One family’s extraordinary journey raises an uncomfortable question: If healthcare exists to help people reclaim their lives, why do we tolerate systems that repeatedly take physicians away from the people they are trying to help?
“They all go back to the importance of the
patient-physician interaction, which is really just a human connection that's
part of delivering good-quality care and getting good outcomes.” — Dr.
Gary Price, President of The Physicians Foundation, speaking to Medical
Economics
A brother stepped forward before his sister asked
Stephanie Johnson lived with polycystic liver disease. Her
liver had enlarged so dramatically that everyday activities became difficult.
Eating was uncomfortable. Climbing stairs left her breathless. Strangers
sometimes mistook her enlarged abdomen for pregnancy.
Her younger brother, Marcus Brown, wanted to help.
Stephanie was reluctant to ask family members to donate an
organ. She believed other people on the transplant waiting list might need a
liver more urgently.
Marcus saw it differently.
He raised the possibility of donating part of his liver with
their mother before Stephanie knew what he was considering.
In February 2026, Marcus donated part of his liver to his
sister at VCU Health’s Hume-Lee Transplant Center in Richmond, Virginia.
The operation involved a multidisciplinary medical team,
including transplant surgeon Dr. Seung Duk Lee, M.D., Ph.D., and Dr. David
Bruno, M.D., FACS. During Stephanie’s recovery, doctors also discovered a
cancerous growth in her colon. Surgical oncologist Dr. Leopoldo Fernandez
subsequently removed it.
Stephanie endured a prolonged hospitalization before finally
returning home.
Then came a small moment with enormous meaning.
She found herself running up the stairs.
Previously, climbing them had left her struggling for
breath. Now, she could move more freely, eat more comfortably, and begin
reclaiming ordinary parts of life.
She could buy clothes in her own size. She could focus more
of her energy on her two sons.
The medical procedures were extraordinary. The victories
that mattered to Stephanie were wonderfully ordinary.
Eating a meal. Climbing stairs. Wearing a shirt without
trying to hide. Getting more of your life back.
Her story is about family, medical expertise, difficult
decisions, and the possibility of recovery.
It is not a story about medical billing, and there is no
evidence that billing problems caused or affected her outcome.
But it reminds us of a principle that should guide every
part of healthcare:
The system exists to serve the patient. The patient does
not exist to serve the system.
That sounds obvious.
So why is it so easy to lose sight of it?
The uncomfortable question: What is healthcare optimizing
for?
Healthcare can perform extraordinarily complex operations.
Surgeons can remove diseased organs and transplant donated tissue. Clinical
teams coordinate medications, imaging, laboratory results, intensive
monitoring, and recovery.
Yet many physicians and their staff still spend hours
tracking down missing information, correcting preventable errors, checking
authorization requirements, and trying to understand why a claim has become
another administrative project.
We can perform miracles in the operating room and still
struggle to get the right information to the right person at the right time.
That is not an argument against technology, administration,
or necessary safeguards.
It is a question about priorities.
Are we optimizing healthcare for the delivery of care—or
for the processing of everything that surrounds it?
There is a difference.
A system can become extremely efficient at processing
paperwork while leaving the underlying reasons for that paperwork untouched.
It can generate a dashboard showing yesterday’s failures
without preventing tomorrow’s.
It can automate a process that should have been simplified.
It can even use artificial intelligence to make a bad
workflow faster.
Congratulations. We have successfully taught the machine to
run in circles.
The harder question is whether the circle needed to exist.
The contrarian thesis: Some billing problems begin before
billing
As a physician and founder of OnnX, I have become
increasingly interested in a question that sits upstream of traditional
revenue-cycle management.
What if some of the work we call billing is actually the
work of reconstructing information that should have been complete, connected,
and usable much earlier?
Consider a typical patient encounter.
A patient schedules an appointment. The practice collects
demographic and insurance information. Staff check eligibility and determine
whether a referral or authorization is required. The physician evaluates the
patient and documents the encounter. Coding and charge capture follow, and a
claim is submitted.
Each stage depends on information from earlier stages.
A missing detail may not be obvious when it first appears.
The workflow continues. Someone else assumes the information is complete. The
problem becomes visible only when the next step fails.
Now someone must investigate.
Was the insurance information correct? Was authorization
required? Did the referral arrive? Does the documentation support the intended
service? Was a payer-specific requirement overlooked? Did the information exist
in another system but fail to reach the person who needed it?
The claim becomes the crime scene.
The actual cause may be several steps away.
This is why I believe healthcare should investigate the origin
of recurring errors, not simply optimize the department that discovers
them.
A denial is an event. It is not always an explanation.
