s
process failed.
All of these may have value.
But practice owners should ask a more demanding set of
questions before adopting a tool.
Does it remove work, or merely move work somewhere else?
Does it reduce the number of times staff must touch the same
issue? Does it catch errors early enough to change the outcome? Does it
integrate with the existing workflow, or force people to maintain another
screen? Does it improve the completeness of information without compromising
clinical accuracy or privacy?
And, critically, can the practice measure the difference?
Useful measures might include:
- The
percentage of claims accepted on the first submission.
- The
frequency of recurring denials by root cause.
- The
number of manual corrections required per claim.
- The
time between identifying a missing item and resolving it.
- The
staff hours spent on avoidable rework.
- The
time from service delivery to claim submission.
- The
percentage of identified errors prevented before submission.
No single metric tells the whole story. First-pass
acceptance, for example, should not be improved by submitting questionable
claims or sacrificing accuracy. Measures need to be interpreted alongside
compliance, clinical integrity, payer requirements, and the patient experience.
But measurement creates something more valuable than another
technology demo: evidence that the workflow is actually improving.
If a system reduces clicks but leaves the number of
corrections unchanged, the practice should know that.
If it improves first-pass acceptance but increases
documentation burden, the trade-off should be visible.
If it catches a problem earlier, the practice should be able
to determine whether that intervention saves time or improves the outcome.
The right question is not “How much AI did we deploy?” It
is “How much unnecessary work did we eliminate?”
That is a far more useful conversation for an independent
clinic trying to protect both its finances and its people.
What Peyton’s story can teach us about the business of
care
Peyton remembered that the hospital staff made him feel at
home. He remembered activities that let him remain a child during a difficult
experience. He remembered a CEO who took time to play basketball with him.
These moments did not replace the medical treatment. They
helped make the experience human.
It would be simplistic to say that kindness alone makes
healthcare excellent. Patients need competent clinicians, appropriate
treatment, safe facilities, reliable operations, and adequate resources.
But the human side of care is not an optional decoration
added after the important work is finished.
It is part of what patients and families experience.
That creates a practical responsibility for healthcare
organizations. The systems surrounding clinical care should support the people
delivering it rather than routinely consuming their attention with avoidable
friction.
This includes revenue-cycle operations.
Billing is not the opposite of compassion. Clinics need to
be paid accurately and on time to remain viable, compensate staff, maintain
services, and care for future patients.
The problem is not that billing exists. The problem is when
unnecessary administrative work absorbs resources that could be used more
productively.
An independent clinic cannot simply absorb unlimited unpaid
labor. It cannot hire a new person every time a process becomes more
complicated. It cannot afford to treat every recurring error as an unavoidable
cost of doing business.
Nor should it have to choose between financial
sustainability and a reasonable workload for its staff.
A healthier revenue cycle should protect both the
clinic’s financial health and its capacity to care.
That is not a sentimental argument. It is an operational
one.
What independent clinics can do now
A practice does not need to wait for a new AI product to
start improving its process. A few focused steps can help reveal where
avoidable rework begins.
1. Stop treating every denial as an isolated event
Review recurring denials and corrections by category. Look
for patterns involving eligibility, authorization, documentation, coding, claim
data, and payer-specific requirements.
The purpose is not to create a longer spreadsheet. It is to
identify repeatable problems that deserve a repeatable solution.
2. Trace the problem backward
When a claim requires correction, ask where the missing or
inconsistent information first appeared.
Was it never collected? Was it entered incorrectly? Did it
fail to transfer between systems? Was it available but difficult to locate? Was
the requirement unclear?
A correction log becomes much more useful when it helps
identify the origin of the problem.
3. Separate necessary review from preventable rework
Some claims need additional scrutiny. Some clinical
situations are complex. Some payer decisions require appeals.
Do not label all manual work wasteful.
Instead, identify tasks that repeatedly occur because a
known requirement was missed, a field was incomplete, or information was
unnecessarily duplicated.
Those are stronger candidates for process redesign and
automation.
4. Fix the handoff, not just the person
If front-desk staff, clinicians, billers, and administrators
repeatedly chase the same information, examine how that information moves
between them.
Clarify responsibilities. Reduce duplicate entry where
possible. Make requirements visible at the appropriate stage. Establish a clear
path for resolving exceptions.
The objective is to make the correct action easier, not to
create another checklist that everyone must remember to check.
5. Measure whether the fix worked
Choose a small number of meaningful baseline measures and
review them after making a change.
Did the correction rate decline? Did first-pass acceptance
improve? Did staff spend less time chasing missing information? Did the change
introduce new problems?
If the answer is unclear, the practice may have implemented
a tool without validating its impact.
6. Keep the human being in the loop
AI can assist with validation and pattern detection, but
clinical meaning, medical necessity, coding decisions, and compliance
obligations require appropriate human oversight.
Automation should make accurate work easier, not make
questionable work easier to submit.