A coding query is a task. It does not automatically reveal
why the documentation was unclear.
A delayed payment is an outcome. It does not tell us whether
the original problem began with registration, coverage, authorization,
documentation, coding, or payer processing.
The location where a problem appears is not necessarily
where it began.
That distinction can change how a practice measures
performance, allocates staff, selects technology, and decides which problems
deserve attention first.
The hidden tax of administrative rework
Imagine a hypothetical physician-owned practice.
A physician sees a patient, makes a clinical decision, and
documents the encounter. The practice expects to submit an appropriate claim.
But the insurance information is incomplete. A referral
requirement was overlooked. The documentation needs clarification.
Each issue appears manageable.
Together, they create a chain of additional work.
A staff member must identify the problem, determine what is
missing, contact someone, retrieve information, and correct the record. The
physician may need to clarify documentation. The claim may be delayed,
corrected, or denied.
The practice has now paid for the encounter in operational
terms twice: once to deliver care and again to repair the process around it.
Sometimes more than twice.
There may be staff time, physician interruptions, additional
communication, delayed cash flow, and patient frustration.
These costs are scattered across departments. Because no
single line item captures the entire burden, the practice may underestimate
what recurring information failures actually cost.
The hidden tax is the labor required to reconstruct
information that was missing, disconnected, misunderstood, or not preserved in
context.
Not every billing problem originates upstream. Some result
from payer decisions, contractual disputes, changing rules, genuine coding
disagreements, or other causes outside a practice’s direct control.
But that makes root-cause analysis more important, not less.
If the practice does not distinguish between problems it can
prevent and problems it must resolve, it may spend money trying to fix the
wrong thing.
The numbers: Administrative burden is not imaginary
The American Medical Association’s 2024 prior authorization
survey reported that physicians and their staff spent an average of
approximately 13 hours per week completing prior authorization work,
with physicians completing an average of 43 prior authorizations weekly.
The survey also found that physicians overwhelmingly viewed
prior authorization as contributing to burnout and delaying necessary care.
These figures describe a specific survey and should not be
treated as universal measurements for every practice. But they illustrate the
scale of the challenge.
Thirteen hours is not a rounding error.
It is more than a working day every week.
And the cost is not simply the salary of the person
completing the authorization. It includes the opportunity cost of what that
person could have done instead.
Could a coordinator have helped a patient understand the
next step in treatment? Could a medical assistant have supported follow-up?
Could a physician have reviewed a complex case without another interruption?
We should not assume that every saved administrative hour
automatically becomes a clinical hour. Staffing, scheduling, and practice
economics are complicated.
But we should ask a straightforward question:
If a process consumes hours every week, what evidence
tells us that those hours are being spent in the best possible way?
A timely warning from Dr. Gary Price
In an interview published October 20, 2026, Medical
Economics spoke with Dr. Gary Price, president of The Physicians
Foundation, about misinformation, burnout, and declining trust between patients
and physicians.
Price emphasized the importance of preserving time for the
patient-physician relationship. He also discussed how fragmentation, insurance
barriers, prior authorizations, and denials can interfere with that
relationship.
His argument is important because it moves the conversation
beyond physician dissatisfaction.
Time with patients is not a luxury feature of healthcare.
It is part of how healthcare works.
A physician who has enough time to listen may discover
something that changes a diagnosis or treatment plan. A physician who can
explain uncertainty may help a patient make a better-informed decision. A
physician who knows the patient's history may recognize a change that a
fragmented encounter obscures.
The human connection is not separate from clinical quality.
Yet administrative systems can consume the very time that
helps make clinical care effective.
This is not a call to eliminate every administrative
requirement. Some protect patients, support appropriate utilization, and
establish accountability.
The challenge is to distinguish necessary controls from
avoidable friction—and then design the necessary controls as well as possible.
The objective should not be to eliminate administration
at any cost. It should be to eliminate the administration that exists because
the system repeatedly fails to work as intended.
Three perspectives that belong in the same conversation
1. Dr. Gary Price: Protect the human connection
Price’s recent comments emphasize that physician-patient
interaction and trust are fundamental to good care.
The operational implication is clear: administrative
processes should support the clinical relationship, not compete with it
unnecessarily.
A technology that saves time is valuable only if it produces
a meaningful improvement in the work or experience that matters.
2. The American Medical Association: Measure the
administrative burden
The AMA’s prior authorization research gives physicians a
way to discuss the burden using more than anecdotes.
The implication is that practices should measure the time
spent on repetitive administrative tasks, identify where delays originate, and
evaluate whether interventions actually reduce the burden.
“Everyone feels overwhelmed” is a reason to investigate.
It is not yet a diagnosis of the operational problem.
3. CMS: Improve information exchange and standardization
The Centers for Medicare & Medicaid Services has
established interoperability and electronic prior authorization requirements
for certain impacted payers through its CMS Interoperability and Prior
Authorization Final Rule.
These changes aim to improve information exchange and make
prior authorization processes more transparent and efficient. Requirements and
implementation timelines vary by provision and organization.
The implication is that electronic exchange matters—but
digitization alone is not the finish line.
An electronic form can still request redundant information.
An automated process can still depend on incomplete data. A faster transaction
can still fail if the underlying requirement is misunderstood.
Digitization changes the channel. Better information
structure can change the quality of the exchange.
These perspectives are not endorsements of OnnX. They
represent different parts of the healthcare landscape that converge on a common
question: how do we make the system support care more effectively?
The OnnX thesis: Fix the signal before repairing the
noise
OnnX is an upstream intelligence platform for healthcare,
built by physicians and medical billers to reduce downstream administrative and
billing noise so physicians can focus on clinical care.
Our premise is that some recurring billing problems begin
with fragmented information, unclear requirements, or disconnected decisions
before a claim is submitted.
The working process is:
Capture → Structure → Preserve → Propagate → Act → Learn
- Capture:
Collect relevant information where it first becomes available.
- Structure:
Organize the information so its meaning and relationships are clear.
- Preserve:
Retain the context needed to interpret it correctly.
- Propagate:
Make the relevant information available to the next appropriate step.
- Act:
Identify gaps, inconsistencies, or requirements that need attention.
- Learn:
Review outcomes and use verified patterns to improve future decisions.
The objective is not to replace medical billers or promise
that every denial will disappear.
It is to test whether better information continuity and
earlier decision support can prevent some of the rework that practices
currently resolve downstream.
Think of a recurring billing error as a smoke alarm.
One option is to hire someone to reset it every time it
sounds.
Another is to investigate whether something is repeatedly
causing the alarm to activate.
The first task may still be necessary. But if nobody
investigates the cause, the practice has created a very efficient
alarm-resetting department.
That is not the same as solving the problem.
Seven practical steps for physician-owned practices
A practice does not need to purchase another platform
tomorrow to start improving its information quality.
Begin with one recurring problem.
1. Pick a specific failure.
Choose something measurable, such as recurring eligibility errors, missing
authorization information, documentation clarification requests, or claims
returned for incomplete data. “Fix billing” is not a useful project definition.
2. Map the information journey.
Document where the information originates, who handles it, where it is stored,
and when it is needed. Ask frontline staff where the process repeatedly breaks
down.
3. Identify the earliest controllable cause.
Separate missing information from misunderstood requirements, unclear
responsibilities, software limitations, and payer-side constraints. These
problems require different interventions.
4. Establish a baseline.
Measure frequency, staff time, recurrence, and impact on claims or patient
services. Without a baseline, a team can mistake a busy month for an
improvement.
5. Fix the source where possible.
Improve the relevant registration field, documentation prompt, authorization
process, responsibility assignment, or information exchange. Do not
automatically send every problem to billing for repair.
6. Use technology for a defined purpose.
Automation may help flag missing fields, compare information, route exceptions,
or surface relevant requirements. It should not invent clinical facts or
silently make consequential decisions without appropriate oversight.
7. Evaluate honestly.
Check whether the intervention reduced the original problem, created new work,
increased false alerts, or shifted the burden to another department. Expand
only when the evidence supports it.
This approach is less glamorous than buying a platform with
a futuristic name.
It is also more likely to reveal what the practice actually
needs.
Metrics that matter more than a shiny dashboard
A useful operational improvement should be measurable.
Depending on the problem, consider tracking:
- First-pass
claim acceptance: The proportion of claims accepted without
correction, using a clearly defined measure.
- Preventable
denial rate: The share of denials linked to causes the practice can
reasonably address.
- Rework
time: Staff time spent investigating, correcting, resubmitting, or
escalating recurring issues.
- Missing-information
rate: How often a workflow reaches a point where required information
is absent or inaccessible.
- Authorization
turnaround: The time required for practice-side processing,
distinguished from payer processing time.
- Repeat-error
rate: How frequently the same underlying issue returns after it has
been corrected.
- Exception
rate: How often an automated process needs human intervention.
- Patient
communication burden: How often patients must contact the practice to
clarify an administrative delay.
No single metric tells the whole story.
A lower denial rate might coincide with slower claim
submission. An automated system might appear efficient because it fails to
identify difficult cases. A new checking process might improve accuracy while
increasing staff workload.