These steps are not glamorous. They will not produce a
dramatic conference demonstration.
But a clinic that eliminates one recurring source of rework
may gain something more useful than a flashy demo: time, predictability, and a
clearer view of its operations.
Where OnnX fits into this conversation
As a physician and founder, I believe medical billing
deserves a different starting point.
The industry often asks how to process claims faster,
identify denials sooner, or automate more of the work after a problem appears.
Those are important questions.
But I believe we should also ask a question that comes
earlier:
How much avoidable correction can we prevent before the
claim exists?
That question is central to the problem OnnX is being
developed to address.
OnnX is an AI-powered medical billing SaaS for small and
medium-sized clinics. The underlying thesis is that some revenue-cycle friction
begins upstream, when clinical and operational information is captured,
structured, or transferred between disconnected workflows.
The opportunity is to help make that information more
complete, consistent, and usable before missing details turn into another round
of manual correction.
This is a product direction, not a claim that every billing
problem can be eliminated or that the system guarantees reimbursement. Payer
policies, coverage decisions, clinical complexity, and other factors will
continue to matter.
The goal is more practical: reduce avoidable friction,
improve the quality of information moving through the revenue cycle, and help
independent clinics spend less time repairing preventable problems.
AI should not replace physicians. It should not ask
clinicians to sacrifice judgment for a cleaner spreadsheet. And it should not
become another layer of software that staff must feed, monitor, and correct.
The technology should earn its place by making the work
better.
If it cannot demonstrate that, the clinic should be
skeptical.
After all, independent practices do not need another
expensive way to discover that they have a problem. They need practical ways to
solve the problems that consume their time and threaten their financial
stability.
The real measure of progress
Peyton Stewart’s story ends, for now, with a bell, a family
celebrating, and a boy looking forward to basketball, school, and a future he
once could not take for granted.
His recovery is a reminder of what healthcare can make
possible when medical expertise, family support, and human connection come
together.
The lesson for healthcare operations is not that every
process can be made perfect. It cannot.
It is that the systems surrounding care should be judged by
whether they help people do their work well, reliably, and without unnecessary
obstacles.
A denial is important. A delayed payment is important. An
incomplete chart is important.
But each is also an opportunity to ask whether the visible
problem began earlier in the chain.
Maybe the next improvement in medical billing will come from
a faster claims engine. Maybe it will come from better automation of follow-up.
Those tools have a place.
But another breakthrough may come from something less
dramatic: capturing the right information at the right time, making it
available to the right people, and preventing a predictable mistake from
becoming another afternoon of rework.
No trumpet fanfare. No futuristic robot in a white coat. No
new dashboard with 47 colors.
Just fewer avoidable corrections and more time for people to
do the work that matters.
That would be progress worth measuring.
And if Peyton Stewart eventually becomes a hospital CEO
after his NBA career, perhaps he will remember the same lesson his hospital
experience revealed: leadership is not just about running an institution. It is
about building an environment where people can help others through their
hardest days.
Healthcare’s administrative systems should be designed with
that same ambition.
One question for physicians, practice owners, and
healthcare operators
If you could eliminate one recurring administrative
problem from your practice tomorrow, which would give your team the most time
back for patient care?
Share your experience in the comments. Your answer may help
another clinic recognize a problem it has mistakenly accepted as normal.
If you believe healthcare needs less avoidable rework and
more time for patients, repost this article so the conversation reaches more
physicians, clinic owners, practice administrators, and medical billers.
A free resource for independent clinics: Find Revenue
Cycle Made Simple for Independent Clinics in the Featured section of my
LinkedIn profile. It is available without a signup.
About the Author
Dr. Daniel Cham is a physician, healthcare
strategist, and founder of OnnX, an AI-powered medical billing SaaS focused on
reducing administrative friction for small and medium-sized clinics. His work
explores healthcare operations, medical billing, clinical data quality, and
responsible technology, with an emphasis on practical improvements that protect
physician time and support patient-centered care.
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Disclaimer
This article is for educational and informational
purposes only. It does not constitute medical, legal, coding, billing,
reimbursement, or compliance advice. Healthcare organizations should consult
qualified professionals and applicable payer and regulatory requirements when
making operational or billing decisions. Peyton Stewart’s story is discussed as
a human-interest example; the reporting cited does not establish that billing
or administrative problems affected his treatment.
Sources and further reading
- PEOPLE:
Peyton Stewart’s recovery and ambition to become a hospital CEO
- WVUE
FOX 8 via Gray News: Peyton Stewart’s life-saving transplant and recovery
- American
Medical Association: Prior authorization reform and physician survey
findings
- Centers
for Medicare & Medicaid Services: Interoperability and Prior
Authorization Final Rule
#MedicalBilling #RevenueCycleManagement
#HealthcareOperations #PhysicianEntrepreneur #IndependentPractice
#HealthcareInnovation #PracticeManagement #PatientCenteredCare #HealthcareAI
#OnnX
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