Pair efficiency measures with quality, safety, and workload
measures.
The goal is not to make the dashboard green.
The goal is to make the underlying work better.
Five myths worth challenging
Myth 1: We just need better billers.
Good medical billers are essential. But even excellent staff
cannot reliably compensate for every upstream information gap. When the same
error recurs, investigate the process as well as the performance of the people
working within it.
Myth 2: More automation means less work.
Automation can move work from one department to another,
generate false alerts, or create new exception queues. Measure the entire
workflow before and after implementation.
Myth 3: AI can solve documentation problems by generating
more text.
More text does not necessarily mean better documentation.
Accurate, relevant, clinically appropriate information matters more than note
length. Clinicians remain responsible for ensuring that the record reflects the
encounter truthfully.
Myth 4: Denials are simply part of doing business.
Some disputes and denials are unavoidable. That does not
make every recurring denial inevitable. Classify the reasons, investigate the
preventable portion, and track whether interventions reduce recurrence.
Myth 5: The newest platform must be the best solution.
Sometimes the right answer is software. Sometimes it is
clearer training, a better template, a defined responsibility, or a simpler
process. Buy technology to address a demonstrated problem—not to avoid
diagnosing it.
The risks of getting it wrong
An upstream intelligence platform can create value, but it
can also introduce new risks.
Clinical integrity: Never fabricate symptoms,
findings, medical necessity, or other clinical information to satisfy a billing
requirement.
Human oversight: Make recommendations understandable
and provide a clear process for review, correction, and escalation.
Privacy and security: Apply appropriate safeguards,
access controls, auditability, and agreements governing the use of patient
information.
Bias and reliability: Test performance across
specialties, payer arrangements, practice sizes, and patient populations. A
system that works in one setting may fail in another.
Transparency: Users should understand why a system
raised an alert and what evidence supports its recommendation.
Compliance: Practices should assess applicable HIPAA
obligations, business associate arrangements, coding standards, payer
contracts, and other requirements with qualified advisers.
Appropriate incentives: Do not encourage unnecessary
services, unsupported diagnoses, or inflated coding in pursuit of better
reimbursement.
There is a subtler risk: a system can make a bad process
harder to question because it now has an algorithm.
An automated recommendation is not automatically correct. A
workflow is not automatically appropriate because it is digital.
Technology should help people make better decisions—not
obscure responsibility for those decisions.
A 30-day experiment
For a physician-owned practice, meaningful improvement can
begin with a small, disciplined test.
Week 1 — Find the recurring problem. Choose one
issue, gather examples, interview the staff handling it, and measure the
baseline.
Week 2 — Trace it upstream. Identify where the
information first becomes incomplete, inconsistent, inaccessible, or
misunderstood. Separate internal causes from external constraints.
Week 3 — Test one intervention. Change a specific
step, train the affected team, and define who handles exceptions. Keep the
experiment limited enough to monitor safely.
Week 4 — Evaluate the evidence. Compare results with
the baseline. Did the problem decline? Did workload actually fall? Did another
error increase? Should the practice expand, modify, or abandon the
intervention?
No giant transformation budget is required.
No presentation needs the phrase “paradigm shift.”
Just a real problem, a measurable test, and the willingness
to discover that the original assumption may have been wrong.
What the future should look like
The future should not be a clinic where every employee
spends the day supervising an endless parade of alerts.
Nor should it be a clinic where AI makes consequential
decisions that nobody understands or checks.
A better future would allow relevant information to be
captured appropriately, structured consistently, preserved with context, and
made available to the people who need it.
Missing information would be identified earlier. Exceptions
would reach the right person. Physicians would not repeatedly reconstruct
details that already existed elsewhere. Medical billers could spend more time
applying their expertise and less time chasing basic information.
Automation would handle appropriate routine tasks while
people retained responsibility for decisions requiring judgment.
Success would be measured not by how many tasks software
completed, but by whether accuracy improved, rework declined, operations became
more predictable, and patients and staff had a better experience.
That is the direction behind OnnX: upstream billing
intelligence.
The ambition is to investigate whether better information
continuity and earlier decision support can reduce preventable downstream
problems for physician-owned practices.
That is a hypothesis to validate through real workflows,
measurable outcomes, and honest testing—not a promise that one platform can
eliminate every problem.
Healthcare needs fewer confident promises and more
disciplined investigations into where its problems begin.
The lesson Stephanie and Marcus leave us
Stephanie Johnson’s recovery is measured in things many
healthy people rarely think about: eating a full meal, climbing stairs, buying
clothes in her own size, and having more energy for her children.
Marcus Brown made an extraordinary personal decision to help
his sister regain those ordinary freedoms.
Their family relied on a specialized medical team whose work
demanded precision, coordination, and careful attention to both donor and
recipient.
Their story reminds us that the purpose of healthcare is not
merely to complete procedures, submit claims, or close tasks.
It is to help people live healthier lives.
Complex medicine will always require expertise, safeguards,
and coordination. Not every administrative requirement can or should disappear.
But we should question avoidable friction wherever it
repeatedly consumes the time, attention, and resources that patient care
requires.
If a process is necessary, make it reliable.
If information is needed, make it available.
If an error keeps recurring, investigate its origin.
If technology is introduced, measure whether it improves the
outcome.
And if physicians and staff are repeatedly asked to
reconstruct information that was lost, duplicated, or poorly structured, ask
whether the process needs to work that way at all.
The future of healthcare may depend not only on what we
teach machines to do, but also on what we finally stop asking people to do over
and over again.
A 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?
Prior authorization? Missing information? Documentation
rework? Eligibility errors? Preventable denials?
Tell me in the comments. The most useful answers may
come from the people who encounter these problems every day.
If this perspective resonates with you, share or repost it
so more physicians and practice teams can join the conversation.
The goal is not to make administration disappear. It is to
make unnecessary administration harder to justify.
Frequently asked questions
1. What is upstream billing intelligence?
It focuses on information quality, context, and decisions before a claim is
submitted, with the aim of identifying gaps early enough to reduce avoidable
downstream rework.
2. Is every medical billing denial preventable?
No. Denials can result from coverage limitations, payer decisions, contractual
disputes, coding issues, incomplete information, and other causes. Practices
should distinguish preventable problems from those requiring appeal or external
resolution.
3. Does better documentation guarantee reimbursement?
No. Reimbursement also depends on coverage, applicable rules, authorization
requirements, coding, contracts, and payer adjudication. Documentation must
remain accurate and clinically appropriate.
4. Can AI eliminate prior authorization?
AI may help organize information, identify requirements, prepare submissions,
and support status monitoring. It cannot eliminate payer requirements or
guarantee approval.
5. How can a small practice begin reducing administrative
burden?
Choose one recurring problem, measure its frequency and impact, trace it to its
earliest controllable cause, and test a targeted improvement before expanding.
6. What should a practice measure?
Depending on the problem, measures may include first-pass claim acceptance,
preventable denial rate, rework time, missing-information rate, authorization
turnaround, repeat-error rate, and staff workload.
7. Does upstream intelligence replace medical billers?
It should not be framed as a replacement for billing expertise. Better
information systems can help billers spend less time repairing avoidable gaps
and more time applying their knowledge.
8. How is OnnX different from a traditional revenue-cycle
management service?
OnnX is being developed around an upstream intelligence thesis: improve how
information is captured, structured, preserved, and acted upon before it
creates downstream billing problems.
9. Should every administrative process be automated?
No. Some processes protect safety, accountability, and compliance. The goal is
to reduce unnecessary friction while preserving accuracy, privacy, appropriate
review, and patient protections.
10. What is the first question to ask about recurring
errors?
Where did this problem first become possible, and what information or
decision would have prevented it?
About the author
Dr. Daniel Cham, MD, is a physician, entrepreneur,
and founder working at the intersection of clinical care, healthcare
operations, and technology. Through OnnX, he is exploring how upstream
information quality and decision support can reduce administrative and billing
friction for physician-owned practices.
Connect with Dr. Cham on LinkedIn.
Continue the conversation
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Knowledge creates possibilities. Start by questioning one
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For a free resource, visit the Featured section of my
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Disclaimer
This article is for general educational and informational
purposes. It is not medical, legal, coding, reimbursement, compliance, or
financial advice. Requirements vary by payer, jurisdiction, specialty, and
practice. Consult qualified professionals for guidance on specific
circumstances.
References and further reading
- VCU
Health: A
brother’s liver donation gives his sister a new lease on life — the
original account of Stephanie Johnson and Marcus Brown.
- Medical
Economics: Misinformation,
burnout and lost trust are “incredibly interrelated” — October 20,
2026, interview with Dr. Gary Price.
- American
Medical Association: Prior
Authorization — research and resources on prior authorization and
physician burden.
- Centers
for Medicare & Medicaid Services: CMS
Interoperability and Prior Authorization Final Rule — federal
requirements and implementation information.
#Healthcare #MedicalBilling #PhysicianEntrepreneur
#HealthcareInnovation #PracticeManagement #HealthTech #PriorAuthorization
#RevenueCycle #PhysicianOwnedPractices #PatientCenteredCare #OnnX
